SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents
Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long, noisy trajectories into fluent but incorrect answers. Diagnosing such failures is difficult, requiring the manual inspection of extremely long execution traces, which could be beyond human capacity. We therefore introduce SearchAuditBench, a benchmark that evaluates whether LLM auditors can localize, attribute, and repair these failures, thereby reducing the human burden. SearchAuditBench comprises 1,243 failed trajectories, averaging 73.1 messages and 65.1K tokens, collected from eight open-weight models on f...
| Authors | Zhixiang Liang, Yifei Liu, Yidan Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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