From Experiments to Decisions: Reusing Evidence in Autonomous Coding Research
TL;DR
This work reconstructs how evidence is reused in a 400-task NeuroGolf campaign, with selected wellbore-prediction records from the same operator as cross-domain comparisons, to preserve not just experimental results, but their limits on subsequent action.
Abstract
Autonomous coding agents can remember an experiment yet carry forward a conclusion it does not justify. We reconstruct how evidence is reused in a 400-task NeuroGolf campaign, with selected wellbore-prediction records from the same operator as cross-domain comparisons. A numerical counterexample exposes an overbroad exclusion; other episodes distinguish what failed, incomplete and revised programs justify doing next. The wellbore records reveal an additional weakness: a coordinator correctly acknowledges a novelty-only rejection, then summarizes it as measured closure. Separately, a prefix-based acceptance gate at three thresholds yields worse target scores than ungated adaptation in the retained experiment. These cases motivate an inspectable handoff linking the tested proposition, its scope, candidate and evaluator identity, check statuses, and reopening condition. A dispatch tree separates investigation, repair, reopening and stopping; a promotion predicate requires that each check both passes and has evidence supporting its use for the requested decision. The practical lesson is to preserve not just experimental results, but their limits on subsequent action.