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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

Researchers have introduced HarnessOpt-Bench, a benchmark for evaluating the performance of language models (LLMs) in optimizing harnesses (the tools and code surrounding them). The study tests 5 leading LLMs across 4 tasks using a shared coding harness and native harnesses. The results show that optimizer models outperform the coding harnesses, native harnesses aren't consistently superior, and gains vary significantly across tasks.

What the Document Says

  • The paper discusses HarnessOpt-Bench, a benchmark for evaluating language models (LLMs) in end-to-end harness optimization under expensive and stochastic evaluation.
  • HarnessOpt-Bench measures the performance of an optimizer (an LLM paired with a coding harness), which edits the given seed harness, proposes a final candidate, and is scored based on its gain over the seed on a held-out test partition.
  • The study evaluates 5 frontier LLMs as optimizers using HarnessOpt-Bench across 4 downstream tasks over 111 runs.
  • The results indicate that optimizer models separate more than the coding harnesses they act through.
  • Native harnesses are not consistently superior to other harnesses.
  • Gains vary substantially across tasks and seed regimes.

Key Concepts Mentioned

  • HarnessOpt-Bench
  • Language Models (LLMs)
  • harness optimization
  • coding harness
  • native harness
  • downstream tasks
  • optimizer models

Claims Made by the Document

  • HarnessOpt-Bench is a useful benchmark for evaluating the performance of LLMs in harness optimization.
  • Optimizer models outperform coding harnesses.
  • Native harnesses are not consistently superior to other harnesses.

Evidence Used

  • The study presents HarnessOpt-Bench as a solution to a lacking common protocol for measuring the performance of LLMs in harness optimization.
  • The results section details the comparisons between optimizer models, coding harnesses, and native harnesses, showing that optimizer models separate more than coding harnesses.

Methods / Approach

  • Introduction of HarnessOpt-Bench benchmark
  • Evaluating 5 frontier LLMs as optimizers using HarnessOpt-Bench across 4 tasks over 111 runs.
  • Using a trusted execution environment to enforce the evaluation boundary, meter target-agent resource use, and preserve candidate versions for audit.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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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.