arXiv AI July 14, 2026 By Hyeong Kyu Choi, Jiatong Li, Wendi Li, Xin Eric Wang, Sharon Li

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 3

Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.

By Sangeun Park, Minhae Kwon

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

arXiv:2410. 02511v2 Announce Type: replace Abstract: With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning.

By Yun Qu, Boyuan Wang, Yuhang Jiang, Jianzhun Shao, Yixiu Mao, Heming Zou, Chang Liu, Cheems Wang, Meiqin Liu, Xiangyang Ji

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 9

Language-based Trial and Error Falls Behind in the Era of Experience

arXiv:2601. 21754v3 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.

By Haoyu Wang, Guozheng Ma, Shugang Cui, Yilun Kong, Haotian Luo, Li Shen, Mengya Gao, Yichao Wu, Xiaogang Wang, Dacheng Tao

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 9

Benchmarking Open-Ended Multi-Agent Coordination in Language Agents

arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.

By Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rockt\"aschel, Amos Storkey

llms agents benchmarks

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

6d ago

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

arXiv:2604. 17244v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs.

By Priya Gurjar, Md Farhan Ishmam, Kenneth Marino

llms agents reinforcement-learning

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Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

Language-based Trial and Error Falls Behind in the Era of Experience

Benchmarking Open-Ended Multi-Agent Coordination in Language Agents

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

DORA Explorer: Improving the Exploration Ability of LLMs Without Training