Search: “models”

Coupled Variational Reinforcement Learning for Language Model General Reasoning

arXiv:2512.12576v3 Announce Type: replace-cross Abstract: While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable

researcharxiv-cs-ai

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Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models

DGX agent

arXiv:2605.24924v1 Announce Type: new Abstract: Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency

safetyarxiv-cs-ro

26 May 2026

Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition

arXiv:2605.25856v1 Announce Type: cross Abstract: Large Language Model interfaces are increasingly verbose, exposing intermediate reasoning traces alongside final answers. Traces are framed as transpa

researcharxiv-cs-ai

Model Releases

From Theory to Decision Rule: Calibrating the Noisy-Label Crossover for Vision-Language Model Weak Supervision Across Three Medical-Imaging Benchmarks

model-releasesarxiv-cs-ai

arXiv:2605.24771v1 Announce Type: cross Abstract: Classical noisy-label theory predicts that downstream performance under weak supervision is bounded above by the labeler's accuracy, implying a sharp

Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models

arXiv:2605.25991v1 Announce Type: new Abstract: We introduce Fuzzy PyTorch, a framework for rapid evaluation of numerical variability in deep learning (DL) models. As DL is increasingly applied to div

High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications

arXiv:2605.24763v1 Announce Type: new Abstract: This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a f

Model Releases

How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning

model-releasesarxiv-cs-ai

arXiv:2605.23926v1 Announce Type: new Abstract: Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis

arXiv:2605.24749v1 Announce Type: cross Abstract: Reward modeling is not only a prediction problem: in KL-regularized policy optimization, the learned reward is exponentiated to define the deployed po

Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information

arXiv:2605.24009v1 Announce Type: cross Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitati

Applications

Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation

arXiv:2605.24647v1 Announce Type: new Abstract: Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interactio

applicationsarxiv-cs-cl

MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules

arXiv:2603.00857v2 Announce Type: replace-cross Abstract: MultiPUFFIN is a domain-informed multimodal foundation model for predicting thermophysical properties of small molecules, addressing a critica

tutorialsarxiv-cs-ai

Hardware

Paris 2.0: A Decentralized Diffusion Model for Video Generation

arXiv:2605.26064v1 Announce Type: cross Abstract: We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv

hardwarearxiv-cs-lg

PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs

arXiv:2601.20539v3 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks'

Polynomial Context-Truncation Sensitivity in Autoregressive Language Models: Sequential Wyner-Ziv Bounds for KV Cache Compression

arXiv:2605.25085v1 Announce Type: cross Abstract: We study the rate-distortion limits of online KV cache compression in autoregressive language models, formulating it as sequential Wyner-Ziv source co

Repeated Sequences Reveal Gaps between Large Language Models and Natural Language

arXiv:2605.24850v1 Announce Type: new Abstract: Evaluating whether large language models (LLMs) capture the structure of natural language beyond local fluency remains an open challenge. Existing evalu

researcharxiv-cs-cl

SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior

arXiv:2605.23915v1 Announce Type: cross Abstract: The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car

safetyarxiv-cs-ro

SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation

arXiv:2605.24371v1 Announce Type: cross Abstract: CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices.

safetyarxiv-cs-cl

SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning

arXiv:2509.05614v3 Announce Type: replace-cross Abstract: Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values. Recently, it has been appl

local-aiarxiv-cs-ai

Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models

arXiv:2605.24564v1 Announce Type: new Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trai

Model Releases

Teaching large language models to reason like expert diagnosticians

model-releasesarxiv-cs-ai

arXiv:2509.12194v2 Announce Type: replace Abstract: Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the

The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models

arXiv:2605.24697v1 Announce Type: cross Abstract: Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden contr

X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models

arXiv:2605.25044v1 Announce Type: new Abstract: Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-t

safetyarxiv-cs-ro

Assessing Predictive Models for Fairness Based on Movement Patterns

arXiv:2605.23234v1 Announce Type: new Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated wi

Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models

arXiv:2605.23275v1 Announce Type: new Abstract: In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects an

Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery

arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generat

Agents

LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation

arXiv:2511.02239v2 Announce Type: replace-cross Abstract: Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (

agentsarxiv-cs-ai

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models

arXiv:2605.22967v1 Announce Type: new Abstract: When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, f

tutorialsarxiv-cs-lg

Point Tracking Improves World Action Models

arXiv:2605.23856v1 Announce Type: new Abstract: Robot policy learning benefits from world-action models that capture environment dynamics, but pixel-level prediction entangles dynamics with nuisance f

safetyarxiv-cs-ro

SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models

arXiv:2605.23345v1 Announce Type: new Abstract: Interactive world models for first-person shooter (FPS) games must resolve high-frequency overlapping control signals at every frame without disrupting

local-aiarxiv-cs-cv

UniReg: A Universal Model for Controllable CT Image Registration

arXiv:2503.12868v2 Announce Type: replace Abstract: Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However

safetyarxiv-cs-cv

USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots

arXiv:2510.07869v4 Announce Type: replace Abstract: Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specif

researcharxiv-cs-ro

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary

arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re

23 May 2026

Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model

arXiv:2605.21568v1 Announce Type: new Abstract: In this work, we extend the Equilibrium Propagation framework to skew-gradient systems and show an equivalence between deep Energy-Based Models and Hami

23 May 2026

Relational Linear Properties in Language Models: An Empirical Investigation

arXiv:2605.22532v1 Announce Type: new Abstract: Linear properties are ubiquitous in the representations of language models; however, testing them experimentally remains a challenging task. This work f

23 May 2026

Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?

arXiv:2605.21642v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly augmented with continuous or latent non-textual tokens intended to support 'visual thinking.' Despite imp

researcharxiv-cs-cv

Enhancing Multimodal Large Language Models for Safety-Critical Driving Video Analysis

arXiv:2605.22185v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding. However, thei

safetyarxiv-cs-cv

Agents

EvoVid: Temporal-Centric Self-Evolution for Video Large Language Models

arXiv:2605.21931v1 Announce Type: new Abstract: Recent Video Large Language Models (Video-LLMs) have demonstrated strong capabilities in video reasoning through reinforcement learning (RL). However, e

agentsarxiv-cs-cv

Agents

General Agentic Planning Through Simulative Reasoning with World Models

arXiv:2507.23773v3 Announce Type: replace-cross Abstract: What does it mean to plan? Current agentic systems, whether scaffolded workflows or end-to-end policies, rely on reactive decision-making: sel

agentsarxiv-cs-cl

Compute Only Once: UG-Separation for Efficient Large Recommendation Models

arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behavior

Deformba: Vision State Space Model with Adaptive State Fusion

arXiv:2605.21308v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional seq

researcharxiv-cs-cv

DNACHUNKER: Learnable Tokenization for DNA Language Models

arXiv:2601.03019v4 Announce Type: replace-cross Abstract: DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are

local-aiarxiv-cs-cl

Dual Quaternion Based Contact Modeling for Fast and Smooth Collision Recovery of Quadrotors

arXiv:2603.14698v3 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs) operating in cluttered environments require efficient and accurate impact modeling to maintain stability post collis

researcharxiv-cs-ro

Efficient numeracy in language models through single-token number embeddings

arXiv:2510.06824v2 Announce Type: replace Abstract: To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long ca

tutorialsarxiv-cs-lg

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data

arXiv:2603.16513v3 Announce Type: replace Abstract: Structured data is widely used in domains such as healthcare, finance, and scientific data management. Recent studies on structured data foundation

LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models

arXiv:2605.21362v1 Announce Type: new Abstract: Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial pro

safetyarxiv-cs-cl

Let EEG Models Learn EEG

arXiv:2605.21280v1 Announce Type: new Abstract: High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent

tutorialsarxiv-cs-cv

Applications

MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space

arXiv:2605.20549v1 Announce Type: new Abstract: Modern vision models achieve strong performance on standard benchmarks, yet their aggregate accuracy reveals little about which scene properties drive t

applicationsarxiv-cs-cv

Mechanisms of Misgeneralization in Physical Sequence Modeling

arXiv:2605.20299v1 Announce Type: new Abstract: Generative sequence models are often trained to plan motion in physical domains, from robotics to mechanical simulations. When constructing a dataset to

local-aiarxiv-cs-lg