Search: “framework”

Geometry-Conditioned Diffusion for Occlusion-Robust In-Bed Pose Estimation

arXiv:2604.23651v1 Announce Type: new Abstract: Robust in-bed human pose estimation under blanket occlusion remains challenging due to the scarcity of reliable labeled training data for heavily covere

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GIFT: Global stabilisation via Intrinsic Fine Tuning

DGX agent

arXiv:2604.23312v1 Announce Type: cross Abstract: Deep reinforcement learning policies achieve strong performance in complex continuous control environments with nonlinear contact forces. However, the

applicationsarxiv-cs-ai

28 Apr 2026

GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction

model-releasesarxiv-cs-cv

arXiv:2604.23941v1 Announce Type: new Abstract: Graphical User Interface (GUI) element grounding (precisely locating elements on screenshots based on natural language instructions) is fundamental for

Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong

arXiv:2604.23523v1 Announce Type: cross Abstract: Safety specifications in cyber-physical systems (CPS) capture the operational conditions the system must satisfy to operate safely within its intended

safetyarxiv-cs-ai

Green Shielding: A User-Centric Approach Towards Trustworthy AI

arXiv:2604.24700v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users p

Tutorials

Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

arXiv:2604.23912v1 Announce Type: new Abstract: Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-

tutorialsarxiv-cs-lg

Group Orthogonal Low-Rank Adaptation for RGB-T Tracking

model-releasesarxiv-cs-cv

arXiv:2512.05359v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning has emerged as a promising paradigm in RGB-T tracking, enabling downstream task adaptation by freezing pretrained pa

Guiding Vector Field Generation via Score-based Diffusion Model

arXiv:2604.24487v1 Announce Type: new Abstract: Guiding Vector Fields (GVFs) are a powerful tool for robotic path following. However, classical methods assume smooth, ordered curves and fail when path

researcharxiv-cs-ro

HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA

model-releasesarxiv-cs-cv

arXiv:2604.23665v1 Announce Type: new Abstract: Recent advances in representation learning have shown that hyperbolic geometry can offer a more expressive alternative to the Euclidean embeddings used

Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena

arXiv:2604.22990v1 Announce Type: cross Abstract: Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, m

HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness

arXiv:2603.17573v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) Models have become the mainstream solution for robot control, but suffer from slow inference speeds. Speculative

applicationsarxiv-cs-lg

Humanoid Whole-Body Badminton via Multi-Stage Reinforcement Learning

arXiv:2511.11218v3 Announce Type: replace Abstract: Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world

Identity-Decoupled Anonymization for Visual Evidence in Multi-modal Retrieval-Augmented Generation

arXiv:2604.23584v1 Announce Type: new Abstract: Multi-modal retrieval-augmented generation (MRAG) systems retrieve visual evidence from large image corpora to ground the responses of large multi-modal

IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting

model-releasesarxiv-cs-lg

arXiv:2604.24224v1 Announce Type: new Abstract: Short-range prediction of convective precipitation from weather radar observations is essential for severe weather warnings. However, deep learning mode

Improved Hardness Results for Learning Intersections of Halfspaces

arXiv:2402.15995v2 Announce Type: replace-cross Abstract: We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly litt

InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

arXiv:2602.23024v2 Announce Type: replace Abstract: Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipu

IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models

arXiv:2604.24002v1 Announce Type: cross Abstract: Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding.

Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics

arXiv:2604.22832v1 Announce Type: cross Abstract: Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scar

safetyarxiv-cs-ai

IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning

arXiv:2604.24114v1 Announce Type: new Abstract: Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consiste

safetyarxiv-cs-cl

JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems

model-releasesarxiv-cs-cl

arXiv:2604.23478v1 Announce Type: new Abstract: Large language models are increasingly deployed as automated judges for evaluating other models, yet the stability of their verdicts under semantically

Judging the Judges: A Systematic Evaluation of Bias Mitigation Strategies in LLM-as-a-Judge Pipelines

arXiv:2604.23178v1 Announce Type: new Abstract: LLM-as-a-Judge has become the dominant paradigm for evaluating language model outputs, yet LLM judges exhibit systematic biases that compromise evaluati

K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology

arXiv:2604.24645v1 Announce Type: cross Abstract: The development of practical (multimodal) large language model assistants for Korean weather forecasters is hindered by the absence of a multidimensio

