GOLLuM combines LLMs with Gaussian processes to boost scientific discovery efficiency
DrDatta_AIIMS · x · 2026-08-30
GOLLuM addresses LLM overconfidence in science by jointly training a language model with a Gaussian process, allowing the model to learn from experimental outcomes under uncertainty.
- Mechanism: Integrates a Gaussian Process with the LLM to introduce uncertainty quantification.
- Results: Ranks first on average across 23 tasks spanning synthesis, materials, and molecular design.
- Efficiency: Matches traditional Bayesian optimization performance while requiring 40% fewer experiments, nearly doubling discovery efficiency.
More from Research
- Pathway CSO pitches BDH as post-Transformer architecture uniting memory, reasoning, adaptation — schwentker · 2026-10-15
- EmbodiedEvo tops RoboDojo at 38.95% success by letting robots inherit failed practice — thetripathi58 · 2026-10-15
- 797GB of n-gram counts in CPU RAM let 1B models match 57% more compute — PangWeiKoh · 2026-10-15
- Google's HARNESS² evolves agent harnesses without verifiers, up to +11.5 points on WorkBuddy-Bench — dair_ai · 2026-10-15
- Subbarao Kambhampati to dissect the 'mythos of LLM thinking tokens' at UMN — rao2z · 2026-10-15
- How 3D Gaussian Splatting gives robots spatial memory of what's out of view — TheTuringPost · 2026-10-15