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Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation

arXiv:2605.29430v1 Announce Type: new Abstract: Automatic speech recognition (ASR) is a core component of human--computer interaction and an increasingly important front-end for LLM-based assistants a

safetyarxiv-cs-ai

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Towards Localized and Disentangled Knowledge Editing for Multimodal Large Language Models

DGX agent

arXiv:2605.29826v1 Announce Type: cross Abstract: Existing methods in Multimodal Knowledge Editing (MKE) have advanced the ability to correct outdated or inaccurate knowledge in Multimodal Large Langu

local-aiarxiv-cs-ai

29 May 2026

Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest

arXiv:2605.29217v1 Announce Type: new Abstract: The amount of fat on the surroundings of the heart is correlated to several health risk factors such as carotid stiffness, coronary artery calcification

Towards Understanding the Shape of Representations in Protein Language Models

arXiv:2509.24895v2 Announce Type: replace Abstract: While protein language models (PLMs) are one of the most promising avenues of research for future de novo protein design, the way in which they tran

local-aiarxiv-cs-lg

Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation

arXiv:2605.29861v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthe

agentsarxiv-cs-ai

TRACE: Toulmin-based Reasoning Assessment through Constructive Elements for LLM CoT Evaluation

arXiv:2605.29656v1 Announce Type: new Abstract: Evaluating open-ended outputs from large language models (LLMs) remains challenging due to the absence of ground truth. Existing metrics rely on final-a

TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces

arXiv:2605.29941v1 Announce Type: cross Abstract: Critical networking workflows require high-fidelity packet captures (PCAPs) for testing, security analysis, and protocol validation, not just statisti

safetyarxiv-cs-lg

TRACER: Persistent Regularization for Robust Multimodal Finetuning

arXiv:2605.29380v1 Announce Type: cross Abstract: Mainstream strategies for finetuning pretrained multimodal models often degrade out-of-distribution (OOD) robustness, a phenomenon known as catastroph

safetyarxiv-cs-ai

Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification

arXiv:2605.29228v1 Announce Type: new Abstract: Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural f

researcharxiv-cs-lg

Train the Agent, Not the Expert: Learning to Harness Heterogeneous Experts for Multi-Turn Visual Reasoning

arXiv:2605.29894v1 Announce Type: new Abstract: Recent progress in computer vision has produced a wide range of powerful specialized models for detection, segmentation, counting, and other visual task

safetyarxiv-cs-cv

Training Deliberative Monitors for Black-Box Scheming Detection

arXiv:2605.29601v1 Announce Type: cross Abstract: As autonomous agents become more capable of performing real-world tasks, distinguishing scheming behavior from benign task pursuit may become a centra

Trajectory Constraints for Imaging Inverse Problems

arXiv:2605.29012v1 Announce Type: new Abstract: Diffusion-based and iterative methods have become effective tools for solving imaging inverse problems. Their reconstruction process naturally forms a t

Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions

arXiv:2605.28833v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) has the potential to substantially reduce manual annotation effort in child speech research by generating automatic

Treatment-Conditioned Diffusion for Forecasting Neurodegenerative Disease Progression

arXiv:2605.29932v1 Announce Type: cross Abstract: Forecasting the progression of neurodegenerative diseases, such as Parkinson's disease, is essential for effective long-term planning and personalized

Trends in AI and Human-AI Interaction in Clinical Trials -- A Hybrid Human-AI Exploration

arXiv:2605.29096v1 Announce Type: new Abstract: This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distri

TriSearch: Learning to Optimize Triangulations via Bistellar Flips

arXiv:2605.30220v1 Announce Type: new Abstract: We introduce TriSearch, a reinforcement learning framework for optimizing objectives over triangulations of a polytope via bistellar flips. The key idea

safetyarxiv-cs-lg

TrojanTO: Action-Level Backdoor Attacks against Trajectory Optimization Models

arXiv:2506.12815v2 Announce Type: replace Abstract: Recent advances in Trajectory Optimization (TO) models have achieved remarkable success in offline reinforcement learning. However, their vulnerabil

researcharxiv-cs-lg

TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance

arXiv:2508.14610v2 Announce Type: replace Abstract: Despite extensive developments in motion planning of autonomous aerial vehicles (AAVs), existing frameworks faces the challenges of local minima and

agentsarxiv-cs-ro

Turbulence-Robust Dynamic Object Segmentation with Multi-Signal Priors and SAM2 Refinement

arXiv:2605.29292v1 Announce Type: new Abstract: This technical report presents our solution for the CVPR 2026 UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence (DOST). We design a trai

UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning

arXiv:2605.29170v1 Announce Type: cross Abstract: Legal NLP benchmarks are overwhelmingly English-centric, leaving failure modes in morphologically rich, non-Latin-script languages undetected. We intr

UI-KOBE: Knowledge-Oriented Behavior Exploration for Lightweight Graph-Guided GUI Agents

arXiv:2605.29534v1 Announce Type: new Abstract: Recent advances in mobile GUI agents have shown strong potential for automating mobile tasks, but most effective systems still depend on large vision-la

local-aiarxiv-cs-ai

Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems

arXiv:2605.28883v1 Announce Type: new Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this de

Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

arXiv:2605.29733v1 Announce Type: new Abstract: Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest

Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field

arXiv:2605.30342v1 Announce Type: new Abstract: We present Gaussian Splatting Anisotropic Visibility Field (GAVIS), a novel framework for uncertainty quantification and active mapping in 3DGS. Our key

Uncertainty Estimation via Hyperspherical Confidence Mapping

arXiv:2605.05964v2 Announce Type: replace Abstract: Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as autonomous driving, healthcare, and manufacturing

safetyarxiv-cs-lg

Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge

arXiv:2505.16178v2 Announce Type: replace Abstract: While fine-tuning is the standard for injecting factual knowledge into large language models (LLMs), the mechanisms enabling reliable fact recall vi

Understanding Safety-Sensitive Expert Behavior in Mixture-of-Experts LLMs

model-releasesarxiv-cs-cl

arXiv:2605.29708v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization rema

Understanding the Ability of LLMs to Handle Character-Level Perturbation

arXiv:2510.14365v4 Announce Type: replace Abstract: This work investigates the resilience of contemporary large language models (LLMs) against frequent character-level perturbations. We examine three

Tutorials

Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection

arXiv:2605.29455v1 Announce Type: new Abstract: Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. Wh

tutorialsarxiv-cs-cv

Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization

arXiv:2605.30227v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dy

local-aiarxiv-cs-ai

UniNote: A Unified Embedding Model for Multimodal Representation and Ranking

arXiv:2605.29287v1 Announce Type: cross Abstract: Item-to-Item (I2I) retrieval is a fundamental part of modern content platforms, supporting critical industrial workflows from recommendation engines t

local-aiarxiv-cs-cv

UniSteer: Text-Guided Flow Matching in Activation Space for Versatile LLM Steering

arXiv:2605.30076v1 Announce Type: new Abstract: Activation-based control steers large language models (LLMs) by intervening on their internal representations during inference, and has emerged as an ef

unix-ctf: Procedural Environments for Unix-Competence Reinforcement Learning

arXiv:2605.29115v1 Announce Type: cross Abstract: Unix competence is the ability to use shell and operating-system primitives as first-class tools, not merely to write programs through a terminal. Cur

agentsarxiv-cs-ai

Unlocking the Working Memory of Large Language Models for Latent Reasoning

arXiv:2605.30343v1 Announce Type: cross Abstract: To improve the reasoning capabilities of large language models, test-time compute is typically scaled by generating intermediate tokens before the fin

Unsupervised Hierarchical Skill Discovery

arXiv:2601.23156v2 Announce Type: replace Abstract: We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent approaches h

researcharxiv-cs-lg

Unsupervised Semantic Segmentation Facilitates Model Understanding

arXiv:2605.29691v1 Announce Type: new Abstract: Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of d

local-aiarxiv-cs-cv

Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization

arXiv:2605.29153v1 Announce Type: cross Abstract: Neural networks trained under different hyperparameter settings can fall into distinct training 'regimes,' with consistent behavior within regimes and

Unveiling the Visual Counting Bottleneck in Vision-Language Models

arXiv:2605.30170v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visu

User-Aware Active Knowledge Acquisition for Emotional Support Dialogue

arXiv:2605.29715v1 Announce Type: new Abstract: Emotional support plays an important role in dialogue systems, and its success depends on adapting to a user's evolving and implicit needs across multi-

safetyarxiv-cs-cl

V2XCrafter: Learning to Generate Driving Scene Across Agents

arXiv:2605.29471v1 Announce Type: new Abstract: Collaborative driving systems leverage vehicle-to-everything (V2X) communication for multi-agent collaborative perception to enhance driving safety, yet

safetyarxiv-cs-cv

Valency Classification of Mapudungun Verbal Roots. Established by the language's own morphotactics

arXiv:2604.00789v3 Announce Type: replace Abstract: In the previous work, a lexical (re)categorisation -- or confirmation of the given category -- of roots identified as verbal was undertaken to deter

ValueFlow: Measuring the Propagation of Value Perturbations in Multi-Agent LLM Systems

arXiv:2602.08567v2 Announce Type: replace-cross Abstract: Multi-agent large language model (LLM) systems increasingly consist of agents that observe and respond to one another's outputs. While value a

safetyarxiv-cs-cl

VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.29564v1 Announce Type: new Abstract: When using reinforcement learning (RL) for contact-rich robotic manipulation, vision can provide task-relevant information that accelerates learning bey

Veda: Scalable Video Diffusion via Distilled Sparse Attention

arXiv:2605.30325v1 Announce Type: new Abstract: Scaling Diffusion Transformers to generate high-resolution, long videos is constrained by the quadratic cost of self-attention, and existing sparse atte

Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering

model-releasesarxiv-cs-cl

arXiv:2605.29648v1 Announce Type: new Abstract: Applying reinforcement learning to improve factual accuracy in knowledge-intensive question answering faces a reward design dilemma. Response-level rewa

VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis

arXiv:2605.28978v1 Announce Type: new Abstract: Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on

agentsarxiv-cs-ai

ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement

arXiv:2605.29302v1 Announce Type: new Abstract: The digital media landscape has seen a pervasive shift toward short-form video advertising on TV, social media and e-commerce platforms. The present stu

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods

model-releasesarxiv-cs-cv

arXiv:2601.12500v2 Announce Type: replace Abstract: Counting and tracking dense crowds in large-scale scenes is a highly practical yet challenging problem. Existing methods mostly rely on fixed-camera