Agents

KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning

arXiv:2604.22779v1 Announce Type: cross Abstract: Enabling large language models (LLMs) to appropriately abstain from answering questions beyond their knowledge is crucial for mitigating hallucination

agentsarxiv-cs-ai

Knowledge Vector of Logical Reasoning in Large Language Models

arXiv:2604.23877v1 Announce Type: new Abstract: Logical reasoning serve as a central capability in LLMs and includes three main forms: deductive, inductive, and abductive reasoning. In this work, we s

researcharxiv-cs-cl

KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters

arXiv:2604.23948v1 Announce Type: cross Abstract: The Korean writing system, extit{Hangeul}, has a unique character representation rigidly following the invention principles recorded in extit{Hunminje

Local Ai

LAMP: Extracting Local Decision Surfaces From Large Language Models

arXiv:2505.11772v3 Announce Type: replace Abstract: We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how r

local-aiarxiv-cs-lg

Large Language Model based Interactive Decision-Making for Autonomous Driving

arXiv:2604.23513v1 Announce Type: new Abstract: In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly cons

Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation

arXiv:2509.06337v2 Announce Type: replace Abstract: Questionnaire-based surveys are foundational to social science research and public policymaking, yet traditional survey methods remain costly, time-

LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation

arXiv:2505.12009v2 Announce Type: replace Abstract: Expressive human pose and shape estimation (EHPS) plays a central role in digital human generation, particularly in live-streaming applications. How

applicationsarxiv-cs-cv

LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization

arXiv:2604.23957v1 Announce Type: new Abstract: Proactive watermarking offers a promising approach for deepfake tamper detection and localization in short-form videos. However, existing methods often

safetyarxiv-cs-cv

Layer Embedding Deep Fusion Graph Neural Network

arXiv:2604.23324v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have demonstrated impressive performance in learning representations from graph-structured data. However, their message-p

Learning-Augmented Robust Algorithmic Recourse

arXiv:2410.01580v3 Announce Type: replace Abstract: Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a

Local Ai

Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM

arXiv:2604.23314v1 Announce Type: new Abstract: Segmentation is central to clinical diagnosis and monitoring, yet the reliability of modern foundation models in medical imaging still depends on the av

local-aiarxiv-cs-cv

Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning

arXiv:2604.23000v1 Announce Type: new Abstract: In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varyi

Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation

arXiv:2604.24681v1 Announce Type: new Abstract: Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding

Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity

arXiv:2604.23867v1 Announce Type: new Abstract: Scientific measurements are often bottlenecked by suboptimal conditions, whether that be noise, incomplete spatial coverage, or limited resolution, rend

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels

arXiv:2504.18184v4 Announce Type: replace-cross Abstract: We consider a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbe

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a

safetyarxiv-cs-cl

Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain

arXiv:2509.10546v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in finance, where unsafe behavior can lead to serious regulatory risks. However, most r

Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs

arXiv:2509.00084v2 Announce Type: replace-cross Abstract: Test-time scaling (TTS) has gained widespread attention for enhancing LLM reasoning. Existing approaches such as Best-of-N and majority voting

safetyarxiv-cs-ai

Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling

arXiv:2604.24717v1 Announce Type: new Abstract: Every Transformer architecture dedicates enormous capacity to learning rich representations in semantic embedding space -- yet the rotation manifold act

applicationsarxiv-cs-ai

LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models

arXiv:2604.23950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant

safetyarxiv-cs-cv

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models

arXiv:2604.23809v1 Announce Type: new Abstract: Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity s

applicationsarxiv-cs-cl

Light 'em Up: Enabling Few-Shot Low-Light 3D Gaussian Splatting with Multi-Scale Explicit Retinex Illumination Decoupling

arXiv:2604.24053v1 Announce Type: new Abstract: Full 360^irc novel view synthesis under low-light conditions remains challenging. Insufficient illumination, noise amplification, and view-dependent pho

Lightweight and Production-Ready PDF Visual Element Parsing

arXiv:2604.23276v1 Announce Type: cross Abstract: PDF documents contain critical visual elements such as figures, tables, and forms whose accurate extraction is essential for document understanding an

LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People

arXiv:2604.23970v1 Announce Type: new Abstract: Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-bu

LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models

arXiv:2603.14882v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) typically assume a uniform spatial fidelity across the entire field of view of visual inputs, dedicating equal precisi

M^2-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills

arXiv:2604.24182v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models predominantly rely on end-to-end fine-tuning. While effective, this paradigm compromises the inherent genera

applicationsarxiv-cs-ro