SciGame benchmark uses counterfactual worlds to expose LLM agents' priors vs evidence
Yusheng Zhao · hf · 2026-11-02
- Problem: Existing scientific discovery benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and don't separate empirical evidence from pretrained priors.
- Method: SciGame converts scientific literature into structured scientific cards; each card yields a pair of original and dimensionally consistent counterfactual environments with identical task structure, separating prior knowledge from empirical evidence.
- Findings: Evaluation reveals exact symbolic law recovery remains a key challenge for agents. From MBZUAI, McGill, and Shanghai AI Lab.
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