ArXiv

Automated Discovery Has No Universally Superior Harness

2026-07-20 · 17:59 UTC ·Akshat Gupta, Jermaine Lei, Alexander Lu... ·1 min read
Authors
Akshat Gupta, Jermaine Lei, Alexander Lu...
Categories
cs.CL, cs.AI
arXiv
https://arxiv.org/abs/2607.18235v1
PDF
https://arxiv.org/pdf/2607.18235v1

Brief

Automated discovery harnesses (decomposing OpenEvolve and TTT-Discover) were compared by Gupta et al. via 30 budget-matched variants on 12 model–problem pairs with over 3.1M LLM rollouts and repeated-trial statistics. They find no universally superior harness—OpenEvolve variants often lag simpler methods—and show early progress reliably predicts final outcomes. An adaptive allocation scheme that prunes weak partial runs and reallocates compute outperforms fixed and non-adaptive ensembles; all run pools and null baselines are released.

Why it matters

Systematic evaluation of 30 budget-matched harnesses (decomposing OpenEvolve-style and TTT-Discover designs) across 12 model–problem pairs using >3.1 million LLM rollouts and repeated-trial statistical analysis (Gupta et al., arXiv 2026-07-20).

Key details

  • No fixed discovery harness is reliably superior across the evaluated pairs; variants of OpenEvolve generally underperform simpler alternatives, so harness choice behaves like a hyperparameter to be tuned per problem.
  • Early discovery progress predicts final performance; a budget-matched adaptive-allocation experiment that starts multiple harnesses, prunes weak partial runs, and reallocates compute to stronger survivors outperformed both committing to a random fixed harness and a non-adaptive harness ensemble. The authors release full run pools and baseline null distributions for reuse.
Source evidence

Abstract

Autonomous discovery systems such as OpenEvolve and TTT-Discover are often used as general-purpose harnesses. However, in practice these are composite systems combining several design choices about archives, parent selection, exploration, and budget allocation into a single recipe. Because discovery runs are expensive and inherently stochastic, existing harnesses are often compared using too few independent trials to distinguish key methodological improvements from run-to-run variance. We systematically decompose OpenEvolve-style evolutionary search and the TTT-Discover search harness into its constituent components and systematically evaluate 30 budget-matched harnesses across 12 model-problem pairs using more than 3.1 million LLM rollouts and repeated-trial statistical analysis. Our results show that discovery harnesses have a generalization problem: No fixed harness is reliably superior across the evaluated model-problem pairs, and variants of OpenEvolve generally underperform simpler alternatives. Thus, harness choice is better viewed as a hyperparameter rather than as a universal recipe, and should be tailored to the specific problem and underlying model. We also find that early discovery progress predicts final performance, and use this property to present a budget-matched adaptive-allocation experiment that starts multiple harnesses, prunes weak partial runs, and reallocates compute to stronger survivors, outperforming both commitment to a randomly sampled fixed harness and a non-adaptive harness ensemble. Together, these results motivate shifting from fixed harness selection to online adaptation guided by early performance. We release all run pools including baseline null distributions for every model-problem pair as reusable statistical infrastructure against for future harness proposals.