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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 33
SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding
Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically structured descriptions of anatomical and disease-related findings.Whitened Advantage Summation Policy Optimization: Correlation-Aware Aggregation for Multi-Reward Reinforcement Learning
Reinforcement learning for language models often combines several rewards by normalizing each one and adding them with fixed weights.Adaptive Localization of Knowledge Negation for Continual LLM Unlearning
With the growing deployment of large language models (LLMs) across diverse domains, concerns regarding their safety have grown substantially.Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space
Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent variables from random noise.Neural Integral Control Barrier Functions for Agile Collision Avoidance
Control barrier functions (CBFs) are a standard way to keep a planner inside a safe set, but they assume control affine dynamics, a barrier function given in closed form, and a known nominal…Recovering Missing Degrees of Freedom in Simulation-Trained Field-Level Generative Models
Simulation-trained generative models are emerging as a powerful framework for field-level inference in cosmology, particularly in strongly nonlinear regimes where analytical approaches become…Susceptibilities for Neural Networks Learning from Physical Data
Neural networks that are trained on data arising from a physical system must somehow learn regularities induced by the underlying physical laws.Evolving Explanations: Complexity-Adaptive Refinement of Faithful Explanations for Symbolic Regression
Natural-language explanations generated by large language models (LLMs) for machine learning models often seem fluent but are not necessarily faithful to the underlying model.Variable names provide priors: semantic grounding enables data-efficient numerical prediction by large language models
Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to code generation.Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which a prediction model systematically over- or under-predicts.SearchInject: Retrieval Exposure Control for Indirect Prompt Injection in Language-Model Search
An indirect prompt injection can affect a search-augmented large language model only after its carrier page reaches model context.Parametric CAD Bench v3: Evaluating Agentic Parametric CAD Authoring from Text and Engineering Drawings
Producing an executable CAD script is not sufficient to establish that an agent has satisfied an engineering design request.Train the Sampler You Run: Few-Step Masked Diffusion Language Models via Mixture-Likelihood Fine-Tuning
When a masked diffusion language model is sampled in a few parallel steps in a random order, the model actually run is a mixture over reveal plans, while cross-entropy training fits token…Chain of Faith: Policy-Internal Rewards for Reasoning Reliance
Chain-of-thought (CoT) explanations can sound plausible while having little influence on a model's final answer.VioTech: Frame-Level Violin Playing Technique Detection with Multi-Scale Mixture-of-Experts
Playing technique is crucial for expressive violin performance, yet few violin technique detection models exist.HistoTx: Early fusion of H&E images and spatial transcriptomics at varying spatial transcriptomics resolution with self-supervised learning
Spatial transcriptomics maps gene expression across tissue sections and, when integrated with whole slide images, jointly captures both morphological architecture and molecular activity to provide a…Accelerating LLM Pretraining with Zeroth-Order Curvature on Idle Serving Compute
LLM pretraining is increasingly compute-intensive, while substantial inference serving compute remains idle during off-peak periods.Human-Oriented World Model
Supporting a person over repeated interactions requires interpreting their current state in context and anticipating its subsequent evolution.GeoGuesser Bench: Benchmarking Vision-Language Agents on Interactive Geolocation
Vision-language model (VLM) agents that act in the open world need two abilities: world knowledge to interpret what they see, and exploration to find what they cannot yet see.Right Ranking, Wrong Scale: Reproducing the Robotic World Model's Dynamics Model and Testing the Uncertainty of Its Follow-up
We reimplement the dynamics model of the Robotic World Model (RWM) and test the uncertainty estimate that its follow-up, RWM-U, uses to penalize reward in offline policy optimization.