# Microsoft presents LongCapHarness, a self-improving harness for long-context reasoning

> Original title: Learn to build a custom harness, folks.

- Source: Elvis Saravia
- Published: 2026-11-10T14:23:02.000Z
- Bad Influence: https://badinfluence.news/items/ajma8zayak0m9xv97aq3hr5rt
- Original: https://x.com/omarsar0/status/2120160305347260443

## Summary

Microsoft researchers present LongCapHarness, a harness that improves long-context reasoning without synthetic data or post-training. Llama3-8B gains 64.18 points on average across long-context benchmarks, and the harness transfers to Qwen2.5-3B. The authors say it beats NExtLong and PolicyLong on HELMET and RULER at less than half their estimated cost.

## Post

Learn to build a custom harness, folks.

This Microsoft work builds a harness to improve long-context reasoning without synthetic data or post-training.

(bookmark it)

They show that Llama3-8B gains 64.18 points on average across long-context benchmarks with no training at all. The gain comes from the harness.

It breaks long-context tasks into capability primitives. A strong agent then rewrites those primitives, the state they track, and the control logic over many rounds of self-improvement on a development set.

The harness built for Llama3-8B transfers to Qwen2.5-3B. On HELMET and RULER, it beats NExtLong and PolicyLong, which rely on data synthesis and training, at less than half their estimated cost.

The harness is written by GPT-5.6-Sol, and results drop sharply when the small models build their own.

The takeaway: before post-training a small model for long context, try improving the harness around it.

https://arxiv.org/abs/2611.10463

Chat with Paper: https://academy.dair.ai/papers/longcapharness-capability-guided-auto-harnessing-for-long-context-reasoning-2611.10463

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