Research Shows AI Agents Spin Silently — Loop Detection Misses Output-Level Stalls

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Researchers at Zhejiang University's ZJU-REAL lab published BEACON (arXiv:2605.06078), a framework studying how long-horizon AI agents get stuck without triggering standard loop-detection alerts. The core finding is that stuck agents rarely repeat identical tool calls; instead, they cycle through superficially varied actions — reading files, running slightly different commands — while producing zero verifiable state changes. Conventional loop detection monitors inputs and call signatures, making it blind to this output-level stalling behavior. BEACON formalizes the problem through milestone-based rewards, where any action segment that fails to reach a defined milestone earns zero reward, regardless of how many steps it took. The paper reports that 39–47% of sampled trajectories complete at least one subgoal but ultimately fail the task, underscoring how common mid-task stalling is in complex agent workflows.

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