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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 text introduces HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing AI systems. The benchmark involves an iterative process where an LLM edits a given harness and submits a final candidate. The candidate is scored based on its improvement over the initial harness. Five frontier LLMs were evaluated across four downstream tasks, with results showing that the optimizer models perform better than the coding harnesses they act through, but the native harnesses don't consistently outperform. The gains vary significantly across tasks and seed regimes.

What the Document Says

  • HarnessOpt-Bench is introduced as a benchmark for evaluating LLMs in harness optimization.
  • The optimizer receives a seed harness, graded evaluation feedback, and a fixed target-evaluation budget.
  • The optimizer edits the harness and nominates a final candidate, which is scored by its normalized gain over the seed on a held-out test partition.
  • A trusted execution environment enforces the evaluation boundary, meters resource use, and preserves candidate versions for audit.
  • Five frontier LLMs were evaluated as optimizers across 4 downstream tasks.
  • 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

  • HarnessOpt-Bench
  • LLMs
  • harness optimization
  • evaluation budget
  • trusted execution environment
  • seed harness
  • candidate
  • normalized gain
  • downstream tasks

Claims Made by the Document

  • HarnessOpt-Bench is important for improving AI systems.
  • 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.

Evidence Used

  • The introduction of HarnessOpt-Bench as a benchmark for end-to-end harness optimization under expensive and stochastic evaluation.
  • Description of the iterative process involving an LLM editing a given harness and submitting a final candidate.
  • Details about the scoring system based on the candidate's improvement over the initial harness.
  • Explanation of how a trusted execution environment is enforced.
  • Information about the evaluation of five frontier LLMs across four downstream tasks.
  • Presentation of the results showing the performance of optimizer models and native harnesses.

Methods / Approach

  • Iterative harness optimization process
  • Scoring system based on normalized gain
  • Use of a trusted execution environment

Limitations Stated by the Authors

  • The text does not provide specific details about the five frontier LLMs used in the evaluation.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The text lacks information about the specific criteria for evaluating the performance of the LLMs.
  • The text does not specify the nature or scope of the downstream tasks used in the evaluation.

Open Questions

  • What are the specific criteria used to evaluate the performance of the LLMs?
  • What are the nature and scope of the downstream tasks used in the evaluation?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The text does not provide information about the specific LLMs, criteria for evaluation, or details about the downstream tasks.

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.