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Active Testing of Large Language Models via Approximate Neyman Allocation

arXiv:2605.10075v1 Announce Type: new Abstract: Large language models (LLMs) require reliable evaluation from pre-training to test-time scaling, making evaluation a recurring rather than one-off cost.

researcharxiv-cs-ai

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ACWM-Phys: Investigating Generalized Physical Interaction in Action-Conditioned Video World Models

DGX agent

arXiv:2605.08567v1 Announce Type: new Abstract: Action-conditioned world models (ACWMs) have shown strong promise for video prediction and decision-making. However, existing benchmarks are largely res

model-releasesarxiv-cs-cv

12 May 2026

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training

arXiv:2605.08408v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems governed by partial differential equation

AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery

arXiv:2605.10741v1 Announce Type: new Abstract: Fine-tuning large language models with LoRA requires choosing a rank r before training starts. Existing approaches either extract rank-1 components sequ

AdaPreLoRA: Adafactor Preconditioned Low-Rank Adaptation

arXiv:2605.08734v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) reparameterizes a weight update as a product of two low-rank factors, but the Jacobian J_{G} of the generator mapping the f

Adaptive 3D Convolution for Remote Sensing Image Fusion

arXiv:2605.09455v1 Announce Type: new Abstract: Remote sensing image fusion aims to create a high-resolution multi/hyper-spectral image from a high-resolution image with limited spectral information a

researcharxiv-cs-cv

Adaptive Action Chunking via Multi-Chunk Q Value Estimation

arXiv:2605.10044v1 Announce Type: cross Abstract: Action chunking emerged as a pivotal technique in imitation learning, enabling policies to predict cohesive action sequences rather than single action

agentsarxiv-cs-ai

Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution

arXiv:2605.10470v1 Announce Type: new Abstract: Super-resolution (SR) is a severely ill-posed problem with inherent ambiguity, as widely recognized in both empirical and theoretical studies. Although

safetyarxiv-cs-cv

Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints

arXiv:2605.09707v1 Announce Type: cross Abstract: Training neural networks to satisfy universal constraints over continuous domains poses unique challenges. Common examples include Lyapunov Neural Net

Applications

Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics

arXiv:2512.13919v2 Announce Type: replace Abstract: This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models

applicationsarxiv-cs-lg

Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum

arXiv:2605.09623v1 Announce Type: cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to DNN p

researcharxiv-cs-ai

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization

arXiv:2510.04988v3 Announce Type: replace Abstract: The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's m

researcharxiv-cs-lg

Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks

arXiv:2511.06216v4 Announce Type: replace Abstract: Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically r

AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting

arXiv:2605.10239v1 Announce Type: new Abstract: This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply comp

researcharxiv-cs-cv

Local Ai

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

arXiv:2410.13181v2 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality

local-aiarxiv-cs-cl

Additive Atomic Forests for Symbolic Function and Antiderivative Discovery

arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,

researcharxiv-cs-lg

Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III

arXiv:2605.08161v1 Announce Type: new Abstract: Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to

researcharxiv-cs-cv

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment

arXiv:2605.09902v1 Announce Type: new Abstract: Adversarial perturbations can mislead Multimodal Large Language Models (MLLMs) recognize a benign image as a specific target object, posing serious risk

safetyarxiv-cs-cv

Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates

arXiv:2605.09337v1 Announce Type: new Abstract: We propose FAR-SIGN (Fully Asynchronous Robust optimization via SIGNed directional projections) for adversary-resilient learning in parameter-server--wo

Affine Tracing: A New Paradigm for Probabilistic Linear Solvers

arXiv:2605.10566v1 Announce Type: cross Abstract: Probabilistic linear solvers (PLSs) return probability distributions that quantify uncertainty due to limited computation in the solution of linear sy

researcharxiv-cs-lg

Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems

arXiv:2605.10555v1 Announce Type: new Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD

agentsarxiv-cs-ai

Agent-Omit: Adaptive Context Omission for Efficient LLM Agents

arXiv:2602.04284v2 Announce Type: replace Abstract: Managing agent context (e.g., thought and observation) during multi-turn agent-environment interactions is an emerging strategy to improve agent eff

Agent-Sentry: Bounding LLM Agents via Execution Provenance

arXiv:2603.22868v2 Announce Type: replace-cross Abstract: Agentic computing systems, while immensely capable, raise serious security, privacy, and safety concerns. A key issue is that the full set of

