Selecting LLM-Generated Hypotheses Raises Truth Beyond What an LLM Panel Expects: A Pre-registered Study on NAS-Bench-201 and HPOBench
Shanglin Yang, Zhan Shi
Abstract
LLM research-ideation systems aim for hypotheses that are both true and non-obvious, yet neither property can normally be checked. We build a testbed in which both can. Generators propose conditional interaction claims about two fully tabulated benchmarks, NAS-Bench-201 and HPOBench; every claim is verified exactly against the tables, and its non-obviousness is scored against a calibrated blind prior elicited from other LLMs. Excess truth measures how much more often a run's claims hold than this panel expects. Across a series of pre-registered stages with Claude and GPT generators, our main positive result is a simple two-stage step found by a harness optimizer: draft a large pool of claims, then select a subset in a separate call. For Sonnet on NAS-Bench-201, this step raised excess truth in every pre-registered test, including against a same-pool ablation that keeps only likely-true claims, without lowering precision. The gain comes both from credit for true claims and from avoiding near-miss false ones. In post hoc checks it held under every calibration, panel and truth rule we tried, and it exceeded random and type-filter selection from the same pools. Because kept claims recur across runs, the evidence weakens when runs and claims are resampled jointly, and it is not significant against Sonnet's own prior. Two secondary findings are also pre-registered. GPT makes real use of the results of cheap probes, beating shuffled and withheld controls, though not more efficiently than asking for more claims. Optimizing the harness for an LLM judge raised judge scores while sharply cutting precision for every generator.