arXiv:2606.23937v1 Announce Type: cross Abstract: Exact-match retrieval recall is often used as a proxy for whether a retriever supplies useful policy context to a downstream decision model. We test this proxy for pre-action policy classification in tau-bench using Qwen2.5-3B/7B classifiers. Under gold-policy conditioning, a compact structured state improves macro-F1 over raw trajectories by 0.13-0.17 after tuning. We then replace the benchmark-designated policy clause with the top-ranked clause retrieved from decision-time context. Although the exact governing clause is retrieved at rank 1 fo
The rapid development of large language models and agentic systems necessitates more precise evaluation methodologies to ensure reliable performance in complex tasks.
Improving the accuracy of evaluation metrics for AI agents directly impacts their reliability, safety, and ultimately their utility in real-world applications.
The understanding of how retrieval metrics can mislead in evaluating long-horizon tool-use agents, prompting a re-evaluation of current methods and the development of more robust proxies.
- · AI evaluation researchers
- · AI safety researchers
- · Developers of AI agent frameworks
- · Developers relying solely on exact-match retrieval metrics
- · Companies with under-tested AI agent deployments
More sophisticated and nuanced evaluation frameworks for AI agents will be developed and adopted.
This will lead to more robust and reliable AI agents capable of performing complex, multi-step tasks with fewer errors.
The increased reliability of AI agents could accelerate their deployment in critical industries, potentially collapsing certain white-collar workflows more effectively.
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Read at arXiv cs.AI