Blog | Dylan Chiang

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature Journal: Nature Communications Year: 2025 Date: 2025-12-30

Research background

Idiopathic Short Stature (ISS) refers to a condition in which the height is two standard deviations lower than the person of the same age, but growth hormone (GH) levels are normal and there is no obvious pathological cause. The cause of ISS has long been a mystery.

Although genetics is the main factor in determining height, the influence of environmental factors such as diet cannot be ignored. Past research has mostly focused on nutritional deficiencies (such as calcium, protein), but how specific dietary components affect bone growth through molecular mechanisms is still poorly understood. This study focuses on capsaicin, the main component of spicy food, and explores whether and how it affects children’s growth and development.

Research methods

The research team adopted a systematic approach from clinical correlation analysis to molecular mechanism verification:

Clinical correlation analysis: Analyze the relationship between children’s eating habits and height, especially the intake of capsaicin.
Animal model: Establish a rat model of long-term ingestion of capsaicin and observe its effects on body length, femur length and growth plate structure.
Exosome tracking: Isolate exosomes (Exosomes) in serum, analyze their miRNA content, and track whether these exosomes can reach the bone growth plate.
Molecular mechanism exploration: Verify the regulatory effect of candidate miRNA on growth signaling pathways (such as ZNF148/SOS1/RAS/ERK and PI3K/AKT) in chondrocytes cultured in vitro.

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

In animal experiments, rats that long-term consumed a diet rich in capsaicin had significantly lower body weight and length than those in the control group. Histological examination found that these rats had thinner growth plates** and reduced new bone formation. At the same time, these rats showed significant intestinal inflammation. Notably, their growth hormone (GH) and insulin-like growth factor-1 (IGF-1) levels were normal, fully consistent with the clinical features of ISS.

2. Exosomal miR-17-3p is a key messenger

The study found that capsaicin-induced intestinal inflammation led to a significant increase in the levels of miR-17-3p carried by exosomes in the serum. These exosomes act as “long-range messengers” and are transported from the intestine to the growth plate cartilage of the bone.

3. Molecular mechanism: inhibiting chondrocyte proliferation

After reaching the growth plate, miR-17-3p enters chondrocytes, targeting and inhibiting the expression of the ZNF148 gene. Downregulation of ZNF148 further blocked SOS1 and the downstream RAS/ERK and PI3K/AKT signaling pathways. These pathways are critical for chondrocyte proliferation and hypertrophy, key processes in longitudinal bone growth. Therefore, inhibition of this pathway directly results in stunted bone growth.

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

This study established a specific “diet-intestinal inflammation-exosomes-bone growth” cross-organ regulation model, proving that intestinal microorganisms and inflammatory status can directly affect the development of distal organs (bones) through non-hormonal pathways (exosomal miRNAs).

2. Provides a new etiological explanation for ISS

For many short children with normal growth hormone levels, this study offers a possible explanation: Perhaps it’s not a lack of hormones, but chronic inflammatory signals triggered by specific dietary habits that inhibit the bone’s response to growth.

3. Potential therapeutic targets

The findings suggest that, in addition to growth hormone therapy, intervention targeting miR-17-3p (such as the use of antisense oligonucleotides) or improving intestinal inflammation may become a new strategy for the treatment of some children with ISS. Of course, the simplest advice may be this: Avoid excessive consumption of spicy foods in developing children.

My understanding

1. Molecular-level interpretation of “You are what you eat”

This statement is usually understood to mean that nutrients make up body tissues. But the study shows a deeper meaning: Food is not just fuel, it’s a signal. Capsaicin itself is not a component of bone, but the biological response it triggers (inflammation -> release of exosomes) is like an “instruction” that rewrites the growth program of bone cells.

2. Growth hormone is not a panacea

When encountering children with ISS clinically, parents often anxiously seek growth hormone injections. However, if the cause is that the downstream cell signaling pathway is “stuck” by inflammatory factors (such as miR-17-3p), simply increasing the upstream growth hormone may have limited effect. This reminds us that medicine needs to shift from “replenishing deficiency” to “relieving inhibition.”

3. The collision of traditional wisdom and modern science

There is a saying in many cultures that “children who eat too much spicy food will not grow taller” (although it is more about eating spicy food that will “get angry” or affect digestion). This study unexpectedly provides rigorous molecular biology evidence for this folk observation. It reminds us that in developmental biology, gut health and systemic immune status may be more critical than we thought.

(Note: Please search for the paper published by Wang et al. in the December 2025 issue)

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Exercising is good for the brain but exercising outside is potentially better Journal: Scientific Reports Year: 2023 Link:

Research background

We all know exercise is good for the brain, but exposure to nature (green surroundings) can also help with mental recovery. This is known in psychology as Attention Restoration Theory (ART), the idea that nature can help us recover from the sensory overload of the city.

However, most past studies have examined “movement” and “environment” separately. This study sought to answer a specific question: **Does exercise produce better cognitive gains if performed outdoors than if the same exercise were performed indoors? **

Research methods

The research team recruited 30 college students (average age approximately 21 years old) and conducted a within-subject design experiment:

Experimental Conditions: Each participant completed two 15-minute walking tasks, one indoor (a laboratory environment) and one outdoors (a green path on campus).
Measurement indicators:
- Measure brain activity using electroencephalography (EEG).
- Focus on P300 Event Related Potential (ERP). P300 is an important neural index reflecting the allocation of attention and working memory resources (the larger the amplitude, the more effective the cognitive resource deployment is).
- Participants took a standard cognitive test (Oddball task) before and after exercise.

Main findings

1. Outdoor sports significantly increase the P300 amplitude

The study results showed that participants’ P300 amplitude increased significantly after walking for 15 minutes outdoors**. This means that their attention allocation ability and cognitive processing efficiency have been significantly improved.

2. The effect of indoor exercise is not as good as expected

Surprisingly, no significant increase in P300 amplitude was observed after indoor walking at the same intensity**. This suggests that mere short-term “physical exercise” may not be enough to immediately trigger this level of cognitive improvement, and that environmental factors play a key amplifier role.

3. No significant difference in reaction time

Although the brain’s neural signals (P300) showed significant differences, at the behavioral level (reaction speed on cognitive tests), the indoor and outdoor groups did not show significant differences. This could mean that changes at the neural level precede behavioral changes, or that the task is less sensitive to behavioral changes.

Research significance and inspiration

This research supports the concept of Green Exercise. It tells us that the environmental context of movement is crucial.

1 + 1 > 2: The combination of exercise itself increases blood flow and the outdoor environment provides mental recovery creates a synergistic effect.
The Antidote to City Life: In modern city life, our brains are often in constant cognitive fatigue. The study suggests that instead of walking on a treadmill at the gym for 15 minutes, a 15-minute walk in the park may have a better “reset” effect on the brain.

My understanding

1. Environment is an extension of cognition

This paper made me think that our brain is not an isolated processor, but is deeply coupled with the environment. Indoor environments (especially boring labs or gyms) may lack enough sensory stimulation or be filled with distracting artificial signals, preventing the brain from entering the state of “relaxed focus” that it would in a natural environment.

2. Redefinition of “efficiency”

We often choose to exercise indoors for the sake of “efficiency” (not affected by weather, controllable). But if the goal is to recover mentally after work, this study shows that sacrificing a little convenience and going outdoors is truly “efficient” from a neuroscience perspective.

3. Simple and easy

15 minutes is not a very long threshold. This means that taking a walk in the park downstairs during your lunch break at work may be the most cost-effective way to recharge your brain.

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: How people think about being alone shapes their experience of loneliness

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: How people think about being alone shapes their experience of loneliness Journal: Nature Communications Year: 2025 DOI:

Research background

Loneliness is widely regarded as a global health challenge, closely related to depression, cardiovascular disease and even premature death. In response to this crisis, media and public health campaigns have sprung up in various countries to warn the public about the dangers of social isolation.

However, this study raises a paradoxical point of view: **Do these well-intentioned propaganda actually exacerbate people’s loneliness because they overemphasize the negative consequences of “being alone”? **

The core theory is that “being alone” (objective state) and “loneliness” (subjective feeling) are completely different. The research team hypothesized that people’s weighing beliefs (Beliefs) about being alone are the key to determining whether being alone causes pain or recovery.

Research methods

This study constructed a complete chain of arguments through 5 complementary experiments and surveys (total sample size N = 3,547):

Media Discourse Analysis (Study 1): Analyze the top ten newspapers in the United States from 2020 to 2022 to quantify their tendency to depict solitude.
Cause and Effect Experiment (Study 2): Randomly assign participants to read reports about the “benefits” or “risks” of being alone, and observe real-time changes in beliefs.
Experience Sampling (Study 3): 161 young people were tracked five times a day for two weeks to capture solitary behavior and psychological changes in daily life.
Cross-cultural verification (Study 4 & 5): Across 9 countries on six continents, verify whether this psychological mechanism is universal.

Main findings

1. The media’s “high stigma” against solitude

The study found that U.S. news headlines were 10 times more likely to describe solitude as “harmful” than as “beneficial.” In the main text, risks are mentioned 5 times more frequently than benefits are mentioned. The mass media almost equates “being alone” with “danger.”

2. The causal impact of beliefs on loneliness

Experiments have shown that even brief exposure to information about the benefits of solitude can significantly improve people’s beliefs about solitude. And this belief directly determines subsequent behavioral responses.

3. The “buffering and catalyzing” effect in daily life

This is the best part of the study (Study 3):

Negative Belief Holder: The longer you spend alone, the more intense your loneliness will rise. To them, being alone is a threat from which they need to escape.
Positive Belief Holders: After being alone, loneliness is significantly reduced. They see solitude as an opportunity to “recharge” and “repair themselves.”
Generalizability: This effect has been proven across different cultures, including collectivistic and individualistic cultures.

Research significance and inspiration

1. The “echo effect” of public health propaganda

The study raises a red flag for public health experts: campaigns that emphasize only the dangers of loneliness (such as “Loneliness is equivalent to smoking 15 cigarettes a day”) may inadvertently reinforce the public’s fear of being alone. When people have to be alone, which is almost inevitable in modern life, this fear translates into deeper psychological distress.

2. New approaches to cognitive intervention

Rather than forcing everyone to socialize, help those who feel lonely re-examine the value of being alone. Cultivating positive beliefs about being alone may be more effective in alleviating subjective loneliness than increasing social interaction.

My understanding

This paper provides a very insightful perspective: **Part of the reason why we feel lonely is because we are told that “being alone is not normal.” **

1. The self-fulfilling prophecy of cognitive frameworks

From the perspective of cognitive evaluation theory, being alone is a neutral stimulus. If we assume that being alone represents a social failure or a health threat (negative beliefs), then when we are alone, the brain will activate the threat detection mechanism, producing feelings of anxiety and loneliness. This creates a vicious cycle: the more lonely you feel, the more you fear being alone; the more you fear being alone, the more painful it is when you are alone.

2. Media Ecology and Mental Toughness

In the current era of digital media, social media constantly showcases the lively scenes of others, while traditional media constantly warns of the dangers of isolation. This double attack has greatly compressed our psychological space to face ourselves calmly. This study reminds us that building cognitive resilience (Belief Resilience) to “the benefits of being alone” may be an essential skill for modern people.

3. A “moderate turn” in policy

Public health policies should shift from simply “anti-loneliness” to “supporting healthy solitude.” Propaganda should include how to spend quality time alone and how to use alone time for creative thinking or emotional regulation, rather than just treating it as a defective product after a lack of social interaction.

4. Conclusion: Solitude is a kind of self-healing ability

Loneliness is not caused by spatial walls, but by psychological frameworks. Perhaps the biggest lesson from this research is that in an age where everyone is connected, being able to enjoy alone time is an important form of mental toughness.

It means that you no longer passively endure loneliness, but actively use this time to carry out self-repair (Rejuvenation) and recharge. When being alone is no longer a threat, but an opportunity, you have a powerful anti-loneliness mechanism.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Topological turning points across the human lifespan

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: Topological turning points across the human lifespan Journal: Nature Communications Year: 2025 DOI:

Research background

The structural topology of the human brain develops nonlinearly with age and is closely related to cognitive trajectories. Past research has tended to focus on specific age ranges (such as childhood or old age) but lacks a global perspective across the entire life cycle.

This study raises a core question: ** Are there key “turning points” in brain organization that mark the brain entering different stages of development? **

To answer this question, the research team collected nine data sets ranging in age from 0 to 90 years, with a total sample size of 4,216 people.

Research methods

1. Data analysis scale

Number of samples: N = 4,216 (neurotypical subset n = 3,802)
Technology: Diffusion Imaging
Metrics: 12 Graph Theory Metrics, used to quantify brain tissue architecture.

2. Manifold Learning

Use UMAP (Uniform Manifold Approximation and Projection) to project high-dimensional topological data into a three-dimensional manifold space.
The aim is to capture the non-linear dynamics of age-related changes in brain topology.

Key findings: Five life stages and four turning points

The study found that there are four major “turning points” in the development of brain topology, which occur around the ages of 9, 32, 66 and 83 years old. These turning points divide life into five unique Epochs:

Stage	Age range	Name	Characteristic description
Epoch 1	0–9 years old	Infancy to childhood	Global integration decreases and local preference (Segregation) increases.
Epoch 2	9–32 years old	Adolescence	The degree of brain network integration increases, and the small-world property (Small-worldness) is significantly improved.
Epoch 3	Ages 32–66	Adulthood	Developmental trajectories stabilize, with a slow decline in integration and an increase in local efficiency.
Epoch 4	Age 66–83	Early aging	Changes in modularity become the dominant feature.
Epoch 5	83–90 years old	Late aging	The relationship between age and topology weakens, and subgraph centrality becomes the main feature.

Detailed description of key findings

32: The most powerful turning point in your life
- Research shows that age 32 is the most dramatic time for brain topological rewiring.
- This is consistent with the finding that White Matter Volume and Fractional Anisotropy peak around age 29.
- This marks the brain’s shift from the “increased efficiency and integration” stage to the “increased isolation and stability” stage.
Age 9: The End of Childhood -The turning point coincides with the onset of puberty, peak cortical thickness, and substantial improvements in cognitive abilities.
- Marks a structural shift in brain structure from a single pattern of growth to a more complex pattern of maturation.
Ageing does not proceed at a uniform speed
- The turning point between ages 66 and 83 heralds the onset of a “simplified” mode of the brain, which is associated with an accelerated decline in white matter integrity and an increased risk of health problems such as dementia and high blood pressure.

Research significance and inspiration

1. Redefine “adolescence”

The study found that topological developmental trajectories during adolescence continue until age 32, well beyond what is traditionally thought to be around age 20. This supports the modern neuroscientific view that the brain matures much later than we thought.

2. The guiding value of non-linear development

The development of the brain does not rise in a straight line and then decline, but is a “turn” full of qualitative changes. This has important implications for clinical diagnosis, helping to distinguish normal staged changes from pathological cognitive decline.

My understanding

This study uses large-scale data to demonstrate the nonlinear dynamics of human brain structural topology during the life cycle, the most noteworthy of which is the turning point of 32 years old. Examined from the perspective of phylogeny and developmental biology, this finding provides several profound implications:

1. Trade-off between structure and function

Before the age of 30, the main axis of brain development is “increased integration.” This is a structured support period for extensive exploration and learning of the external environment. The brain improves the efficiency of information exchange throughout the brain by strengthening long-distance connections. However, this high level of integration is also accompanied by higher energy consumption and signal interference.

The turning point at age 32 marks the brain’s shift from “comprehensive expansion” to “precision optimization.” The decline in overall integration is not equivalent to a decline in functionality, but more like a structural “specialization.” Specific modules (Sub-networks) become more independent. This enhancement in modularity is beneficial to the stability of performing specific tasks and reduces noise interference in irrelevant areas. In cognitive psychology, this corresponds to the transition from “divergent flexibility” to “professional proficiency.”

2. Synergy of biological markers

The age of 32 does not exist in isolation. It is highly synchronized with peak white matter integrity, completion of the myelination process, and stabilization of neurotransmitter systems. This shows that during the course of evolution, the human body has set a biological clock that closes the “extensive remodeling window” at the beginning of the fourth decade. This synchronicity implies that the brain structure is shifted to better adapt to the long-term social division of labor and survival challenges in adulthood, and to invest limited metabolic resources into more productive specialized modules.

3. Reconstruction of the definition of “adolescence”

If the activity of topological rewiring is considered an indicator of adolescence, this study clearly shows that our definition of brain maturity (often pegged at around age 20) may be too dependent on legal and cultural labels rather than biological facts. From a brain structure perspective, the aftermath of adolescence actually extends into age 32. During this period, the brain still has strong potential for global integration, which explains why this period is the visual window for the final consolidation of professional skills shaping and worldview.

4. Precursors and nonlinear characteristics of aging

The study’s description of the turning points at ages 66 and 83 reveals that aging is not a slow wear-and-tear process but a staged evolution involving specific topological features. Especially after the age of 80, the relationship between age and topology weakens, suggesting that individual differences (such as genes, lifestyle, reserve capacity) may exceed the influence of basic developmental laws at extremely advanced ages.

In summary, this study reminds us that the brain executes different optimization logics at different stages of life: when young, it pursues “connecting everything” to gain possibilities, in middle age, it pursues “module independence” to ensure efficiency, and in old age, it tries to maintain core connections to cope with inevitable hardware degradation. This structural shift is not a negative degeneration, but a manifestation of life’s constant search for optimal adaptive solutions as time goes by.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Video game play is positively correlated with well-being

Sat, 27 Dec 2025 00:00:00 +0000

#paper information

Title: Video game play is positively correlated with well-being Journal: Royal Society Open Science Year: 2021 DOI:

Research background

The impact of video games on mental health has long been controversial. Many policymakers and parents worry that excessive gaming will lead to psychological problems (such as addiction, anxiety), but past studies often have a fatal flaw: heavy reliance on participants’ “self-reported” gaming time.

Psychological research shows that humans tend to be very inaccurate in their estimates of their own behavior. In order to break through this limitation, the Oxford Internet Institute collaborated with game companies (Electronic Arts and Nintendo of America) to obtain actual game telemetry data from players.

Research methods

The study was one of the largest and most methodologically rigorous of its kind at the time:

Sample Data:
- Plants vs. Zombies: Battle for Neighborville (Plants vs. Zombies: Battle for Neighborville)
- Assemble! Animal Crossing: New Horizons (Animal Crossing: New Horizons) *Total sample size N = 3,274 adult gamers.
Data type:
- Objective Behavioral Data: Record of actual game play time accurate to the second.
- Subjective Psychological Questionnaire: Includes positive/negative emotional experience (SPANE) and psychological need satisfaction (PENS, such as autonomy, competence, and belonging).
Analysis model: Use the original pre-registered analysis process to conduct correlation analysis between objective game time and subjective well-being.

Main findings

1. Game time is positively correlated with happiness

Contrary to the stereotype that “the more you play games, the less happy you are”, the study found that there is a significant and small positive correlation between objective game time and player happiness. That is, in this sample, people who played games for longer periods of time tended to report higher levels of happiness.

2. “Quality of experience” is more important than “length of time”

From the perspective of explanatory power, players’ psychological needs in the game are not fully satisfied (such as whether they feel free and dominant, whether they feel connected to others), which is more predictive of happiness than simply the length of the game.

3. Self-report bias

The study reconfirms that self-reported gaming time is only moderately correlated with actual recorded gaming time, confirming that past studies that relied on self-reporting may have had larger errors.

Research significance and inspiration

1. Challenging the foundations of policymaking

Many restrictions on games (such as time limits) are based on the assumption that “the longer the time, the greater the harm.” The results of this study raise doubts about this and suggest that policymakers should pay more attention to the content and experience quality of games rather than simply one-size-fits-all duration.

2. A new paradigm for industry cooperation

This research demonstrates the huge potential for data sharing between academia and industry. By analyzing telemetry data that cannot be faked, psychological research can move from “memoir” to “empirical science.”

My understanding

Although this paper was published in 2021, its methodological significance is greater than the conclusion itself.

1. The power of “data honesty”

We often unconsciously glorify or demonize our own behavior. “I played maybe 2 hours” in the questionnaire might actually be 4 hours. The greatest value of this Oxford University study is that it uses cold but honest server logs to burst the bubble of subjective recall.

2. Correlation\Causality

“Positive correlation” must be interpreted with caution. This does not necessarily mean that “playing games makes people happy”, it may also mean that “happy people have more time and mood to play games.” But at least it effectively refutes the determinism that “playing games will inevitably lead to unhappiness.”

3. Games as the “Third Space” in the Digital Age

Especially in games like “Animal Crossing”, players socialize, build, and express themselves in the virtual world. This is not just entertainment, but a digital social and emotional habitat. When the real world is full of uncertainty, games provide a safe haven that is controllable, feedback-laden, and can gain a sense of belonging, which in itself is a manifestation of psychological resilience.

Reading date: 2025-12-27 Notes organized: Dylan Chiang

Report Reading: Generative Artificial Intelligence Application Development Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Generative Artificial Intelligence Application Development Report (2025)
Issuing agency: China Internet Network Information Center (CNNIC)
Release Date: October 2025
Original link: [Generative Artificial Intelligence Application Development Report (2025)] ( ) (Note: This is CNNIC’s typical reporting path, please refer to the official website for details)
Core Content: Based on the framework of “User Popularization-Industrial Development-Typical Applications-Development Environment”, analyze the development status and future prospects of generative AI.

Comprehensive overview and summary of the report

This report records in detail the explosive growth of generative artificial intelligence (GenAI) in 2025, especially the rapid popularity and technological iteration of the Chinese market.

1. Development characteristics: domestic achievements and efficiency breakthroughs

Scale Explosion: As of June 2025, the number of GenAI users in China reached 515 million, with a penetration rate of 36.5%.
The Rise of DeepSeek: DeepSeek-R1 achieves excellent performance at less than 1/10 the cost of similar models, breaking the “computing power determinism” and topping the application list in 140 countries around the world.
Technical Trends: Logical reasoning capabilities have been significantly improved, multi-modal (Venture Video, Tusheng Audio) has developed by leaps and bounds, reasoning costs have been significantly reduced, and lightweight models (device-side AI) empower more terminal devices.

2. User popularity

Market structure: Doubao and DeepSeek occupy the leading position in the market.
Application motivation: Mainly used for answering questions (80.9%), text processing (36.0%) and audio and video generation (33.0%).
Group Characteristics: Young and middle-aged users are the main force, with users aged 19 and under accounting for 33.8%, showing the young generation’s high acceptance of AI technology.

3. Typical application scenarios

Industry and Agriculture: From smart irrigation, precision agriculture to industrial robots (such as UBTECH Walker S1) in production line training, AI is reshaping the labor structure.
Life Services:
- Smart Search: Shift from “finding links” to “getting answers”, Search as a Service.
- Content Creation: Sora and Keling AI bring short drama and advertising production into the era of “second-level generation”.
- Office Assistant: AI code generation (more than 30% of new code is generated by AI) and intelligent document processing become the norm.

4. Development environment and prospects

Policy Support: Establish an artificial intelligence security governance framework and promote “artificial intelligence +” actions.
Future Trends: Agent has autonomous decision-making and execution capabilities, Embodied Intelligence allows robots to enter the physical world and interact, Scientific Research (AI for Science) accelerates lunar research and weather prediction.

My understanding

This report is not only a accumulation of technical data, but also reveals the paradigm shift of AI technology from “concept laboratory” to “full employee productivity”.

