HarnessMM: Recursive Self-Improvement for Harness Token Efficiency
Yuhua Jiang, Chang Xu, Feifei Gao, Li Zhao
Abstract
HarnessMinusMinus (HarnessMM) is a research project on improving
the efficiency of agent harnesses, the software coordinating
model reasoning, tool execution, and feedback. This technical
report consolidates its shared framework, implementations,
and empirical studies across coding, tool-based, and visual-agent
settings. Agent harnesses often invoke reasoning after each tool
result, even when earlier calls have already determined subsequent
actions. HarnessMM addresses this overhead through guarded decision
reuse and recursive self-improvement (RSI).
RSI refines author prompts, context settings, and adapter rules
using training feedback, with validation selecting deployment
configurations. At inference time, a controller reuses resolved
decisions while keeping actor weights fixed. Executable Decision
Graphs encode actions, arguments, dependencies, and guards.
Guarded Decision Reuse activates eligible decisions from observed
feedback, while Certified Callback Substitution executes them
through native tool interfaces, replacing redundant reasoning
callbacks. The original actor resumes when new reasoning is
required or reuse conditions are unmet.
On HumanEval, HarnessMM preserves Codex success at 90.82\%
while reducing tokens per task from 66.72k to 47.02k
(29.54\% saving). For DeepSeek Harness, success improves
from 71.43\% to 85.71\% while tokens decrease from 96.28k
to 11.62k (87.93\% saving). An MBPP ablation shows
26.91\% lower cost than single-use graphs and 19.52\%
lower cost than retained callbacks. The report also
summarizes the HarnessMM-V visual-agent study and documents
optimization overhead and execution time where measured.
Together, these studies investigate guarded decision reuse across diverse agent harness settings.
Code is available at https://github.com/YuhuaJiang2002/HarnessMM.