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

We identify a 34% blind spot in probe-based LLM agent failure monitoring on Qwen3.6-27B SWE-bench Pro: the WANDERING sub-class where probe says success but agent never emits finish_tool. We test six detector designs across three signal channels (text, residual cross-layer, action entropy) and find that tool-use entropy collapse is the breakthrough signal—WANDERING agents collapse onto a small set of repeated tool calls (W/S median ratio 0.41 in Qwen and Llama, 0.71 in GPT-5), enabling a Tier-3 autonomous-termination detector at 70% recall × 5% false-positive cost. Cross-architecture validation: Llama-70b (n=2,315, p