Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties
Philipp J. Schneider, Lin Tian, Marian-Andrei Rizoiu

TL;DR
This paper introduces a multi-agent LLM simulation framework where agents learn social behaviors through in-context learning and coaching signals, resulting in emergent social ties and network structures similar to real online communities.
Contribution
It presents a novel simulation framework that models human-like social dynamics in LLM agents using behavioral rewards and in-context learning.
Findings
Agents develop stable interaction patterns
Emergent social ties form in the network
Network structures resemble real online communities
Abstract
Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily, reciprocity, and social validation -- and what memory and learning mechanisms enable such dynamics to emerge? We present a multi-agent LLM simulation framework in which agents repeatedly interact, evaluate one another, and adapt their behavior through in-context learning accelerated by a coaching signal. To model human social behavior, we design behavioral reward functions that capture core drivers of online engagement, including social interaction, information seeking, self-presentation, coordination, and emotional support. These rewards align agent objectives with empirically observed user motivations, enabling the study of how network structures and group formations emerge from individual decision-making. Our experiments show that coached LLM…
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Taxonomy
TopicsMental Health via Writing · Topic Modeling · Language and cultural evolution