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

arXiv:2605.10365v1 Announce Type: new Abstract: Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly dra

Local Ai

Agent-X: Full Pipeline Acceleration of On-device AI Agents

arXiv:2605.10380v1 Announce Type: new Abstract: LLM-based agents deliver state-of-the-art performance across tasks but incur high end-to-end latency on edge devices. We introduce Agent-X, a software-o

local-aiarxiv-cs-ai

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators

arXiv:2605.08647v1 Announce Type: cross Abstract: Multi-agent systems achieve state-of-the-art outcomes through peer collaboration. However, when an agent in the pipeline silently drops a constraint,

AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems

arXiv:2605.08715v1 Announce Type: cross Abstract: LLM-based multi-agent systems are increasingly deployed on long-horizon tasks, but a single decisive error is often accepted by downstream agents and

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread

agentsarxiv-cs-ai

Agentic MIP Research: Accelerated Constraint Handler Generation

arXiv:2605.09186v1 Announce Type: new Abstract: Mixed-integer programming (MIP) research is both mathematically sophisticated and engineering-intensive: testing an algorithmic hypothesis within a bran

Agentic Performance at the Edge: Insights from Benchmarking

arXiv:2605.10384v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is a natural fit for Internet of Things (IoT) and edge systems, but edge deployments are often constrained to model

AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization

arXiv:2605.08704v1 Announce Type: new Abstract: Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore divers

AgentReview: Exploring Peer Review Dynamics with LLM Agents

arXiv:2406.12708v3 Announce Type: replace Abstract: Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exp

safetyarxiv-cs-cl

AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks

arXiv:2605.10286v1 Announce Type: new Abstract: Building effective clinical decision support systems requires the synthesis of complex heterogeneous multimodal data. Such modalities include temporal e

AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems

arXiv:2605.08813v1 Announce Type: new Abstract: Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal com

agentsarxiv-cs-lg

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

arXiv:2605.08756v1 Announce Type: new Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs). Recent works show t

Applications

AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases

arXiv:2505.18184v2 Announce Type: replace-cross Abstract: The increase in cardiac and pulmonary diseases presents an alarming and pervasive health challenge on a global scale responsible for unexpecte

applicationsarxiv-cs-cv

AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care

arXiv:2605.08480v1 Announce Type: new Abstract: Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their abili

Applications

AI Native Asset Intelligence

arXiv:2605.09115v1 Announce Type: cross Abstract: Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although

applicationsarxiv-cs-ai

AIPO: : Learning to Reason from Active Interaction

arXiv:2605.08401v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capabilities, largely stimulated by Reinforcement Learning with

ALAM: Algebraically Consistent Latent Transitions for Vision-Language-Action Models

arXiv:2605.10819v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by the scarcity of action-labeled robot data, whereas action-free videos provide abundant evide

Alice v1: Distillation-Enhanced Video Generation Surpassing Closed-Source Models

model-releasesarxiv-cs-cv

arXiv:2605.08115v1 Announce Type: cross Abstract: Wepresent Alice v1, a 14-billion parameter open-source video generation model that achieves state-of-the-art quality through consistency distillation

Align and Shine: Building High-Quality Sentence-Aligned Corpora for Multilingual Text Simplification

arXiv:2605.09476v1 Announce Type: cross Abstract: Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including

AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2601.01762v2 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. W

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling

arXiv:2604.08178v2 Announce Type: replace Abstract: In classical Reinforcement Learning from Human Feedback (RLHF), Reward Models (RMs) serve as the fundamental signal provider for model alignment. As

Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis

arXiv:2605.10415v1 Announce Type: new Abstract: Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single

safetyarxiv-cs-cl

Aligning Validation with Deployment: Target-Weighted Cross-Validation for Spatial Prediction

arXiv:2603.29981v2 Announce Type: replace Abstract: Reliable estimation of predictive performance is essential for spatial environmental modeling, where machine-learning models are used to generate ma

safetyarxiv-cs-lg

Alignment as Jurisprudence

arXiv:2605.08416v1 Announce Type: new Abstract: Jurisprudence, the study of how judges should properly decide cases, and alignment, the science of getting AI models to conform to human values, share a

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels

arXiv:2509.20294v4 Announce Type: replace Abstract: We study spectral algorithms in the setting where kernels are learned from data. We introduce the effective span dimension (ESD), an alignment-sensi

safetyarxiv-cs-lg