Social Learning
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Related Articles from SNS
Performance Evaluation of Social Learning
arXiv:2606.09176v1 Announce Type: new Abstract: Social Learning is a decentralized decision-making paradigm in which spatially dispersed agents collect streaming observations regulated by one of a finite number of models (the hypotheses). The agents are interested in assigning probability scores (the beliefs) to the possible hypotheses. To this end, the agents exchange their beliefs according to a certain communication graph.
Social Learning with Limited Attention: Negative Reviews Persist under Newest First
arXiv:2406.06929v4 Announce Type: replace Abstract: We study a model of social learning from reviews where customers are computationally limited and make purchases based on reading only the first few reviews displayed by the platform. Under this limited attention, we establish that the review ordering policy can have a significant impact. In particular, the popular Newest First ordering induces a negative review to persist as the most recent review longer than a positive review.
SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice
arXiv:2606.04155v1 Announce Type: new Abstract: Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world. However, scalable and effective training remains a significant challenge due to the scarcity of expert coaching. In this paper, we introduce SocialCoach, a holistic LLM-powered agentic tutoring system for personalized social skill development at scale.
Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings
arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP systems collapse this variation into a single ground-truth label, ignoring the diversity of interpretations. In this work, we model social dimensions along a perspectivist spectrum, capturing how interpretations vary across demographic groups on a dataset consisting of 28k human annotations.
TRANS: Terrain-aware Reinforcement Learning for Agile Navigation of Quadruped Robots under Social Interactions
arXiv:2602.12724v3 Announce Type: replace Abstract: This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL) framework for quadrupedal social navigation over unstructured terrains. Conventional quadrupedal navigation typically separates motion planning from locomotion control, neglecting whole-body constraints and terrain awareness. On the other hand, end-to-end methods are more integrated but...
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
arXiv:2605.18936v2 Announce Type: replace Abstract: Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance.
Agentopia: Long-Term Life Simulation and Learning in Agent Societies
arXiv:2606.07513v1 Announce Type: new Abstract: Humans learn from social life. Simulating this process with LLM-powered agents represents a promising research direction, raising a natural question: whether LLMs can learn from such simulated social experience to better understand and replicate human behavior. However, prior agent society simulations typically operate at the scale of days, limiting the depth of social interactions and long-term growth.
Semantic knowledge guides innovation and drives cultural evolution
arXiv:2510.12837v4 Announce Type: replace Abstract: Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans. While social learning enables the transmission of such innovations, the cognitive processes that generate them remain poorly understood. Classical theories typically treat innovation as random variation, a simplification insufficient for explaining the complexity of human cultural evolution.
Behind the Feed: A Taxonomy of User-Facing Cues for Algorithmic Transparency in Social Media
arXiv:2602.03121v3 Announce Type: replace Abstract: People who use social media are learning about how the companies that run these platforms make their decisions on who gets to see what through visual indicators in the interface (UI) of each social media site. These indicators are different for each platform and are not always located in an easy-to-find location on the site. Therefore, it is hard for someone to compare different social media platforms or determine whether transparency leads...
How returning to school helped this social worker deepen his practice
How returning to school helped this social worker deepen his practice Through applied learning, mentorship and a community of fellow adult learners, Mr Kevin See strengthened the way he serves families, leads teams and mentors future practitioners. During a meeting about a study corner used by children living in rental flats, Mr Kevin See found himself speaking up against plans to remove the space. To him, the corner was more than just a place for tuition sessions.