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Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

arXiv:2603.03332v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the ro

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Frequency-Aware Flow Matching for High-Quality Image Generation

DGX agent

arXiv:2604.15521v1 Announce Type: new Abstract: Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively

model-releasesarxiv-cs-cv

20 Apr 2026

From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts

arXiv:2604.16115v1 Announce Type: new Abstract: Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from

applicationsarxiv-cs-cv

From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text

arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solu

From Competition to Coopetition: Coopetitive Training-Free Image Editing Based on Text Guidance

arXiv:2604.15948v1 Announce Type: new Abstract: Text-guided image editing, a pivotal task in modern multimedia content creation, has seen remarkable progress with training-free methods that eliminate

safetyarxiv-cs-cv

From Intention to Text: AI-Supported Goal Setting in Academic Writing

arXiv:2604.15800v1 Announce Type: cross Abstract: This study presents WriteFlow, an AI voice-based writing assistant designed to support reflective academic writing through goal-oriented interaction.

safetyarxiv-cs-ai

Local Ai

From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization

arXiv:2503.07520v5 Announce Type: replace Abstract: Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cros

local-aiarxiv-cs-cv

From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?

arXiv:2604.01608v2 Announce Type: replace Abstract: Multi-agent systems (MAS) tackle complex tasks by distributing expertise, though this often comes at the cost of heavy coordination overhead, contex

agentsarxiv-cs-ai

From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation

arXiv:2604.15805v1 Announce Type: cross Abstract: Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to

applicationsarxiv-cs-ai

From Vulnerable Data Subjects to Vulnerabilizing Data Practices: Navigating the Protection Paradox in AI-Based Analyses of Platformized Lives

arXiv:2604.15990v1 Announce Type: cross Abstract: This paper traces a conceptual shift from understanding vulnerability as a static, essentialized property of data subjects to examining how it is acti

From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark

arXiv:2604.15654v1 Announce Type: new Abstract: Ultra-high-definition (UHD) image restoration poses unique challenges due to the high spatial resolution, diverse content, and fine-grained structures p

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents

model-releasesarxiv-cs-cl

arXiv:2602.01566v2 Announce Type: replace Abstract: Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research o

FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users

arXiv:2502.19312v2 Announce Type: replace-cross Abstract: Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curati

applicationsarxiv-cs-ai

Fusing Cellular Network Data and Tollbooth Counts for Urban Traffic Flow Estimation

arXiv:2604.15782v1 Announce Type: new Abstract: Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data.

tutorialsarxiv-cs-lg

Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)

arXiv:2604.15772v1 Announce Type: new Abstract: Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons, where sparse or fixed rewards seve

agentsarxiv-cs-ro

GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow

arXiv:2604.15612v1 Announce Type: cross Abstract: Gaussian splatting has recently gained traction as a compelling map representation for SLAM systems, enabling dense and photo-realistic scene modeling

tutorialsarxiv-cs-cv

GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos

arXiv:2604.16214v1 Announce Type: new Abstract: Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments.

applicationsarxiv-cs-cv

GenHSI: Controllable Generation of Human-Scene Interaction Videos

arXiv:2506.19840v2 Announce Type: replace Abstract: Large-scale pre-trained video diffusion models have exhibited remarkable capabilities in diverse video generation. However, existing solutions face

researcharxiv-cs-cv

Geometric regularization of autoencoders via observed stochastic dynamics

arXiv:2604.16282v1 Announce Type: new Abstract: Stochastic dynamical systems with slow or metastable behavior evolve, on long time scales, on an unknown low-dimensional manifold in high-dimensional am

safetyarxiv-cs-lg

Local Ai

GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology

arXiv:2604.15495v1 Announce Type: new Abstract: Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans

local-aiarxiv-cs-ai

Local Ai

Graph self-supervised learning based on frequency corruption

arXiv:2604.15699v1 Announce Type: new Abstract: Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web app

local-aiarxiv-cs-lg

GroupDPO: Memory efficient Group-wise Direct Preference Optimization

arXiv:2604.15602v1 Announce Type: new Abstract: Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single

safetyarxiv-cs-cl

GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows

arXiv:2604.15715v1 Announce Type: cross Abstract: The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows

Hallucination as Trajectory Commitment: Causal Evidence for Asymmetric Attractor Dynamics in Transformer Generation

arXiv:2604.15400v1 Announce Type: cross Abstract: We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by asymmetric attractor dyn

HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?

arXiv:2604.15415v1 Announce Type: cross Abstract: Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous

Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation

arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning dema

Hero-Mamba: Mamba-based Dual Domain Learning for Underwater Image Enhancement

arXiv:2604.16266v1 Announce Type: new Abstract: Underwater images often suffer from severe degradation, such as color distortion, low contrast, and blurred details, due to light absorption and scatter

Heterogeneous Sheaf Neural Networks

model-releasesarxiv-cs-lg

arXiv:2409.08036v2 Announce Type: replace Abstract: Heterogeneous graphs, whose nodes and edges may belong to different types and feature spaces, arise in a wide variety of real-world domains such as

Hierarchical Active Inference using Successor Representations

arXiv:2604.15679v1 Announce Type: cross Abstract: Active inference, a neurally-inspired model for inferring actions based on the free energy principle (FEP), has been proposed as a unifying framework

tutorialsarxiv-cs-ai

Hierarchical Codec Diffusion for Video-to-Speech Generation

arXiv:2604.15923v1 Announce Type: cross Abstract: Video-to-Speech (VTS) generation aims to synthesize speech from a silent video without auditory signals. However, existing VTS methods disregard the h

safetyarxiv-cs-cv

HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images

arXiv:2603.02210v3 Announce Type: replace Abstract: Human-product images, which showcase the integration of humans and products, play a vital role in advertising, e-commerce, and digital marketing. Th

tutorialsarxiv-cs-cv

HiPreNets: High-Precision Neural Networks through Progressive Training

model-releasesarxiv-cs-lg

arXiv:2506.15064v3 Announce Type: replace Abstract: Deep neural networks are powerful tools for solving nonlinear problems in science and engineering, but training highly accurate models becomes chall

Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification

model-releasesarxiv-cs-lg

arXiv:2504.15214v3 Announce Type: replace Abstract: Parameter-efficient transfer learning (PETL) methods adapt large artificial neural networks to downstream tasks without fine-tuning the entire model

How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models

arXiv:2604.15873v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly studied as repositories of linguistic knowledge. In this line of work, models are commonly evaluated both

researcharxiv-cs-cl

How people use Copilot for Health

arXiv:2604.15331v1 Announce Type: cross Abstract: We analyze over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversati

safetyarxiv-cs-ai

HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning

arXiv:2604.15678v1 Announce Type: new Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods

HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning

model-releasesarxiv-cs-cl

arXiv:2604.15648v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain

HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design

arXiv:2604.15642v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rap

IA-CLAHE: Image-Adaptive Clip Limit Estimation for CLAHE

arXiv:2604.16010v1 Announce Type: new Abstract: This paper proposes image-adaptive contrast limited adaptive histogram equalization (IA-CLAHE). Conventional CLAHE is widely used to boost the performan

Impact of Nonlinear Power Amplifier on Massive MIMO: Machine Learning Prediction Under Realistic Radio Channel

arXiv:2604.15977v1 Announce Type: new Abstract: M-MIMO is one of the crucial technologies for increasing spectral and energy efficiency of wireless networks. Most of the current works assume that M-MI

researcharxiv-cs-lg

Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies

arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and join

Improving Reasoning Capabilities in Small Models through Mixture-of-Layers Distillation with Stepwise Attention on Key Information

arXiv:2604.15701v1 Announce Type: new Abstract: The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-o

safetyarxiv-cs-cl

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

arXiv:2512.07173v4 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic n

researcharxiv-cs-lg

In-Context Distillation with Self-Consistency Cascades: A Simple, Training-Free Way to Reduce LLM Agent Costs

arXiv:2512.02543v2 Announce Type: replace Abstract: Deploying LLM agents at scale typically requires choosing between quality and cost. Existing cost-reduction approaches fail to preserve agility: the

agentsarxiv-cs-lg

InfoChess: A Game of Adversarial Inference and a Laboratory for Quantifiable Information Control

arXiv:2604.15373v1 Announce Type: cross Abstract: We propose InfoChess, a symmetric adversarial game that elevates competitive information acquisition to the primary objective. There is no piece captu

agentsarxiv-cs-ai

Information-Consistent Language Model Recommendations through Group Relative Policy Optimization

arXiv:2512.12858v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in business-critical domains such as finance, education, healthcare, and customer suppo

safetyarxiv-cs-ai

Information Router for Mitigating Modality Dominance in Vision-Language Models

arXiv:2604.16264v1 Announce Type: new Abstract: Vision Language models (VLMs) have demonstrated strong performance across a wide range of benchmarks, yet they often suffer from modality dominance, whe

applicationsarxiv-cs-cv

InstructTable: Improving Table Structure Recognition Through Instructions

arXiv:2604.02880v2 Announce Type: replace Abstract: Table structure recognition (TSR) holds widespread practical importance by parsing tabular images into structured representations, yet encounters si