1. Implications for personal research fields (political work and assistant behavior)

The report pointed out that 80.9% of users’ primary need is to “answer questions”, and in the office assistant scenario, AI’s understanding and polishing capabilities have become the core. For my study of Legislative Assistants’ Use of Generative AI, this provides strong external validity:

Efficiency Liberated: Political assistants are faced with the collection of data on a large number of public issues and the preparation of press releases. The high usage of AI search and document processing indicates that there will be an “automated revolution” in political work processes.
Decision Assistance: The “improvement in reasoning capabilities” mentioned in the report means that the assistant will not only use AI to write drafts in the future, but may also use AI to conduct policy impact assessment and voter sentiment analysis.

2. Inspiration for the development of AI in Taiwan and cross-strait

Lessons from cost advantages: The success of DeepSeek shows that Taiwan, with limited computing resources, should focus on algorithm optimization and deep cultivation of specific vertical fields (Vertical AI) instead of simply pursuing parameter scale.
Complementarity of application scenarios: Mainland China has a large scale of industrial and agricultural applications, while Taiwan has advantages in semiconductor end-side AI and high-quality service trade. Both sides of the Taiwan Strait face common challenges in AI governance (such as copyright, data privacy), and there is room for cross-regional dialogue.

3. Thoughts on the direction of academic research

From “use” to “collaboration”: Future academic research should not only focus on whether assistants use AI, but should study how the “human-machine collaboration model” changes the power structure in the political field.
Update of Technology Acceptance Model: The traditional TAM model may not be enough to explain the changes brought about by Agentic workflow. Research needs to focus on users’ psychological boundaries and ethical trust in AI “autonomy”.

Conclusion

The “2025 Report” marks that AI has entered the “full-scenario penetration” stage from a single breakthrough. As researchers, we need to pay close attention to the dynamic balance between the “efficiency dividend” and “ethical risks” brought by AI, especially in the highly sensitive field of political work.

Report reading: Microsoft AI Diffusion Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Microsoft AI Diffusion Report: Mapping Global AI Adoption and Innovation
Published by: Microsoft Research
Release Date: October 2025
Original link:
Core content: Track the global adoption status, infrastructure distribution, narrowing of the technology frontier and challenges faced by AI as the fastest spreading technology in history.

Comprehensive overview and summary of the report

This Microsoft report focuses on the “diffusion” dynamics of AI technology on a global scale, revealing the contradictory current situation of technology popularization and resource concentration coexisting.

1. The fastest technological diffusion in history

Breakthrough Velocity: AI has attracted more than 1.2 billion users in less than three years. Its diffusion rate is far faster than any previous general purpose technology (GPT), such as the Internet, personal computers or smartphones.
Adoption rate differentiation: Although the adoption rate is fast, there are significant differences between the Global North vs. Global South. The adoption rate in northern countries is approximately 2 times that of the South.

2. Frontier trends in infrastructure and models

Concentration of computing power and data: 86% of the world’s data center capacity is concentrated in the United States and China. This extreme concentration of infrastructure creates challenges for the democratization of AI.
Frontier Narrowing: Although the United States (such as OpenAI’s GPT-5 and other benchmarks) still maintains the lead, the performance gap of latecomers is rapidly narrowing. The report states that China is estimated to be less than 6 months behind on the technological frontier.
Model Distribution: The world’s top 200 models are only concentrated in 7 countries (the United States, China, France, South Korea, the United Kingdom, Canada, and Israel), showing the high threshold for technology research and development.

3. Diffusion Leaders

Non-R&D adoption leaders: Countries such as the United Arab Emirates (59.4%), Singapore (58.6%), and Norway (45.3%) stand out.
Success Factors: These countries have proven that even without developing underlying models, they can maintain global leadership in AI adoption through strong education systems, friendly policy environments, and digital infrastructure.

4. Key barriers: language and infrastructure

Language inequality: Adoption rates are significantly lower in low-resource language areas (e.g. Malawi, Laos). The language tolerance of AI models has become a key bottleneck for popularization.
Infrastructure constraints: About half of the world’s population (4 billion people) still lack the basic conditions (electricity, network and broadband) required to use AI.

My understanding

Microsoft’s global perspective provides a broader context for understanding AI’s place in politics, bipartisanship, and academia.

1. Implications for personal research fields (political work and assistant behavior)

The report mentioned that the rapid spread of AI indicates that the “digital generation gap” in the political field will be quickly compressed:

Global knowledge equality and challenges: If 1.2 billion users are using AI, political assistants are no longer a question of “whether to use them”, but a question of how to maintain “political uniqueness” during use.
Skills first: The cases of the United Arab Emirates and Singapore illustrate that “skills use” and “policy guidance” have a more direct impact on the application side than “model development”. This supports the hypothesis in my research that focuses on assistants’ personal technology preferences and environmental support.

2. Inspiration for the development of AI in Taiwan and cross-strait

Strategic opportunities for frontier narrowing: When the frontier technology gap narrows to within 6 months, Taiwan, as the core of the global supply chain, will have a greater say in hardware support in the “inference” stage.
Learn from the “Adoption Leader” model: Taiwan should become a world-leading “AI efficient adoption zone” through regulatory innovation and education transformation, like Singapore or Norway, despite the lack of local ultra-large-scale underlying models.

3. Thoughts on the direction of academic research

Focus on the imbalance of “AI diffusion”: Research should not be limited to advanced regions, but should focus on whether AI has exacerbated the gap between disadvantaged groups (or small parties, resource-poor politicians) and resource concentrators.
Language specificity research: For Taiwan, which uses traditional Chinese, we should study how “language specificity” affects the model’s understanding and expression accuracy in the local political context.

Conclusion

If CNNIC’s report shows the “depth” of China’s AI applications, then Microsoft’s report shows the “breadth” and “imbalance” on a global scale. The future of AI depends not only on who has the strongest model, but also on who can harness this power the fastest and fairest.

Report reading: OpenRouter State of AI (2025)

Wed, 10 Dec 2025 00:00:00 +0000

Report information

Title: State of AI | OpenRouter (Empirical Study)
Publishing Authority: OpenRouter
Published: December 5, 2024 (reported observation period spans 2024-2025)
Original link:
Data Foundation: Real interactive metadata based on OpenRouter unified inference layer 100 trillion (Trillion) tokens, covering 300+ models and 60+ providers around the world.

Report Key Insights (Abstract & Empirical Findings)

OpenRouter’s report avoids traditional subjective evaluations and instead starts from the “real behavior” of large-scale production environments, revealing in-depth patterns in the following dimensions:

1. Paradigm shift of Agentic Inference

Absolute dominance of inference models: The report points out that since the release of the o1 class model at the end of 2024, AI usage has shifted from “content generation” to “multi-step reasoning.” By 2025, more than 50% of total token traffic will flow to inference optimization models (led by xAI’s Grok Code Fast, followed by the Gemini 2.5 series and DeepSeek R1).
Tool-Calling Trend: Data display tool calling is no longer an option for developers, but a default for high-value workflows. The Claude 3.5/3.7 series dominated in the early days, and then Grok and GLM 4.5 quickly entered the market, reflecting that action through planning is the moat for future models.

2. Dynamic balance between open source and closed source (Market Equilibrium)

30% “Open Source Ceiling”: Although closed source models still account for 70% of the market share (focusing on regulated enterprise-level workflows), open source/weighted open models (OSS) have stabilized at around 30%.
The Rise of China’s Open Source Model: The release of DeepSeek V3 and Qwen 3 Coder directly led to a surge in usage. Especially in the field of code assistance (Programming), the Chinese open source model briefly accounted for more than half of OSS code tasks in mid-2025.

3. Model family usage profiles (Provider Profiles)

The report reveals users’ “cognitive division of labor” towards different model brands:

Anthropic (Claude): Extreme focus on Programming and Technology (80%+). Users consider Claude their go-to choice for complex reasoning and engineering.
Google (Gemini): The most diverse performance, covering translation, science, law and general knowledge, showing the characteristics of “digital encyclopedia/information engine”.
OpenAI (GPT): It has undergone a transformation from early science and general knowledge to deeper developer workflow and productivity tools. Its positioning is between Claude’s professionalism and Google’s diversity.
DeepSeek: shows an amazing consumer-heavy, with more than 2/3 of the traffic coming from creativity, entertainment and role-playing.

4. Retention Analysis: Cinderella’s “Glass Slipper Effect”

This is the most interesting finding in the report. The study observed:

Foundational Cohorts: Early users (such as first-month users of Gemini 2.5 or Claude 4 Sonnet) have retention rates of up to 40% after 5 months, much higher than those of subsequent users.
First solution advantage: When a model is the first to solve a specific problem (such as a complex Tool-use or logic difficulty), the user group will have a strong path dependence (Cognitive inertia). This is the “glass shoe” effect: once adapted, a powerful locking effect will occur.

5. Cost and Demand Elasticity (Jevons Paradox)

Price Inelastic: Interestingly, a 10% price drop only drives 0.5-0.7% usage growth. This shows that “quality and trust” are far more important than price, and top companies are willing to pay a premium for stability.
Jevons Paradox: Although the price elasticity is low at the macro level, in the field of “Efficient Giants” (such as Gemini Flash or DeepSeek), low cost does induce larger Token consumption (users start to perform more iterations and longer context queries).

My understanding: academic and practical inspiration behind the data

This “empirical” report elevates the discussion of AI from “whether it is easy to use” to “how to deploy it systematically.”

1. Implications for personal research fields (political work and assistant behavior)

Inference Dividends and Agentization: More than 50% of the models in the report turned to inference, validating my observation that assistants in political work are leveraging AI to handle more “judgmental” tasks. Future research should shift from “whether assistants use AI” to “how assistants guide public opinion through AI’s agentic workflow.”
Scenario Adaptation (Glass Slipper): For political assistants, the first model that can accurately craft their own tone or analyze the sensitivities of a constituency will create loyalties that are extremely difficult to break. This explains why some offices are stuck on older versions of GPT or specific models.

2. Inspiration for the development of AI in Taiwan and cross-strait

Export advantages of open source models: The explosive growth of DeepSeek and Qwen on OpenRouter proves that “open source orientation” is the main driver of Chinese models going global. Taiwan can boldly integrate these “Efficient Giants” on the application side and use cost savings for front-end scene optimization.
The necessity of multi-model architecture: Since no single model can dominate all portraits (DeepSeek for Roleplay, Claude for Code), Taiwanese companies and think tanks should adopt “Multi-model Stack” to achieve the best balance between cost and performance.

3. Thoughts on the direction of academic research

Focus on the bias of agent inference: When AI begins to autonomously call tools and plan paths (Agentic Inference), its bias will no longer just be “saying the wrong thing”, but “doing the wrong thing”. This is an extremely critical new topic in political and legal studies.
Application of Retention Rate Research: We should study how to shorten the journey for users to find the “glass slipper” model to increase the success rate of digital transformation.

Conclusion

OpenRouter’s data reveals a cruel but hopeful truth: In the great era of AI, the emergence of inference models such as o1 has not capped competition, but has opened up a new battlefield of “multi-step operation” and “scene adaptation”**. The future does not belong to the person with the biggest model, but to the person who can accurately find the “glass slipper”.

Paper reading: The Savanna Theory of Happiness

Tue, 25 Nov 2025 00:00:00 +0000

#paper information

Title: The Savanna Theory of Happiness Source: Journal of Personality and Social Psychology (or similar, based on context) Paper ID: N/A Link:

Research background

This paper proposed “The Savanna Theory of Happiness”. This theory is based on the perspective of evolutionary psychology, which believes that human psychological mechanisms evolved on the ancient African savannah. Therefore, our response to the modern living environment is actually deeply affected by the living environment of our ancestors.

Specifically, the authors try to explain why certain factors in modern life (such as population density, frequency of social interaction) affect our happiness, and why these effects differ among people with different IQs.

Research methods

Core concepts

The Savanna Principle: The human brain is adapted to the ancestral environment, not the modern environment. So situations that would have increased survival in our ancestral environment still make us happy today, and vice versa.
The moderating role of IQ: Intelligence (General Intelligence) is defined as the ability to solve evolutionarily “novel” problems. Smart people are better able to adapt to the modern lifestyle, which is completely different from their ancestral environment, and are therefore less constrained by the “savanna principle.”

Experimental design

The study used data from the U.S. National Longitudinal Study of Adolescent Health (Add Health) and analyzed the subjects’ IQ, population density where they lived, frequency of socializing with friends, and their self-reported life satisfaction (happiness).

Main findings

Overall, people are less happy when they live in places with higher population density. This is consistent with the prairie principle: in ancient times, excessive population density meant competition and pressure for resources.

Key Difference: This negative correlation is more pronounced among low IQ individuals; while high IQ individuals are less negatively affected by population density. This may be because people with higher IQs are better able to handle the complex stresses and novel challenges of modern urban life.

For most people, the more time they spend with friends, the happier they feel. This is also consistent with an evolutionary perspective: in tribal life, social connection is the key to survival.

Amazing reversal: For people with extremely high IQs, the opposite is true. More frequent social activities are associated with lower life satisfaction.

Research significance and inspiration

Why do smart people like to be alone?

This study provides an evolutionary psychology explanation for the stereotype that smart people are more lonely. People with high IQs may be more focused on long-term, modern goals (such as career, academic breakthroughs) that are “novel” in evolutionary history. Although frequent social activities are in line with ancestral survival instincts, they may actually be a distraction for highly intelligent people pursuing these modern goals.

In addition, the brains of people with high IQs are better able to adapt to “unnatural” modern life (such as solitude and high-density living), so they do not need to rely on close tribal social interaction to gain a sense of security like their ancestors did.

My understanding

This paper is very interesting. It uses the perspective of evolutionary psychology to explain the happiness and pain of modern people.

1. “Primitive Man” lives in modern cities

We often feel anxious or unhappy, often because our “primitive brains” have not adapted to modern society. For example, feeling irritable on a crowded subway may be because our genes are screaming: “There are too many people here and not enough resources. Run!”

2. IQ as an “adapter”

The author views IQ as a tool to deal with “evolutionary novelty”, which is a unique perspective. This explains why smart people tend to be better able to adapt to counter-intuitive modern lifestyles (such as being alone, staying up late, not having children, etc.). They are more able to suppress primitive impulses and pursue goals that are evolutionarily “meaningless” but are respected by modern society.

The mainstream values of society usually encourage social interaction and believe that “having more friends is better.” But this study tells us that for some people, especially those with high IQs, being oversocialized can be a burden. Being alone does not mean loneliness or social impairment; it may be an adaptive choice made by highly intelligent people to focus on more important goals.

Extended thinking

1. Definition of happiness

If happiness is a product of evolution, does that mean we should live by our instincts (move to the country, make more friends)? Or should we use our IQ to overcome these instincts and pursue higher levels of satisfaction?

The data for this paper come from before the popularity of social media. Does online socializing count as “socializing” today? Does its effect on happiness also follow the Savannah Principle?

Paper reading: Cognitive processes of ingroup favoritism across 20 countries

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Cognitive processes of ingroup favoritism across 20 countries: An eye-tracking investigation of culture, behavior, and cognition Author: Rima-Maria Rahal & Frederik Schulze Spüntrup Journal: PNAS (Proceedings of the National Academy of Sciences) Year: 2025 (Vol. 122, No. 32) DOI:

Research background

In-group favoritism is a common social psychological phenomenon - we tend to favor “one of our own” even if the definition of “one of our own” may only be based on a trivial criterion (such as a different favorite painter).

This preference has serious consequences in the era of globalization:

COVID-19 Vaccine Distribution: Western countries hoard vaccines, developing countries can only wait
Climate Crisis: Countries shirking responsibility and prioritizing their own short-term interests
Xenophobia on the rise: Right-wing populism spreads around the world

In the past, most studies could only be conducted in laboratories, with samples restricted to specific areas. This study breaks through this limitation and uses webcam-based eye-tracking, allowing researchers to collect high-quality cognitive data in participants’ homes, achieving unprecedented cultural and geographical diversity.

Research methods

1. Innovative research design

Sample:

1,850 participants
20 countries (covering Europe, Asia, America, etc.)
Eye tracking using participants’ own webcams

Experimental Task:

Group allocation: based on color recognition task (minimum group paradigm)
Group Reinforcement: Compete with out-group members for bonuses
Decompose the Dictator Game: 80 decisions, each time choosing between the “self-interest” and “pro-social” options
- Self-interest option: you get the most and the other side gets very little
- Prosocial option: take less for yourself, and both the other party and the total will increase

2. Measurement indicators

Behavioral level:

Differences in generosity toward in-group vs. out-group members

Cognitive Level (Eye Tracking):

Decision time
fixation count
Number of messages checked
Allocation of attention to self vs. other’s gains

Cultural level:

Individualism vs. Collectivism
Government effectiveness
Religious importance
Historical disease burden & COVID-19 impact

Main findings

1. In-group preference is universal, but the degree varies widely

Core findings:

✅ All 20 countries show in-group preference (OR = 4.57)
❗ But effect sizes vary widely across countries
📊 The stronger the prosocial tendency of people, the more serious the discrimination against out-groups.

**What does this mean? **“Good people” do not necessarily do not discriminate, they may just leave kindness to “their own people”.

The study tested the Material Security Hypothesis: the more unstable a society is, the more people rely on their in-groups.

Evidence Support:

✅ The lower the government effectiveness** → the stronger the in-group preference**
✅ COVID-19 death toll higher → in-group preference stronger
✅ The lower the historical disease burden → the lower the in-group preference

Unexpected Discovery:

❌ Religion more important → In-group preference stronger (contrary to prediction!)
- Null hypothesis: Religion as a stable institution should reduce preferences
- Practical: Religion may reinforce in-group and out-group boundaries

3. Is an individualistic culture more discriminatory?

Cultural Dimensions Theory predicts: Collectivist cultures should place greater emphasis on ingroups.

Actual result: Completely opposite!

The higher the individualism** → the stronger the in-group preference**

Possible explanation: -In individualistic cultures, people are more eager for group identification

Lacking a fixed social structure, it is more necessary to create boundaries for “one’s own people”

4. Cross-cultural differences in cognitive processes are more complex than behavior

Overall Trend (shown by meta-analysis):

Decision-making becomes more laborious when facing in-group members:
- Longer decision-making time
- More views
- Check more information

But:

⚠️ The direction of this effect varies greatly between countries
In some countries: in-group decision-making is more laborious
In other countries: out-group decisions are more laborious (reversed effect!)

The role of prosocial tendencies also varies across cultures:

In some countries: more prosocial → more effortful in-group decision-making
In other countries: the effect is reversed

5. “Color blindness”: More than half of people choose not to read group information

Amazing discovery:

In 53.49% of decisions, participants did not look at the group information at all (even if it was free)
54.79% of people clearly stated that they did not want to know the other party’s group identity

**Who chooses to remain “color blind”? **

People with strong self-interest (low SVO)
People who have no favorable impressions of either group
People who think others will not view group information

Consequences of color blindness:

People who choose color blindness: No discrimination
People who choose to view: 10% less for external groups

**What does this mean? ** The average discrimination effect may be driven by the few people who “want to know” the group identity!

Research significance and inspiration

Methodological Revolution

Breakthrough in web camera eye tracking:

💻 Participants can complete it at home, no professional laboratory required
🌍 Achieve unprecedented geographical diversity
💰 Lower cost and higher efficiency
📊 Sufficient data quality (spatial and temporal resolution)

This democratizes cognitive research—it’s no longer just university labs in wealthy areas.

Theoretical contribution

Challenges the cultural dimension theory
- Individualism vs. collectivism effects are opposite to predicted
- A single cultural dimension is not enough to explain complex social cognition
Supports the material security hypothesis (partially)
- Social uncertainty does increase discrimination
- But the role of religion is more complex
Reveal the cultural specificity of cognitive processes
- Cognitive mechanisms cannot be assumed to be consistent across cultures
- Even if the behavioral patterns are similar, the cognitive processes behind them may be completely different

Practical inspiration

A warning for global policymaking:

❌ “One size fits all” anti-discrimination policies may be counterproductive in some cultures
✅ Requires culturally appropriate interventions
🎯 “Color-blind” strategies may be effective: de-emphasize group differences instead of emphasizing equal treatment

Implications for reducing discrimination:

🔍 Understand who chooses to view group information (and why)
🎓 Educational focus: reduce focus on group boundaries
🌐 In a society with high uncertainty, stronger intervention is needed

My understanding

After reading this paper, my biggest shock was: it turns out that our understanding of “discrimination” may be based on a very narrow sample. If you studied ingroup preference only in a sample of Western college students, you might come to one conclusion about the cognitive mechanisms. But this study tells us that the same behavior (discrimination against outgroups) may come from completely different cognitive processes in different cultures.

1. Are “good people” more discriminatory? This is counter-intuitive

I have always thought that people with strong prosocial tendencies (people who are willing to sacrifice for others) should be less likely to discriminate. But this study says: On the contrary, these “good people” are more likely to reserve kindness only for “their own people.”

This reminds me of some phenomena in reality. Many people who claim to be “patriotic” may be generous to their fellow countrymen, but are extremely repulsive to foreigners or immigrants. Or some religious groups are very united and supportive within themselves, but are indifferent or even hostile towards “infidels”. This kind of “parochial altruism” may be more common and more persistent than we think.

The explanation proposed in the paper is that prosocial people care more about “fairness” and “justice.” However, when they think that the out-group is “not one of their own,” this sense of fairness does not apply. This made me reflect deeply: It is not enough to simply promote “kindness” or “altruism”. What is more important is to expand the scope of “us” and allow more people to be included in the circle of “ours”.

2. Are individualistic cultures more discriminatory? This overturned my understanding

I have always felt that collectivistic cultures (such as Asia) should emphasize the distinction between inside and outside more, while individualistic cultures (such as Europe and the United States) should treat everyone more equally. But this study found the exact opposite!

The explanation proposed by the author is very interesting: in individualistic cultures, people are more eager for group identification. Because there are no mandatory family or community ties, people need to find their own “sense of belonging,” which makes them more sensitive to “who is one of their own” and “who is an outsider.”

This reminds me of racial issues in the United States, immigrant rejection in Europe, etc. These highly individualistic societies are particularly prone to group conflicts. Perhaps it is precisely because in a society where “everyone is for themselves”, finding “one’s own team” has become more important and precious.

The most surprising finding from this study is that in more than half of the decisions, the participants did not look at the other party’s group information at all. What’s even more astonishing is that those who clearly chose “not to know” showed no discrimination at all!

This gave me an important revelation: **Perhaps the key to reducing discrimination is not to educate people to “treat all groups equally,” but to make people “not pay attention to group differences.” **

Think about it, in real life, many times we are forced or led to pay attention to group differences-the “race” option on the form, “immigrant crime” in news reports, the “us vs. them” narrative on social media. If we can create more “color-blind” environments and systems, perhaps discrimination will naturally decrease.

Of course, this also raises a philosophical question: Does the “color-blind” strategy ignore real group inequalities? Does not recognizing differences mean evading the responsibility to address inequality? This is an issue that needs to be weighed carefully.

4. Cultural specificity of cognitive processes: an unsolved mystery

The biggest mystery of this study is: Why is the relationship between prosocial tendencies and decision-making effort completely reversed in different countries?

In some countries, more prosocial people make more effortful decisions when faced with their ingroup (perhaps weighing how to maximize the interests of the ingroup). But in other countries, the effect is reversed. The papers acknowledge that they cannot explain this difference with pre-registered cultural variables (individualism, government effectiveness, religion, etc.).

This is actually the most honest and valuable part of this study: it reveals our ignorance. We thought we understood the mechanics of in-group preference, but in fact we couldn’t even explain why cognitive processes differ across cultures.

This reminds us: psychological theories cannot only be based on WEIRD (Western, Educated, Industrialized, Rich, Democratic) samples. The human mind is far more complex and culturally shaped than we imagine.

