Otap:Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
Factual evidence
What the source reports
Research introduces Otap, a new evaluation metric using optimal transport for LLM agent trajectories, assessing planning, tool use, and execution beyond binary success.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 5 November 2026
- Collected by OneBench
- 21 Jul 2026, 08:29 UK
Stored source excerpt
arXiv:2607.17082v1 Announce Type: cross Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics…
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OneBench interpretation
Institutional assessment
So what
Better evaluation of agentic workflows enables robust G-SIB enterprise deployments, especially for complex financial tasks requiring multiple steps and tool integrations.
Do what
This research provides a pathway to more reliable validation frameworks for agentic AI, informing your model risk assessments and deployment strategies for multi-step intelligent automation.