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arXiv:2604.08495v1 Announce Type: cross Abstract: This paper addresses the decentralized non-uniform area coverage problem for multi-agent systems, a critical task in missions with high spatial priori

agentsarxiv-cs-ro

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Safety

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

DGX agent

arXiv:2604.06210v2 Announce Type: cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks fac

safetyarxiv-cs-ai

10 Apr 2026

DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification

arXiv:2604.07622v1 Announce Type: new Abstract: Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its

DMin: Scalable Training Data Influence Estimation for Diffusion Models

arXiv:2412.08637v4 Announce Type: replace Abstract: Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models (DMs), yet existing

researcharxiv-cs-cv

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism

arXiv:2601.05524v2 Announce Type: replace Abstract: Parallel Speculative Decoding (PSD) accelerates traditional Speculative Decoding (SD) by overlapping draft generation with verification. However, it

Applications

DQA: Diagnostic Question Answering for IT Support

arXiv:2604.05350v2 Announce Type: replace Abstract: Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user repo

applicationsarxiv-cs-cl

Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification

arXiv:2512.03745v2 Announce Type: replace Abstract: Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first perform

Dynamic Context Evolution for Scalable Synthetic Data Generation

arXiv:2604.07147v1 Announce Type: cross Abstract: Large language models produce repetitive output when prompted independently across many batches, a phenomenon we term cross-batch mode collapse: the p

Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction

model-releasesarxiv-cs-cl

arXiv:2604.07659v1 Announce Type: new Abstract: Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hal

Efficient PRM Training Data Synthesis via Formal Verification

arXiv:2505.15960v3 Announce Type: replace Abstract: Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over rea

EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World

arXiv:2604.07607v1 Announce Type: cross Abstract: Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human da

safetyarxiv-cs-cv

Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models

arXiv:2604.06893v1 Announce Type: cross Abstract: Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy

researcharxiv-cs-lg

Agents

Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

arXiv:2604.07133v1 Announce Type: cross Abstract: This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose

agentsarxiv-cs-ai

Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models

arXiv:2604.08456v1 Announce Type: cross Abstract: Despite rapid progress, pretrained vision-language models still struggle when answers depend on tiny visual details or on combining clues spread acros

Epistemic Robust Offline Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2604.07072v1 Announce Type: new Abstract: Offline reinforcement learning learns policies from fixed datasets without further environment interaction. A key challenge in this setting is epistemic

Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems

arXiv:2604.06914v1 Announce Type: new Abstract: In this paper, we study a vehicle-to-infrastructure (V2I) system where distributed base stations (BSs) acting as road-side units (RSUs) collect multimod

safetyarxiv-cs-lg

Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios

arXiv:2604.06742v1 Announce Type: cross Abstract: Large Language Models (LLMs) are driving a shift towards intent-driven development, where agents build complete software from scratch. However, existi

Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study

model-releasesarxiv-cs-cl

arXiv:2510.04641v3 Announce Type: replace Abstract: Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases, creating a re

Evaluating PQC KEMs, Combiners, and Cascade Encryption via Adaptive IND-CPA Testing Using Deep Learning

arXiv:2604.06942v1 Announce Type: cross Abstract: Ensuring ciphertext indistinguishability is fundamental to cryptographic security, but empirically validating this property in real implementations an

researcharxiv-cs-lg

Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles

arXiv:2604.07378v1 Announce Type: new Abstract: Autonomous vehicles in interactive traffic environments are often limited by the scarcity of safety-critical tail events in static datasets, which biase

safetyarxiv-cs-ro

Event-Level Detection of Surgical Instrument Handovers in Videos with Interpretable Vision Models

arXiv:2604.07577v1 Announce Type: new Abstract: Reliable monitoring of surgical instrument exchanges is essential for maintaining procedural efficiency and patient safety in the operating room. Automa

safetyarxiv-cs-cv

EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration

arXiv:2604.07070v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, their potential for purpose-driven exploration in dynamic geo-spatial