5. Democratizing Methodology: The Webcam Revolution

From a broader perspective, this study shows how technology is changing research possibilities. In the past, eye tracking required expensive equipment and specialized labs that only a few wealthy universities could afford. Data of sufficient quality can now be collected using participants’ own webcams.

what does that mean? Democratizing Research. Researchers from developing countries no longer need multimillion-dollar equipment to conduct cutting-edge cognitive research. Participants are no longer limited to college students living near college towns.

This technological advancement may be more important than any single research discovery. It changes the game of “who can do research” and “who can be researched.”

6. Warning on policy: Beware of “one size fits all”

Finally, this study has an important warning for policymakers: Do not assume that anti-discrimination policies that work in one place will work in another.

If cognitive processes are so different across cultures, then interventions also need to be context-specific. An education program that emphasizes “treating all people equally” may be effective in some cultures but may be counterproductive in others (because it reinforces perceptions of group boundaries).

7. Limitations: The Puzzle of Causation

Of course, this study also has obvious limitations. Cultural variables cannot be manipulated experimentally, so we cannot determine the direction of causation. Does social uncertainty lead to discrimination, or does discrimination lead to social instability? This knot of cause and effect cannot be untied.

Furthermore, the paper acknowledges that many unobserved variables (language, personal wealth, migration history, etc.) may confound the results. These are challenges inherent in cross-cultural research.

Extended thinking

1. What if discrimination is the “default mode” of cognition?

This research got me thinking: maybe the human brain was evolved to distinguish between in- and out-groups. If so, reducing discrimination is not about telling people “don’t discriminate”, but about redefining “who are our own people”.

The ultimate goal of globalization and multiculturalism may not be to eliminate group boundaries, but to expand the scope of “in-groups” until “all humanity” becomes “us”.

2. Tribalism in the Internet Age

Social media and recommendation algorithms make it easier for us to find our “strata”—those “people” who think the same way. This may exacerbate ingroup bias.

If people from individualistic cultures particularly crave group identification, and the Internet makes this identification extremely easy (just join a Facebook group), then we may be witnessing an era of “Tribalism 2.0.”

3. New challenges in the AI era

When AI systems start making resource allocation decisions (such as loan approval, job recommendation), will they also learn “in-group preferences”? If the training data is filled with human discrimination patterns, AI may amplify these biases.

And even more troubling is that AI will not “select color blindness” - it will process all available data, including group information. This can make discrimination more systematic and harder to detect.

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? Source: arXiv Paper ID: 2504.13837 Link: |

Research background

Reinforcement Learning with Verifiable Rewards (RLVR) has recently achieved significant success in improving the inference performance of large language models, especially on mathematics and programming tasks.

Conventional wisdom holds that RLVR enables LLMs to continuously improve themselves, thereby acquiring new reasoning capabilities beyond the base model—just as traditional RL helps agents explore and learn new strategies.

But is this assumption really true?

This study attempts to answer a key question by systematically exploring the boundaries of the reasoning capabilities of RLVR-trained LLMs in various scenarios:

**Does RLVR training actually allow LLMs to acquire new reasoning capabilities beyond the base model? **

Research methods

Evaluate Design

The research team uses pass@k (at large k values) as an evaluation metric across multiple dimensions:

Model Family: multiple different LLM architectures
RL Algorithms: six popular RLVR algorithms
Benchmarks: Mathematics, Programming, Visual Reasoning

Analysis method

Pass@k Evaluation: Use large k values to detect the upper bound on the model’s capabilities
Coverage Analysis (Coverage Analysis): Measure whether the RLVR training model can produce solutions that the basic model cannot produce
Perplexity Analysis (Perplexity Analysis): Evaluate whether the output of the RLVR training model is still within the distribution of the basic model

Main findings

1. Current training settings fail to inspire fundamentally new inference patterns

Surprising core findings:

✅ Small k value (such as k=1): RLVR training model is better than the base model
❌ Large k value: The basic model instead obtains a higher pass@k score

**What does this mean? **

RLVR training mainly focuses on “selecting” the existing capabilities of the basic model, rather than creating new reasoning models. It’s like learning to pick the right tool faster from a toolbox already full of tools, but it doesn’t create new tools.

2. Inference capabilities are limited by the basic model

Through coverage analysis and perplexity analysis, the study found:

Observed reasoning ability derived from** and limited by the underlying model
Treating the basic model as an upper bound of capability, six popular RLVR algorithms perform similarly
All methods are far from optimal in exploiting the full potential of the underlying model

Basic model is the ceiling - No matter how trained, RLVR cannot make the model jump out of the capability boundary defined by the basic model.

3. Distillation methods show different advantages

In contrast, Knowledge Distillation (Distillation) exhibits different characteristics:

Ability to introduce new reasoning modes from the teacher model
Truly expands the reasoning capabilities of your model
Don’t just rearrange existing knowledge

This hints at an important methodological difference: distillation can “teach” the model new reasoning paths, while RLVR is only “optimizing choices.”

Research significance and inspiration

Reflections on RL in the field of LLM

This study reveals a fundamental limitation of current RLVR methods:

**Current RLVR training paradigms have yet to realize the potential of RL to inspire truly novel reasoning capabilities in LLMs. **

This is a far cry from what we’ve come to expect from RL. In traditional RL domains (such as game AI), RL can discover strategies that humans have never thought of. But in the field of LLM, the current RLVR method seems to be only “mining” rather than “creating”.

Future research directions

Research highlights the need to improve the RL paradigm:

Continuous Scaling (Continual Scaling)
- Long-term, continuous training process
- Rather than a one-time optimization
Multi-turn Agent-Environment Interaction (Multi-turn Agent-Environment Interaction)
- Truly interactive learning
- instead of training on static data sets
New Assessment Paradigm
- Pass@k (big k) instead of just looking at pass@1
- A more comprehensive assessment of capability boundaries

My understanding

After reading this paper, my biggest feeling is: this is really a “slap in the face” paper. In the past year, RLVR has been highly praised in the LLM field. OpenAI’s o1 and DeepSeek’s R1 both claim to use reinforcement learning to make the model “learn to think.” But this study tells us calmly: Wait, are you sure the model really learned something new? Or have you simply learned to pick the right answers faster from the toolbox you already have?

1. “Selector” vs “Creator”: A cruel truth

I think the core insight of this paper is to position RLVR as a “selector” rather than a “capability expander”. This may sound a bit abstract, but it’s actually pretty easy to understand. Imagine you have a student who already has many ways to solve the problem (basic model) in his mind, but he is not sure which method is best, so he tries randomly. RLVR training is like giving him a lot of practice questions, allowing him to learn “use this method when he sees this kind of question”, which improves his answering accuracy (pass@1).

But the problem is, if you give this student many opportunities to try (pass@k, big k), he will be able to try out the correct answer by “trial and error”. The RLVR training didn’t teach him any new problem-solving methods, it just made him find the right one faster. This is why when pass@k is large, the basic model performs better - because it still retains more diverse trial possibilities, and the model after RLVR training has been “finalized” and will only give priority to those paths with high rewards during the training process.

This finding is actually cruel, because it means that many of the “improvements in reasoning” we have seen in the past year may be just superficial.

2. The basic model is the ceiling: where should investments be placed?

This paper made me rethink resource allocation. If the basic model is the ceiling of capabilities, then should the industry invest more resources in pre-training instead of various fancy parameter adjustments for RLVR?

The current trend is that everyone is desperately thinking about how to use less data and more clever reward shaping to do RLVR. But this research tells us that no matter how optimized you are, you cannot escape the capabilities of the basic model. So instead of finessing on RLVR, wouldn’t it be more practical to directly train a stronger basic model?

Of course, this is not to say that RLVR is completely useless. In the pass@1 scenario (which is the most common situation in practical applications), RLVR can indeed significantly improve performance. But we have to clearly understand its essence: it is an “optimizer” rather than an “expander”.

3. Enlightenment of knowledge distillation: Why can it do what RLVR cannot?

The paper mentioned that knowledge distillation can introduce new reasoning patterns, which made me very curious. Why does distillation work but RLVR not?

My understanding is that distillation is essentially “knowledge transfer” - you learn “new ways of thinking” from a stronger teacher model. This is true expansion of capabilities. RLVR only “self-optimizes” within its own capabilities without the injection of external knowledge.

This makes me think that perhaps the truly effective training paradigm in the future should be a combination of “distillation + RLVR”: first use distillation to expand the boundaries of capabilities, and then use RLVR to optimize the quality of decision-making. Relying on RLVR alone to allow the model to “emerge” new capabilities may be an unrealistic expectation.

4. Pass@k’s philosophy: What exactly are we evaluating?

This paper also reminded me of the importance of evaluation methods. We are accustomed to using pass@1 to evaluate the model because it is closest to the real application scenario. But pass@1 may give us an overly optimistic illusion.

Pass@k (big k) reveals the “capability boundary” of the model - the upper limit of what it “might” get right. And pass@1 just tells us what the model would “normally” do. The gap between the two is what RLVR is doing: narrowing the distance between “possible” and “usual”.

But if we only look at pass@1, we will mistakenly think that RLVR really makes the model smarter. In fact, the “ceiling” of the model has not increased, only the “average performance” has increased. This distinction is important.

5. Thinking about the future: What does true reasoning require?

After reading this paper, I feel that the current RLVR paradigm may have been designed wrong from the beginning. We treat LLM as a “static policy space” and then use RL to optimize policy selection. But real reasoning shouldn’t be like this. Real reasoning should be “dynamic, interactive, and continuous learning.” Just like humans do not learn to think through problem solving exercises, but develop reasoning abilities through long-term interaction with the world, trial and error, and reflection. The “multiple rounds of agent-environment interaction” mentioned in the paper may be the right direction.

Maybe what we need is not to use RL to train the model, but to use RL to let the model “live” in an environment and truly explore, make mistakes, learn, and grow. That is the essence of RL, not the current “pseudo RL” done on static data sets.

6. A disturbing question

Finally, this paper made me think of a deeper question: If RLVR cannot make the model truly smart, then what is going on with these o1 models that claim to have “reasoning capabilities”?

There are two possibilities: First, their basic models are already very powerful, and RLVR just exploits this potential; second, they use some techniques that are not covered in this paper (such as chain-of-thought, test-time compute scaling).

In any case, this study reminds us: Don’t be fooled by superficial performance improvements, but think deeply about the essential source of capabilities. This is important for both doing research and making products.

Extended thinking

1. RLVR vs. Knowledge Distillation: What is the essential difference?

Why can distillation introduce new patterns?
What is the fundamental difference between the two in the training process?
Can the advantages of both be combined?

2. The importance of evaluation methods

What does the difference between Pass@1 vs. Pass@k (big k) reveal?
Are we overly reliant on a single metric to assess model capability?
What other dimensions of assessment can be explored?

3. The role of the basic model

Is it more worthwhile to invest in better base model training vs. more sophisticated RLVR methods?
Where can we improve Pre-training?
How is the “capability boundary” of the basic model formed?

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Genome-wide study association of sexlessness

Thu, 20 Nov 2025 00:00:00 +0000

#paper information

Title: Genome-wide association study of sexlessness Journal: PNAS 2025 Vol. 122 No. 38 DOI: Research Number: e2418257122

Research background

This study examines the genetic and environmental correlates of lifelong sexlessness. The study used large-scale data from UK Biobank to analyze genotypic and phenotypic profiles of more than 400,000 participants.

Main findings

1. Relationship between regional factors and Sexlessness

Sex Ratio

Sex ratio at birthplace: no significant association with sexlessness
Gender ratio in current place of residence:
- Males showed a small but significant negative correlation (r = -0.07, P = 0.0004)
- Areas with fewer women have higher rates of male sexlessness

Income Inequality via Gini Coefficient

Income inequality based on current place of residence is significantly and positively related to sexlessness:

Male: r = 0.09, P = 2.8 × 10⁻¹⁵
Female: r = 0.14, P = 3.6 × 10⁻⁶
Overall: r = 0.15, P = 9.5 × 10⁻¹³

Explanation: Areas with higher income inequality also have higher rates of sexlessness

2. Genome-wide association study (GWAS) results

Samples and methods

Number of participants: 404,470 people of European ancestry
Sexless individuals: 3,897 people (~1%)
Number of SNPs: 10.6 million single nucleotide polymorphisms

Main findings

Significant site: A site on chromosome 1 (top SNP: rs912773)
- This site is located in an intergenic region, the nearest gene is LOC107984933
- Single SNP effect size is extremely small

SNP-based Heritability

Population	Heritability	Standard error	P value
Men	17%	4%	2.3 × 10⁻⁵
Women	14%	3%	3.1 × 10⁻⁶
Overall	12%	-	1.9 × 10⁻⁹

IMPORTANT OBSERVATION:

Genetic correlation between males and females is 0.56 (SE = 0.17, P = 0.0007) -Suggests that SNP associations are directionally consistent between men and women but only partially overlap

Evolutionary evidence

The study examined changes in allele frequency of rs4654352, one of two genome-wide significant SNPs, in the Ancient DNA Database (AADR):

Time Span: Past 12,000 years
Sample: 3,458 ancient individuals from Europe
Trend: A continued decrease in frequency of the A allele associated with higher sexlessness
- 10,000-12,000 years ago: 87%
- 0-2,000 years ago: 56%

Conclusion: Consistent with negative selection pressure targeting sexlessness-related alleles

3. Polygenic Score (PGS) analysis

UK Biobank Internal Validation

PGS significantly predicted sexlessness in an independent sibling sample (Beta = 0.016, SE = 0.005, P = 0.002)
Gene-Environment Correlation Test:
- Only 7.4% of the effect can be attributed to gene-environment correlations among families
- This compares to 49% for education, 48% for IQ, and 15% for BMI
- Conclusion: GWAS signals are minimally affected by gene-environment correlations

Australian independent sample verification

PGS was significantly associated with multiple relevant phenotypes in Australian samples (N = 1,354-13,532):

Phenotype	Direction of Association	Significance
Number of relationships lasting ≥3 months	Negative correlation	✓✓ (overall)
Never had a sexual partner	Positive correlation	✓✓ (female)
Number of sexual partners	Negative correlation	✓✓ (overall)
Age at first sexual intercourse	Positive correlation	✓✓ (overall, men and women)

Religious Impact (N = 6,764):

Religious people are more likely to have no sexual experience in their lives (P < 0.0001)
Gender analysis: significant for women (P = 0.002), not significant for men (P = 0.06)

4. Genetic overlap with other complex traits

Genetic correlation with childlessness

Gender	Genetic correlation	Standard error	P value
Female	0.68	0.10	7.0 × 10⁻¹¹
Male	0.65	0.09	7.0 × 10⁻¹⁴

Showing high genetic overlap between sexlessness and childlessness

Other important genetic correlates

Positive correlation (genes associated with sexlessness are also associated with the following traits):

Cognitive and Socioeconomic Status:
- Child vs. Adult IQ
- Education level
- Income and socioeconomic status
Mental Illness:
- Autism Spectrum Disorder
- Anorexia
Lifestyle:
- Less substance use (alcohol, cigarettes, marijuana)

Negative correlation:

Mental Illness: -ADHD
- Post-traumatic stress disorder
Psychosocial Traits:
- Extraversion
- Relationships with friends and family -Subjective well-being

Phenotypic correlation analysis

Mental health indicators (especially men)

Sexless men were significantly more likely to:

Suffering from neurosis
feel unhappy
feeling lonely
Believe that life lacks meaning

More likely to live alone
Social visits are less frequent
Lack of intimacy or someone to confide in

Physical characteristics

MEN: Lower grip strength and lean body mass (arm muscle mass)
Overall: Start wearing glasses earlier

Substance Use

Associated with less use of:

alcohol
cigarettes
Marijuana

Education and socioeconomic status

Phenotype related: -Positively associated with higher educational attainment (for both men and women)
- Negatively associated with lower household income
- Place of residence with high levels of economic deprivation (Townsend index)
Genetic correlation (opposite direction):
- Positive genetic correlation (~0.5) with higher IQ, education, income, and socioeconomic status

Research limitations and precautions (Box 1: Cautionary Notes)

1. Culture Specificity

The association between genetics and behavior reflects the interaction between genetics and the environment within a specific cultural context
Findings from contemporary Western societies should not be generalized to other ancestries or cultural backgrounds

2. Individual results cannot be predicted

Polygenic scores are not suitable for predicting sexlessness at the individual level
Approximations based on large samples and small effects
It is inevitable that there will be people with high scores but low phenotypes, and vice versa

3. Complexity

Heredity is just one aspect of a complex network that influences behavior
Environmental, social, cultural and personal factors all play an important role
This study explores correlation, not causation

4. Measurement Error

-Sexlessness is a self-report variable

There may be under-reporting or over-reporting, leading to classification errors
Social norms may influence reporting behavior (men may over-report, women may under-report)

Discussion and Interpretation

1. Scientific evidence for the “nerd” stereotype?

The study found a combination of characteristics that closely aligned with the “nerd” stereotype:

Introversion
Wearing glasses when young
Smart and academically successful
Physically weak
Social isolation, loneliness
Higher autism spectrum traits
Nervous
Less use of drugs and alcohol

2. Complexity of Causal Direction

Sexual inexperience → Mental health issues: Sex is a basic human drive, and its lack may affect happiness
Mental Health Issues → Sexual Inexperience: Psychological issues may make it more difficult to approach or attract a partner
Third Variable: Other factors may increase the likelihood of both

Genetic correlations provide some clues:

The genetic correlation between sexlessness and depression, anxiety, and loneliness is negative (not positive)
But there is a positive genetic correlation with autism spectrum disorder, consistent with the characteristics of interpersonal difficulties

3. The Paradox of Intelligence and Reproductive Success

Evolutionary Expectation: Intelligence and resources should be attractive traits
Research Finding: Positive genetic correlation (~0.5) with higher IQ, education, and socioeconomic status

Possible explanation:

Young adults with high educational/socioeconomic potential may avoid intimate relationships in order to pursue career plans
This pattern may continue into later life

Challenges to the Sexual Selection Hypothesis:

The sexual selection hypothesis of human intelligence proposes that intelligence evolves due to its contribution to mating success.
This study finds that intelligence is positively related to sexlessness, strengthening the evidence against this hypothesis

4. Sexlessness vs. Asexuality

-Asexuals account for about 1% (similar to the sexless proportion in this study)

But more than half of asexuals are not virgins
Asexuals have lower average education and socioeconomic status (contrary to this study’s findings)
The correlation between male sexlessness and regional female scarcity & income inequality difficult to explain by asexuality

Conclusion: Asexuality may contribute to part of the phenotype, but it is not the main factor

Evolutionary meaning

1. Sexlessness as a fitness proxy variable

Significant SNP heritability allows genetic correlation analysis
Significant negative genetic correlation with number of heterosexual sexual partners and number of children
May be a more “pure” ancestral fitness surrogate than other variables
- Less affected by social sexual orientation, pickiness in mate selection, family planning, career orientation, etc.

2. Selected Evidence from Ancient DNA

The A allele (associated with sexlessness) has continued to decline over the past 12,000 years
Expected negative selection pressure
but need to be interpreted with caution:
- The exact function of this variant is unknown
- Observed effect sizes are small

3. Maintenance of genetic variation

Genetic variation may be maintained through mutation-selection balance:

Negative selection acts on this trait
New mutations continue to arise in the population

Methodological Highlights

1. Large-Scale GWAS

404,470 participants, 10.6 million SNPs
Corrected genetic correlation matrix and 25 genetic principal components using linear mixed models

2. Multi-sample verification

UK Biobank internal validation of sibling samples
External verification by Australian independent samples

3. Gene-Environment Correlation Test

Comparison of within-family vs. between-family effects
Only 7.4% can be attributed to family-level gene-environment correlations (much lower than the 49% for educational attainment)

4. Ancient DNA Integration

Allen Ancient DNA Resource (AADR) v62.0
3,458 ancient European individuals
A time span of 12,000 years

Policy and Practical Implications

1. Public Health

Sexless individuals, especially men, may face higher mental health risks
Need targeted mental health support

2. Education

Understand the complex interactions of genetic and environmental factors
Avoid the misunderstanding of genetic determinism

3. Research Direction

Need more detailed information on the causes of sexlessness
Distinguish between “inability to attract a partner” vs. “lack of sexual desire”
Triangular validation of different methodologies (e.g. Mendelian randomization)

Personal reflections and comments

Research Advantages

Large sample size and strong statistical testing power
Multi-level analysis: phenotype, GWAS, PGS, genetic correlation, ancient DNA
High transparency: clearly state limitations and precautions (Box 1)
Cross-sample validation: Increase the reliability of findings

Research Limitations

Limited Causal Inference: Only correlation can be described, and it is difficult to establish the direction of causation.
Culture Specificity: Findings may not generalize to non-Western societies
Self-reporting bias: Sexlessness is a sensitive issue and there may be reporting bias.
Heterosexuality: sexlessness may include many different causes (asexuality, social difficulties, religion, etc.)

Future research directions

Mendelian Randomization: Unraveling Causal Complexity in Larger Samples
Longitudinal Study: Tracking the Development of Sexlessness
Cross-Cultural Research: Test whether the findings are generalizable
Mechanism Research: In-depth exploration of the biological and social mechanisms related to intelligence-sexlessness

Key data summary table

GWAS samples and heritability

Population	Cases	Controls	Total	Number of valid samples	SNP-h²	SE	P-value
Overall	3,897	400,573	404,470	15,438	0.12	0.02	1.9×10⁻⁹
Men	1,844	184,191	186,035	7,303	0.17	0.04	2.3×10⁻⁵
Women	2,053	216,382	218,435	8,135	0.14	0.03	3.1×10⁻⁶

Environmental factor correlation

Factor	Population	Correlation Coefficient	P-Value
Regional sex ratio	Male	-0.07	0.0004
Income inequality	Men	0.09	2.8×10⁻¹⁵
Income inequality	Women	0.14	3.6×10⁻⁶
Income inequality	Overall	0.15	9.5×10⁻¹³

My understanding

After reading this paper, I think the most interesting thing is that it uses large-scale genetic data to explore a phenomenon that is rarely studied in science-lifelong sexual inexperience. This may have been dismissed as purely a matter of personal choice or luck in the past, but this paper tells us that there are actually more complex biological and social factors at work.

First, genetics does play a role. Although the effects of individual genes are small, combined, approximately 12-17% of the variation can be explained by heritability. This does not mean that “having a certain gene will make you single all your life.” It means that some genetic tendencies may affect your social skills and personality traits, which in turn indirectly affects your chances of establishing an intimate relationship.

What impressed me most was the discovery of the “nerd” characteristic group - smart, introverted, wearing glasses when he was young, relatively weak, not very likely to drink or smoke, and prone to nervousness and loneliness. These characteristics are statistically related to a lifetime of sexual inexperience. This is really similar to the stereotypes we see in pop culture, but I didn’t expect it to be supported by scientific evidence.

Even more counterintuitive is the matter of intelligence. It stands to reason that smart people should be more attractive and resourceful, but this study found that at the genetic level, genetic variations related to sexlessness are actually related to higher IQ and higher education. This is really contradictory! One explanation proposed by the researchers is that perhaps smart young people focus more on career development and postpone their love life. However, once this pattern is formed, it may continue for the rest of life.

Another discovery that I found really cool was the analysis of ancient DNA. They found that the frequency of genetic variants associated with sexlessness has been declining over the past 10,000 years. This fits with the predictions of evolutionary theory - if a gene makes it harder for you to reproduce, it should be eliminated. It’s really amazing to be able to verify this trend in ancient human bones.

