RIGOR: Representation-Instrumented Generation with Outcome-Gated Refinement for Budgeted Discovery

AI scientist systems built on large language models (LLMs) can generate hypotheses and design experiments, but evaluating a candidate can be costly in time, computation, or materials, so the choice of the next experiment matters. We present Representation-Instrumented Generation with Outcome-Gated Refinement (RIGOR), a framework for budgeted discovery that describes candidate designs with an explicit rubric of interpretable dimensions, which record how designs differ rather than how good they are. Measuring a candidate on these dimensions gives its rubric-based representation, which hypothesis generation and outcome prediction share. An LLM outcome predictor estimates each candidate's outcome and uncertainty from its complete design, its rubric-based representation, and previous outcomes, and an upper confidence bound (UCB) rule selects the next experiment. As evidence accumulates, RIGOR may propose new rubric dimensions, but it activates a dimension only if it reduces prediction error by at least 5% on the next five outcomes, with every forecast recorded before the outcome is observed. Across four scientific-design tasks and four MLS-Bench algorithm-design tasks, with Opus 4.7 and GPT-5.5, a budget of 24 evaluations, and three runs per setting, RIGOR outperforms existing baselines: it achieves the highest mean final best score in 11 of 16 model–task settings, the highest mean evaluated outcome in all 16, and the lowest within-run variation over the second half of the budget in 15. Its prospective gate admits only 16 of 86 proposed dimensions, each of which improves prediction of subsequent outcomes.