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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 introduce HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing their own harnesses. The study examines five frontier LLMs on four tasks and finds that these models can separate more than the coding harnesses they act through, but gains vary significantly across tasks and seed regimes.

What the Document Says

  • The text discusses the importance of automated harness optimization as a route to improving AI systems.
  • HarnessOpt-Bench is introduced as a benchmark for end-to-end harness optimization under expensive and stochastic evaluation.
  • The benchmark tests an optimizer (an LLM paired with a coding harness), which edits the harness and nominates a final candidate.
  • Results are based on experiments with five frontier LLMs across four downstream tasks, over 111 scored runs.
  • Findings show that optimizer models separate more than the coding harnesses they act through.
  • Native harnesses are not consistently superior to optimized ones.
  • Gains vary substantially across tasks and seed regimes.

Key Concepts Mentioned

  • Automated harness optimization
  • Large Language Models (LLMs)
  • HarnessOpt-Bench
  • Coding harness
  • Downstream tasks

Claims Made by the Document

  • Optimizer models separate more than the coding harnesses they act through.
  • Native harnesses are not consistently superior to optimized ones.
  • Gains vary substantially across tasks and seed regimes.

Evidence Used

  • Results from experiments with five frontier LLMs on four downstream tasks, over 111 scored runs.

Methods / Approach

  • Evaluating LLMs as optimizers using HarnessOpt-Bench benchmark.

Limitations Stated by the Authors

  • Not specified in the text.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study focuses on five frontier LLMs, which may not represent the entire spectrum of LLMs. The generalizability of the findings to other models is unknown.

Open Questions

  • What are the specific methods used for optimizing the harnesses by the LLMs?
  • How does the performance of lesser-known or smaller LLMs compare to the ones tested in this study?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The text does not provide details on the specific methods used for optimizing the harnesses by the LLMs.
  • Additional research may be necessary to understand the performance of lesser-known or smaller LLMs.

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.