Modern depth predictors adopt physically unsupported edge cues as real geometry, causing global structural hallucinations that local repair cannot fix.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2representative citing papers
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
citing papers explorer
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Geometric Collapse: When Vision Models Fail to Verify Physical Causality
Modern depth predictors adopt physically unsupported edge cues as real geometry, causing global structural hallucinations that local repair cannot fix.
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Mesh Based Simulations with Spatial and Temporal awareness
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.