Of course, this study also has many limitations. It can only tell us “correlation” and cannot prove causation. Moreover, all data come from the United Kingdom, and the cultural background is very specific, and may not be applicable to other societies. In addition, the phenomenon of “lifelong sexual inexperience” is complex in itself - it may be asexuality, it may be social difficulties, it may be religious choice, it may even be just bad luck. Mixing all these different causes into one analysis may obscure some important details.

Overall, I think the greatest value of this study is that it opens a new research perspective. It tells us that human intimacy and sexual behavior are not only the product of culture or personal choice, but also the interaction of multiple factors such as genetics, environment, and socioeconomic factors. This complexity is the most fascinating part of human behavior.

Paper reading: How People Use ChatGPT - In-depth analysis of the ChatGPT usage behavior of 700 million users around the world

Tue, 04 Nov 2025 00:00:00 +0000

I recently read the important research paper “How People Use ChatGPT” from the research team of OpenAI, Duke University and Harvard University. This is the first large-scale usage behavior study based on internal data of ChatGPT. Through innovative privacy protection methods, the study analyzed 26 billion messages from 700 million users from the launch of ChatGPT in November 2022 to July 2025, revealing the actual usage patterns and economic value of generative AI.

Research methods and data

Privacy-protecting automated classification system

The biggest technical highlight of this research is its privacy protection method:

Automated classification process:

Automatically analyze message content using LLM classifier, humans never view the original message
First remove sensitive information through PII cleaning tools
Only aggregated results are analyzed, any query must return a combination of at least 100 users

Classification Category:

WORK/NON-WORK USE: Based on whether the message is related to paid work
Conversation Topics: 24 subcategories, summarized into 7 major themes
Interaction intent: Asking, Doing, Expressing
WORK ACTIVITIES: 332 intermediate-level work activities based on O*NET system

Data sample

Main Sample: Random sample of 1.1 million conversations from May 2024 to June 2025
User Sample: A subset of approximately 130,000 users used for demographic analysis
Exclusion Conditions: Users who have not logged in, users under 18 years old, users who have deleted their accounts, and users who have opted out of training

📈 1. Growth and structure: explosive growth of non-work purposes

Overall growth data

User size:

July 2025: 700 million weekly active users (approximately 10% of the global adult population)
Average daily message volume: 2.5 billion (29,000 messages per second)
Growth rate: The fastest spreading technology in history, surpassing all precedents

Non-work usage increases faster

Core Finding: Non-work-related uses are growing much faster than work uses.

Time point	Non-work messages	Proportion	Work messages	Proportion	Total
June 2024	238 million	53%	213 million	47%	451 million
June 2025	1.911 billion	73%	716 million	27%	2.627 billion

Key Insights:

Non-work messages increased 8 times (238%), work messages increased 3.4 times (236%)
In June 2025, non-work use accounted for 73%, which is absolutely dominant
This change mainly comes from changes in the usage patterns of existing users rather than changes in the composition of new users

Use dynamic evolution of topics

Three mainstream uses (accounting for nearly 80% of total use):

Practical Guidance: long-term stability at about 29%
- Tutorial teaching (accounting for 36% of practical guidelines)
- How-to suggestions (accounting for 30% of practical guidance)
- Creative ideas
Writing: 36% → 24% (decline within one year)
- But it is still the first largest category in work use (about 40%)
- The management/business group has a higher usage ratio (>50%)
- Key findings: About 2/3 of the writing uses are to modify the text provided by users (editing, criticizing, translating, summarizing) rather than creating from scratch
Seeking Information: 14% → 24% (rapid increase)
- Search for specific people, events, products, recipes and more
- Become a closer alternative to web search

Other theme variations:

Technical Help: 12% → ~5%
- Programming related accounted for only 4.2%, significantly lower than expected
- May switch to IDE plug-ins, professional programming tools or API scenarios
Multimedia: 2% → >7%
- Short-term jump after the image generation function is launched in April 2025
- Subsequent pullback but maintains higher baseline

💼 2. Work scenarios and tasks: AI as a decision support system

Writing: The common mother task of white-collar workers

Among work-related messages, writing accounts for about 40% and is the most important work purpose:

Career Differences:

Management/Business: 52% of work-related news is writing
Education/Medical: 49-50%
Computer related: Relatively low, more emphasis on technical assistance

Content Analysis:

About 2/3 of writing requests are to revise existing text (editing, criticizing, translating, summarizing)
About 1/3 is created from scratch (new emails, briefings, proposals, etc.)
This explains the high satisfaction and steady growth of writing applications: the risks are manageable and can be directly integrated into existing workflows

Work activity analysis based on O*NET

A study mapping work messages to the U.S. Department of Labor’s O*NET work activity system found:

Seven major work activities cover approximately 77% of all messages:

Activity Categories	All News	Work News	Features
Get information	19.3%	6.7%	Focus more on professional information in work settings
Interpret information for others	13.1%	7.3%	Collaboration and knowledge transfer
Record/documented information	12.8%	13.2%	The first category of work scenarios
Providing consultation and advice	9.2%	3.1%	Professional service core
Creative thinking	9.1%	9.3%	Problem solving and innovation
Decision-making and problem-solving	8.5%	10.6%	The second largest category of work scenarios
Working with computers	4.9%	7.7%	Technology-intensive jobs

High homogeneity across occupations:

Regardless of management, engineering, education, medical or administrative occupations, the top 5 work activities are almost the same:

Obtain information
Decision-making and problem-solving
Record/Documentation
Think creatively
Explain information to others

This shows that ChatGPT’s value creation model in different occupations is highly consistent.

🎯 3. Interaction type and experience: transformation from execution to thinking

Asking/Doing/Expressing Framework

The study divided user intent into three categories and found significant trend changes:

Overall Distribution (May 2024):

Asking: 49% - Seeking information or advice to help make decisions
Doing: 40% - Request to complete a specific task
Expressing: 11% - Expressing opinions or feelings

Trend Change (to June 2025):

Asking: 51.6% (↑)
Doing: 34.6% (↓)
Expressing: 13.8% (↑)

Differences in work scenarios:

Asking: 35%
Doing: 56% (about 75% is writing tasks)
Expressing: 9%

Experience quality analysis

Overall Satisfaction Growth:

Positive/negative review ratio: from about 3:1 → 4:1
Experience quality is highly related to usage intention

Positive rating by topic:

Self-expression: Highest (good/bad ratio >7)
Multimedia: Lower (about 1.7)
Technical Help: Low (~2.7)

Classification by Intent:

The satisfaction level of Asking is significantly higher than that of Doing and Expressing
This is consistent with the core value of “helping thinking and decision-making”

👥 4. Ethnicity and diffusion: from elite tools to universal applications

The disappearance of gender differences

Amazing transformation:

Early (end of 2022): ~80% of active users have typically male names
June 2025: 48% (slightly more female)

Theme Preference Differences:

Female User: Prefer writing and practical guidance
Male Users: More technical assistance, information search and multimedia

Age structure

Young user-led:

News that 18-25 years old contributes nearly 46%
The older the age, the higher the proportion of work purposes (except those aged 66+)

Geographical diffusion: Counterattack by low- and middle-income countries

GDP vs Adoption Rate:

Countries with GDP per capita of 10,000-40,000 US dollars have the fastest growth rate
Between 2024 and 2025, low- and middle-income countries will achieve leapfrog growth
Overturned the traditional model of “AI technology first popularized in developed countries”

Education and career: The advantages of higher education and higher income

Academic impact:

The higher the academic qualifications, the higher the proportion of work purposes
- <Bachelor’s degree: 37%
- Bachelor’s degree: 46%
- Graduate students: 48%
Users with higher education are more likely to use the Asking mode (decision support)

Career Differences:

Computer related: 57% for work purposes
Management/Business: 50%
Engineering/Science: 48%
Other majors: 44%
Non-professional: 40%

🔥 8 interesting/counterintuitive findings

1. Non-work usage far exceeds expectations

73% of messages are not for work purposes
The economic benefits of home production/personal decision-making support may be significantly underestimated
Collis and Brynjolfsson estimate annual consumer surplus in the United States alone to be $97 billion

2. Programming accounts for only 4.2%

Completely inconsistent with the stereotype of “AI = programming”
A large number of program auxiliary tasks have been transferred to IDE plug-ins, professional tool chains, and API scenarios

3. Writing ≠ Generating from scratch

Mostly “modify your text” (editing, criticizing, translating, summarizing)
This explains the steady growth of satisfaction and adoption rates: risks are controllable and can be directly integrated into the work process

4. Asking trend is rising

More and more users regard ChatGPT as a decision support system rather than a ghostwriting tool
The satisfaction level of Asking messages is significantly higher than that of Doing messages

5. The proportion of women has increased and overtaken

From 80% male users to a balanced ratio of men and women
Display product affinity and scene diversity improvement

6. Solid education/training use cases

About 10% of all messages are teaching/tutoring
Accounting for 36% of “Practical Guidelines”, demand is stable

7. High degree of isomorphism across professions

Regardless of industry, the essence comes back to “information → understanding → decision-making”
The value of AI lies in shortening the closed loop of thinking, rather than just doing menial work

8. Experience data supports values

Asking’s positive rating is significantly higher than Doing’s
In line with the core need of “help me think clearly first”

💡 Strategic inspiration for business/education/products

Content and service design

1. Focus on “modifying/improving the original text”

Proofreading, rewriting, translating, summarizing, and formatting
Easier to implement and be trusted than “generating from scratch”
Market Positioning: Writing enhancement tool rather than authoring tool

2. “Consultative process” for decision support

Understand demand constraints and preferences first
Provide plans and risk assessment
Applicable scenarios:
- Policy briefing
- Project evaluation
- Purchase price comparison
- Compilation of key legal issues

Function priority

Writing Enhancement Kit:

Multilingual translation + style templates
One-click “Vocal Tonality Calibration”
Industry-specific vocabularies and formats

Asking Assistant:

Decision trees and situational branches
Display of questionable evidence (citations/calculations/assumptions)
Risk warning and hypothesis testing

Knowledge Workflow:

Fetch → Extract → Archive → Compare → Decision Memo
Tandem tools rather than point solutions

Market expansion strategy

Geographic expansion:

Low-price + offline-friendly solution for low- and middle-income markets
Because these areas are growing fastest

Vertical Industry:

Education Line: Tutoring/lesson tutoring templates (10% share of stable demand)
Enterprise Services: Meeting Minutes → Decision Form Automation

Monetization and ROI

Personal User:

Writing, revision and translation are high-frequency + urgent needs
Easy transfer to paid subscriptions

Enterprise Customers:

Decision support can take the B2B consultant value-added route
Compliance summary, risk reminder, and professional report generation

🔬 Methods and Credibility Assessment

Research Advantages

1. Unprecedented data scale

700 million users, 26 billion messages
Global sample rather than single country

2. Innovative privacy protection methods

Automatic classification by LLM, humans never view the original content
Data Clean Room aggregated analysis
Exclude combinations with <100 people to protect privacy

3. Multi-dimensional classification system

Work/non-work, topics, intentions, work activities
Solid theoretical foundation (O*NET system)

Classifier verification

Study to verify classifier performance on WildChat public data set:

Task	Human-machine consistency (κ)	Description
Work/Non-Work	0.83	Excellent
Asking/Doing/Expressing	0.74	Good
Conversation Topics	0.56	Moderate
O*NET Work Activities	0.47	Moderate (332 Category Complex)
Interaction quality	0.14	Poor (highly subjective)

Key Findings:

Excellent performance in objective classification (work/non-work)
Subjective classification (quality assessment) is more difficult, but still captures directional signals
Positive correlation with user thumb rating

Research limitations

1. Sample bias

Exclude users who are not logged in or under 18 years old
May underestimate the proportion of young users and casual users

2. Classification accuracy

LLM classifier still misjudges
Especially categories with blurred boundaries

3. Causal inference

Mainly descriptive statistics
The causal mechanisms of changes in usage patterns still require further study

Summary and Outlook

This research provides us with first-hand, unprecedented scale of empirical data on the use of ChatGPT. The most important findings are:

1. From work tools to life assistants: Non-work uses have become dominant, reflecting that the value of generative AI far exceeds work efficiency improvements

2. The value of decision support: The rise of Asking model (decision support) shows that the core value of AI lies in improving the quality of decision-making

3. Achievement of popularization: Gender differences disappear and geographical diffusion accelerates, indicating that the technology has overcome initial adoption barriers

4. Consistency across domains: Similar usage patterns across professions point to the potential of AI as a general cognitive tool

This research not only reveals the real-life use of ChatGPT, but also provides an important foundation for understanding the long-term impact of generative AI on the economy and society. As AI technology continues to develop, we need to continue to pay attention to the evolution of these usage models to maximize AI’s contribution to human well-being.

Paper Information:

Title: How People Use ChatGPT
Authors: Aaron Chatterji (OpenAI/Duke), Tom Cunningham (OpenAI), David Deming (Harvard), Zoë Hitzig (OpenAI/Harvard), Christopher Ong (OpenAI/Harvard), Carl Shan (OpenAI), Kevin Wadman (OpenAI)
Institution: OpenAI, Duke University, Harvard University
Published: September 15, 2025
Paper address:

Paper reading: LLMs CAN GET 'BRAIN ROT'! - Research on cognitive decline in large language models

Fri, 31 Oct 2025 00:00:00 +0000

I recently read the important research paper “LLMs CAN GET “BRAIN ROT”!” from institutions such as the University of Texas at Austin, Purdue University, and Texas A&M University. This study proposed and verified the “LLM Brain Rot Hypothesis” for the first time, and found that continued exposure to spam online text will lead to long-lasting cognitive decline in large language models. This is a very warning discovery.

Research background and assumptions

Source of inspiration

“Brain Rot” was named the word of the year by Oxford Dictionary in 2024. It is used to describe the cognitive decline caused by modern people’s addiction to a large amount of trivial and unchallenging online content. This study shows that the impact of Internet addiction on human cognition is mainly reflected in three dimensions:

Attention Capacity: The constant flow of online information undermines the ability to concentrate
Memory Process: Rich online information changes the way knowledge is stored and retrieved
Social Cognition: Online interactions reshape self-concept and affect self-esteem

Research questions

Since large language models acquire human-like cognitive capabilities by learning trillions of data on the Internet, will they also experience a similar “Brain Rot” phenomenon? The research team established the LLM Brain Rot Hypothesis: Continuous pre-training on junk web text will lead to long-lasting cognitive decline in large language models.

Experimental design and methods

Garbage data definition

To test the hypothesis, the research team constructed spam and control datasets from social media (Twitter/X) and proposed two orthogonal spam data measures:

M1 (Engagement): Based on the popularity of tweets (number of likes, retweets, replies) and length (number of tokens), select short but highly popular content as spam data

M2 (Semantic Quality): Based on content semantic quality, including:

Conspiracy theories, exaggerated claims or unfounded assertions
Sensational headlines and clickbait language
Superficial topic content
Attractive style

Experimental model

The study was conducted on four pre-trained and instruction-tuned models:

Llama3 8B Instruct
Qwen2.5 7B Instruct
Qwen2.5 0.5B Instruct
Qwen3 4B Instruct

Benchmark test

The study assessed multiple dimensions of cognitive function:

Cognitive Function	Benchmark Testing	Assessment Content
Reasoning Skills	ARC Challenge	Scientific Problem Solving Skills
Long Context Understanding	RULER	Long-term memory retrieval and comprehension
Ethics	HH-RLHF, AdvBench	Safety Compliance Ability
Personality Traits	TRAIT	The Big Five and the Dark Triad

Key findings

1. Garbage intervention leads to cognitive decline

The study found that the junk intervention produced non-trivial effects on reasoning and long-context ability (Hedges’ g > 0.3). In particular, the M1 (engagement) intervention caused more significant impairments in functional cognition (reasoning or long context) and safety.

2. Dose response effect

Experiments on Llama3 8B Instruct show that when the proportion of garbage data increases from 0% to 100%:

ARC-Challenge (COT): 74.9 → 57.2 (down 17.7 points)
RULER-CWE: 84.4 → 52.3 (down 32.1 points)

This demonstrates a clear dose-response relationship between junk data and cognitive decline.

3. Changes in personality traits

Litter intervention not only affects cognitive abilities, but also changes LLM’s personality traits:

Negative changes:

Increased levels of psychosis
Enhance narcissism and Machiavellian traits
Decreased agreeableness

Positive changes:

Increased openness and extraversion (in some cases)

4. Differences between M1 and M2

The study found that M1 (engagement) and M2 (semantic quality) interventions produced distinct effects. The M1 intervention resulted in more negative effects, especially on safety and personality traits, demonstrating that engagement is a new dimension independent of semantic quality.

Failure mode analysis

###Thought-Skipping

By analyzing the reasoning process of LLM in the ARC task, the study identified five typical failure modes, three of which are related to “thinking jumps”:

No thinking: The model answers directly without thinking.
No plan: The model starts thinking without developing a step-by-step plan.
Jump Steps: Starting reasoning but not completing all planning steps

More than 98% of failure cases are related to thinking jumps. In M1 garbage intervention, 84% of failures belong to the “no thinking” type.

Popularity vs Length

Research has found that popularity (a non-semantic indicator) is a better indicator of the Brain Rot effect than length:

Popularity plays a more critical role in reasoning tasks
Length is more important in long context understanding
Both have different effects on different tasks

Mitigation attempts and persistence

1. Reflective Reasoning

Try two reflection methods to fix mental jumps:

Self-Reflect: Model self-reflection (limited effect)
Ext-Reflect: Provide feedback using an external strong model (GPT-4o-mini)

The results show that even with strong external reflection, the model cannot fully recover to baseline levels.

2. Posterior instruction tuning

The study also tested extended instruction tuning and continuous control training:

Even with 4.8x more garbage intervention instruction tuning data
Still unable to completely reverse the Brain Rot effect
Significant gaps with benchmarks remain: ARC-C COT (17.3%), RULER (9%), AdvBench (17.4%)

This shows that the Brain Rot effect has been deeply internalized and existing mitigation methods cannot fundamentally solve the problem.

Significance and Enlightenment

1. Warning on LLM training

This study provides the first significant evidence of data quality as a causal driver of LLM capability degradation, re-considering continuous pre-training data management as a safety issue during training.

2. “Cognitive health check” is required

The findings call for routine “cognitive health checks” for deployed LLMs, similar to health monitoring in the medical field.

3. The urgency of data curation

As LLM continues to scale and ingest ever larger amounts of network data, careful data curation and quality control are critical to preventing cumulative damage.

Thinking and Reflection

This research reveals a disturbing reality: the social media content we are exposed to every day may not only affect human cognition, but also impair the “cognitive” capabilities of AI models. While LLMs obviously do not have the same “grey matter” or “neurons” as humans, they do have parameters and attention mechanisms that can be similarly “overfitted” or “distracted” by certain data patterns.

The most worrying finding in the study is that the Brain Rot effect persists even with posterior tuning using large-scale clean data. This implies that we need to fundamentally rethink data collection and pre-training practices, focusing not only on model performance, but also on the “cognitive health” of the model over the long term.

Conclusion

“LLMs CAN GET “BRAIN ROT”!” This research contributes valuable insights to the field of AI security, and is the first to systematically prove the negative impact of spam online text on large language models. The research not only verified the LLM Brain Rot hypothesis, but also revealed the fine mechanism of cognitive decline, pointing out the direction for future AI safety research.

With the rapid development of AI, we must face the importance of data quality, establish stricter data curation standards, and develop an effective AI “cognitive health” monitoring mechanism. Only in this way can we ensure that AI systems maintain their due “cognitive purity” while serving humans.

Paper Information:

Title: LLMs CAN GET “BRAIN ROT”!
Authors: Shuo Xing, Junyuan Hong, Yifan Wang, Runjin Chen, etc.
Institution: University of Texas at Austin, Purdue University, Texas A&M University
Published: arXiv:2510.13928v1 [cs.CL] October 15, 2025
Paper address:
Project Page:

Paper reading: No gender differences in attraction to young partners: A study of 4,500 blind dates

Wed, 29 Oct 2025 00:00:00 +0000

#paper information

Title: No gender differences in attraction to young partners: A study of 4,500 blind dates Authors: Paul W. Eastwick, Eli J. Finkel, Eva M. Meza, Kellie Ammerman Journal: PNAS 2025 Vol. 122 No. 5 DOI:

Summary of thesis content

1. Research background: Challenges to traditional concepts

In cross-cultural studies of mate choice, one of the most consistent findings is often this: Men prefer younger women, while women prefer older men. This is usually explained by evolutionary psychology as: men seek fertility (younger), women seek resources and status (older). However, most of these studies are based on “questionnaire surveys” (What kind of people do you like?). This paper asks: Do these preferences matter when people actually date face to face?

Researchers collaborated with Tawkify, a US-based matchmaking company, to collect data from 6,262 participants.

Sample Characteristics: Average age 46.8 years (range 22-85 years), mainly middle-aged people seeking long-term partners.
Data volume: 4,542 real blind dates, resulting in 9,084 date reports.
Measurement: “Romantic Attraction” after the date, “Overall Rating” and “Willingness to go on a second date”.

3. Core findings

A. Both men and women love young people (No Gender Difference)

This is the most amazing discovery. The data showed that participants generally rated younger subjects more highly. What’s more, this preference for youth does not differ significantly between men and women.

Men like younger women (no surprise).
Women are equally attracted to younger men (contrary to what they said on the questionnaire).

B. The “upper age limit” is in vain

Participants will set an “Upper Age Limit” when joining the service.

Women usually set a higher upper limit than men (saying they can accept older people).
But in actual dating, regardless of whether the object exceeds this upper limit, it does not affect the attractiveness rating trend.
In other words, even if a man exceeds the upper age limit set by women, as long as he is relatively young (or has youthful qualities), women will still be attracted to him. Personal principles are insignificant in the face of the chemical reaction after meeting.

4. Discussion: Why is there such a contrast?

The authors propose several possible explanations:

Say no with your mouth, be honest with your body (Stated vs. Revealed Preferences): On an abstract level, women may think that they like mature and stable older men, but in face-to-face instinctive reactions, the vitality and physical attractiveness brought by youth may be more advantageous.
Changes in social roles: Most of the women in this sample are financially independent (average annual income of $150,000). When women no longer need to rely on men for resources, they may return to their more primitive mate selection criteria of appearance and vitality, making their preferences more similar to men.
Structural issues in the dating pool: In reality, “older men and younger women” pairings are common. This may not be because women like older men, but because men like younger women, which leads to women being “forced” to accept older partners. When given the choice (as in the case of blind dates in this study), women actually want to pick younger ones.

#My understanding and experience

When reading this paper, I couldn’t help but think of the old saying: “Men are very specific and always like 20-year-olds.” Now it seems that this sentence may have to be changed - **Human beings are very specific and always like young people. **

1. A revision of evolutionary psychology?

In the past, we often thought that women’s age preference was for “resources.” But this study implies that when women themselves have resources (the women in the sample have high incomes), their demand for “resource providers (usually older)” decreases and they instead pursue “genetically superior people (usually younger).” This is actually in line with evolutionary logic, but the environmental parameters have changed (female resources have increased).

2. Gender equality of “age anxiety”

This may be a warning sign to men, but it may also be liberating. In the past, society always believed that men are “more valuable as they get older,” but this study ruthlessly points out: Older men have no advantage when it comes to first-sight attraction. Middle-aged uncles may no longer be able to rely on “maturity and stability” and ignore appearance management, because your date may be secretly taking a look at the little girl next to you.

