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This research aims to identify failure patterns in agent WANDERING through tool-use entropy collapse monitoring.

May 26, 2026Open Access

Tool-Entropy Collapse: A Cross-Architecture Signature of Agent WANDERING Failure

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Randomized trial uncovers tool-use entropy collapse in WANDERING agents, indicating failure monitoring issues in AI models.

This research aims to identify failure patterns in agent WANDERING through tool-use entropy collapse monitoring.
Analyzed 20 Qwen3.6-27B SWE-bench Pro agents to identify 34% blind spot in monitoring.
Tested six detector designs across text, cross-layer residuals, and action entropy for effectiveness.
Validated findings with cross-architecture setups including Llama-70b and GPT-5.
Tool-use entropy collapse leads to a W/S median ratio of approximately 0.41 in Qwen and Llama, 0.71 in GPT-5.
Achieved 70% recall with 5% false-positive rate using entropy collapse as a detection method.
Cross-task validation returned non-significant results (p=0.81), limiting findings to specific task scenarios.

Caio Vicentino (2026) studied this question.

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May 26, 2026Open Access

View Full Paper

Ask AI

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Authors

Randomized trial uncovers tool-use entropy collapse in WANDERING agents, indicating failure monitoring issues in AI models.

This research aims to identify failure patterns in agent WANDERING through tool-use entropy collapse monitoring.
Analyzed 20 Qwen3.6-27B SWE-bench Pro agents to identify 34% blind spot in monitoring.
Tested six detector designs across text, cross-layer residuals, and action entropy for effectiveness.
Validated findings with cross-architecture setups including Llama-70b and GPT-5.
Tool-use entropy collapse leads to a W/S median ratio of approximately 0.41 in Qwen and Llama, 0.71 in GPT-5.
Achieved 70% recall with 5% false-positive rate using entropy collapse as a detection method.
Cross-task validation returned non-significant results (p=0.81), limiting findings to specific task scenarios.

Caio Vicentino (2026) studied this question.

View Full Paper

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