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Where Does Harness-Optimization Value Live? Localized Gains and the Budget-Splitting Trap in Self-Evolving LLM Agents
Not provided in the abstract
harness optimizationbudget allocationlarge language modelsagent evolution
2609.02889
Builder Relevance
1h ago70%
Abstract
This paper investigates the optimization value in the harness of large language models, revealing that significant gains are localized in the reflection/control slot and that uniform budget splitting can be detrimental.
Reality Card
Core Claim
The study demonstrates that nearly all useful optimization value in harness evolution is found in the reflection/control slot, and that uniform budget splitting can hinder performance.
Method / Result
HARNESSEVO achieved a leave-one-in gain of +0.119 in the reflection/control slot.
Limitations
The results are task-contingent, with some tasks showing no improvement, indicating potential limitations in generalizability.
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