arXiv AI
The Silent Failures in Agentic Data Science
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Can Coding Agents Reproduce Findings in Computational Materials Science?
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DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?
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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
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