The paper investigates how communication affects trading agents that operate independently rather than as a coordinated team. Over seven trading days, 200 agents from four model families were grouped under five communication regimes, from isolation to shared message boards, and their reading logs were recorded before each decision. Results showed that in two model families, communicating groups held more of the same stocks than isolated groups, and a group’s return was largely driven by shared holdings; agents were more likely to reuse phrases from groupmates, and communicating groups tended to sell in step more often than isolated ones, though isolated groups also converged on similar stocks, indicating that herding assessments must compare against a no‑peer baseline.
arXiv:2610.20360v1 Announce Type: new
Abstract: Gossip, defined as evaluative talk about absent third parties, is widely seen as a uniquely human social practice. This study examines whether comparab...
By Yong-Hui Hua, Chih-Han Lin, Shu-Kai Hsieh
arXiv:2606. 03544v1 Announce Type: new Abstract: Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior.
By Linyue Pan, Yaoming Zhu, Lin Qiu, Xuezhi Cao, Xunliang Cai
The Flag Game is a toy model designed to study how AI agents form collective beliefs. In the game, each agent sees only a private crop of a hidden country flag and can share beliefs with peers, leading to complex phenomena such as non‑monotonic performance scaling, accuracy gains from social awareness, and polarization that degrades performance at large population sizes. The authors introduce social circuit attribution to identify key agents and views, and develop a statistical mechanical theory to explain collective belief collapse and polarization in larger populations.
By Elizabeth Pavlova, Hidenori Tanaka
arXiv:2608. 16801v1 Announce Type: new Abstract: We study how teams of AI coding agents coordinate while solving programming tasks.
By Giuseppe Destefanis, Tomaso Aste
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
By Batu El, Jinhee Paeng, Fatih Dinc, Shiye Su, Mete Erdogan, Aneesh Pappu, Haotian Ye, Wanjia Zhao, Surya Ganguli, James Zou
arXiv:2609.07675v1 Announce Type: cross
Abstract: Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets,...
By Zelin Li, Yiyun Su, Matt White, Zhipeng Wang, Xiao-Yang Liu, Tianyu Shi
arXiv:2610.33011v1 Announce Type: cross
Abstract: While recent advances in autonomous agents enable them to coordinate on increasingly complex tasks, prior work shows that this growing capacity for c...
By Eric Onyame, Runtao Zhou, Kowshik Thopalli, Bhavya Kailkhura, Chirag Agarwal
arXiv:2610.24833v1 Announce Type: cross
Abstract: As AI agents become increasingly connected through networks and take on tasks that require negotiation and joint decision-making, a systematic approa...
By Jin Gao
The study investigates how limited reading capacity and claim wording influence consensus outcomes in language‑model networks. By modeling message capacity as the number of messages an agent reads, the authors show that when agents read fewer than about 6.4 messages on average, a wrong consensus becomes unreachable. However, the wording of a claim—its inherent threshold—can override this effect, leading to incorrect consensus even when most agents start correct.
By Makoto Fukushima
The study investigates how generative agents in simulated societies revise their stances and reorganise interactions. By combining stance-change metrics, source evaluations, and temporal network analysis across multiple GPT‑4o runs, the authors find that environmental rational persuasion leads to the largest overall stance shift, while economic emotional persuasion produces the highest rate of low‑trust stance changes. In both experimental settings, agents often partially accept new positions yet maintain low trust in the source, and sustained coordination can coexist with ongoing disagreement.
By Hanzhong Zhang, Siyang Song, Jindong Wang
The paper introduces SILICA, an open instrument designed to evaluate whether large language model (LLM) agent societies replicate human behavioural distributions. Using five environments with human‑anchored data and perturbations, the study finds that most LLMs only match human behaviour at initial stages, failing to reproduce end‑state cooperation or correct acceptance thresholds. The results suggest that current LLM societies can support exploratory claims but do not yet reliably emulate human social dynamics.
By Raad Bin Tareaf