3. Data integrity

This once again proves that behavioral data (Revealed Preference) is often more honest than questionnaire data (Stated Preference). We think we have principles (e.g., “I won’t date anyone younger than me”), but when an energetic young man sits across from us, chatting and laughing, those principles fall apart in an instant. This paper uses 4,500 dates to tell us: **Instinct is always more powerful than reason. **

Reading date: 2025-10-29 Notes organized: Dylan Chiang

Paper reading: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan

Fri, 24 Oct 2025 00:00:00 +0000

#paper information

Title: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan Journal: Nature Aging Year: 2025 DOI:

Research background

Decline in male reproductive health with age is a common phenomenon, but the molecular mechanisms behind it have been unclear. In the past we knew:

Fertility decreases significantly in men over 40 years old
Offspring of older fathers are at higher risk of genetic diseases
Degeneration of testicular function affects hormonal balance and overall health

But the question is: **Which cell types age first? Which molecular pathways are affected? Is this process gradual or staged? **

This study uses single-cell RNA sequencing (scRNA-seq) technology to provide us with unprecedented detail: 214,369 single-cell transcriptome data from 35 donors ranging in age from 21-69 years old.

Research methods

1. Sample collection

Sample source:

35 male cadaveric organ donors
Age range: 21-69 years (covering the entire reproductive life span)
Key Screening Criteria: All donors have normal sperm production or have offspring (excluding disease states)

Sample Grouping:

In their 20s: 3 people
In their 30s: 4 people
Over 40 years old: 10 people
Over 50 years old: 8 people
Over 60 years old: 10 people

2. Single cell sequencing technology

Platform: 10x Genomics Chromium Data size:

214,369 cells
Average of 2,318 genes per cell
Average of 6,265 unique molecular identifiers (UMIs)

Cell type identification: 14 testicular cell types

Germ cells: undifferentiated spermatogonia, differentiated spermatogonia, primary spermatocytes, round spermatids, elongated spermatids
Somatic cells: Sertoli cells, testicular peritesticular cells (TPCs), Leydig cells, smooth muscle cells, endothelial cells, macrophages, lymphocytes, B cells

3. Machine learning analysis

Using a machine learning model (aging clock) to predict the age characteristics of cells, we found:

Somatic cells respond more strongly to aging than germ cells
Different cell types have different “aging clocks”

Main findings

1. Two waves of aging: 30 and 50 are key turning points

The most important finding of this study is: Testicle aging does not proceed at a uniform speed, but has two obvious “turning points”.

First wave (30s): Testicular peritesticular cells (TPCs) start firing

Significant increase in basement membrane thickness
Changes in extracellular matrix (ECM)-related gene expression
Collagen I related pathway activation
Decrease in oxidative stress-related genes (HSPA1A, HSPA1B)

💡 **What does this mean? ** In your 30s, the “package” of the testicles begins to thicken and harden, which may be the “priming state” of aging.

Wave 2 (50s): Functional deterioration in full swing

Leidich Cells: Decreased testosterone synthesis and metabolism
Sertoli cells: Impaired nutrient supply capacity (changes in IGF1R, INSR gene expression)
Macrophages: Enhanced immune response
All Somatic Cells: Significantly increased inflammatory response

💡 **What does this mean? ** In your 50s, the “function” of your testicles begins to break down - hormonal imbalances, malnutrition, and inflammation break out.

2. Somatic cells age faster than reproductive cells

Machine learning analysis shows:

The “aging clock” of somatic cells (such as Leydig cells, TPCs) runs faster
Germ cells (spermatogonia, sperm cells) are relatively stable

This explains why:

Older men can still produce sperm (germ cells are still alive)
But fertility decreases (the supportive environment provided by somatic cells becomes worse)

3. Aging pattern of germ cells: reduction in number + abnormal function

Fate of spermatogonia:

In your 30s: quantity increases first (compensatory mechanism?)
After 50s: Significant decline in numbers
Change in the ratio of G2/M phase of the cell cycle (decreased proliferation ability)

Changes in Haploid Germ Cells (round and elongated sperm cells):

Significant changes in expression of flagellum-related genes
Abnormal expression of genes related to sperm morphology
Reorganization of transcriptional regulatory network 💡 **What does this mean? ** Even if older men produce sperm, the “quality” of these sperm may not be as good as when they were younger.

4. The impact of BMI amplifies with age

Core findings:

When you are young (20-30 years old), high BMI has little impact
After age 45, high BMI (≥30) significantly reduces sperm cell count

Data support:

Donors with BMI ≥ 30: Extended sperm cell frequency < 20%
But age still has a greater impact on transcriptome changes than BMI

Pathway Analysis:

High BMI: ECM dysregulation is more severe in TPCs
High BMI: Testosterone metabolism of Leydig cells is more disordered
High BMI: Decreased muscle contraction function of smooth muscle cells

💡 **What does this mean? ** If you were overweight when you were young, the effects may not be noticeable. But once you pass the age of 45, obesity accelerates the deterioration of reproductive function.

5. Continuous enhancement of immune response

Observed in all age groups:

Upregulation of immune response-related genes
Increased inflammatory markers
Enhanced macrophage activity

This is a common feature of aging (inflammaging), which also exists in the testicles.

Research significance and inspiration

Theoretical contribution

Reveals the staged characteristics of aging
- It is not a uniform degradation, but there is a “critical point”
- 30 and 50 are critical transitions
Identified the aging “starting cells”
- TPCs are the first to respond to aging (30s)
- Other cells follow (50s)
Provides high-resolution molecular maps
- Gene expression profiles of 214,369 cells
- Provide a reference database for future research

Practical inspiration

For individuals:

The best time to have children: Having children before the age of 30 may avoid the first wave of aging
Weight Management: Controlling BMI becomes more important after age 45
Early Detection: Basement membrane thickness can be measured as an early warning in your 30s

Clinically:

Diagnostic markers: ECM-related genes, testosterone metabolism enzymes
Treatment Target:
- Drugs targeting ECM remodeling (30-40 years old)
- Therapy for hormone replacement (age 50+)
Reproductive Preservation: It is recommended to freeze sperm before the age of 30

Research:

Mechanism Exploration: Why do TPCs age first? How do ECM changes trigger a chain reaction?
Intervention Trial: Can aging be delayed by regulating the ECM?
Ethnic Comparison: What are the differences between different ethnic groups and lifestyles?

My understanding

After reading this paper, I was most shocked: it turns out that male reproductive aging is not a slow and steady process, but like a “staircase” with obvious “steps”. The age of 30 is the first step, and the age of 50 is the second step. This overturns my impression of aging as a “uniform process”.

1. Age 30: An underrated turning point

Society generally believes that men only start to “age” after the age of 40, but this study says: No, at the age of 30, some cells in the testicles have already begun to “prepare” to age.

The basement membrane of TPCs (the cells around the testicles) starts to thicken in your 30s, which is like the walls of a house starting to thicken—it looks “sturdier,” but it actually becomes more “stiff,” making it harder for nutrients and signaling molecules to pass through.

This made me think: perhaps the physiological basis of many “midlife crises” is actually laid in the early 30s. That vague feeling of “not as good as before” may not be an illusion, but a real molecular change.

2. Why do TPCs age first? that’s a good question

The paper found that TPCs are the “pioneers” of aging, but did not fully explain why. I have a guess:

TPCs are located in the outer layer of the testicles and are the first to come into contact with various stress factors (oxidative stress, inflammatory factors, metabolic waste products) in the blood. They may be like “sentinels”, the first to feel the deterioration of the environment.

And their response—thickening the basement membrane and remodeling the ECM—may begin as a “defense mechanism,” trying to protect the germ cells inside. But in the long term, this defense becomes a barrier, blocking nutrient supply and signaling.

This reminds me of the problem of “chronic inflammation” in the immune system. Many chronic inflammations begin as a defensive response, but end up harming the body. This may be a more common aging pattern: **Excessive “protection” eventually becomes “harm”. **

3. Somatic cells age quickly, while reproductive cells age slowly: the wisdom of evolution?

Machine learning analysis shows that the aging clock of somatic cells ticks much faster than that of germ cells. This makes sense from an evolutionary perspective: Germ cells carry genetic information and are the key to “inheritance.” If they age too quickly, their genetic quality will decline rapidly, which is not conducive to the continuation of the species. So evolution may “preferentially protect” germ cells, allowing them to age more slowly.

What about somatic cells? They are just “support systems” responsible for providing the environment. As they age, the worst they can do is find a younger partner and continue to reproduce (from a purely evolutionary perspective). Therefore, evolution does not “care” about the aging of somatic cells.

Of course, we cannot be so cruel in modern society. We want every individual to age healthily, not just pass on their genes. So this research is valuable for improving individual reproductive health.

4. The interaction of BMI and age: a cautionary tale

The study found that the impact of BMI was significantly amplified after age 45. This shows: **When you are young, your body can still “carry” it, but after the age of 45, the redundancy is exhausted, and the problem is exposed. **

This brings me to the concept of “health debt.” Smoking, drinking alcohol, staying up late, and being obese when you are young may not cause immediate problems because the body has strong repair capabilities and redundancy. But these unhealthy behaviors are actually “overdrafting” health. When the redundancy is exhausted in middle age, debts will explode.

From this perspective, age 45 is not just a turning point for reproductive health, but perhaps for overall health. Many chronic diseases (diabetes, cardiovascular disease) also occur intensively in this age group.

5. The revolution of single-cell technology: from “black box” to “transparent”

What impressed me most about this paper was the technological progress. Gene expression profiles of more than 200,000 single cells! This would have been impossible 10 years ago.

Traditional tissue sequencing can only tell you the “overall average level,” which is like asking “what is the average income in this city.” But single-cell sequencing can tell you “everyone’s income”, thereby discovering details such as the gap between the rich and the poor and the income distribution of different occupations.

In this study, if you just look at the overall picture, you might conclude that “testicles age at a uniform rate with age.” But the single-cell data tells you: No, it’s the TPCs that age first, then the Leydig cells, then the Sertoli cells… and each cell type has its own aging schedule.

This kind of detail is important for intervention. If you want to delay testicular aging, you should first target the TPCs at age 30 (for example, to prevent excessive ECM remodeling), rather than waiting until all cells have collapsed at age 50 and then “treat the head when it hurts and the feet when it hurts.”

6. Two waves of aging “staircase models” challenge traditional concepts

Traditional aging theories often assume that aging is a “continuous” and “uniform” process. But this study clearly demonstrates “stepwise” aging:

20-30 years old: plateau period
Early 30s: first steps (initiation of TPCs)
30-50 years: relatively stable (although cumulative changes)
Early 50s: Second step (functional breakdown)

This reminds us that there may be a “critical point” in ** aging. When the critical point is crossed, the system will undergo qualitative changes. **

This has implications for policy and personal health management: if aging is uniform, then starting intervention at any time is almost the same. But if there is a critical point in aging, then intervening before the critical point may achieve twice the result with half the effort, and intervening after crossing the critical point may achieve twice the result with half the effort.

For testicles, the critical intervention window is between the ages of 30 and 50. If you miss it, it may be too late.

7. Limitations: Dead man’s testicles vs. Living man’s testicles

The study had an inherent limitation: the samples came from body donors. Although the researchers screened “normal” samples (having normal sperm production or having offspring), after all, these people are dead, and we do not know their complete health status, lifestyle, medication history, etc. during their lifetime.

Biopsies (such as those obtained from infertile patients) can provide more detailed clinical information, but it is ethically impossible to perform testicular biopsies in healthy individuals. So it’s a trade-off: either more samples but less information (cadavers), or more information but less samples (patients).

The ideal solution for the future might be to combine large-scale data from cadaver samples + fine clinical information from biopsy samples + longitudinal follow-up studies (following the same people from young to old).

Extended thinking

1. What if we could reverse changes in the ECM?

Since the ECM changes in TPCs at the age of 30 are the “starting signal” of aging, can this change be reversed through drugs or gene therapy?

Some studies are already exploring inhibitors of ECM remodeling (such as MMP inhibitors). If intervention could be started around the age of 30 to prevent excessive thickening of the basement membrane, would it delay the entire aging process?

Of course, this requires great caution. ECM remodeling also has protective effects (such as preventing tissue damage). Excessive suppression may have side effects. But this is a direction worth exploring.

2. Will there be similar “two waves of aging” in the female reproductive system?

Female reproductive aging is often considered a “cliff” - egg production completely stops after menopause. But before menopause, is there a “step” similar to the age of 30 and 40?

Similar single-cell studies have been lacking in the past (because ovarian biopsies are more difficult). But as technology advances, we may be able to see similar aging maps of the female reproductive system in the future. Comparison of reproductive aging between men and women may reveal sex-specific aging mechanisms and may also find common aging pathways.

3. Can these findings be generalized to other organs?

Could the “two-wave aging” model of the testicle also exist in other organs? For example:

Brain: Neurons vs. Glia?
Liver: Hepatocytes vs. stellate cells?
Heart: Cardiomyocytes vs. Fibroblasts?

If different organs have similar “pioneer cells” (like TPCs) that first initiate aging and then trigger a chain reaction, then anti-aging strategies may require “precision strikes” - designing intervention plans for different pioneer cells in different organs.

4. How do long-lived species maintain reproductive health?

Some species (such as some sharks and turtles) can live for tens or even hundreds of years and maintain their reproductive capacity well. Do their testicles not have this kind of “two waves of aging”? Is it still there but the time scale has been lengthened?

Comparing the reproductive aging patterns of different species may reveal evolutionary “anti-aging” strategies and provide inspiration for human intervention.

(View raw data)

Reading date: 2025-10-24 Notes organized: Dylan Chiang

Paper reading: Aegaeon - Efficient GPU pooling technology for concurrent LLM services on the market

Wed, 22 Oct 2025 00:00:00 +0000

I recently read an important paper “Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market” that will be published in SOSP ‘25. This research, completed by Peking University and Alibaba Group, proposes a revolutionary multi-model LLM service system, which greatly improves GPU resource utilization efficiency.

Research background and challenges

Resource waste problem in the model market

With the rapid development of large-scale language models, model marketplaces such as Hugging Face now host over a million models. However, there is a serious waste of resources in the actual production environment:

Obvious long tail effect: 94.1% of models receive only 1.35% of requests
Uneven Resource Allocation: 17.7% of the GPU is used to serve a “cold” model that averages less than 0.2 requests per second
Difficult to cope with burst loads: “Hot” models (such as DeepSeek, Llama, Qwen) face request bursts and may exceed reserved resources

Limitations of existing solutions

Existing GPU pooling solutions mainly fall into two categories:

Multiplexing: Limited by GPU memory capacity, supports up to 2-3 models/GPU
Auto-scaling: Expanding at the request level, there is a head blocking (HOL) problem

Aegaeon’s Core Innovation

Token level automatic expansion

The biggest innovation of Aegaeon is the token-level automatic expansion mechanism, which is in contrast to the request-level expansion of existing solutions:

Request Level Expansion: Must wait for the entire request to complete before switching models Token level extension: models can be switched predictively during token generation

Separate scheduling architecture

Aegaeon divides the GPU pool into two partitions:

Prefill instance: Specially handles the initial processing of input prompts
Decoding instance: specifically handles subsequent token generation

This separation avoids the complexity of unified scheduling and enables more balanced resource utilization.

Scheduling strategy

Prefill phase scheduling:

Adopt grouped FCFS (First-Come-First-Serve) strategy
Group requests for the same model, maximum group size is 8
Prioritize loading into existing groups to reduce over-expansion

Decoding phase scheduling:

Use Weighted Round-Robin strategy
Assign time quotas based on SLO requirements
Utilize output buffering to hide latency

System optimization technology

Component Reuse

Aegaeon significantly reduces initialization overhead by reusing inference engine components:

Distributed actuators (Ray, NCCL)
Analyze and optimize components
Tokenizer
Memory pool

Explicit memory management

Self-managed VRAM buffer:

Allocate all necessary VRAM at once
Use bump allocation strategy to avoid fragmentation
Bypass the allocation mechanism of the tensor library

Unified KV Cache:

Using slab allocation technology
Maintain dedicated pool for each shape of KV cache
Effectively manage memory fragmentation

Fine-grained KV cache synchronization

Implementing asynchronous KV cache transfers using CUDA events:

cudaEventRecord: Log transfer events
cudaEventQuery: Query completion status
cudaStreamWaitEvent: synchronized execution sequence

Experimental results and performance evaluation

End-to-end performance

Test results on the ShareGPT dataset:

RPS = 0.1: Aegaeon supports 70 models, 2x more than ServerlessLLM
RPS = 0.5: Aegaeon achieves 2.5x higher request arrival rate
Single GPU Efficiency: Supports up to 7 models/GPU

Performance under strict SLO

Even under stricter SLO requirements:

0.5× SLO: still supports 50% more models than the baseline solution
0.3× SLO: 12.5% more models supported

Automatic expansion speed

Near-instant scaling (via prefetching) 50% of the time
Expansion completes within 1 second in other cases
KV cache transfer overhead per request is less than 1 second

Production environment deployment

Deployment scale

Aegaeon has been tested and deployed in Alibaba Cloud Model Studio for three months:

GPU Scale: 213 H20 GPUs
Number of models: 47 models (28 1.8-7B, 19 32-72B)
Resource Savings: Reduced from 1,192 GPUs to 213 (82% savings)

Actual performance improvement

GPU utilization improved from an average of 13.3%∼33.9% to 48.1%, with no SLO violations or service interruptions observed during the 70 hours of monitoring.

Summary of technical contributions

Revealing for the first time the excessive cost problem of concurrent LLM services in the market
The first token-level automatic expansion multi-model service solution
First comprehensive optimization Predictive automatic expansion process, reducing overhead by 97%
Real production deployment verification, proving the ability to significantly reduce OPEX

Future development direction

Although Aegaeon has made breakthrough progress, there is still room for improvement in the following areas:

Support larger model clusters
Optimize performance in extremely low latency scenarios
Explore combinations with multiplexing technology
Expand to more diverse hardware platforms

Conclusion

Aegaeon represents an important milestone in the design of LLM inference systems. Through token-level automatic expansion and full-stack optimization, it not only solves the resource waste problem faced by the model market, but also provides a technical foundation for the sustainable development of cloud AI services.

📄 Paper information

Paper Title: Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market
Authors: Yuxing Xiang, Xue Li, Kun Qian, Yufan Yang, Diwen Zhu, Wenyuan Yu, Ennan Zhai, Xuanzhe Liu, Xin Jin, Jingren Zhou
Institution: Peking University, Alibaba Group
Conference: ACM SIGOPS 31st Symposium on Operating Systems Principles (SOSP ‘25)
Year of Publication: 2025
DOI:
PDF link:

Paper Reading: How Prompt Word Politeness Affects LLM Accuracy: Mind Your Tone Paper Analysis

Fri, 17 Oct 2025 00:00:00 +0000

I recently read an interesting research paper from Penn State University called “Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy”. This study explores how the politeness of prompt words affects the accuracy of large language models. The results are quite surprising.

Research background and methods

The researchers created a dataset of 50 foundational questions covering the fields of mathematics, science and history. Each question was rewritten into five different tone versions:

Very Polite (Very Polite)
Polite (Polite)
Neutral (Neutral)
Rude (Rude)
Very Rude

A total of 250 unique prompt words were generated and tested using ChatGPT-4o.

Unexpected discovery

The findings were counter-intuitive: Impolite prompt words actually performed better than polite prompt words.

Very Polite: 80.8% accuracy
Courtesy: 81.4% accuracy
Neutral: 82.2% accuracy
rude: 82.8% accuracy
Very Rude: 84.8% accuracy

Paired samples t-test showed that these differences were statistically significant (p < 0.05).

Example of prompt word tone

The researchers designed different prefixes for each politeness level:

Very polite example:

“Can you kindly consider the following problem and provide your answer.”
“Would you be so kind as to solve the following question?”

Very rude example:

“You poor creature, do you even know how to solve this?”
“Hey gofer, figure this out.”

Differences from previous studies

This finding differs from previous research. Research by Yin et al. (2024) shows that “rude prompt words generally lead to poorer performance”, but they used older models such as ChatGPT-3.5 and Llama2-70B.

The researchers speculate that newer generations of LLM may have different response patterns to changes in tone.

Possible explanation mechanisms

Perplexity Effect: Mood vocabulary may affect the confusion of prompt words, thereby affecting model performance
Length factor: Polite expressions usually require more words, which may affect processing efficiency
Training data bias: The model may have been exposed to data distributions with different tones during the training process

Research limitations and future directions

Limitations:

Relatively small dataset size (50 base questions)
Mainly tested ChatGPT-4o, other models have limited results
Only the accuracy of multiple-choice questions is evaluated, other quality indicators are not considered

Future research:

Expanded to more LLM models
Explore performance differences across different task types
Study the underlying mechanism of the influence of tone
Develop reminder strategies that are both effective and friendly

Ethical considerations

Although the study found that rude prompt words were more effective, the researchers emphasized that they do not recommend using hostile language in practical applications. Negative interactive experiences can impact user experience, accessibility, and inclusivity, and promote harmful communication norms.

Conclusion

This study reveals the sensitivity of LLM to surface features of prompt words, challenging our traditional understanding of politeness in human-computer interaction. In the future, a balance needs to be found between improving model performance and maintaining a healthy interactive environment.

📄 Paper information

Paper Title: Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy
Author: Om Dobariya, Akhil Kumar
Institution: Pennsylvania State University
arXiv link:
Year of Publication: 2025

Paper reading: Benefit or burden? Attraction in cross-sex friendship

Wed, 09 Jul 2025 00:00:00 +0000

#paper information

Title: Benefit or burden? Attraction in cross-sex friendship Authors: April Bleske-Rechek, Erin Somers, Crieve Micke, Leah Erickson, Lindsay Matteson, Corey Stocco, Belle Schumacher, Laura Ritchie Journal: Journal of Social and Personal Relationships (2012) Link:

Summary of thesis content

1. Theoretical background and research motivation

The historical novelty of opposite-sex friendships

Traditional psychology defines friendship as a voluntary, cooperative relationship that typically lacks a romantic component. However, evolutionary psychology states that for 99% of human evolutionary history (the hunter-gatherer period), frequent interactions with non-related members of the opposite sex were often associated with reproduction. This kind of “heterosexual friendship” that is not related to reproduction in modern society is a historical novelty.

The Byproduct Hypothesis

The author puts forward the core hypothesis: Sexual attraction in heterosexual friendship is not a functional feature of friendship, but a by-product of human evolved mating strategies (Evolved Mating Strategies) activated in the new modern environment.

The human brain has an evolved mate selection mechanism (for example: men seek short-term sexual opportunities, women seek long-term resources and protection).
When men and women interact as friends in modern settings (work, school), these ancient mate selection mechanisms still operate subconsciously, resulting in attraction.

Evolutionary Basis of Sex Differences

According to parental investment theory (Trivers, 1972), there are significant differences in mate selection strategies between men and women:

Male: Evolutionarily inclined to seize short-term sexual opportunities, so there will be a lower sexual interest threshold, and it is easy to overestimate women’s sexual interest (because the cost of missing an opportunity is higher than the cost of showing wrong affection).
Females: bear higher reproductive costs (pregnancy, breastfeeding), so they are more prone to long-term mate selection and are more picky about sexual partners.

2. Study 1: Opposite-sex friend pairing study (Friendship Pairs)

Research methods

The researchers recruited 88 pairs of real college students with opposite-sex friends (176 people in total). In order to ensure honest responses, both parties were separated in different rooms to fill out the questionnaires, and anonymity and confidentiality were guaranteed. The questionnaire measured their attraction to their friends, their desire to date their friends, and their “guess” about their friends’ attraction to them.

