arXiv AI August 11, 2026 By Tim R. Davidson, Adam Fourney, Saleema Amershi, Robert West, Eric Horvitz, Ece Kamar

The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation

arXiv:2511. 02687v2 Announce Type: replace Abstract: The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with different information, privileges, and tools.

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

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

Jun 8

More Capable, Less Cooperative? When LLMs Fail At Zero-Cost Collaboration

arXiv:2604. 07821v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation fails.

By Advait Yadav, Sid Black, Oliver Sourbut

llms agents

arXiv AI

Aug 10

Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration

arXiv:2608. 06381v1 Announce Type: cross Abstract: Explainable AI (XAI) has shown promise for human-agent collaboration, yet results rely on hand-crafted policies in custom environments, limiting generalizability to state-of-the-art teaming research.

By Mateus Levi Sim\~oes Fernandes, Alberto Sardinha

agents reinforcement-learning benchmarks

arXiv AI

Jun 4

The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.

By Xinyu Lu, Tianshu Wang, Pengbo Wang, zujie wen, Zhiqiang Zhang, Jun Zhou, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

agents benchmarks safety

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 16

OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models

arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.

By Tianyi Lin, Chuanyu Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang

llms agents reinforcement-learning benchmarks

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

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Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration

The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

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OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models