Harness-Search: Guiding Long-Horizon Search through Multi-Agent Coordination
Shanyong Wang, Yanyu Xu and colleagues at Xiaohongshu with Shandong University and UIUC introduce Harness-Search, which splits a long-horizon search agent into three permission-bounded roles that propose, commit and audit.
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Roles. A Retrieval Policy proposes search operations, a Memory Operator validates and commits evidence to shared working memory, and a Summary Auditor accepts or rejects termination based on whether the curated evidence is sufficient.
Results. With GPT-OSS-20B as the policy, Harness-Search raises Recall by 4.60 to 27.92 points and Final-Answer Recall by 12.34 to 30.13 points over the strongest harness baseline on each evidence-retrieval benchmark, across seven benchmarks.
Long trajectories. Evidence coverage keeps growing as the history lengthens, with less redundant retrieval; at 32 turns it reaches 65.57 against 56.25 for Harness-1.
Transfer and SFT. Gains hold across policy models from Qwen3-8B to Gemini-3.1-pro, and trajectories from the harness also work as supervised fine-tuning data.
Abstract
Long-horizon search requires agents to gather evidence across multiple steps and synthesize it into well-supported answers. The recent agent harnesses provide a natural and promising framework to support such long-running search processes. As interaction histories grow, one single agent in harnesses might get stuck and cause the policy to lose track of unresolved questions, overlook useful evidence, or terminate before sufficient support has been collected. One of promising way is to decouple three distinct responsibilities of proposing retrieval actions, updating persistent state, and deciding when to stop rather than concentrating them within a single policy. Targeted at it, we introduce Harness-Search, a multi-agent search harness to reduce the local errors propagating across subsequent exploration, evidence curation, and termination decisions. In particular, Harness-Search assigns these responsibilities to three permission-bounded authorities: a Retrieval Policy that proposes search operations, a Memory Operator that validates and commits persistent-state updates, and a Summary Auditor that accepts or rejects termination based on the sufficiency of the curated evidence. Together, these roles form a Propose-Commit-Audit loop in which actions are proposed, persistent evidence is selectively committed, and stopping decisions are subjected to an explicit sufficiency check. Across seven long-horizon search benchmarks, Harness-Search improves both retrieval and answer generation under the same policy backbone, increasing Recall by 4.60-27.92 points and Final-Answer Recall by 12.34-30.13 points over the strongest harness-based baseline on each evidence-retrieval benchmark. Moreover, trajectory-level analyses show that Harness-Search continues to accumulate useful evidence and expand evidence coverage with less redundant retrieval as the search history grows.