# HarnessMM cuts agent tokens per task by 88% with cached decisions

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

- Source: Elvis Saravia
- Published: 2026-10-26T14:53:02.000Z
- Bad Influence: https://badinfluence.news/items/ledm54xg9ooewczvjbdzmaufo
- Original: https://x.com/omarsar0/status/2114732037827662278

## Summary

Researchers propose HarnessMM, which stores settled agent decisions in an Executable Decision Graph and reuses them when guards hold. On a DeepSeek harness, tokens per task fall from 96.28k to 11.62k while success rises from 71.43% to 85.71%. A recursive self-improvement loop tunes the harness configuration from training feedback without changing model weights, and with Codex on HumanEval, success stays at 90.82% while tokens per task drop 29.54%.

## Post

Learn to build a custom harness, folks!

There is so much alpha in understanding how to optimize the harness.

This work shows the benefits of a smart harness configuration and caching the decisions your agent harness has already made.

(bookmark it)

They propose HarnessMM, which cuts tokens per task on a DeepSeek harness from 96.28k to 11.62k while success rises from 71.43% to 85.71%.

Harnesses often call the model again after tool feedback even when an earlier call already settled the next action. HarnessMM stores those settled decisions in an Executable Decision Graph with exact actions, arguments, dependencies, and guards. When the guard holds, the controller reuses the decision. Anything new goes back to the model.

A recursive self-improvement (RSI) loop tunes the harness configuration from training feedback, and the model weights never change.

With Codex on HumanEval, success stays at 90.82%, and tokens per task drop 29.54%.

Before you pay for a bigger model, check how many of your harness calls repeat a decision the agent already made.

Paper: https://arxiv.org/abs/2610.32813

Chat with Paper: https://academy.dair.ai/papers/harnessmm-recursive-self-improvement-for-harness-token-efficiency-2610.32813

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