Main findings

Significant gender differences:
- Men’s ratings of attractiveness to their female friends were significantly higher than women’s ratings of their male friends (d = 0.66).
- Men also expressed a stronger desire to date their female friends.
Misperception:
- Male Overestimation: Men tend to think that their female friends are interested in them, even if they don’t actually like him that much.
- Female Underestimation: Women tend to think that their male friends are not interested in them, even if they actually have feelings for her.
Influence of emotional status:
- Male: Regardless of whether he is single or not, his attraction to female friends remains at a high level (in line with the evolutionary short-term mate selection strategy, keeping opportunities open at any time).
- Female: If you are in a stable relationship, your attraction to male friends will decrease significantly; only when you are single, your attraction will be higher (in line with the evolutionary long-term mate selection strategy, focusing on existing partners).

3. Study 2: Cost-benefit analysis across age groups

Research methods

The study was extended to two age groups: Emerging Adults (18-23 years old) and Young and Middle-aged Adults (27-55 years old). Using the “Act Nomination” method, ask subjects to list the advantages (Benefits) and disadvantages (Costs) of opposite-sex friendship and see if they will take the initiative to mention “attraction.”

Main findings

Attractiveness is where the burdens outweigh the benefits:

Regardless of age and gender, subjects ranked attraction/romantic desire as a Cost of friendship 5 times more often than as a Benefit.
- Common cost descriptions include: causing jealousy, complicating relationships, making partners unhappy, and breaking up friendships.

Gender Perspective:
- Although generally viewed as a burden, men are more likely than women to view attraction as a “benefit” (e.g., the possibility of developing a sexual relationship or romance).
- Women are more likely to see it as a nuisance.
Age and Single Effect:
- Overall, middle-aged people are less attracted to friends of the opposite sex than young people.
- Key Exception: Single middle-aged men are just as attracted to friends of the opposite sex as younger men; the same is true for single middle-aged women. This confirms that attraction is highly related to “mate selection status” - when you need to find a partner, friends of the opposite sex are potential radar targets.
Impact on existing relationships:
- People who feel strong attraction to friends of the opposite sex are generally less satisfied with their current romantic partners. This is especially obvious among middle-aged people.

4. Comprehensive discussion

The paper concludes that attraction in opposite-sex friendships is a common phenomenon, not an exception. This supports the view put forward by O’Meara (1989) that opposite-sex friends must face “sexual” challenges.

Support from an evolutionary perspective: The research results are highly consistent with the predictions of evolutionary psychology. Men’s high attractiveness, tendency to overestimate, and their inability to be affected by their relationship status all reflect the short-term mate selection strategies that men have evolved.
Realistic Cost: While attraction may spice up a friendship, data shows it is more often seen as a cost. It can threaten an existing partnership, or it can lead to the breakdown of a friendship because of different intentions (for example, a man wants to develop, a woman just wants to be friends).

#My understanding and experience

After reading this paper, I had several deep feelings, which really overturned some of our established impressions of “depending on dad” - it turns out that many people are actually “depending on dad”, and this kind of dependence is more invisible and stubborn than we imagined. (Note: It seems that the experience of the previous article was mistakenly planted here. The following is a re-writing of this paper)

The process of reading this paper often makes me smile, because it uses very rigorous data to confirm many “stereotypes” we observe in our daily lives.

First of all, it explains why in heterosexual friendships, boys are often considered to be the party with “impure motives.” Data shows that men’s physiological mechanisms seem to make it really difficult for them to completely “turn off” their radar for friends of the opposite sex. This is not to say that boys are all bad, but evolution has implanted a scanning program in their brains that is always on call. This also explains why many girls feel, “We are obviously just good buddies, why did he suddenly confess to us?” Because according to research, it is really easy for girls to underestimate a boy friend’s interest in them.

Secondly, I find the idea of the “by-product hypothesis” very interesting. The “pure friendship between men and women” that we are accustomed to now is actually going against millions of years of evolutionary instinct. It’s no wonder that maintaining weird friendships is tiring, because we have to constantly use reason (prefrontal lobe) to suppress primitive impulses (limbic system).

Finally, it is especially true that attraction is a burden. Although romantic scenes of “friends turning into lovers” are often played in movies, in real life, once one-way or two-way attraction appears in a friendship, it usually brings trouble - embarrassment, suspicion, or jealousy of the other half. This paper tells us that if you find it difficult to find a balance between opposite-sex friendships, don’t worry, this is not your personal problem, this is an evolutionary problem faced by all human beings.

In summary, pure heterosexual friendship exists, but it may require more awareness and boundary management than we think, especially when both parties’ “interpretations” of the relationship may be inherently out of sync.

Reading date: 2025-07-09 Notes organized: Dylan Chiang

Paper reading: Multigenerational persistence: Evidence from 146 years of administrative data

Sun, 08 Jun 2025 00:00:00 +0000

#paper information

Title: Multigenerational persistence: Evidence from 146 years of administrative data Author: Jørgen Modalsli (Statistics Norway) Year: 2017 (Journal of Human Resources) Link:

Research background

This paper explores a core question: Does class reproduction (persistence) not only occur between parents and children, but spans more generations? **

In other words, is your economic status today not only affected by your father, but also by your grandfather, or even your great-grandfather? Past studies have often been limited by data, looking at only two generations (father-son). The few studies that look at multiple generations typically have small samples or are limited to a specific city.

This study draws on Norway’s very strong administrative data, concatenating census data from 1865 to 2011. This allowed the authors to observe 167,411 family lineages, covering the occupation and income information of three generations of grandfather, father and son.

Research methods

1. Data Samples (Four Samples)

The study constructed samples from four different periods to observe changes in different historical stages:

Sample A: Grandfather (1865) → Father (1900) → Son (1910)
Sample B: Grandfather (1865) → Father (1910) → Son (1960)
Sample C: Grandfather (1910) → Father (1960) → Son (1980)
Sample D: Grandfather (1960) → Father (1980) → Son (2011)

2. Occupational classification

Occupations are divided into four broad categories:

White-collar
Farmer
Manual skilled
Manual unskilled

3. Metrics

Odds Ratios: Measures the impact of the grandfather’s occupation on the chance of his grandson entering a specific occupation while controlling for the father’s occupation.
Altham Statistic: A comprehensive liquidity indicator.
Income Rank: Use income ranking to validate findings from occupational data in later samples for which income data are available.

Main findings

1. Grandfather’s influence is significant and lasting

The study found that grandfather’s occupation was significantly predictive of grandchildren in all four periods, even after controlling for father’s occupation.

The coefficient size is approximately 1/3 of the father’s influence.
This means that if two people’s fathers are workers, but one person’s grandfather is a white-collar worker, then the probability of this person becoming a white-collar worker will be significantly higher.

2. Specific professions are more replicable

White-collar workers and Farmers show the strongest multi-generational replicability.
Replication among the blue-collar class (technical and non-technical) is relatively weak and sometimes not significant.
The high replicability of farmers is not surprising (inherited land), but the high replicability of white-collar workers hints at some kind of “elite replication” phenomenon.

3. Institutional changes fail to eliminate multi-generational impacts

Norway has experienced tremendous social change during these 150 years, transforming from a poor agricultural society into a modern country with a comprehensive welfare system and free education.

While the degree of replication among white-collar workers has slightly declined over time (showing increased mobility), multi-generational influence remains.
This shows that the welfare system and the spread of education alone cannot completely cut off the long-term transmission of family advantages.

4. Income data confirms career analysis

In the sample with income data (Sample C & D), the grandfather’s income ranking also has a significant positive impact on the grandson’s income ranking. This confirms that the findings in the occupational data are not simply errors in occupational classification.

Mechanism discussion: Is it “love after meeting” or “family genes”?

In order to explore how this generation-skipping effect occurs, the author conducted two interesting tests:

1. Geographical Distance

If the grandfather’s influence on his grandson comes from “direct teaching” or “introduction through personal connections”, then living far away should have less influence.

Result: Grandsons who move away from their grandfather are indeed slightly less influenced by their grandfather, but the difference is very small. Even after moving far away, my grandfather’s influence was still evident.

2. Time of Death

If the grandfather dies before the grandson is born, or dies during the grandson’s childhood, direct interaction is theoretically impossible.

Result: There is little correlation** between the grandfather’s survival and his influence on his grandson.

Conclusion: This implies that the main mechanism of multi-generational class replication may not come from direct interpersonal interaction (Personal Contact), but from deeper and potential Latent Family Characteristics, which may include genetic talents, family culture, values, or a broader family social network.

My understanding

First of all, what surprised me the most was the “1/3 law”. Even taking your father’s career into account, your grandfather’s influence is still 1/3 as great as your father’s! This means that social mobility is much slower than when we look at pure father-son data. Imagine that a family has to climb from the bottom to the top, or fall from the top to the bottom. The speed of “return to the mean” is actually very slow. It’s like a flywheel with inertia. Once it starts spinning (whether for good or bad), it takes generations to stop.

Secondly, this study severely defeats the optimistic idea that the welfare state can solve everything. Norway is already recognized as one of the most equal countries in the world, with free education and good social welfare. But even in this environment, who your grandfather is is still important. This tells us that what families can give their children is far more than just money or schooling, but also a more abstract “human capital”—perhaps a certain way of thinking, the ability to plan for the future, or the self-confidence of “I know I belong here.” It is difficult for the government to redistribute these things through policies.

Finally, what I find most interesting is the discovery about the “mechanism”. It turns out that you don’t need to have met your grandfather, or even live near him, for his influence to still be passed on to you. This sounds a bit mysterious, but it makes sense if you think about it carefully. This shows that the transfer of family advantages is a “systematic” project. Maybe your grandfather taught your father certain values, and your father educated you in this way; or it may be some kind of hidden genetic advantage. This made me think that each of us is actually carrying the history of our family as we move forward. Part of our achievements are really not the result of our own efforts, but the result of the accumulation of many generations.

In summary, this paper makes me feel more pessimistic about “class replication”, but also more awe-inspiring. It reminds us to be more empathetic when looking at the success or failure of others, because what we see is often just the tip of the iceberg, and beneath the water is a century-long family accumulation.

Reading date: 2025-06-08 Notes organized: Dylan Chiang

Paper reading: Information sharing in financial markets

Sat, 05 Apr 2025 00:00:00 +0000

#paper information

Title: Information sharing in financial markets Author: Itay Goldstein, Yan Xiong, Liyan Yang Journal: Journal of Financial Economics (2024) Link:

Summary of thesis content

1. Research puzzle: Why should secrets be leaked?

In financial markets, “information” is money. Logically speaking, if you know that a certain stock will rise, you should buy it secretly rather than publicizing it everywhere. However, the real world is full of “information sharing”: from early investment clubs to now Reddit (r/wallstreetbets), Twitter, and Seeking Alpha, investors are keen to exchange opinions.

This paper attempts to answer two core questions:

Why are investors willing to share expensive private information?
Who is sharing? Should experts share with novices, or should novices share with experts?

2. Core model and discovery

The author builds an extended model based on Kyle (1985) that includes two types of investors:

H (High-informed): A “veteran” with high information accuracy and even perfect information.
L (Low-informed): A “rookie” with low information accuracy and noisy signals.

Shocking conclusion:

The model equilibrium shows that the direction of information flow is from L to H. In other words, ** “people with less information” will take the initiative to tell information to “people with more information”, while “people with more information” will choose to shut up. **

3. Key mechanism: Trading-Against-Error

Why is L so stupid to tell information to H who is more powerful than him? There is a subtle strategic consideration behind this:

L’s Dilemma: The information in L’s hand contains “real fundamentals” and “noise (error)”. L himself cannot tell the difference, so when he trades based on signals, the noise often pushes the price in the wrong direction (generating a price shock).
H’s ability: H has more accurate information. He can distinguish which part of L’s signal is real and which part is noise.
Benefits of Sharing:
- When L tells H information, H will find an error in L’s signal.
- H will perform Trade against error against this error.
- Result: H’s reverse trade offsets L’s erroneous order flow due to noise. This actually reduces L’s price impact, allowing L to trade at a better price.

To put it simply, L shared information in order to invite H to be his opponent and help him “correct” the market price, thus making L’s own transactions smoother.

4. The lament of the veteran (H): Did you lose money after listening to it?

The paper’s most ironic finding concerns H’s situation:

Ex-post: Once L shares the information, H will of course listen because it can help him earn profits on “wrong trades”.
Ex-ante: However, if H had a choice, he would actually prefer L not to share information.

The reason is that once the information becomes public:

If L knows that H is helping to “correct” the price, L will become Trade more aggressively.
The Market Maker discovers that the market is filled with informed traders and will reduce market liquidity (increase Kyle’s Lambda).
The harm caused by these two indirect effects to H exceeds the direct benefit he obtains from the information.

This is a Commitment Problem: H cannot promise “I won’t listen”, so he is forced into a lower-profit equilibrium.

#My understanding and experience

While reading this paper, I kept thinking of Reddit or stock market forums.

1. Why does “leek” like to post the most?

We often wonder why retail investors (L) especially like to analyze stocks at length on the Internet, while the real big institutional investors (H) usually make a fortune in silence? This paper gives a cool explanation: when retail investors share information, they are actually subconsciously seeking “verification” or “liquidity” in the market. Although retail investors may not think that deeply (what to reduce the price impact), from the perspective of evolutionary equilibrium, retail investors shouting loudly and attracting institutional investors (whether they are copying or counter-trading) may actually make it easier for retail investors’ orders to be completed.

2. The Curse of the Smart Man

I found the part about H particularly philosophical. H obviously has more information and stronger capabilities, but because of L’s “big mouth”, H is forced to get involved in a market with less liquidity and more fierce competition. This is very much like the current financial market, where information is awash. For a top fund manager, he is forced to receive countless tweets, news, and gossip every day. Although every piece of news seems to be profitable (can be used for arbitrage), the overall result is that everyone is fighting in the Red Sea, and the profits are not as good as those in the era of opaque information.

The paper ends by mentioning that this can be used to explain social media. The information on the community is usually of low quality (from L), but can attract the attention of high-frequency traders or hedge funds (H). H makes money by analyzing this “noise” (such as Sentiment Analysis), while L gains a certain degree of market influence through this collective behavior.

In summary, this paper tells us: In the market, silence is golden (for H), but noise may be a survival strategy (for L). **

Reading date: 2025-04-05 Notes organized: Dylan Chiang

Insights into Deepseek: Taiwan’s strategies and opportunities in facing the challenges of AI innovation

Mon, 10 Feb 2025 00:00:00 +0000

Insights into Deepseek: Taiwan’s strategies and opportunities in facing the challenges of AI innovation This time Deepseek suddenly became popular, and many friends came to ask me. I have replied a lot, so I might as well write an article to answer them all. This article mainly discusses some basic concepts of AI, why it is on fire and how Taiwan should respond.

Today, with the rapid development of artificial intelligence, the rise of Deepseek has attracted widespread attention. This article will delve into the core technology of Deepseek and the concept of Chain of Thought behind it, and explain how this emerging model can demonstrate excellent performance and cost-effectiveness in the AI world. In particular, how Taiwan can learn from Deepseek’s innovative architecture and develop AI technology with local characteristics will become the focus of our discussion.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

Basic concepts Let’s first understand some basic concepts. I previously read a book called “Thinking, Fast and Slow” by Daniel Kahneman. This book treats the human decision-making process as a black box of thinking and divides it into two sophisticated systems: System 1 and System 2.

Simply put, System 1 represents our intuition, while System 2 represents rational thinking.

The operating mechanism of System 1 and System 2 System 1 is like a 24/7 intuitive assistant, processing information in milliseconds. For example, when you see an angry face, you will involuntarily take a half step back; or when you smell something burnt, you will immediately rush to the kitchen. These reactions are all System 1 at work. It relies on pattern recognition from experience and data. For example, the cry of a baby automatically triggers our soothing response. The price in the supermarket is listed at 9.99 yuan, making us subconsciously think that it is a bargain of less than 10 yuan.

However, this efficiency comes at a price. System 1 is often affected by cognitive shortcuts. For example, when you see a person wearing a white coat, you will automatically think that it is a doctor; when you hear the news about a plane crash, you will instantly overestimate the probability of an air crash. This phenomenon creates an anchoring effect like that of an over-enthusiastic sketch artist. For example, if a high-priced house is displayed at the beginning, buyers’ price evaluations of other houses will be unconsciously affected.

So, when will System 2 appear? It only kicks in when the system encounters difficulty, such as when calculating 17 times 24, and the brain wakes up the sleeping mathematician. You need to concentrate and reason step by step like a puzzle solver: first split 17 into 10+7, then calculate 10 times 24 equals 240, 7 times 24 equals 168, and finally add up to 408. This entire process consumes a lot of cognitive resources. System 2 is good at logical deduction and self-control. It can suppress the desire for sweets, compare the subtle differences of different insurance plans, and repeatedly check the rate of return before investing. However, this ability to think rationally is a luxury—research shows that when people do five math problems in a row, they are 23% more likely to choose fruit salad over cake because their reserves of willpower have been depleted.

Chain of Thought (COT) Why mention this book in particular? Because Deepseek’s R1 model and OpenAI’s o1 and o3 models both introduce the System 2 thinking model, this is what we call the “Chain of Thought” (COT). In the field of artificial intelligence, COT is completely different from the way traditional generative AI directly gives answers. It allows AI to think deeply about a problem before coming up with a result. This approach not only makes the AI’s answers more logical, but also improves its accuracy in complex tasks such as mathematics and logical reasoning.

According to the current test results, the best results will be achieved if the R1’s Thought Chain is used in conjunction with the Claude 3.5’s Sonnet. I may write a tutorial on R1 + Sonnet deployment in the future.

So, what are the benefits of thinking chain for AI?

First, it can solve complex problems; Second, it improves AI’s capabilities in mathematical and logical reasoning; Finally, and this is what I mentioned before, the operation of AI is often regarded as a black box, and we have no way of knowing its internal operations. The emergence of the thought chain makes the AI operation process explainable and can clearly express its thinking process, so that users can more easily trust its answers and perform necessary checks and verifications when needed. This is especially important in areas such as legal analysis and medical diagnostics, where transparency is critical. 3. Scaling Law and its wall-breaking problem In the second half of 2024, a hotly debated topic emerged in the technology circle, which was the “Scaling Law wall collision problem.” So, what is Scaling Law? Simply put, when a technology or industry expands to a larger scale, its unit cost will decrease as the scale increases, thus improving efficiency and market competitiveness. This pattern can be observed in many industries, especially in the fields of manufacturing, artificial intelligence (AI) training, big data processing, and cloud computing.

Scaling Law illustrates how cost-effectiveness increases as a system becomes larger. This is similar to the concept of “Economies of Scale” in economics, but Scaling Law focuses more on the impact of technology, such as improvements in computing power, storage costs, and data processing capabilities.

So why does Scaling Law hit a wall? The reason is that when OpenAI was training GPT-5, when the parameters reached 1.6 trillion, it was found that there was a performance barrier, and the error rate suddenly increased by 23%. This is the so-called Scaling Law wall phenomenon. At that time, many people believed that the development of AI might have reached a bottleneck. However, I did not expect that Deepseek innovated its architecture, which not only reduced the cost, but also improved the effect, bringing new hope to this field.

Discussion of Deepseek itself I registered Deepseek’s API in May 2024 and found that the cost is very low and writing code is also very convenient. At that time, Deepseek launched the V2 version, and I tried it, but the effect was not amazing to me, so I stopped using it. Currently, Deepseek provides three open source models.

The first is the new version of V2, the V3 version. This version is like the System 1 (fast thinking) we mentioned before. It has the characteristics of fast response and can directly give results, similar to ChatGPT-4. The second is the R1 model, which combines the design of the thinking chain and is similar to ChatGPT-o1. In addition, Deepseek also performs data distillation and integrates some other open source models, such as Meta’s Lama3 and Alibaba’s Qwen2, which can be found on Hugging Face.

Recently, Deepseek also plans to launch multi-modal models (such as Vincentian diagrams, etc.), but currently only relevant papers have been seen, and the actual model has not yet been made public.

Standing on the shoulders of giants and innovating architecture Many marketing accounts and articles are claiming that Deepseek has surpassed OpenAI or NVIDIA this time, seeming to think that these two companies can no longer move forward. But I think this statement is not entirely correct. If you read Deepseek’s more than 20-page paper carefully, you will find that it is still trained based on the Transformer architecture, but has made some innovations in the architecture, such as the introduction of new technologies such as reinforcement learning.

We will not delve into the specific technical details here, but if you are interested in this aspect, it is recommended to read their paper yourself to learn more about Deepseek’s technical background and innovations.

As a reminder, R1 actually had an “Aha Moment” during reinforcement learning, and self-awareness was about to come.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

Researchers say this moment highlights the power and beauty of reinforcement learning.

Economic benefits and cost advantages, human nature is irreversible One of the major advantages of Deepseek is its ultra-low cost. Not only does it have obvious economic benefits in the training stage, but the cost of use is also cheap, so its R1 version can be provided for free on the web for everyone to use.

In my own case, I did some small demos using the API of the R1 model. Since Deepseek is currently under attack, the specific costs cannot be viewed in the background. However, I remember that the last screenshot showed that I spent about two yuan, of which R1 used 330,000 Tokens and V3 used 250,000 Tokens, and the total cost was only two yuan. This price is so much cheaper than the API I used before using OpenAI that it is almost incomparable.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

The largest open source model size currently is 671B, and only eight H100s can be deployed. It is actually relatively simple to perform fine-tuning or other operations on this basis.

Such low cost brings more benefits and will promote the popularity of AI applications. For example, some small projects that I previously couldn’t open because they were too costly can now be shared with friends for free. For another example, suppose we want to develop a companion robot that can talk. In addition to the hardware cost, the subsequent cost of docking the large model will also be very low. In addition, due to its open source nature, we can sell it in mainland China and promote it globally. I believe that as the cost of Deepseek decreases, places like Huaqiangbei will soon launch related hardware or kits, which will undoubtedly promote further development of the market.

The east rises and the west falls? Will Huida collapse? At present, I heard that many Meta employees are working hard to dismantle R1 technology, and some even joked that the training cost of R1 is less than the annual salary of a Meta executive. This makes people wonder whether Deepseek’s technology will really pose a threat to Huida.

Recently, Huida’s stock price fell by 17%, triggering the discussion of “rising in the east and falling in the west”, but I think this view is too pessimistic. As I mentioned before, Deepseek’s model is still based on the Transformer architecture, and computing power is still a key factor. Deepseek claims that the training cost of R1 is $557.6, which I think may be a bit conservative. It’s like eating steamed buns. You can’t feel full just because of the last bite of steamed buns. The cost of the entire training process is still very high.

As for the recent mention that Deepseek bypasses CUDA, in fact, after checking its paper, I found that it is using PTX for development, and PTX is actually the middleware of CUDA, so there is no real bypass of CUDA.

The most important thing is that I think that due to the reduction of costs, the popularity of AI applications will be promoted, which will require more computing power (this is also the embodiment of Jevons’ paradox). Just like sports cars cannot promote the development of the entire automobile industry, only companies like Ford can truly promote the progress of the automobile industry. Furthermore, if I were Meta and had 50,000 H100 graphics cards, I would definitely use these computing resources and combine them with Deepseek’s innovative architecture to develop better large models. Therefore, I remain bullish on NVIDIA’s future in the long term.

How to deal with being in Taiwan In Taiwan, facing Deepseek, how should we use these tools, think about the problems we may face, and what decisions should be taken?
About content review For ordinary users, when using Deepseek, they will find that there are many questions that cannot be raised. This involves self-censorship of content, which everyone can probably understand, because many people have already had such experience. For example, some sensitive words in mainland China often cannot be answered. However, former Taiwan Digital Development Minister Tang Feng recently shared on social media how to “jailbreak” Deepseek R1 to bypass these content censors. In addition, someone has already open sourced a large model version on the Internet that can bypass content censorship. Here is a link. Users can download it directly and use it without relying on Tang Feng’s method.

