HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
2026-08-06 • Artificial Intelligence
Artificial IntelligenceComputation and LanguageMachine Learning
AI summaryⓘ
The authors introduce HarnessOpt-Bench, a test to measure how well large language models (LLMs) can improve the systems surrounding them, like prompts and tools, called harnesses. They set up a controlled environment where an LLM tries to edit and optimize a given harness using feedback, under resource limits. Their experiments with five different LLMs show that the ability to improve depends on both the model and the harness, with no clear winner for the best setup across tasks. The authors demonstrate that optimizing harnesses is a meaningful challenge with room for progress.
Large Language ModelsHarness OptimizationAgentic SystemsPrompt EngineeringBenchmarkingEvaluation FeedbackResource ManagementCode OrchestrationTrusted Execution Environment
Authors
Varun Ursekar, Apaar Shanker, Yash Maurya, Shehab Yasser, Vijay S. Kalmath, Veronica Chatrath, Yuan Xue
Abstract
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.