arXiv AI
Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues
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arXiv AI
Sep 21
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
arXiv:2609.21423v1 Announce Type: new Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to...
arXiv AI
Jul 8
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
arXiv:2607. 05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons.
Hugging Face Trending Papers
Jul 7
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv AI
3d ago
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Kolab is a new dataset comprising over 115 hours of screen recordings from 50 professionals across 40 occupations who collaborate with AI agents on realistic, long‑horizon tasks. The dataset includes participants’ judgments of collaboration quality and output, revealing varied coordination patterns and the need for agents to anticipate how experts will use or modify their contributions. Analysis shows current state‑of‑the‑art models overestimate collaboration success and struggle to differentiate high‑ from low‑quality interactions, indicating a gap in understanding long‑horizon human‑agent teamwork.
arXiv AI
4d ago
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems
arXiv:2610.18780v1 Announce Type: new Abstract: Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Y...
arXiv AI
Aug 17
AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
arXiv:2608. 14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horizons.