LongHarness 论文：GLM-5.3-Flash 在 OfficeQA Pro V2 上 exact match 从 4.4% 提升至 61.1%
Elvis Saravia 介绍了一篇论文，提出模型无关的长上下文 harness——LongHarness，面向 100M tokens 及以上的语料构建。论文报告称，使用同一模型 GLM-5.3-Flash，LongHarness 使 OfficeQA Pro V2 的 exact match 从 4.4% 提升到 61.1%，收益来自 harness 本身。
Learn to build custom harnesses, folks!
Here is an impressive paper showing the impact of a good custom harness on long-context work.
GLM-5.3-Flash goes from 4.4% to 61.1% exact match on OfficeQA Pro V2 with the same model.
The authors report that the gain comes from the harness.
The model-agnostic harness name is LongHarness, built for corpora of 100M tokens and more.
Why it works:
Standard agent harnesses keep evidence and conclusions mixed into one growing chat history. Retrieval keeps expanding without closing the actual gap, and corrections stop reaching the answer.
LongHarness stores both as explicit state. Open conclusions decide what to retrieve next, and each new piece of evidence confirms or revises the conclusions that asked for it.
On the same benchmark, it beats Claude Code by 12.1 points on V1 and Codex by 18.9 points on V2.
arxiv.org/abs/2610.13075
Chat with Paper: academy.dair.ai/papers/longh…
来源：omarsar0 · x.com