Stanford's SHELDON does dual search over hypothesis and observation spaces, +26.7 points
StanfordUniversity · hf · 2026-10-14
Stanford researchers introduce SHELDON, an LLM scientific reasoning harness inspired by the hypothetico-deductive method and dual-space models of scientific discovery.
- Existing test-time scaling approaches couple observation discovery to current hypotheses, favoring local refinement over exploring unknown hypothesis regions
- SHELDON separately represents and jointly explores hypothesis and observation spaces: it actively seeks observations its current hypotheses cannot explain, using those unexplained observations to expose gaps, motivate new investigations, and generate hypotheses beyond refinements
- Across diagnosis, abstract reasoning, and optimization benchmarks, it outperforms evaluated baselines by up to 26.7 points and generates up to 4.5x more novel solutions
More from Research
- Visual SLAM Match Moving: Reconstructing Camera Pose to Composite CG Into Footage — rsasaki0109 · 2026-10-14
- FlashVSR+ runs untiled 4K video super-resolution on one RTX 4090 with 2x speedup — MMLab-CUHK · 2026-10-14
- Sber's GreenWAM world foundation model spans 20 robot families as both predictor and policy — SberRoboticsCenter · 2026-10-14
- Tencent's LifeSearch benchmark: best deep research agent scores just 63.4% on real-life tasks — Tencent-Hunyuan · 2026-10-14
- Science: Menopause may explain why women face higher dementia risk than men — AnnaCiaunica · 2026-10-14
- When will AI start delivering breakthroughs in physics like it has in math? — burny_tech · 2026-10-14