Modified DeepSeek Harness Uses 87.93% Fewer Tokens in Coding Test
Photo: TokenPost
A modified DeepSeek Harness used 87.93% fewer model tokens per task and completed more assignments on a coding benchmark, pointing to lower resource use for AI agents. The Oct. 23 preprint tested 98 HumanEval coding tasks. With HarnessMM and other changes to the agent setup, average combined input and output token use fell from 96,280 to 11,620 tokens per task. The success rate rose from 71.43% to 85.71%. HarnessMM stores actions the model has already selected and attaches conditions based on a later tool result.
Source: TokenPost
