Tsinghua and Microsoft researchers cut DeepSeek Harness token use by 88% with HarnessMM

A new preprint shows AI coding agents can skip predictable model calls, using far fewer tokens while succeeding more often AI coding agents talk to themselves a lot. A new paper from Tsinghua University and Microsoft Research Asia suggests much of that chatter is unnecessary. Applied to the DeepSeek Harness, the researchers’ technique, called HarnessMM, cut tokens per task from 96.28k to 11.62k, an approximately 88% reduction. Task success rates went up at the same time, which is not usually how efficiency trade-offs work. The preprint is titled “HarnessMM: Recursive Self-Improvement for Harness Token Efficiency.” It was submitted on October 23, 2026, by Yuhua Jiang, Chang Xu, Feifei Gao, and Li Zhao. Some vocabulary first. A “harness” is the scaffolding around a language model that turns it into an agent.

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