HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation
Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python. Existing methods often rely on LLM reflectors to propose harness updates, which can fix observed failures but also disrupt previously correct behavior. Text-to-code tasks offer a natural way to mitigate this issue: we can execute generated outputs, allowing us to evaluate and verify the effects of proposed updates directly. Building on this signal, we introduce \textsc{HERO} (\textbf{H}arnessing \textbf{E}vidence for \textbf{R}efinement and \textbf{O}ptimization), which combines offline and test-time harness o...
| Authors | Mingyu Lu, Timothy Ossowski, Tristan Naumann et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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