FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games

Researchers introduce FALSIFYBENCH, an evaluation framework that tests whether large language models can perform inductive reasoning through hypothesis-driven discovery tasks. Testing 12 LLMs reveals that reasoning models outperform instruction-tuned models, with success primarily driven by the ability to actively falsify hypotheses rather than confirm them.

FALSIFYBENCH addresses a critical gap in LLM evaluation by testing whether these systems can engage in the type of hypothesis-driven reasoning central to scientific discovery. Rather than measuring performance on benchmarks that reward pattern matching, the framework forces models to iteratively propose examples, receive feedback, and revise beliefs—mirroring the scientific method itself. The findings carry important implications for deploying LLMs in research contexts.

The research reveals a significant capability gap: reasoning-specialized models substantially outperform general instruction-tuned variants, yet even the best performers fall far short of optimal strategies. The most revealing insight is that successful models actively seek disconfirming evidence through negative testing, while unsuccessful ones gravitate toward confirmation bias. This mirrors a well-documented human cognitive bias and suggests LLMs inherit similar reasoning limitations unless specifically trained to counteract them.

For the AI industry, these results carry sobering implications about LLM readiness for autonomous scientific work. Organizations considering deploying LLMs for hypothesis generation, experimental design, or literature review should recognize that current models struggle with the structured falsification reasoning that separates genuine scientific discovery from pattern recognition. The turn-level analysis methodology introduced here provides a diagnostic tool for understanding where models fail, enabling more targeted improvements.

Future work should focus on whether targeted training on negative testing strategies can close the gap between current LLM performance and human-level scientific reasoning. This research establishes a valuable benchmark for measuring progress toward trustworthy AI agents in scientific domains.

• →Reasoning-specialized models outperform instruction-tuned models at hypothesis-driven discovery tasks, though none approach optimal performance.
• →Active hypothesis falsification—not confirmation—is the primary driver of success in inductive reasoning benchmarks.
• →LLMs exhibit systematic failure patterns tied to how they navigate hypothesis space, detectable through fine-grained turn-level analysis.
• →Current LLMs demonstrate limitations that could affect their reliability for autonomous scientific research and discovery tasks.
• →FALSIFYBENCH provides a structured methodology for evaluating and improving LLM reasoning capabilities in hypothesis-driven domains.