NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents

Understand

Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science.

• However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization.
• Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive exploration of complex model systems.
• To address these critical gaps, we introduce NewtonBench, a benchmark comprising 324 scientific law discovery tasks across 12 physics domains.