Tabular foundation models use distinct similarity-based readouts such as attention-weighted votes or class-conditional means, with invariances tracing to removable positional parameters.
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cs.LG 3years
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PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
Caracal is a Fourier-based sequence mixing architecture that achieves causal autoregressive modeling with standard operators and competitive performance on long sequences.
citing papers explorer
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A Mechanistic Study of Tabular Foundation Models
Tabular foundation models use distinct similarity-based readouts such as attention-weighted votes or class-conditional means, with invariances tracing to removable positional parameters.
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PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
PhysEDA folds separable Manhattan-distance exponential decay into linear attention and potential-based rewards, cutting complexity to linear while improving zero-shot transfer and sparse-reward performance on decoupling-cap placement, macro placement, and IR-drop prediction.
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Caracal: Causal Architecture via Spectral Mixing
Caracal is a Fourier-based sequence mixing architecture that achieves causal autoregressive modeling with standard operators and competitive performance on long sequences.