EviSnap: Faithful Evidence-Cited Explanations for Cold-Start Cross-Domain Recommendation

arXiv:2604.06172v1 Announce Type: cross Abstract: Cold-start cross-domain recommender (CDR) systems predict a user's preferences in a target domain using only their source-domain behavior, yet existin

researcharxiv-cs-ai

Tutorials

ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits

arXiv:2604.06559v1 Announce Type: cross Abstract: Understanding and explaining the structure of generated test inputs is essential for effective software testing and debugging. Existing approaches--in

tutorialsarxiv-cs-lg

Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection

arXiv:2604.08159v1 Announce Type: new Abstract: The rapid advancement of facial forgery techniques poses severe threats to public trust and information security, making facial DeepFake detection a cri

Tutorials

Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration

arXiv:2603.16570v2 Announce Type: replace Abstract: Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models

tutorialsarxiv-cs-cv

Fast Spatial Memory with Elastic Test-Time Training

arXiv:2604.07350v1 Announce Type: cross Abstract: Large Chunk Test-Time Training (LaCT) has shown strong performance on long-context 3D reconstruction, but its fully plastic inference-time updates rem

researcharxiv-cs-lg

Local Ai

FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

arXiv:2604.06795v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensit

local-aiarxiv-cs-ai

Local Ai

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization

arXiv:2604.06833v1 Announce Type: cross Abstract: As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving p

local-aiarxiv-cs-lg

Fighting AI with AI: AI-Agent Augmented DNS Blocking of LLM Services during Student Evaluations

arXiv:2604.02360v1 Announce Type: cross Abstract: The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipse

FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On

arXiv:2604.08526v1 Announce Type: new Abstract: Given a person and a garment image, virtual try-on (VTO) aims to synthesize a realistic image of the person wearing the garment, while preserving their

FLeX: Fourier-based Low-rank EXpansion for multilingual transfer

arXiv:2604.06253v1 Announce Type: cross Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language

FlowGuard: Towards Lightweight In-Generation Safety Detection for Diffusion Models via Linear Latent Decoding

arXiv:2604.07879v1 Announce Type: new Abstract: Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) content

Applications

Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning

arXiv:2604.06985v1 Announce Type: cross Abstract: Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited

applicationsarxiv-cs-ai

GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents

arXiv:2604.07429v1 Announce Type: new Abstract: Towards an embodied generalist for real-world interaction, Multimodal Large Language Model (MLLM) agents still suffer from challenging latency, sparse f

GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting

arXiv:2604.07728v1 Announce Type: new Abstract: High-fidelity interactive digital assets are essential for embodied intelligence and robotic interaction, yet articulated objects remain challenging to

researcharxiv-cs-cv

Tutorials

Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges

arXiv:2604.08230v1 Announce Type: new Abstract: Object detection models trained on a source domain often exhibit significant performance degradation when deployed in unseen target domains, due to vari

tutorialsarxiv-cs-cv

Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline

arXiv:2412.19685v2 Announce Type: replace-cross Abstract: Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insi

Generative Phomosaic with Structure-Aligned and Personalized Diffusion

arXiv:2604.06989v1 Announce Type: cross Abstract: We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color-base

researcharxiv-cs-ai

Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030

arXiv:2604.06215v1 Announce Type: cross Abstract: The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical

safetyarxiv-cs-ai

Applications

GraphWalker: Graph-Guided In-Context Learning for Clinical Reasoning on Electronic Health Records

arXiv:2604.06684v1 Announce Type: new Abstract: Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) offer

applicationsarxiv-cs-lg

GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning

model-releasesarxiv-cs-cl

arXiv:2604.07808v1 Announce Type: new Abstract: Full-parameter fine-tuning of large language models is constrained by substantial GPU memory requirements. Low-rank adaptation methods mitigate this cha

GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations

arXiv:2604.06358v1 Announce Type: cross Abstract: Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains cha

Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation

arXiv:2604.08046v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current res

Hallucination as output-boundary misclassification: a composite abstention architecture for language models

arXiv:2604.06195v1 Announce Type: cross Abstract: Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally generated

researcharxiv-cs-ai

Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution

arXiv:2604.07833v1 Announce Type: new Abstract: Embodied agents are evolving from passive reasoning systems into active executors that interact with tools, robots, and physical environments. Once gran

safetyarxiv-cs-ro

Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization

arXiv:2604.06285v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual inform

safetyarxiv-cs-ai

Hardware

HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models

arXiv:2604.07812v1 Announce Type: new Abstract: In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making th

hardwarearxiv-cs-cv