arXiv cs.AI· Songtao Huang, Zhijie Zhong, Xiaohan He, Shuaiyu Zhang, Zhiyin Yu, Li Kang, Heng Zhou, Yutao Fan, Xiangchao Yan, Bo Zhang, Shiyang Feng·· 6 天前AI 评分51
LongHarness：通过有状态证据管理在 1 亿 Token 语料上推进智能体推理
LongHarness: Advancing Agentic Reasoning over 100M-Token Corpora via Stateful Evidence Management
AI 导读
LongHarness 是一个模型无关的 Agent 框架，通过证据与结论协同演化以及证据生命周期管理，让检索聚焦于尚未解决的信息需求，避免无界探索和证据状态漂移。
正文 · 原文
Authors:Songtao Huang, Zhijie Zhong, Xiaohan He, Shuaiyu Zhang, Zhiyin Yu, Li Kang, Heng Zhou, Yutao Fan, Xiangchao Yan, Bo Zhang, Shiyang Feng
Abstract:Corpus-level long-context reasoning requires agents to acquire and integrate evidence across document collections that exceed their context windows. Existing agent harnesses enable incremental access to these corpora, but acquired evidence and evolving conclusions often remain entangled in a growing interaction history. This implicit state management can lead to two coupled failure modes: unbounded exploration, where retrieval expands the interaction history without resolving decision-relevant information gaps, and evidence-state drift, where previously acquired evidence or subsequent corrections fail to inform the current conclusion. We present LongHarness, a model-agnostic harness that makes the interaction between acquired evidence and evolving conclusions explicit. Its core mechanism, Evidence-Conclusion Coevolution, uses unresolved conclusions to guide exploration toward missing information, while newly acquired evidence strengthens or revises the conclusions that directed the search. LongHarness couples this process with Evidence Lifecycle Management, which keeps accumulated evidence current and traceable throughout the interaction. Together, these mechanisms focus retrieval on remaining information needs and keep conclusions aligned with current evidence. We evaluate LongHarness on OfficeQA Pro V1 and V2, AA-LCR, and LongBench v2 using GLM-5.3-Flash and Qwen3.8-Flash. Compared with direct reasoning using the same backbone, LongHarness improves GLM-5.3-Flash exact match on OfficeQA Pro from 15.8% to 60.2% on V1 and from 4.4% to 61.1% on V2. Against state-of-the-art agent harnesses, it surpasses Claude Code by 12.0 percentage points on V1 and Codex by 18.9 points on V2. With Qwen3.8-Flash, LongHarness reaches 61.7% on V1, outperforming Claude Code by 8.3 points and Codex by 12.0 points.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.13075 [cs.AI] |
| (or arXiv:2610.13075v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13075 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Songtao Huang [view email]
[v1]
Fri, 9 Oct 2026 06:32:02 UTC (404 KB)
来源：arXiv cs.AI · arxiv.org