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DynSTEER: Dynamic Stage-wise Trajectory Evaluation and Execution-time Review for Agents

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Large language model agents are increasingly deployed for long-horizon task execution, raising a central granularity question for trajectory evaluation: whole-trajectory verification is too coarse to capture concrete failures and their associated evidence in long trajectories, while atomic-step scoring is too fine-grained, noise-sensitive, and computationally expensive. This granularity gap makes a single-reference trajectory paradigm inadequate for assessing the rich space of valid agent execution paths and delays timely feedback and early stopping in long-horizon tasks. To address these issu

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