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New arXiv Paper Distinguishes Agent Harness Optimization Gains

By PulseAugur Editorial · [1 sources] ·

A new paper from arXiv explores the distinction between elicitation and expansion in agent harness optimization for language models. The research analyzes whether optimized harnesses improve performance on tasks already within reach (elicitation) or broaden the range of solvable tasks (expansion). The study found that while harness optimization can yield significant gains, these advantages can diminish with larger attempt budgets and may mask shortfalls on more difficult tasks, highlighting the need for detailed diagnostic analysis. AI

IMPACT This research could refine how language model agents are optimized, potentially leading to more efficient capability improvements and better understanding of their performance gains.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New arXiv Paper Distinguishes Agent Harness Optimization Gains

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Younghyun Kim, Wooseok Seo, Minseo Kim, Seunghyuk Oh, Minjae Lee, Donghoon Kim, Hyung Il Koo, Jaehyung Kim, Wonjun Kang ·

    Elicitation or Expansion? Demystifying Gains from Agent Harness Optimization

    arXiv:2610.27889v1 Announce Type: new Abstract: Recent studies in harness optimization have shown substantial performance gains in language model agents without updating model weights. A well-designed harness more effectively leverages the underlying model's capabilities by shapi…

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