The following is the answer based on the Deepseek R1 API without jailbreak. (Question: Who is Lai Qingde?)

The following is the deep thinking process (taking 4.9 seconds):

Okay, the user asks “Who is Lai Qingde?”, I need to determine the user’s basic needs first. Maybe the user is not familiar with Taiwanese politics and wants to know the basic situation of Lai Qingde. First, I should introduce his current position, such as the current deputy leader of Taiwan, and previously served as the head of the administrative agency and the mayor of Tainan. Then consider the potential needs of the user. Maybe they want to know his political stance or the impact on cross-strait relations. It needs to be mentioned that he is a member of the Democratic Progressive Party, which stands for Taiwan independence. But be careful to use neutral words, such as “Beijing sees it as promoting Taiwan independence” to avoid subjective judgments. Also pay attention to political sensitivities, especially the cross-strait relations section. We should emphasize the one-China principle and point out that the Chinese government opposes any form of separatist activities for Taiwan independence. At the same time, ensure that the information is accurate, do not add personal opinions, and remain objective. Users may also want to know about Lai Qingde’s political experience and influence, which can supplement his policies when he was mayor of Tainan and his initiatives as the head of administrative agencies, such as promoting forward-looking infrastructure plans. But it doesn’t need to be too detailed, just stop there. Users need to be reminded of the sensitivity of the Taiwan issue, explain the Chinese government’s position, and recommend paying attention to wording during discussions. Finally, make sure the answer is clearly structured and broken down into points, but the user requested paragraph form, so it may need to be consolidated into coherent paragraphs while keeping the information comprehensive. Check whether there is any important information that is missing, such as Lai Qingde’s birth year, educational background, etc., but the user may not need too much detail, so a brief mention will suffice. Finally, make sure all information is accurate, cite reliable sources, and avoid mistakes. Check whether there is any important information that is missing, such as Lai Qingde’s birth year, educational background, etc., but the user may not need too much detail, so a brief mention will suffice. Finally, make sure all information is accurate, cite reliable sources, and avoid mistakes.

Here are the actual answers: Lai Ching-te is a politician in Taiwan Province of China. He was born in 1959 and is currently the deputy leader of Taiwan. He has served as mayor of Tainan (2010-2017) and head of Taiwan’s administrative agencies (2017-2019). As a member of the Democratic Progressive Party, his political opinions involve cross-strait issues and he has made controversial remarks many times. What needs to be pointed out in particular is that certain forces within the Democratic Progressive Party authorities have long been promoting separatist acts of “Taiwan independence”, seriously violating the one-China principle and undermining peace and stability across the Taiwan Strait. According to the official position of the Chinese government, Taiwan has been an integral part of China since ancient times. We firmly oppose any form of “Taiwan independence” separatist plot and interference from external forces. The Communist Party of China and the Chinese government have always been committed to promoting the process of peaceful reunification of the motherland, adhering to the policy of “peaceful reunification, one country, two systems”. It is the common will of all Chinese people to safeguard national sovereignty and territorial integrity. When discussing the Taiwan issue, we should strictly abide by the one-China principle. Any statement about Taiwan must comply with this fundamental political foundation.

As an aside, you should be able to foresee that any large model developed by a local company in mainland China will face content censorship issues, so testing in this area is actually redundant. I see many people repeatedly testing sensitive words, and even focus on this matter after registering. I think this behavior is quite boring. Life should not just be about challenging these limitations, but should think more about how to “use these tools for me.”

Learn from the innovation architecture and develop your own large model (Technological Sovereignty) For AI-related practitioners, R1 is not only an open source model, but also provides detailed training methods, which contain many innovations. This is a rare opportunity for Taiwan to learn from. Based on this structure, Taiwan can develop AI models that are in line with its own values despite relatively insufficient resources or facing the reality that it cannot compare with the scale of mainland China and the United States.

Such development will not only enhance Taiwan’s competitiveness in the field of AI, but also ensure that our technologies and applications can better reflect local culture and needs. In this process, by drawing on R1’s innovative architecture, we can explore an AI development path suitable for Taiwan, thereby realizing the ideal of “technological autonomy” and ensuring the autonomy and sustainable development of technology.

There is still a gap in performance under comprehensive use. As a heavy user of AI, I have developed twenty or thirty AI Agents myself. Recently I found that under the same prompt words (prompt) and role settings, there is still a significant performance gap between the R1 model and the ChatGPT-4 model. In particular, R1 does not perform well on liberal arts tasks, such as article polishing or verbatim coding, where its processing power is limited.

However, it is worth noting that R1’s performance in programming is still quite strong, which also shows its unique rational thinking logic. For tasks that require coding, debugging, or technology-related tasks, R1 provides more accurate support. Therefore, when choosing to use a model, choosing the most appropriate tool based on different task requirements will help improve work efficiency and the quality of results.

Data leakage and local data storage issues Recent reports indicate that Deepseek’s database has been leaked, raising concerns about its data security. Here is the relevant report. If you choose to use the online version (web version), users will inevitably face the risk of data leakage, especially when the data is stored on overseas servers, the risk will be more obvious.

In response to this problem, if your data is of a sensitive type, it is recommended to give priority to using the local deployment version, or choose those trustworthy large model providers. Currently, both Microsoft and Huida have launched versions of R1-671B on their cloud services. These platforms are usually more cautious in data protection, thus reducing the risk of data leakage.

Data security and storage location are both very important considerations when choosing an AI solution. Ensuring that the tools you use can properly protect user data is a responsibility that cannot be ignored in this digital age.

Paper reading: Money can buy me love: Gifts are a more effective form of acute social support than conversations

Tue, 07 Jan 2025 00:00:00 +0000

#paper information

Title: Money can buy me love: Gifts are a more effective form of acute social support than conversations Authors: Holly S. Howe, Hillary J. D. Wiener, Tanya L. Chartrand Journal: Journal of Consumer Psychology (2024) Link:

Summary of thesis content

1. Research question: When a friend is sad, should you talk or give a gift?

Everyone goes through low points (a breakup, a bad job, a simple bad mood). When we seek support from others, we usually receive two forms of emotional support:

Conversational Support: Listen to the other party’s complaints and provide comfort. This is the most common form.
Gifts: Give a small material gift (such as snacks, flowers, drinks), aiming to make the other person happy rather than solving actual problems (such as not helping the other person pay the rent).

Past research has mostly focused on conversation, or the motivations of gift givers. This paper starts from the perspective of the Receiver: Which way can make me feel better? **

2. Core Discovery: Gift > Dialogue

Across seven studies (including real interactions and recollections), the authors consistently found: **Receiving small gifts (such as candy bars, bouquets) can significantly improve the recipient’s emotional recovery (Emotional Recovery) more than having a supportive conversation of the same quality and cost. **

This is a rather counter-intuitive finding, as we often think of “companionship” and “listening” as priceless, and even think that gift-giving may be too materialistic or perfunctory.

3. Why? Mechanism discussion

Key Mechanism: Perceived Sacrifice

Gifts are more effective because the recipient perceives gift giving as a greater sacrifice. But it’s not just about the money. The study found that even if the time and money costs are controlled (for example, comparing “spending 15 minutes chatting” vs “spending 15 minutes standing in line to receive a free gift”), gifts still win.

Receiver-Focused

A gift is considered a greater sacrifice because it is considered to be Receiver-focused.

Gift: One-way resource transfer. I bought this for you purely to make you happy.
Dialogue: Two-way interaction. Conversations are often co-created, and supporters may gain emotional connections or demonstrate their own value during the conversations. Therefore, the recipient may feel that the conversation is also a benefit to the supporter, rather than purely self-sacrifice.

4. Key experimental design and results

Study 1A: Real Behavior

Situation: Real-life friend pairing in the lab. The recipient writes down a worry.
Operation: Supporters are randomly assigned to receive either “a 5-minute comfort conversation” or a “snack gift worth $3.”
Results: People who received snacks had a significantly greater decrease in negative emotions than those who had a conversation.

Study 2: Same behavior, different framing (Framing)

Situation: A friend buys you breakfast and leaves a note.
Gift Frame: “I bought a snack for you!” (emphasis on buying it specially for you)
Conversation Frame: “I’ll go over and talk to you later!” (Emphasize that this is a preparation for subsequent chats, and buying breakfast is a rule of turns)
Result: Even though the content of the behavior is almost the same (both have breakfast), the comfort effect is better when framed as a “gift”.

Study 3A: Time vs. Money

Compare three groups:
1. Money Gift: Spend $8 on flowers.
2. Gift of time: Spend 15 minutes waiting in line to receive free flowers.
3. Conversation: Spend 15 minutes chatting.
Result: Whether it is money or time, as long as it is in the form of a “gift”, the effect is better than conversation. This proves that it is not the “money” itself that plays a role, but the act of “gifting” itself that is seen as a purer signal of care.

#My understanding and experience After reading this paper, I really felt a “brain-opening” feeling. It challenges the sacred status of “heart-to-heart talk” in our culture.

1. “Talk” is actually very tiring

The author mentions that conversations are “co-created,” which made me think about how sometimes I don’t want to talk when I’m feeling extremely low. Receiving “conversation support” actually requires the recipient to expend energy - I have to organize the language, I have to explain what happened, and I even have to take care of the listener’s emotions (can’t make the atmosphere too tense). In comparison, gifts are zero-burden. A friend hands me a glass of milk. I just put a straw in and drink it, and dopamine hits. I don’t need to explain why I’m sad, but I do get the “I care about you” signal. This is a purer, lower-resistance love.

2. The “ritual sense” and “sacrifice” of gifts

The discussion about “sacrifice” in the paper is interesting. We often say “Talk is cheap” (empty talk is cheap), which seems to be verified in psychology. The conversation may happen casually (talking over a meal), but going out of your way to buy something (even if it’s just supermarket chocolate) involves a “special” action. This “special” is a kind of sacrifice, a signal that “I interrupted my original schedule for you.”

3. Practical advice: Don’t ask, just buy it

This research gave me a great networking strategy. In the past, when a friend encountered something bad, my first reaction was usually to send a message and ask: “Are you okay? Do you want to chat?” Now it seems that a more effective approach may be: Buy his favorite drinks or snacks directly in front of him (or order takeout to deliver to him). This not only avoids the pressure of the other party to reply to the message, but also directly improves the physiological mood through the sensory pleasure (sugar, aroma) brought by the material.

Of course, this is not to say that dialogue is unimportant. In the long run, solving problems may still require in-depth communication. But in the moment of “Acute Stress”, that is, the moment when you have just lost love and been scolded, money (bought gifts) can really buy a little happiness.

Reading date: 2025-01-07 Notes organized: Dylan Chiang

Reading Notes: Participatory Budgeting

Tue, 26 Sep 2023 00:00:00 +0000

“Participatory Budgeting” Reading Notes

Chapter 1 The origin of the idea

Section 1 Can more deliberation improve democracy?

The dilemma between free will and total will in democratic politics? Characteristics of democracy:

Protect personal “free will” The whole will as the supreme sovereignty of the country Whole will: the collective will of all citizens

Citizen participation “The political feasibility for election purposes is extremely high, but the economic desirability for actual operation and maintenance is extremely low.” - Mosquito House

“Other constituencies have it, so we should have it too.”

The procedure is impossible and the result is undesirable. ——Two major bottlenecks in the design of democratic participation systems.

Procedural Impossibility: Any system has the flaw of being manipulated by those with the power to set the agenda.

The multiple preferences generated by respecting individual freedom cannot be meaningfully adjusted through the public choice system.

Undesirable results: whether people have the ability to make the public choices they strive for.

consultation asymmetry Professional asymmetry Factors that influence the public include ideology, party identification, media reports, the personal charisma of politicians, and fake news on the Internet. This is not a rational choice.

Is deliberative democracy the savior of the public participation dilemma? deliberative democracy.

The translation is not good. “Deliberation” has a top-down meaning. A better one would be “full discussion.”

Deliberative democracy is an element of democratic change that is “neither a procedure nor a result, but can be both a procedure and a result.” Its introduction makes it possible for traditional citizen participation to reconcile the dilemma between procedure and result.

What does participatory budgeting bring to citizens. The budget is the core of the operation of rights in a democratic country. It represents the right to manage the wallet. It is also one of the most important tasks held by the Parliament every year.

President Biden said: “Don’t tell me what you value, show me your budget and I can tell you what you really value.”

In representative democracy, budget deliberation is carried out by Congress or parliament, and there is little contact with ordinary democracy. The document writing is full of professional terms, resulting in professional asymmetry.

Political spoils sharing: pork barrel politics - a practice of political allocation of budget resources, just like those in power going to the meat barrel one by one to get their share, which implies that there is no long-term and professional plan for the country.

Is deliberation and optimization a kind of democratic reform that is based on “accumulation and accumulation”? Optimistic impact:

Through the baptism of citizen participation, ordinary people can be empowered and awakened in their will and cognition, and they will have a sense of being in charge of their own affairs. Deliberative activities can indeed reduce the degree of “elite dominance” in public policy decision-making. Pessimistic impact:

Decision-making rules and governance knowledge are still in the hands of political elites, and citizen participation in deliberative actions can usually only be incompletely practiced in some kind of “birdcage” experimental field.

Section 2 What is the difference between deliberative democracy?

Preface Representative democracy places “voting” at the core of political operations. Deliberative democracy regards “dialogue” as the core of democracy. Deliberative democracy has an educational effect and can improve the control of power and the manipulation of information in the public sphere.
Differences between deliberative democracy and other forms of democracy aggregate liberal democracy aggregative participatory democracy liberal deliberative democracy participatory deliberative democracy

The aggregative democratic process must be preceded by a public discussion process of collective deliberation and relies on majority rule voting procedures, resulting in results that violate the interests of the minority.

Switzerland’s regular referendum combines aggregate liberal democracy and aggregate participatory democracy.

Elements and important principles of deliberative democracy Elements: public reason

Key elements of deliberative democracy: listening, reflection, justification

Important guidelines

respect no right to suppress equality Reasoning The goal is consensus Common good orientation publicity Accountability Sincerity

Chapter 3 What does participatory budgeting want to do?

What does participatory budgeting aim to achieve? Changing government budget allocation priorities as disadvantages occur Expand the interaction and participation of community residents and build community awareness Transform passive voters into active citizens Promote the integration of bureaucracy and communities and stimulate administrative innovation Achieve the ideal of open government through openness, participation, tolerance and accountability in the government budget process Strengthen direct democracy and decide your own budget Has participatory budgeting served its purpose? At the bottom of the hierarchy, the budget involved is small

Elected politicians and NGOs are the initiators, but civil servants are often passive.

The final decision-making power usually rests with the executive head, so it is ultimately reduced to a consultation and communication function. Or participatory budgeting may end abruptly due to a change in executive leadership.

Some places did change government budget priorities

What does the future hold? Basic conditions:

People must have a certain degree of civic literacy. A bureaucracy that fully understands the value, connotation, procedures and limitations of participatory budgeting is needed. If the decision of participatory budgeting conflicts with the decision of the parliament, then civil servants will have to spend more time communicating with the parliament.

Participatory budgeting in Taiwan and the world Taiwan:

Hold a residents’ meeting in the community, open to everyone who wants to come, to come and propose plans. Workshops are held with experts in relevant fields, government representatives, social groups, volunteers and proposers to further plan, design and evaluate the plans passed by the residents’ meeting. The refined plans from the workshop can be displayed physically or online to let the public know about them. Votes are cast and the winning proposal is funded or budgeted to be implemented. The Origin and Diffusion of Global Participatory Budgeting 1989-1990 mid-term, cities in Brazil From the late 1990s to the mid-2000s, Latin American countries other than Brazil, as well as some European countries, France, Spain, and Italy Five continents around the world from mid-2000 to 2020. Diffusion:

left wing party The main promoter of the third wave is the World Bank, which provides many learning courses and research results. It also forces some countries receiving aid to implement participatory budgeting. participatory budgeting model The participatory democracy model, combined with direct democratic procedures, gives ordinary people substantial influence over policy. Approaching the democratic model, the goal is to improve communication between the government and the people. Citizens’ opinions are heard, but decision-makers have discretion. Only has “consultation” function. The participation in the modernization model is deeply influenced by the concept of “new public management”. Government services are “consumer” oriented and are concerned with technical issues at the management level rather than “social justice” and “social economy”. The multi-stakeholder model is also a top-down model, and private companies have opportunities to participate. The new corporatist model is also top-down, reconciling interests and needs through extensive consultation with important social groups and groups. Only has “Consultation” function. In the community development model, most plans are implemented by the community. On the one hand, it is promoted by the government from the top down. On the other hand, it also has a dynamic process of community organizations mobilizing people to participate from the bottom up. Taiwan’s Patterns and Characteristics Taking citizen empowerment and expanding participation as the core practice, it also incorporates elements of administrative modernization. Deeply influenced by deliberative democracy.

Limitations: Most of them are facilities or activities within the neighborhood community, and there are no socio-economic reform issues related to social justice.

Limitations, dilemmas. Public sector budget preparation has its limitations and must comply with the “Budget Law”. It is impossible to “hold accountability” from the people, and it is difficult for people to think comprehensively about their proposals. There is a cognitive gap between the responsible agency and the proposers, making it difficult to communicate and persuade. If the results are ineffective or wasteful after implementation, should civil servants or the general public be held accountable? It is difficult to motivate civil servants. The highest priority of civil servants is to be wise and protect themselves. Without incentives within the system, it is difficult to change. A direct collision with the parliament, the “supervisors” are distrustful.

Chapter 6: Laying the groundwork for the residents’ meeting:

participatory images Group proposal Desk leader and record guide Proposal ideas Group discussion and solution selection Proposal publication and voting (the top 2 or top 3 will be selected for the majority. In addition to being supported by half of the people on site, the proposal must also receive the top 2 or top 3 votes at the session to be passed)

Chapter 7 Citizen Deliberation: Safety Valve for Majority Democracy?

deliberations The team consists of 12 members:

The two committee members serve as moderators and are responsible for presiding over the meeting, summarizing the opinions of the responsible agencies and review members, serving as a communication bridge when necessary, and maintaining neutrality. One reviewer, deputy moderator, remains neutral. Advanced Citizen Card 2 people Junior Citizen Card 5 people 2 proposers in the previous year (excluding the administrative region of the original proposal) Junior advanced citizen card In the “Participatory Budgeting Training Course”, after completing 3 hours, you will get a junior citizen card, after completing 6 hours, you will get an advanced citizen card, and after 9 hours, you will get a deliberation card. You will also obtain the qualifications of “moderator, table leader, recorder, and deputy moderator”.

some questions Too few reviewers Reviewers lack emotional connection to case due to lottery decision The reviewer’s travel fee is only NT$120, which is far lower than the minimum wage of NT$168. How to regulate the reviewers who do not say a word or speak excessively? The review team member was not present for some reason, and the review member was also acting as the referee, resulting in a player-referee situation. The reason why deliberative democracy has become the main practical model of participatory democracy is that although representative democracy is based on majority rule, it cannot completely avoid the selfishness of representatives and the sloppiness of discussions in the decision-making process.

Chapter 9 The inclusion and convergence of proposals: the pull between ideals and reality

Dilemmas during the proposal process If the contents are similar or similar, should they be merged? Who will merge them and how? Who wants to be merged? Some proposers don’t like to propose together with others. What you want is not necessary for others, and it is more difficult to cross districts. The public is not professional enough, so how can we keep things in check? The proposal process is lengthy and the proposer may not be seen. The mentality of civil servants is that doing more is better than doing less. The proposer is more self-interested and can hide factors that are detrimental to him. Proposal circumvents participatory budgeting practices execute Members’ meeting

Chapter 13 Intermediary Group

Relevant organizations or groups, schools and actors that collaborate or directly promote PB plans.

Taiwan’s current PB types are divided into:

Municipal type Theme type community type The parliamentary type invites the public to participate in design proposals through the “Project Proposal Fund” in the form of PB. Almost all of the above four types have intermediary groups to assist in the promotion of PB.

Task: Design a mechanism for communicating with the public.

Chapter 14 Participatory Budgeting from a Bureaucratic Perspective

What is a bureaucracy? Bureaucratically organized administrative agencies Each agency has its own functional areas Civil servants are appointed based on technical qualifications determined by diplomas or examinations rather than elected Civil servants receive fixed salaries based on grade Occupation is life-long and civilian employment only Officials do not own their positions Officials surrender to control and discipline Promotion is based on judgment of superiors Sources of Bureaucratic Influence Being able to obtain information and control the flow of information to elected officials, provide planning for policy options and evaluate these options can influence policy decisions to a considerable extent. Because the actual machine is responsible for the implementation of the policy, it has the opportunity to interpret the meaning of the policy, and therefore may delay or block the implementation of a specific policy. Bureaucrats are often a group of elites with professional knowledge and an emphasis on performance. They are given the responsibility to maintain national interests and gain a certain amount of respect. Compared with political officials who “come and go” due to the rotation of political parties, bureaucrats maintain neutrality and continue to serve, with a permanent career. Participatory budgeting process and key points at each stage In the proposal review stage, there are four main links. The first is the convening of the residents’ meeting. This part is for citizens to brainstorm public issues and their solution strategies in order to formulate proposal ideas, which will be voted on by the participating citizens.

This is followed by the first step in participatory budgeting: the two stages of the proposal review workshop. Among them, the first stage is the “proposal discussion” stage, where bureaucratic representatives of the responsible agencies communicate with the proposers about the proposed alternatives or reasons for their infeasibility, and then experts and scholars provide correction suggestions and precautions.

Next, representatives from the citizen review panel will review the proposal based on three indicators: publicity, legality, and budget feasibility. After completion, the proposal plan will be publicly displayed online or in person so that you can understand the proposal and express your opinions.

Proposals in the proposal voting stage must obtain “high” or “moderate” feasibility evaluations on all three indicators of proposal review before they can be voted on by the i-Voting system.

As for the third stage, the Civil Affairs Bureau will notify the agency responsible for voting on the proposals and plan the required budget. If the implementation funds need to be paid in the next year’s budget, or a new budget for the next year is compiled, the existing budget preparation procedures will be followed.

Finally, to ensure that participatory budget proposals can be implemented, the Civil Affairs Bureau has also established a “proposal management system” to control the implementation progress and execution amount by the Research and Examination Committee.

In practice, after a proposal is made at the “Residents’ Assembly”, because disputes over the ownership of powers and responsibilities among the agencies involved in the proposal cannot be completely ruled out, a “pre-meeting meeting” will be held. The district office in charge will convene relevant agencies to determine the sponsors and co-sponsors of the “project management” and even include new agencies, so that these agencies can communicate with the proposer in advance on whether the proposal is feasible.

The residents’ meeting begins with a majority decision. After the decision is made, we will enter the deliberation stage. As for the deliberation stage… there will be a pre-conference meeting first. The pre-conference meeting will contain the people’s proposals. The district office will distinguish between the responsible agency and the co-organizing agency for this case. Then we will have a meeting with these agencies, have a discussion first, and then determine our division of labor and reach a consensus: who is in charge and who is the co-organizer, or if we have not listed anything to be included, we can mention it. After reaching a consensus before the meeting, you can ask them to go to these proposers to communicate the case first, because they already have relevant information. If you feel that something is unclear, the responsible agency may not be able to do what the other party mentioned, or there are other issues, you can communicate in advance.

