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原文摘要

Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics reduce such a trajectory to a binary success flag, compare it against a reference by exact matching, or delegate judgment to another language model. A success flag cannot distinguish sound solutions from lucky ones, nor explain why runs fail. Exact matching penalizes valid plans reordered or decomposed differently. We reframe trajectory evaluation as a distance between the agent's execution graph and a set of valid solution graphs, and instantiate it via an unbalanced fused Gromov-Wasserstein transport problem over attributed dependency graphs. The resulting score, termed OTAP (Optimal Transport for Agentic Planning), is a normalized discrepancy score. Its idealized, globally minimized entropy-free objective is invariant to dependency-preserving relabelings of a fixed graph and has a redundant-insertion sensitivity analysis under stated assumptions. Its unbalanced marginals handle missing or hallucinated steps without forcing a match; granularity invariance remains an open problem that soft coupling mitigates only in part. On controlled perturbations and three public benchmarks, OTAP separates valid from invalid trajectories in a regime where semantics-only metrics score below chance. Its performance is encouraging across these settings, while controlled edge-noise experiments reveal sensitivity to dependency extraction. Where a formal verifier exists, strict surface metrics predict validity better than OTAP does, which places OTAP in open-ended domains where no verifier is available. A blinded study of 236 trajectories finds the strongest agreement with OTAP among the six compared metrics. Structure-aware baselines achieve higher clean-graph AUROC, while OTAP has the smallest absolute AUROC decline under the tested edge noise.