GPT-6 Astra solved 49 of 50 unpublished physics research problems
Why it matters
TheoPhysEval, a new benchmark from 35 researchers at ICTS, TIFR, IISc, Harvard, KU Leuven, Google DeepMind and other institutions, sets 50 unpublished, research-level theoretical physics problems; a typical one is about a day's work or less for a world expert. Off-the-shelf AI agents got web search and coding tools but no human hints. GPT-6-Astra in Codex solved 49 of 50 (98%), at under $3 per problem on average (about $116 for the full run); Claude Opus 5.5 in Claude Code solved 46, Gemini 3.8 Flash 34, open-weight DeepSeek-V4.1-Flash 27 and GLM 5.3 21. Several Astra answers first graded wrong turned out to expose subtle errors in the experts' reference solutions. With web search off, Astra and Opus still solved 46 each, which the authors read as heavy memorization of the physics literature. The authors say some frontier models may already have surpassed the best human researchers at concrete calculations. Caveat: these are "turn-the-crank" problems that extend known results, not breakthroughs; each agent ran once; and agents still lack "taste" in picking problems.
Read the source: arXiv (TheoPhysEval) (opens in a new tab)
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