世界モデルにおける観測者効果：適応的介入が潜在物理を損なう
The Observer Effect in World Models: Invasive Adaptation Corrupts Latent Physics
自己教師あり学習で得た凍結表現から物理量が線形に読み出せるかを調べる非侵襲的評価法PhyIPを提案し、適応ベースの評価が潜在構造を崩すことを示した。
著者: Christian Internò, Jumpei Yamaguchi, Loren Amdahl-Culleton, Markus Olhofer, David Klindt, Barbara Hammer
分類: cs.LG, cs.AI
原文アブストラクト
Determining whether neural models internalize physical laws as world models, rather than exploiting statistical shortcuts, remains challenging, especially under out-of-distribution (OOD) shifts. Standard evaluations often test latent capability via downstream adaptation (e.g., fine-tuning or high-capacity probes), but such interventions can change the representations being measured and thus confound what was learned during self-supervised learning (SSL). We propose a non-invasive evaluation protocol, PhyIP. We test whether physical quantities are linearly decodable from frozen representations, motivated by the linear representation hypothesis. Across fluid dynamics and orbital mechanics, we find that when SSL achieves low error, latent structure becomes linearly accessible. PhyIP recovers internal energy and Newtonian inverse-square scaling on OOD tests (e.g., $ρ> 0.90$). In contrast, adaptation-based evaluations can collapse this structure ($ρ\approx 0.05$). These findings suggest that adaptation-based evaluation can obscure latent structures and that low-capacity probes offer a more accurate evaluation of physical world models.