arXiv AI
Jul 7
Separating Representation from Reconstruction Enables Scalable Text Encoders
arXiv:2607. 04011v1 Announce Type: cross Abstract: While decoders have rapidly scaled, encoders have remained largely unchanged since BERT.
A Mechanistic Study of AI-Text Detection Neurons in Frozen BERT: Sparse Probing and Activation Patching on RAID
The study investigates which neurons in a frozen BERT-base-uncased encoder support AI‑text detection using the RAID benchmark across six generators. By applying an L1‑to‑L2 sparse‑probing protocol to all 9,216 CLS hidden‑state dimensions, the authors identify a stable set of fewer than 1% of neurons per generator that largely preserves detection accuracy. Bidirectional activation patching confirms the causal relevance of this set, while mean‑ablating the neurons shows the signal is redundantly distributed, and cross‑generator analysis reveals a bipartite structure with instruction‑tuned generators concentrating more stable neurons in the final layer. "whyItMatters":"The findings demonstrate that a small, stable subset of BERT neurons can reliably support AI‑text detection across diverse generators, enabling efficient detector design without re‑identifying neurons for each new generator."
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arXiv AI
Jun 18
Rescaling MLM-Head for Neural Sparse Retrieval
arXiv:2606. 18811v1 Announce Type: cross Abstract: Learned sparse retrieval (LSR) models such as SPLADE have traditionally used BERT-style masked language models as backbone encoders.
arXiv Machine Learning
Aug 27
The Von-Neumann State-Space Transformer for neural decoding
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arXiv AI
Jul 2
Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps
arXiv:2607. 00004v1 Announce Type: cross Abstract: While advanced foundation models like ModernBERT significantly outperform older architectures in dense retrieval, they surprisingly lag behind the aging BERT-base baseline in learned sparse retrieval (LSR).
arXiv Computation and Language
Sep 7
Large Language Models with At Most One Spike per Neuron
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arXiv AI
Jul 10
Agentic Neural Architecture Search
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
Hugging Face Trending Papers
Aug 25
Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
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arXiv Computation and Language
Sep 16
Persistent Recurrent Memory Between Transformer Layers - Improves Language Model Generalization
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arXiv Machine Learning
Jun 8
Sparsely gated tiny linear experts
arXiv:2606. 07414v1 Announce Type: new Abstract: Sparsity allows scaling model parameters without proportionally increasing computational cost.
arXiv AI
Sep 1
Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models
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arXiv AI
Jun 26
Discovering Millions of Interpretable Features with Sparse Autoencoders
arXiv:2606. 26620v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a powerful tool for decomposing superposed language model representations into sparse and interpretable features.
arXiv AI
Jun 12
Language Model Circuits Are Sparse in the Neuron Basis
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