TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
org/abs/2003.04887
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Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
Convolutional and graph neural networks outperform the Wiener filter by factors of 15-80 in predicting Newtonian noise from single seismic events on synthetic seismometer array data.
TaperNorm gradually removes internal normalization in pre-norm transformers via learned gates that reach zero, revealing final norm as a scale anchor and enabling up to 1.18x faster KV-cached decoding with small loss increases.
Review Residuals add an update-conditioned gate to transformer residual connections, yielding depth-stable training and performance gains that emerge and grow with model size from 590M parameters upward.
HAARES is a lightweight residual basis router that augments block summaries with an RMS-matched half-split detail vector and reports consistent gains over Block AttnRes in 48-layer 201M models across language modeling benchmarks.
Attention Residuals replaces fixed residual summation with input-dependent softmax attention over preceding layers, and a blocked variant is shown to improve uniformity and downstream performance in a 48B-parameter model pre-trained on 1.4T tokens.
citing papers explorer
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TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
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PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
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Deep learning-based phase-field modelling of brittle fracture in anisotropic media
A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
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NNNN: Neural Networks for Newtonian Noise Mitigation at the Einstein Telescope
Convolutional and graph neural networks outperform the Wiener filter by factors of 15-80 in predicting Newtonian noise from single seismic events on synthetic seismometer array data.
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Gated Normalization Removal and Scale Anchoring in Pre-Norm Transformers
TaperNorm gradually removes internal normalization in pre-norm transformers via learned gates that reach zero, revealing final norm as a scale anchor and enabling up to 1.18x faster KV-cached decoding with small loss increases.
-
Review Residuals: Update-Conditioned Residual Gating for Transformers
Review Residuals add an update-conditioned gate to transformer residual connections, yielding depth-stable training and performance gains that emerge and grow with model size from 590M parameters upward.
-
HAARES Half-Split Residual Basis Routing for Deep Transformers
HAARES is a lightweight residual basis router that augments block summaries with an RMS-matched half-split detail vector and reports consistent gains over Block AttnRes in 48-layer 201M models across language modeling benchmarks.
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Attention Residuals
Attention Residuals replaces fixed residual summation with input-dependent softmax attention over preceding layers, and a blocked variant is shown to improve uniformity and downstream performance in a 48B-parameter model pre-trained on 1.4T tokens.