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. What HarnessMM actually does 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. It decides when the model gets called, which tools it can use, and how results get fed back into the loop. Tokens are the chunks of text a model reads and writes. Every token costs compute, so an agent that burns fewer of them is cheaper and faster to run. HarnessMM gives an AI agent something like that muscle memory. The method builds what the author...