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How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks

Paper recorded by Signals 4 on 2026-09-11 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.AI · 人工智能 · first seen 2026-09-14

Abstract

Low reported scores on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index (2026), suggest that frontier language models still struggle with advanced physics, a demanding test of their scientific reasoning and quantitative problem-solving abilities. Yet this impression does not always align with domain experts' experiences using these models in their

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#100 most recent of 300 cs.AI papers we have recorded · ↑ newer: Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for L · ↓ older: GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling th
Cite this page: How Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks: the #100 most recent of 300 cs.AI papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/how-good-are-frontier-models-at-physics-expert-re-grading-reveals-broken-evaluat.html
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