Yarin Gal: open-ended research makes LLMs collapse problems into known work
yaringal · x · 2026-09-23
Oxford professor Yarin Gal tested Fable as a "student" researching an open-ended hypothesis and found himself arguing with it when it misread a paper as refuting his hypothesis. His takeaway: LLMs handle well-defined optimization objectives fine, but on open-ended problems they collapse the task onto known work instead of actually answering it — a useful data point on the limits of AI-scientist products.
Related event: Oxford's Yarin Gal: LLMs are poor at real science without scaffolding(3 posts)→
More from Research
- SGLang Megakernel Pushes Muse Spark to 1,109 tok/s, 2.1x Over Baseline — hsu_byron · 2026-11-12
- ArcGrav: a cross-model Agent Control Language to cut token overhead — ArchitecturalGravity · 2026-11-12
- Open-source dual-arm robot solves tangram puzzles with 90 demos, ~60% success — DanGrover · 2026-11-12
- FutureHouse's deslopper trains Kosmos to write indistinguishably from human experts — proteinrosh · 2026-11-12
- Fleuret amplifies debate: AI can't slash wet-lab experiments in unmapped biology — francoisfleuret · 2026-11-12
- Recursive bets on open-endedness: agents running massive parallel experiments like scientist communities — agihouse_org · 2026-11-12