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arXiv:2510.13920v2 Announce Type: replace Abstract: Query-focused table summarization requires generating natural language summaries of tabular data conditioned on a user query, enabling users to acce

agentsarxiv-cs-cl

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Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation

DGX agent

arXiv:2505.21072v4 Announce Type: replace Abstract: Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in

researcharxiv-cs-cl

20 Apr 2026

Faster LLM Inference via Sequential Monte Carlo

arXiv:2604.15672v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates language model inference by drafting tokens from a cheap proposal model and verifying them against an expensive

researcharxiv-cs-cl

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use

arXiv:2604.15646v1 Announce Type: new Abstract: Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet wr

researcharxiv-cs-cl

Fed3D: Federated 3D Object Detection

arXiv:2604.15795v1 Announce Type: new Abstract: 3D object detection models trained in one server plays an important role in autonomous driving, robotics manipulation, and augmented reality scenarios.

local-aiarxiv-cs-cv

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics

arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode

local-aiarxiv-cs-lg

FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound

arXiv:2512.22278v2 Announce Type: replace Abstract: The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal h

Find, Fix, Reason: Context Repair for Video Reasoning

arXiv:2604.16243v1 Announce Type: new Abstract: Reinforcement learning has advanced video reasoning in large multi-modal models, yet dominant pipelines either rely on on-policy self-exploration, which

safetyarxiv-cs-cv

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation

arXiv:2604.16298v1 Announce Type: new Abstract: UAV vision-language navigation (VLN) requires an agent to navigate complex 3D environments from an egocentric perspective while following ambiguous mult

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models

arXiv:2604.15488v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offer

safetyarxiv-cs-ai

FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale

arXiv:2604.16265v1 Announce Type: new Abstract: Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited rep

researcharxiv-cs-lg

Flexible Empowerment at Reasoning with Extended Best-of-N Sampling

arXiv:2604.15614v1 Announce Type: new Abstract: This paper proposes a novel method that incorporates empowerment when reasoning actions in reinforcement learning (RL), thereby achieving the flexibilit

safetyarxiv-cs-lg

Follow the Flow: On Information Flow Across Textual Tokens in Text-to-Image Models

arXiv:2504.01137v3 Announce Type: replace Abstract: Text-to-image generation models suffer from alignment problems, where generated images fail to accurately capture the objects and relations in the t

safetyarxiv-cs-cl

Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions

arXiv:2604.15395v1 Announce Type: new Abstract: Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towa

applicationsarxiv-cs-ro

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

Frequency-Aware Flow Matching for High-Quality Image Generation

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

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

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

Agents

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

researcharxiv-cs-ai

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

Agents

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

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

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

researcharxiv-cs-ai

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

researcharxiv-cs-ai

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