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. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts. This paper synthesizes 27 benchmark, taxonomy, and audit papers (2023-2026), spanning 19 distinct benchmarks, into a cross-cutting taxonomy of agent limitations. To our knowledge, this is the first synthesis that integrates evidence across tool use,
This paper synthesizes recent research (2023-2026) on LLM agent failures, suggesting a maturation of the field where foundational limitations are being systematically cataloged.
A strategic reader should care because understanding the systematic limitations of LLM agents is critical for realistic deployment strategies and for identifying next-generation research opportunities.
The focus shifts from merely reporting benchmark scores to a more nuanced understanding of where LLM agents consistently fail, which will influence development roadmaps and investment priorities.
- · AI safety researchers
- · Developers of robust LLM architectures
- · Enterprise AI implementers
- · Companies over-promising AGI capabilities
- · Benchmark-driven AI development
- · Investors in undifferentiated LLM agent startups
Systematic understanding of LLM agent failure modes will lead to more targeted research and development efforts.
This improved understanding could accelerate the deployment of more reliable, albeit still limited, AI agents in complex real-world scenarios.
The documented limitations could temper over-enthusiasm for fully autonomous AI, shifting public and investor focus towards augmented intelligence solutions.
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Read at arXiv cs.AI