aiqbench analysis
TC-Bench exposes a critical gap where Vision Foundation Models rely on visual shortcuts rather than structural invariants, causing them to fail in extreme regimes despite high predictive accuracy. This study establishes scientific alignment as a necessary, distinct objective for representation learning, proving that scaling alone does not guarantee physical reasoning capabilities. The release of a reproducible benchmark for tropical cyclone research provides a concrete protocol for evaluating causal interpretability in scientific domains.
The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
arXiv:2605.24782v2 Announce Type: replace Abstract: While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models may look correct without reasoning correctly, a discrepancy we term the Perception-Physics Paradox. To address this gap, we introduce scientific alignment as an implicit objective for representation learning in scientific domains. We study a principled, testable aspect of scientific alignment through structural isomorphism, which requires latent representations to uniquely identify physical systems up to a linear reparameterization. This perspective induces a hierarchy of necessary conditions and yields a systematic probing protocol for physical and causal interpretability. To operationalize this framework, we release TC-Bench, a global, reproducible benchmark dataset with an automated construction pipeline for tropical cyclone research, and show that current VFMs rely on visual shortcuts that collapse in intense regimes, indicating that scientific alignment does not arise as a natural byproduct of scaling alone.
Abstract:While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models may look correct without reasoning correctly, a discrepancy we term the Perception-Physics Paradox. To address this gap, we introduce scientific alignment as an implicit objective for representation learning in scientific domains. We study a principled, testable aspect of scientific alignment through structural isomorphism, which requires latent representations to uniquely identify physical systems up to a linear reparameterization. This perspective induces a hierarchy of necessary conditions and yields a systematic probing protocol for physical and causal interpretability. To operationalize this framework, we release TC-Bench, a global, reproducible benchmark dataset with an automated construction pipeline for tropical cyclone research, and show that current VFMs rely on visual shortcuts that collapse in intense regimes, indicating that scientific alignment does not arise as a natural byproduct of scaling alone.
Machine Learning (cs.LG)
arXiv:2605.24782 [cs.LG]
(or arXiv:2605.24782v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.24782 Focus to learn more arXiv-issued DOI via DataCite
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