Stories about HARNESSEVO
1 related stories
Where Does Harness-Optimization Value Live? Localized Gains and the Budget-Splitting Trap in Self-Evolving LLM Agents
AI InsightBy decomposing the harness into slots and attributing their contributions, this research reveals that optimization value is not uniformly distributed, but concentrated in specific local components. This implies that flat budget allocation across all slots wastes resources, and prompt engineering should shift from whole-string rewriting to targeted optimization of high-leverage slots. Its value lies in providing a finer-grained budget allocation basis for automated prompt engineering.Key TakeawayPrompt optimization for LLM agents is shifting from flat-string editing to structured slot decomposition and attribution.Why It MattersThe study reveals a budget-splitting trap, showing optimization resources should focus on key slots rather than being uniformly distributed. This directly affects iteration costs in automated prompt engineering and agent systems, and provides a methodological basis for building more efficient self-evolving agents.Who's Affected- BeneficiaryLLM Agent DevelopersCan use slot attribution to identify high-value optimization targets and avoid budget waste.
- BeneficiaryPrompt Optimization ToolsStructured decomposition can inform more efficient automated prompt optimization strategies.
What's NextFuture observation should focus on whether HARNESSEVO reproduces similar high-value slot distributions across more benchmarks, and whether attribution results can reliably guide budget allocation, which would validate its generality and practicality.Importance 62/100