arXiv:2605.24782v1 Announce Type: new 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
The proliferation of Vision Foundation Models across scientific domains makes it critical to address their potential limitations in truly understanding underlying physics, which this paper directly tackles.
A strategic reader should care because unchecked reliance on visually correlated AI without true scientific alignment can lead to flawed insights and decisions in critical scientific and industrial applications.
The explicit introduction of 'scientific alignment' and the 'Perception-Physics Paradox' provides a crucial framework for evaluating and developing more robust and trustworthy AI in scientific research.
- · AI safety researchers
- · Scientific research institutions
- · High-stakes AI application developers
- · Explainable AI developers
- · Developers of un-aligned VFMs
- · Industries relying solely on black-box AI predictions
- · Companies making critical decisions based on superficial AI insights
Increased scrutiny on the methods used to validate AI models in scientific discovery and industrial applications.
Development of new benchmarks and evaluation metrics focused on 'scientific alignment' rather than just predictive accuracy.
A potential shift in AI funding and research priorities towards models that demonstrate deeper causal understanding versus purely correlational performance.
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Read at arXiv cs.LG