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Source note3 min read

HarnessOpt-Bench: Evaluating LLMs at Harness Optimization

Source

arXiv

arxiv.org/abs/2608.06301v1

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Plain-Language Brief

The paper introduces HarnessOpt-Bench, a benchmark for evaluating the ability of Language Models (LLMs) to optimize the harnesses surrounding them, which includes prompts, tools, control flow, memory, and orchestration code. The benchmark tests 5 frontier LLMs across 4 downstream tasks, demonstrating that these models can outperform native harnesses and show significant variation in performance across tasks and seed regimes.

What the Document Says

  • The paper discusses the importance of automated harness optimization, which is the improvement of a harness by an AI system through iterative and evaluation-guided processes.
  • HarnessOpt-Bench is introduced as a benchmark for measuring the performance of Language Models (LLMs) in end-to-end harness optimization under expensive and stochastic evaluation.
  • The optimizer receives a seed harness, graded evaluation feedback, and a fixed target-evaluation budget to edit the harness and nominate a final candidate.
  • A trusted execution environment enforces the evaluation boundary, meters resource use, and preserves candidate versions for audit.
  • The paper evaluates 5 frontier LLMs as optimizers both under a shared coding harness and under their native harnesses across 4 downstream tasks, over 111 scored runs.
  • Experiment results show that optimizer models separate more than the coding harnesses they act through.
  • Native harnesses are not consistently superior.
  • Gains vary substantially across tasks and seed regimes.

Key Concepts Mentioned

  • Language Models (LLMs)
  • Harness Optimization
  • Automated Harness Optimization
  • HarnessOpt-Bench
  • Trusted Execution Environment

Claims Made by the Document

  • LLMs can improve the performance of a harness through automated harness optimization.
  • Optimizer models separate more than coding harnesses they act through.
  • Native harnesses are not consistently superior to optimizer models.

Evidence Used

  • The introduction and description of HarnessOpt-Bench.
  • Results from the evaluation of 5 frontier LLMs.

Methods / Approach

  • Experimental evaluation using HarnessOpt-Bench benchmark

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them. This makes automated harness optimization -- the iterative and evaluation-guided improvement of a harness by an AI system -- both an important route to improving AI systems and a demanding capability for AI systems themselves. Yet the community lacks a common protocol for measuring how well frontier LLMs perform at this task. We introduce HarnessOpt-Bench, a benchmark for end-to-end harness optimization under expensive and stochastic evaluation. An optimizer, an LLM paired with a coding harness, receives a target agent's seed harness, graded evaluation feedback, and a fixed target-evaluation budget. It edits the harness and nominates a final candidate, which is scored by its normalized gain over the seed on a held-out test partition that remains inaccessible throughout search. A trusted execution environment enforces the evaluation boundary, meters target-agent resource use, and preserves candidate versions for audit. We evaluate 5 frontier LLMs as optimizers both under a shared coding harness and under their native harnesses across 4 downstream tasks, over 111 scored runs. Experiment results show that optimizer models separate more than the coding harnesses they act through, native harnesses are not consistently superior, and gains vary substantially across tasks and seed regimes. These results establish harness optimization as a measurable and discriminative capability with large space for improvement.