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Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images

arXiv:2605.13049v1 Announce Type: new Abstract: Infrared and Visible Image Fusion (IVIF) has shown promise in visual tasks under challenging environments, but fusion under unregistered conditions face

safetyarxiv-cs-cv

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Model Releases

Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion

DGX agent

arXiv:2605.13816v1 Announce Type: new Abstract: Digital phenotyping enables continuous passive monitoring of behavior and physiology, offering a promising paradigm for early detection of psychotic rel

model-releasesarxiv-cs-lg

14 May 2026

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

arXiv:2605.12562v1 Announce Type: cross Abstract: Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learnin

tutorialsarxiv-cs-ai

Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization

arXiv:2605.12756v1 Announce Type: cross Abstract: Large language models (LLMs) are pretrained by minimizing the cross-entropy loss for next-token prediction. In this paper, we study whether this optim

Understanding and Accelerating the Training of Masked Diffusion Language Models

arXiv:2605.13026v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known

Understanding Catastrophic Forgetting In LoRA via Mean-Field Attention Dynamics

model-releasesarxiv-cs-lg

arXiv:2402.15415v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is the dominant parameter-efficient fine-tuning method due to its favorable compute-performance trade-off, yet it suffers

Understanding Generalization through Decision Pattern Shift

arXiv:2605.13148v1 Announce Type: cross Abstract: Understanding why deep neural networks (DNNs) fail to generalize to unseen samples remains a long-standing challenge. Existing studies mainly examine

Unified generalization analysis for physics informed neural networks

arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use

Unify Robot Actions in Camera Frame

arXiv:2511.17001v2 Announce Type: replace Abstract: Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations

tutorialsarxiv-cs-ro

Unifying Entropy Regularization in Optimal Control: From and Back to Classical Objectives via Iterated Soft Policies and Path Integral Solutions

arXiv:2512.06109v3 Announce Type: replace-cross Abstract: This paper develops a unified perspective on several optimal control formulations through the lens of Kullback-Leibler (KL) regularization. We

safetyarxiv-cs-lg

Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions

arXiv:2605.13158v1 Announce Type: new Abstract: Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather

UniJEPA: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning

arXiv:2510.10642v3 Announce Type: replace-cross Abstract: Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage kn

UNIV: Unified Foundation Model for Infrared and Visible Modalities

model-releasesarxiv-cs-cv

arXiv:2509.15642v3 Announce Type: replace Abstract: Joint RGB-infrared perception is essential for achieving robustness under diverse weather and illumination conditions. Although foundation models ex

Universal Representation of Generalized Convex Functions and their Gradients

model-releasesarxiv-cs-lg

arXiv:2509.04477v3 Announce Type: replace-cross Abstract: A wide range of optimization problems can often be written in terms of generalized convex functions (GCFs). When this structure is present, it

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2605.13835v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkab

Unweighted ranking for value-based decision making with uncertainty

arXiv:2605.13601v1 Announce Type: new Abstract: As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concern

Useful Memories Become Faulty When Continuously Updated by LLMs

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation

arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic

Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction

arXiv:2605.13407v1 Announce Type: new Abstract: Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer int

VectorSmuggle: Steganographic Exfiltration in Embedding Stores and a Cryptographic Provenance Defense

model-releasesarxiv-cs-lg

arXiv:2605.13764v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems convert sensitive content into high-dimensional embeddings and store them in vector databases that

VERA-MH Concept Paper

arXiv:2510.15297v4 Announce Type: replace-cross Abstract: We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in

VERA-MH: Validation of Ethical and Responsible AI in Mental Health

arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Valid

VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority

arXiv:2605.12571v1 Announce Type: cross Abstract: Long video question answering requires locating sparse, time-scattered visual evidence within highly redundant content. Although current MLLMs perform

ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence

model-releasesarxiv-cs-cv

arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason

VIP-COP: Context Optimization for Tabular Foundation Models

arXiv:2605.12904v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabul

VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

tutorialsarxiv-cs-ai

Visual Accommodation: Rethinking Image Scale as a Learnable Variable for Object Detection

arXiv:2412.06341v2 Announce Type: replace-cross Abstract: We propose Ciliary-DETR (previous name: Elastic-DETR), a framework for test-time resolution adjustment analogous to biological accommodation.

tutorialsarxiv-cs-ai

Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?

arXiv:2605.12684v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of thes

Applications

ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation

arXiv:2506.15953v2 Announce Type: replace Abstract: Dexterous manipulation is a cornerstone capability for robotic systems aiming to interact with the physical world in a human-like manner. Although v

applicationsarxiv-cs-ro

Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition

arXiv:2605.13087v1 Announce Type: cross Abstract: Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance, a p

VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence

arXiv:2605.13798v1 Announce Type: new Abstract: Cross-modal 3D medical image analysis requires voxelwise representations that remain anatomically consistent across imaging contrasts, scanners, and acq

WARDEN: Endangered Indigenous Language Transcription and Translation with 6 Hours of Training Data

arXiv:2605.13846v1 Announce Type: cross Abstract: This paper introduces WARDEN, an early language model system capable of transcribing and translating Wardaman, an endangered Australian indigenous lan

Watermarking Should Be Treated as a Monitoring Primitive

arXiv:2605.13095v1 Announce Type: cross Abstract: Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under adversa

WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning

arXiv:2605.13621v1 Announce Type: new Abstract: Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specif

safetyarxiv-cs-cv

Weakly Supervised Segmentation as Semantic-Based Regularization

arXiv:2605.13674v1 Announce Type: cross Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes,

Weakly-Supervised Spatiotemporal Anomaly Detection

arXiv:2605.13746v1 Announce Type: cross Abstract: In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-leve

Agents

What Do You Think I Think? Accounting for Human Beliefs Using Second-Order Theory of Mind

arXiv:2605.12745v1 Announce Type: cross Abstract: Discrepancies between an agent's actual knowledge and what a person thinks the agent knows can hinder interactions. If an agent could detect such disc

agentsarxiv-cs-ai

What Happens Before Decoding? Prefill Determines GUI Grounding in VLMs

arXiv:2605.12549v1 Announce Type: new Abstract: Existing training-free approaches for GUI grounding often rely on multiple inference runs, such as iterative cropping or candidate aggregation, to ident

What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition

arXiv:2605.13032v1 Announce Type: new Abstract: Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph

What is Learnable in Valiant's Theory of the Learnable?

arXiv:2605.13840v1 Announce Type: cross Abstract: Valiant's 1984 paper is widely credited with introducing the PAC learning model, but it, in fact, introduced a different model: unlike PAC learning, t

Agents

What Limits Vision-and-Language Navigation ?

arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr

agentsarxiv-cs-ai

What properties of reasoning supervision are associated with improved downstream model quality?

arXiv:2605.13290v1 Announce Type: new Abstract: Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the

What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models

arXiv:2605.13105v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual sh

safetyarxiv-cs-ro

Applications

When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation

arXiv:2605.13067v1 Announce Type: cross Abstract: As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference co

applicationsarxiv-cs-ai

When and Why is Optimistic Multiplicative Weights Slow? The Geometry of Energy Dissipation

arXiv:2605.13242v1 Announce Type: cross Abstract: This paper studies the convergence of the Optimistic Multiplicative Weights Update algorithm (OMWU) in two player zero-sum games. Recent works have id

When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction

arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions

When Backdoors Meet Partial Observability: Attacking Real-World Reinforcement Learning

arXiv:2601.14104v2 Announce Type: replace-cross Abstract: Backdoor attacks can cause reinforcement learning (RL) policies to behave normally under clean inputs while executing malicious behaviors when

safetyarxiv-cs-cv

When Diffusion Breaks Constraints: Sequential Autoregressive Generation with RL and MCTS

arXiv:2512.01242v3 Announce Type: replace-cross Abstract: Data-driven generative models excel in language and vision, but diffusion models often fail in constrained planning and design tasks, exhibiti