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Machine Learning
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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 67
Geometric and algebraic inference in humans and neural networks
Humans possess a remarkable ability to draw inferences from limited data.Transformer-Based Reinforcement Learning for Phase I Dose Allocation
Phase I dose-finding trials must learn from sparse toxicity observations while limiting exposure to overly toxic doses.VESPER: Online Learning of Free Energies with a Multi-Tempered Variational Objective
Computing molecular free-energy landscapes over collective variables (CVs) is central to understanding molecular mechanisms, but poses a difficult learning problem without training data available a…MXCode: Vector Quantization for MX-Native Inference at Scale
Memory capacity and bandwidth are major bottlenecks in LLM inference, motivating weight compression beyond conventional 4-bit quantization.Behavior Graphs: Unsupervised Graph Structures for Interpreting Robot Deployment Data
Data flywheels promise continuous improvement via insights from deployment data.Hacking or Just Failing? A More Capable LLM Monitor Better Separates Reward Hacking from Honest Failure
An agent's visible score can exceed its real progress because it gamed the score or because it simply made mistakes, so a useful reward-hacking monitor must tell the two apart.TileWeave: Heterogeneous Tiling for Sparse Prefill on Mobile SoCs
Smartphone applications increasingly require large language models to process long contexts, whereas high prefill latency and memory demands constrain local inference.On the Framing Effect of AI Red-Teaming Instructions: Evidence from Prior Exercises and Controlled Simulations
AI red-teaming, adversarially probing generative AI systems to surface harms, has become a central evaluation method.Nonconformal Distortion and Local Orientation Reversal in the Fourier Circuit of a Modular-Addition Transformer
We study the local differential geometry of a Fourier-based modular-addition circuit in a one-layer transformer whose algorithm has previously been reverse-engineered.RankLane: Efficient MoE Prefill on Accelerators with Ranked Expert Capacities
Mixture-of-experts (MoE) models expand model capacity by activating only a subset of experts for each token.A Spectral Renormalization-Group View of Learning
We study how the layers in deep neural networks (NNs) converge, using Wilsonian Renormalization-Group (RG) ideas to organize the spectral picture.Spoofing the Latent State: Imperceptible Goal-Image Attacks Derail Planning in LeWorldModel
Joint-embedding predictive architectures (JEPAs) have recently emerged as general-purpose visual world models for control.Do Multimodal Models Decide Consistently on Equivalent Optical Spectra?
A spectrum can be given to a model as a table in nanometers or as a plot in photon energy, and the decision should not depend on which one it sees.Local Spatial TreeSHAP: Geographically Explainable AI Revealing Urbanization-Dependent Spatial Heterogeneity in Flood Resilience
Urban flood resilience emerges from complex interactions among natural, built, and socio-economic systems, yet most recovery policies implicitly assume that resilience mechanisms are spatially…Statistical Inference under Performativity: From Policy Feedback to Self-Evolving Systems
Performativity arises when prediction-informed decisions change the data-generating process, creating feedback between deployment and learning.OmniMechanism Design for Human-AI Collaboration with Impressionable Minds
As AI systems are increasingly deployed in high-stakes domains such as healthcare, effectively integrating human expertise with AI outputs is critical for reliable decision-making.CLiS: Plug-and-Play Attention Acceleration for Video Diffusion on TPUs via Certified Stabilization
High-resolution video diffusion relies on attention over long token sequences, making attention efficiency critical to generation latency.Learning to evolve by matching velocities
Learning the continuous-time evolution of nonlinear dynamical systems relies on trajectory-based objectives, which require simulating the learned dynamics during training and backpropagating through…SpBench: An Agent-Native Framework for Cross-Engine Lakehouse Benchmarking
SpBench is an agent-native framework for cross-engine lakehouse benchmarking, in production use for benchmarking Snowpark, Snowpark Connect for Apache Spark, and other execution environments across…From Lookups to Lessons: Turning Retrieved Knowledge into Parametric Memory
Retrieval-augmented generation (RAG) helps large language models (LLMs) answer factual questions beyond their training data, but it adds storage and inference costs.