Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Summary
This paper synthesizes 27 benchmark, taxonomy, and audit papers from 2023-2026 into a unified taxonomy of LLM agent limitations, identifying six failure clusters including tool invocation errors, planning failures, long-horizon degradation, multi-agent coordination issues, safety concerns, and measurement validity problems.
View Cached Full Text
Cached at: 07/08/26, 04:38 AM
# Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents Source: [https://arxiv.org/abs/2607.05775](https://arxiv.org/abs/2607.05775) [View PDF](https://arxiv.org/pdf/2607.05775) > 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, planning, long\-horizon reasoning, multi\-agent coordination, safety, and measurement validity into a single, unified taxonomy of LLM agent limitations\. We identify six failure clusters: \(1\) tool invocation and parameter\-level errors, \(2\) planning and constraint\-satisfaction failures, \(3\) long\-horizon degradation from context accumulation, \(4\) multi\-agent coordination failures, \(5\) safety and security failures under adversarial or underspecified conditions, and \(6\) measurement validity problems\. The taxonomy was derived iteratively by grouping independently reported error categories into themes corresponding to distinct stages of the agent reasoning\-to\-action pipeline\. Across the literature, we find that failures compound nonlinearly with task length, that strong performance on individual sub\-tasks does not reliably translate into end\-to\-end success, and that additional scaffolding does not consistently improve reliability\. At the same time, substantial progress has been demonstrated in single\-turn tool use, short\-horizon web navigation, and narrowly scoped coding tasks\. ## Submission history From: Wael Albayaydh \[[view email](https://arxiv.org/show-email/08b90aa9/2607.05775)\] **\[v1\]**Tue, 7 Jul 2026 03:05:13 UTC \(175 KB\)
Similar Articles
When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
The ToolMaze benchmark evaluates LLM agents' ability to handle real-world tool failures, revealing that implicit semantic failures cause the largest performance drops and that dynamic replanning remains a critical bottleneck not addressed by scaling or prompting.
PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems
PlanBench-XL is a new benchmark that evaluates LLM agents' ability to plan and adapt in large tool ecosystems with limited visibility and dynamic disruptions. Experiments show GPT-5.4 achieves only 51.9% accuracy in block-free settings and collapses to 11.36% under severe blocking, highlighting significant challenges in long-horizon planning.
@tli104: New paper: "Self-Compacting Language Model Agents" LM agents build up long traces of reasoning and tool calls. As the t…
New paper proposes self-compacting language model agents that can decide when to clean up their own traces of reasoning and tool calls to avoid accumulating mistakes and stale information.
AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators
This paper introduces AgentCollabBench, a diagnostic benchmark for multi-agent systems that evaluates behavioral risks like instruction decay and context leakage across four major LLMs. It argues that communication topology is a critical factor in multi-agent reliability, often overshadowing raw model capability.
Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability
Introduces ToolBench-X, a benchmark for evaluating large language model agents under various tool-environment reliability hazards, revealing a substantial gap in performance compared to clean environments.