A paper from Tsinghua and Microsoft proposes HarnessMM; DeepSeek Harness on HumanEval…
A new paper from Tsinghua and Microsoft proposes HarnessMM, which lets the model pre-write subsequent commands and their corresponding check conditions; when tool results pass the checks, the commands are executed directly, skipping LLM calls. On 98 HumanEval tasks, DeepSeek Harness used 87.93% fewer tokens than the original version, and its success rate rose from 71.43% to 85.71%; Codex maintained a 90.82% success rate while using 29.54% fewer tokens than standard Codex.
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The paper presents HarnessMM's token savings and success rate changes on HumanEval, offering insight into how agents can skip redundant LLM calls.
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New Tsinghua and Microsoft paper finds that DeepSeek Harness used 87.93% fewer tokens than its original version on 98 HumanEval tasks while success rose from 71.43% to 85.71%.
Agent tools like Codex and DeepSeek Harness call the LLM after each tool result to choose the next step. Often that step was already decided, like running tests once the code compiles.
HarnessMM lets the model write future commands with exact arguments, plus a check for each, such as a successful build. When a tool result passes the check, the agent runs the command and skips the LLM call.
On 98 HumanEval tasks, Codex kept its 90.82% success while using 29.54% fewer tokens than plain Codex. DeepSeek Harness rose from 71.43% to 85.71% success and used 87.93% fewer tokens than the original.
If your agent runs predictable chains like write, build, test, have the model plan them once with pass checks instead of re-asking each step.