Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery
Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured. We introduce Prospective Hypothesis Discovery (PHD), which asks models to autonomously construct grounded, discriminative, and testable hypothesis spaces from inconclusive evidence, including anomalous observations and fragmented records, to guide subsequent investigation. To evaluate this capability, we introduce HypoArena, comprising HypoData, a benchmark of 988 cases across six scientific and analytical domains, and HypoEval, an evaluation framework for open-ended hypothesis sets. To construct HypoData at sc...
| Authors | Tianyun Zhong, Wangyi Jiang, Wei Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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