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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 52
Where Does EEG Help Wearable Freezing of Gait Detection?
Freezing of gait (FoG) detectors built on inertial measurement units (IMUs) are accurate while the legs move, but they become unreliable when the legs are nearly still, which is exactly when…The Continuum Limit of the Deep Linear Network
The Deep Linear Network (DLN) is a simple version of a Multilayer Perceptron (MLP) in which the activation function is the identity and the bias vector is the zero vector.Weights That Stay: On-Chip Megakernels for Serial Heads in Speculative Decoding
Parallel speculative drafters use a lightweight serial head to recover token dependencies within a block generated in parallel.Beyond Blackwell: Constrained Data Processing Inequalities
We study the comparison of statistical experiments from a decision-theoretic standpoint, working with Markov transitions as basis-free linear maps on dual spaces.Efficient and Scalable Multi-View Masked Autoencoder for Heterogeneous Graph Self-Supervised Learning
Recent years have witnessed the flourishing of self-supervised learning on heterogeneous graphs, e.g., masked autoencoder-based methods.Behavioral Steering of Vision-Language-Action Models in the Low-Parameter Regime
Reinforcement learning (RL) is increasingly used to adapt pretrained vision-language-action models (VLAs).When Does Spatial Cooperation Help? Contamination Regimes in Decentralized Online Spectrum Anomaly Learning
Learning-based spectrum monitors adapt online to benign propagation changes, but persistent rogue transmissions can gradually become incorporated into the learned background.BitLLM: Efficient Low-Bit, Conversion-Hiding LLM Decoding on Mobile GPUs
The rapid scaling of large language models (LLMs) has unlocked remarkable capabilities across various domains.From Weeks to Hours: Fast and Principled SFT Data Generation for Reasoning LLMs
Reasoning-oriented post-training enables large language models (LLMs) to solve complex tasks via multi-step inference.Can agents reliably design therapeutic antibodies?
Recent work has demonstrated agents driving protein design workflows, either through orchestration of specialized tools or direct manipulation of sequences.TabAbax: A Tabular Foundation Model Technical Report
Tabular foundation models (TFMs) enable in-context prediction on unseen tabular datasets without task-specific training.The First-Order Value of Free Covariates in Budgeted Risk Monitoring
A deployed model sees its inputs for free, but checking whether its risk has risen requires costly labels.Evolving Scientific Intelligence: From Fragmented Evidence to Distributed Experimental Loops
Scientific discovery advances when evidence from different investigations is brought into contact.Conic Certification of Transformer Attention
Attention is the computational core of large language models, yet the verifiers that bound how far an attention head's output can move under input perturbations give no guarantee on how loose their…Dataset Mismatch Matters in Group Relative Policy Optimization for Reinforcement Learning from Verifiable Rewards
Recently, reinforcement learning from verifiable rewards (RLVR) is practically very important, where the leading approach is group relative policy optimization (GRPO) that typically faces a…TVP: TV is Almost All You Need for Speculative Decoding
Speculative decoding accelerates LLM inference with a drafter whose proposed tokens are verified by the target model in parallel.Guarded Compression of Adam Optimizer State with Shared-Gradient Bounds
Adam keeps two FP32 moments per parameter, twice the bytes of the FP32 weights, yet compressing them is risky: the update divides by the square root of the second moment, so small reconstruction…Do Orthogonality Constraints Help Language-Model Training Beyond Sphere Optimization?
A growing body of work reports benefits from orthogonality constraints in fine-tuning large language models (LLMs).Sparse Autoencoder Attribution Graphs Are Decided at the Margin
Attribution graphs over the features of sparse autoencoders (SAEs) are used to predict what an ablation does and are compared across training seeds.Trail: Accurate Local Smart Home Control Using Decision Models
Natural-language driven smart home control remains challenging: cloud-hosted Large Language Models (LLMs) raise privacy concerns, while local Small Language Models (SLMs) can generate invalid…