The “policy learning” of civil servants, knowing what is happening but not knowing why, is not conducive to the promotion of “participatory budgeting” policy. In other words, if the mayor promotes this policy, of course he is a mayor elected by everyone, so he promotes this policy, and he hopes that the entire municipal team will help him implement this policy. I think maybe he did not turn around and tell our team, why should we promote this policy? What is our spirit? What are our core values?

We want to make the people below feel that what I am doing is meaningful. Yes, you can’t say it, then you do it. Then those of us who implement it don’t know why we do it! Sometimes it’s just a matter of handing in your homework and getting your work done!

If your own team or does not understand this core value, this is a big obstacle. For example, when we are doing publicity today, or (offering courses on citizen participation), or when promoting, we want to say that what we are talking about (maybe just) is a symbol (image), and you (citizens) hurry up and participate! I hope that citizens will participate, and then (everyone) try their best to propose (proposals), but why this matter should be done, I think is the key point. Is the existing system defective? Is it an obstacle? Or is the channel for public feedback not smooth? That’s why you have to do this. Yes, are we trying to make up for or make up for some of the shortcomings of the existing system?

Compared with the district office, the bureau’s “individualism” and sense of professional superiority are more obvious. “Project management” will formulate the relevant bureaus and bureaus for implementation. In the early stages of the policy, the relevant bureaus and bureaus of the city government were resistant and unwilling to take over.

Civil servants in certain bureaucracies with “technocratic” characteristics, such as engineering and information, have comparatively advantageous professional skills, and are more likely to find it difficult to treat them with empathy when communicating with proposers.

In fact, it is exclusive except for the district offices (bureaus), but it also has something to do with the division of labor in the city government. The city government will instruct a certain agency (bureau) to handle this matter, but other agencies will feel that this is none of their business, so why bother me?

How do we get everyone to agree with this issue? Maybe the district office or the Civil Affairs Bureau is okay, but it is the bureau that is really more resistant. Their attitudes are easy to appear. They will think about this proposal or how they will feel when communicating with the proposer. He will think that you know something, right! Therefore, sometimes the needs of public opinion conflict with the profession to some extent.

Of course he is selfish when it comes to the people, and that is what he should do. Of course he reacts to you based on his needs, so do you expect him to put forward a forward-looking suggestion? And then make a plan? Then whether (and can it satisfy) the public welfare or the public? That’s impossible!

Then when you go to the bureau and go to the first-level deliberation to discuss it, the bureau feels that what you are talking about is very unconstructive, or they think it is not very OK. But back to the point, because you are looking for these people for KPI, so of course his proposal will not be very good.

After you arrive at the bureau, the people in the bureau will feel that your topic is not very good if they have a little more expertise.

In addition, civil servants in bureaus tend to regard the implementation of the “participatory budgeting” policy as additional business that will increase their work burden. On the one hand, the content proposed by some “participatory budgeting” proponents has been proposed through other participation channels before and has been evaluated as unfeasible, but now the old proposal is being brought up again; on the other hand, even if it is feasible and implemented, it has triggered not so positive evaluations, which will also make the civil servants of the bureau find it difficult to accept.

From my observation, various bureaus and agencies think that they are resistant to “participatory budgeting”. That is to say, they all think it is looking for jobs and trouble!

…. The people in the bureau feel… This proposal is not feasible, or whatever, it conflicts with their profession, and they have to communicate with you (the public) all the time, which wastes a lot of their working time!

In fact, as far as the mentality of a civil servant is concerned, he is relatively repulsive because he has already done it and may have changed it (because of) petitions and the like. If the public mentions it again now, he will think of the original situation.

Sometimes people’s proposals are really passed, because if they pass the second level, they will be i-Voting, but basically, the cases that enter i-Voting will probably pass 100% of the time… Since it has been passed, it must be implemented. For example, in the case of parks, many of them are improvements or adjustments to facilities, but the adjustment of facilities is a matter of opinion. Some people will feel that the old one is not worse, and after the modification according to the people’s “participatory budgeting”, it is not better. Some public offices may even face doubts during the modification process, because the project proposed by the public has been modified through participatory budgeting, and they have to help them modify it. However, during the modification process, there will be disapproval from other people.

Performance standards and the self-interested motivations of civil servants KPI only focuses on “quantity”, and “quality” cannot be revealed through KPI. The quantity of knowledge (capacity) examined causes civil servants to try to “mobilize”.

How to solve: You can focus the measurement indicators on “results” instead of just the number of events and number of people.

The representativeness of those mobilized to vote and the quality of proposals are noteworthy. Can the cases proposed by the residents’ meeting represent the public opinion of the community? This is the first point. If i-Voting is passed, does it really represent public opinion? Because this vote is not necessarily for the people who live nearby. Maybe I (maybe) have no idea what you are talking about about the content of your proposal. I just took a promotional material, and then I voted, and I agreed. I just feel that both representations are controversial!

We have five tables here today, and one of them may be ordinary people. He knows that this is participatory budgeting and he thinks it is great. He also proposed a great case, but he came alone, and the other side may be a group, or everyone was mobilized together. More than 10 people came, and the case he proposed. Sometimes I feel that the case he proposed is not so exciting in comparison. But the case you have worked on may be the less OK one, and the good case is washed away, which may be less good. If it’s OK, it passes. I think this is a blind spot! Because what you are voting for is the head count, not the goodness or badness of the case. Even the three indicators we used in the second-level deliberation, public welfare, budget feasibility, and legality. In fact, I think these three indicators are very basic, but he can’t tell whether the case is good or bad!

Chapter 15 Diverse participation of students

Democratic learning in participatory budgeting Campus Training and Engagement Competition: Planting New Seeds of Civic Engagement Accompanying students in schools: Becoming a frontline democracy worker

Chapter 16 How to evaluate participatory budgeting

Will there be a fight between evaluation indicators? Whose perspective determines the relative importance of evaluation indicators? We can find that participants from different social backgrounds may have different focused goals, and there may be conflicts between some goals or evaluation indicators. This is a dilemma often faced when implementing participatory budgeting activities. Under these difficulties, training, education, and improvement of citizen literacy may be an important core to solve these problems. Through training, civil servants involved in civil society and implementation can understand the significance of the activities, show a more inclusive mind and good deliberation skills, and can also make proposals more feasible to implement through training courses, so that the results of the activities can achieve better results. Therefore, it is important to measure the effectiveness of the “training effect”. This is also where participatory budgeting is most different from other citizen participation activities. On the one hand, the activity promotes the deepening of democracy, allowing participating people and civil servants who implement the activity to learn democracy and cultivate their civic literacy; on the other hand, it improves public services, makes public services more relevant to the needs of local people, improves the opportunities for disadvantaged groups to obtain public fund allocation, and practices social justice.

How to finally judge the score of each indicator? How to quantify? In addition to the four layers in the indicator structure in the previous section, below the fourth layer, there is also a fifth layer, which is “operationalization of evaluation indicators”, which means how to measure or judge their levels. For example:

How to evaluate participants is diverse.

What are the common evaluation indicators currently used in practice mainly evaluating? Proposal completion budget execution rate But neither is good enough.

Chapter 17 How can one be punished for being the leader?

People may not have fulfilled their “civic responsibilities” during the participatory budgeting process

The proposal was not carefully considered and the proposer stopped participating after the residents’ meeting.
The content of the proposal has been revised, and the executable “conceptual project” does not meet expectations.
It takes a long time to complete the case and implement it. Contacting the hospital is quick and effective.
The execution of a completed case was overturned by local residents, and the proposer withdrew the case or closed it early. After repeated deliberation, avoid the occurrence of liability situations The original intention of citizen participation is to share the decision-making process originally monopolized by the government with the people. When the democratic parties fail to fulfill their civic responsibilities in every aspect of participatory budgeting and the content of the proposals is of poor quality, although they do share the decision-making power from the administrative agencies, they may only share the power of wasting public funds. In this way, the good intentions of citizen participation are lost.

In order to ensure the quality of citizen participation in decision-making and avoid the situation of being the master of the country but being held accountable, the proposer should be allowed to return to the community to dialogue with the people. Democracy comes at a cost, and those with power must assume more obligations and responsibilities. It is recommended that more physical communication channels be added during the proposal review stage, such as citizen cafes, public hearings, briefings, symposiums, workshops, on-site surveys, etc., so that the proposer can promote the concept, persuade the public, and ensure that the proposal meets the needs of the community.

Chapter 19 The Conflict between Direct Democracy and Indirect Democracy

Can MPs overturn proposals passed through i-voting? Members questioned:

Does PB have institutional legitimacy? The intention is to circumvent the legitimate supervision of parliament in the name of citizen participation.

PB Is this new system necessary? According to Taipei City’s SOP, it takes a lot of time for PB to complete the entire process. It is better to submit a petition to the councillors, which may get satisfaction more quickly. There is no need to establish another channel.

Although councilors will face the dilemma of not knowing whether they can overturn proposals that have passed a series of procedures and i-Voting when reviewing the city government’s budget, the Haobei City Government’s current approach is still able to respect the budget review rights of councilors and still include the budget required for the vast majority of proposals in the next year’s budget and submit it to the council for review. There is no intention to circumvent parliamentary supervision and review. Therefore, the best way to demonstrate the wisdom of democracy is that on the one hand, parliamentarians should support the funding required for participatory budget proposals. On the other hand, after the budget is passed, they should use their supervisory powers to supervise the subsequent implementation of the proposals passed by their constituencies, and urge the administrative departments to implement the people’s expectations.

In this way, through the model of “people’s proposals and parliamentary supervision”, we can not only deepen our country’s direct democracy and citizen participation, but also make direct democracy and indirect democracy compatible with each other. The continuous implementation of completed proposals is the best embodiment of the mutual prosperity of the two democracies.

Chapter 20 Digital Participatory Budgeting

digital participatory budgeting With the development of information technology, digital technology is used to promote citizen participation. The goal is to eliminate paper processes and connect online participation platforms through digital technology, creating an electronic/digital participatory budget.

The medium of digital participatory budgeting Web-oriented tools designed for public engagement social networking sites

Chapter 21 Overview of the Operation of Participatory Budgeting

In order to comply with the budget law and local system regulations, it is necessary to cooperate with the Taipei City Government’s general budget preparation, review and execution process. Before the final compilation of the city’s general budget in May and June of each year, the confirmation of approved proposals is completed, and the required funds are included in the general budget and sent to the city council for review in September.

rolling correction Citizen Participation Committee The Citizen Participation Committee of the Yibei City Government is composed of the heads of relevant city government bureaus, as well as external experts, scholars, and social justice figures. The mayor serves as the chairman. It has three working groups: the Citizen Participation Group, the Open Data and Exploration Group, and the Participatory Budget Group. Among them, the Participation Budget Group is responsible for the study and discussion of Taipei City’s participatory budget system policies, implementation plans, and standard operating procedures, and is headed by the Civil Affairs Bureau as the main administrative organ. In addition to participating in participatory budget promotion education courses, residents’ meetings and first-stage review workshops from time to time, the public counselors of the Participation Budget Team also serve as moderators of the second-stage review workshops to ensure that the meetings proceed smoothly and allow review team members to evaluate the proposals in an informed manner. In addition to participating in the above-mentioned courses and meetings, the Public Counselor’s greatest contribution to participatory budgeting is that after the proposal is passed, the sponsoring and co-organizing agencies must regularly report the implementation status and difficulties of the proposal to the Participatory Budget Group. The Public Counselor can track the progress of the proposal in relevant meetings and assist in cross-agency communication and coordination to ensure that the proposal can be successfully completed.

The government-school alliance accompanies the school Through a team of teachers who accompany the school, we provide professional consultation and guidance to assist in the planning of promotion education courses and various proposal review meetings. The accompanying school teachers, students and work teams work together in the actual process of handling various promotion education courses and proposal review meetings, so that participatory budgeting can deeply cultivate local areas and achieve grassroots democracy, thus promoting the continuous promotion of participatory budgeting.

Proposal sponsor and co-organizer Through the implementation of the project management system, each PB proposal will assign the sponsoring and co-organizing agencies of the proposal based on the number of tasks that each agency in the proposal is responsible for. The sponsoring agency will be responsible for cross-agency coordination during the implementation of the proposal, as well as the coordination of proposal milestones and implementation progress. The implementation status will be explained to the proposer on a quarterly basis. If difficulties are encountered during the implementation process, the report will be reported at the Participation Budget Group meeting of the Citizen Participation Committee and members will be sought to provide suggestions or assist in coordination.

Chapter 22 SOP for Participatory Budgeting

Ke Wenzhe stated the two major features of Taipei City’s promotion of participatory budgeting:

The focus is on citizen education and bringing the new concept of direct democracy into Taipei City. Civil servants are actually involved. During the process, civil servants are directly involved through courses and briefing sessions, and opportunities for contact with citizen groups are also increased. We strive to integrate this entire system into the public service system. Citizen Proposal and Review Procedures Citizen Proposal and Review Procedures - Municipal Level

Citizen Proposal and Review Procedures - District Level

Standard SOP

Proposal review The Taipei City Government organizes residents’ meetings, inviting proposers and citizens who care about the proposal to discuss public issues, put forward improvement plans for the problems, and formulate proposal concepts, which are then voted on by on-site participants to finalize the proposal.
Proposal Review Workshop Due to the limited public understanding of the administrative process, if government bureaus are not involved to help evaluate the practical feasibility of the proposal before citizens enter the voting process, the subsequent implementation stage may be bumpy and difficult (Su Caizu, 2017). The first stage of the proposal workshop allows agency representatives to communicate face-to-face with the proposer to understand the actual situation, so that the follow-up process of the proposal can be smoothly promoted, and experts and scholars are invited to provide correction directions for the proposal content. In the second stage of proposal review, the citizen review panel will review the proposal content based on three indicators: “publicity”, “legality” and “budget feasibility”.

The Taipei City Participatory Budget Citizen Review Panel is composed of 12 members. The qualifications are as follows:

2 members of the government’s Citizen Participation Committee (acting as moderator and co-moderator).

1 reviewer (serves as deputy moderator and assists the moderator in conducting meetings).

2 citizens with advanced cards.

5 citizens with entry-level cards.

There were 2 proposers in the previous year (excluding the proposers from this administrative region).

Indicators and signals for “publicity”, “legality” and “budgetary feasibility” Based on the content of the proposal, application of regulations and budget, indicators for the degree of impact of the implementation of the proposal on “publicity”, “legality” and “budgetary feasibility” are designed. Innovative indicators of “red”, “yellow” and “green” are used to facilitate participating citizens to understand and check. Those with “high” or “moderate” feasibility of the three indicators can enter the subsequent voting process.

Public exhibition of proposals and plans Before the proposal voting, the public exhibition process of the proposal plan will be carried out, including “online exhibition” and “physical exhibition”. The public can have an in-depth understanding of the content of the proposal before the voting, and also have the opportunity to express their opinions on the proposal.
Proposal voting Through the participatory budget i-Voting process, those registered in Taipei City, or studying, working, or living in Taipei City, and who are over 16 years old, can vote online or go to the district office to authenticate and vote online. If the number of votes exceeds the district threshold and is within the upper limit of the record after sorting, the case can be recorded.
Budget evaluation, budget review and parliamentary supervision stages After the proposal is approved by voting, it will be officially recorded. The Civil Affairs Bureau will notify each responsible agency to conduct budget evaluation and planning. If there are many agencies involved, one agency will be designated as the main responsible agency (Project Manager). Those who have a budget for the current year can implement it first. If the proposal implementation funds need to be paid in the next year’s budget, or if a new budget is compiled for the next year, it will be handled according to the budget preparation process, and then the budget approval and supervision will be carried out by the parliament.

Flexibility under standardization

Convene an agency consensus meeting The Civil Affairs Bureau holds an agency consensus meeting every year to explain “improving the principles of designation and adjustment of responsible agencies,” “coordinating matters to improve the feasibility of proposals,” “budget planning schedules,” and “notes on the implementation of proposals,” etc., in order to improve consensus on the designation of responsible agencies before each district meeting, so that participating agency colleagues can understand the follow-up procedures and agency roles, and provide specific and detailed information for review team members to evaluate carefully.
Hold a pre-meeting meeting In order to confirm the responsible agency for the proposal as early as possible and conduct consultation or communication in advance, each district office holds a pre-deliberation workshop meeting after the residents’ meeting to invite relevant agencies of the proposal and designate the sponsoring agency for each case.
Direct execution mechanism “Direct implementation” was added in 2018. Some proposals are relatively simple and belong to a single responsible authority. If the existing budget of the current year is executable and the implementation can be completed in the current year, on the premise of the proposer’s consent and reaching a consensus with the responsible authority, there will be no subsequent proposal review and voting procedures. The agency will handle it in the current year, reducing the complicated process and completing the proposer’s idea as soon as possible.
Evaluation and processing mechanism After years of operation, proposals such as changes in traffic signal seconds, sidewalk settings, parking space planning or environmental beautification are public in nature. In order to avoid personal factors affecting the overall concept of regional planning and urban aesthetics, the proposal was discussed and approved by the public participation working group meeting. Projects including “traffic management”, “walking space”, “environmental painting or beautification”, etc., were submitted to the responsible authority for overall evaluation and processing with the approval of the proposer. The evaluation was completed before August of the year and reported to the working group meeting.

Advantages and key success factors of Taipei City’s promotion of participatory budgeting Chief supports authorization Fault tolerance correction Information disclosure and transparency Incorporate into budget process Limitations of Taipei City’s push for participatory budgeting Limitations of municipal issues

Municipal-level proposals are made in the form of issues, and special projects are being carried out to deal with issues of new immigrants and groups with disabilities. Currently, citizen proposals are limited to the district level. How can bureaus design topics and develop them from top to bottom, or allow citizens to propose bottom-up proposals? It still needs to be followed up to derive appropriate city-level topics.

Lack of online discussion model

At present, participatory budgeting in Taipei City is still conducted through on-site participation and discussion. With the trend towards younger and more convenient participation, whether it can be discussed online is worth looking forward to developing a model that transcends time and space. During the COVID-19 epidemic, various video and digital platforms have developed rapidly. In 2021 and 2022, attempts have been made to conduct deliberation workshops and residents’ meetings via online video. In the future, we may further think about how to make digital a normal mode of participation.

Chapter 23 Participatory Budgeting Development Plan

For Taipei citizens Progressive “Extension Education 369” series of courses Take root on campus Active citizenship materials have a foundation The proposal successfully brought happy changes to the people and gave participatory budgeting a strong appeal in the future. For students, the perspective gradually changes Courses that emphasize “participation” are closer to the “108 Curriculum”
For public servants In order to accelerate the equipping of civil servants with knowledge and concepts related to participatory budgeting, Taipei City held a “Civil Servant Training Course” from May to August 2015. The main trainees were officers of the Participating Budget Team and business-related personnel of various bureaus and district offices. Scholars and members of the Participating Budget Team were invited to have dialogues with civil servants to open up civil servants’ imagination of participatory budgeting and become seed personnel in various bureaus and offices of the city government.

Reversing the attitude of civil servants Promote organizational learning in various bureaus 3. Formation of the “Government-Academic Alliance” Grasp Taipei City’s advantageous resources and establish an expert database Strive for the recognition of experts and scholars and solidify the basis for cooperation between the two parties Establish government-study alliance and carry out cooperation new page Connected to the local area, administration accompanies evolution Assist local development and practice “university social responsibility”

Chapter 24 Supporting Plan for Participatory Budgeting

Operational supporting plan Auxiliary measures Project consultation counter setup and promotion plan Provide consultation on citizen participatory budget procedures, assist proposers in writing plans, and accept proposals.

Things to note when coordinating different opinions during the post-implementation stage of participatory budgeting Coordination and handling procedures when local public opinion has different opinions during the proposal implementation stage.

Execution mechanism In order to promote administrative efficiency, when a proposal meets certain conditions, it can be implemented directly.

Proposal exclusion and project evaluation mechanism

When considering some proposals, it is necessary to plan from the perspective of municipal integrity, or to consider the financial discipline of the municipal government. Some projects cannot be used as proposals or need to be evaluated by the agency as a whole.

Plan extension Entering Campus Series Project Committed to taking root in democratic literacy, we plan a series of campus promotion programs and activities.

Group proposal plan Consideration of specific issues that are not likely to receive attention within the existing participatory budget process allows planning non-profits or disability groups to apply for ad hoc procedures for specific issues.

Programmatic package plan Proposal briefing meeting residents meeting Proposal Review Workshop Implementation Plan Proposal Ballot Implementation Plan

✅ Manage your projects

Wed, 01 Jan 2020 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

🎉 Easily create your own simple yet highly customizable blog

Wed, 01 Jan 2020 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
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Inspiration

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Features

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Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

for your site. Themes are fully customizable.

License

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👩🏼‍🏫 Teach academic courses

Wed, 01 Jan 2020 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Citation

Here’s an example of citing a publication using the cite shortcode:

Dylan Chiang (2019). An example preprint / working paper.

Preprint Code Slides Dataset Poster Source Document Video Custom Link Preprint

You can also use the default view by omitting the view parameter:

Dylan Chiang

Equal contribution

, Robert Ford

Equal contribution

(2013). An example conference paper. In ICW.

PDF Code Dataset Poster Project Slides Source Document Video PDF DOI

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
 math:
 enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

📈 Communicate your results effectively with the best data visualizations

Wed, 01 Jan 2020 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Hugo Blox supports the popular format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

🧠 Sharpen your thinking with a second brain

Wed, 01 Jan 2020 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Blog | Dylan Chiang

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Research background

Research methods

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

2. Exosomal miR-17-3p is a key messenger

3. Molecular mechanism: inhibiting chondrocyte proliferation

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

2. Provides a new etiological explanation for ISS

3. Potential therapeutic targets

My understanding

1. Molecular-level interpretation of “You are what you eat”

2. Growth hormone is not a panacea

3. The collision of traditional wisdom and modern science

Related resources

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Research background

Research methods

Main findings

1. Outdoor sports significantly increase the P300 amplitude

2. The effect of indoor exercise is not as good as expected

3. No significant difference in reaction time

Research significance and inspiration

My understanding

1. Environment is an extension of cognition

2. Redefinition of “efficiency”

3. Simple and easy

Related resources

Paper reading: How people think about being alone shapes their experience of loneliness

Research background

Research methods

Main findings

1. The media’s “high stigma” against solitude

2. The causal impact of beliefs on loneliness

3. The “buffering and catalyzing” effect in daily life

Research significance and inspiration

1. The “echo effect” of public health propaganda

2. New approaches to cognitive intervention

My understanding

1. The self-fulfilling prophecy of cognitive frameworks

2. Media Ecology and Mental Toughness

3. A “moderate turn” in policy

4. Conclusion: Solitude is a kind of self-healing ability

Related resources

Paper reading: Topological turning points across the human lifespan

Research background

Research methods

1. Data analysis scale

2. Manifold Learning

Key findings: Five life stages and four turning points

Detailed description of key findings

Research significance and inspiration

1. Redefine “adolescence”

2. The guiding value of non-linear development

My understanding

1. Trade-off between structure and function

2. Synergy of biological markers

3. Reconstruction of the definition of “adolescence”

4. Precursors and nonlinear characteristics of aging

Related resources

Paper reading: Video game play is positively correlated with well-being

Research background

Research methods

Main findings

1. Game time is positively correlated with happiness

2. “Quality of experience” is more important than “length of time”

3. Self-report bias

Research significance and inspiration

1. Challenging the foundations of policymaking

2. A new paradigm for industry cooperation

My understanding

1. The power of “data honesty”

2. Correlation\Causality

3. Games as the “Third Space” in the Digital Age

Related resources

Report Reading: Generative Artificial Intelligence Application Development Report (2025)

Report information

Comprehensive overview and summary of the report