arXiv AI July 17, 2026 By Hao He, Chris J. Kuhlman, Xinwei Deng

Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments

arXiv:2607. 14574v1 Announce Type: new Abstract: Collective problem solving often requires that group members consider the tradeoff between exploitation of known solutions and exploration for new ones, where information of known solutions can be disseminated among individual members through communication networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 30

Collective cooperation without individual fidelity in LLM agents

arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.

By Henrique Ferraz de Arruda, Carlos Gracia L\'azaro, Alberto Aleta, Yamir Moreno

llms agents benchmarks

arXiv AI

3d ago

From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs

arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to wor...

By Kunal Jha, Max Kleiman-Weiner, Natasha Jaques

llms agents

arXiv AI

Jul 14

Multi-Agent LLMs Fail to Explore Each Other

arXiv:2607. 11250v1 Announce Type: cross Abstract: Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another.

By Hyeong Kyu Choi, Jiatong Li, Wendi Li, Xin Eric Wang, Sharon Li

llms agents

arXiv AI

Jun 17

Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models

arXiv:2504. 03991v2 Announce Type: replace-cross Abstract: Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making.

By Siddharth Srikanth, Varun Bhatt, Boshen Zhang, Werner Hager, Charles Michael Lewis, Katia P. Sycara, Aaquib Tabrez, Stefanos Nikolaidis

llms agents reinforcement-learning

arXiv AI

Sep 21

Scaling Discovery through Test-Time Communication

The paper demonstrates that test‑time communication among agents can significantly outperform independent parallel attempts on complex tasks. In experiments on the ARC‑AGI‑3 benchmark, a team of $k$ communicating agents matched the success rate of $4k$ independent agents, with the advantage growing as the team size increased. The study also shows that communication enables solving tasks that no single agent can solve, and that these benefits transfer to research‑oriented problems such as polyomino packing and MNIST classifier compression, where communicating agents surpassed prior best scores.

By Jongho Park, Vasilis Kontonis, Shivam Garg, Akshay Krishnamurthy, Dimitris Papailiopoulos

agents benchmarks

arXiv AI

Jul 2

Large language models replicate and predict human cooperation across experiments in game theory

arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.

By Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal, Max Pellert

llms agents safety

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Collective cooperation without individual fidelity in LLM agents

From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs

Multi-Agent LLMs Fail to Explore Each Other

Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models

Scaling Discovery through Test-Time Communication

Large language models replicate and predict human cooperation across experiments in game theory