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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 235
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively.On Collusion-Free Teaching in Planar Graphs
Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion.AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface
The rapid advancement of image-to-text extraction systems and large language models (LLMs) poses new challenges for educational assessment by making assignment materials easy to transcribe and solve…Learning Source Structure: Bayesian Prediction with Transformers
A transformer trained on sequences whose source parameters are drawn afresh each time can learn only what the sequences share: the source family and its prior.On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination
Natural Language Autoencoders (NLAs) are proposed as oversight tools for language models: an Activation Verbaliser (AV) writes an English explanation of an activation vector, and an Activation…Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains
Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive models for this new, target domain.SP$^2$ec: Adaptive Self-Speculative Decoding for Vision-Language Models
Speculative Decoding (SD) has demonstrated its efficacy in accelerating large language model inference, but for Vision-Language Models (VLMs), the diverse input modalities make it difficult for a…Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization
Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response reuse.A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bounds for stochastic gradient…Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks
Gauge-equivariant graph neural networks generate ordered products of gauge links through local message passing, but the geometry of these internal structures is not captured by the receptive field…Registration is a Representation Problem: Solving Whole-Body PET/CT Registration
Raw image intensities are a poor surrogate for similarity across images in correspondence problems.SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection
Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes.Depth Memory
Residual connections propagate information through a network by additively accumulating layer outputs.Statistical Vignettes on the Grokking Phenomenon
In this work, we study grokking in a simple, analytically tractable setting whose rich phenomenology allows us to disentangle the effects of initialization, signal strength, weight decay, sample…Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and…RoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations…Scaling Online Optimal Transport Minibatches for Flow Matching
A central challenge in scaling flow models is maintaining stability in training and integration.Fast Rates for Offline Q-Learning with KL Regularization
This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization.Who Improves the Robot? Toward Physical Recursive Self-Improvement
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven.MFG: Multi-turn Feedback Guided Molecular Optimization
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall…