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Lookahead Drifting Model

arXiv:2605.04060v1 Announce Type: cross Abstract: Recently, a new paradigm named drifting model has been proposed for mapping distributions, which achieves the SOTA image generation performance over I

researcharxiv-cs-cv

LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)

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Human DGX agent

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arXiv:2605.05187v1 Announce Type: new Abstract: This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and

model-releasesarxiv-cs-cv

7 May 2026

Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction

arXiv:2605.05134v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination. While existing detection method

Low-Latency Quasi-Static Modeling of UAV Tether Aerodynamics

arXiv:2512.22588v3 Announce Type: replace Abstract: One of the main limitations of multirotor UAVs is their short flight time due to battery constraints. A practical solution for continuous operation

applicationsarxiv-cs-ro

Low-Rank Adaptation of Geospatial Foundation Models for Wildfire Mapping Using Sentinel-2 Data

model-releasesarxiv-cs-cv

arXiv:2605.04989v1 Announce Type: new Abstract: Wildfire burned-area mapping is essential for damage assessment, emissions modeling, and understanding fire-climate interactions across diverse ecologic

LTGS: Long-Term Gaussian Scene Chronology From Sparse View Updates

arXiv:2510.09881v3 Announce Type: replace Abstract: Recent advances in novel-view synthesis can create the photo-realistic visualization of real-world environments from conventional camera captures. H

applicationsarxiv-cs-cv

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems

arXiv:2605.04323v1 Announce Type: new Abstract: Understanding soil is fundamental to agriculture, carbon cycling, and environmental sustainability, yet progress is limited by fragmented and heterogene

MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization

arXiv:2501.03972v2 Announce Type: replace Abstract: The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems of

researcharxiv-cs-ro

Magic-Informed Quantum Architecture Search

model-releasesarxiv-cs-ai

arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed

Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification

model-releasesarxiv-cs-cl

arXiv:2511.05080v4 Announce Type: replace Abstract: The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer sol

Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption

arXiv:2605.03078v1 Announce Type: new Abstract: While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integra

applicationsarxiv-cs-ai

Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning

arXiv:2604.01345v2 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses. Adaptive IRL aims to reconstruct t

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

arXiv:2312.06423v3 Announce Type: replace-cross Abstract: Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid prolif

Manifold of Failure: Behavioral Attraction Basins in Language Models

arXiv:2602.22291v3 Announce Type: replace Abstract: While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a comprehens

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior

arXiv:2605.05115v1 Announce Type: new Abstract: Neural representations carry rich geometric structure; but does that structure causally shape behavior? To address this question, we intervene along pat

Hardware

Massively Parallel Exact Inference for Hawkes Processes

arXiv:2604.01342v2 Announce Type: replace Abstract: Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as O(N^2) i

hardwarearxiv-cs-lg

Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining

arXiv:2605.04278v1 Announce Type: new Abstract: Materials science workflows rely on structured and unstructured data from the vast body of available scientific literature. However, most of the experim

agentsarxiv-cs-cl

Tutorials

Materialist: Physically Based Editing Using Single-Image Inverse Rendering

arXiv:2501.03717v3 Announce Type: replace Abstract: Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on n

tutorialsarxiv-cs-cv

Measuring Psychological States Through Semantic Projection: A Theory-Driven Approach to Language-Based Assessment

arXiv:2605.04873v1 Announce Type: new Abstract: Recent advances in natural language processing have enabled increasingly accurate estimation of psychological traits from language. However, most existi

researcharxiv-cs-cl

Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc

arXiv:2605.03847v1 Announce Type: new Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates

safetyarxiv-cs-ai

MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs

arXiv:2605.04180v1 Announce Type: new Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, a

safetyarxiv-cs-cl

Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering

arXiv:2605.04116v1 Announce Type: cross Abstract: We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be

Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping

arXiv:2605.04308v1 Announce Type: new Abstract: Continual incorporation of new knowledge is essential for the long-term evolution of large language models (LLMs). Existing approaches typically rely on

MEMTIER: Tiered Memory Architecture and Retrieval Bottleneck Analysis for Long-Running Autonomous AI Agents

model-releasesarxiv-cs-ai

arXiv:2605.03675v1 Announce Type: new Abstract: Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over

MenuNet: A Strategy-Proof Mechanism for Matching Markets

arXiv:2605.03216v1 Announce Type: cross Abstract: Strategy-proofness is a fundamental desideratum in mechanism design, ensuring truthful reporting and robust participation. Stability is another centra

safetyarxiv-cs-ai

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent

arXiv:2602.19837v3 Announce Type: replace-cross Abstract: Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle

agentsarxiv-cs-lg

Tutorials

Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning

arXiv:2605.04102v1 Announce Type: cross Abstract: A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these direct

tutorialsarxiv-cs-lg

MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education

arXiv:2605.04772v1 Announce Type: new Abstract: Access to diverse, well-annotated medical images with interactive learning tools is fundamental for training practitioners in medicine and related field

applicationsarxiv-cs-cv

Misaligned by Reward: Socially Undesirable Preferences in LLMs

arXiv:2605.05003v1 Announce Type: new Abstract: Reward models are a key component of large language model alignment, serving as proxies for human preferences during training. However, existing evaluat

safetyarxiv-cs-cl

Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment

arXiv:2605.04363v1 Announce Type: new Abstract: TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synth

Local Ai

MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction

arXiv:2605.04744v1 Announce Type: new Abstract: Plant breeding underpins global food security through incremental, accumulating improvements in crop yield, quality and sustainability, achieved via rep

local-aiarxiv-cs-lg

Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks

arXiv:2605.04134v1 Announce Type: new Abstract: We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be e

Modular Reinforcement Learning For Cooperative Swarms

arXiv:2605.04939v1 Announce Type: new Abstract: A cooperative robot swarm is a collective of computationally-limited robots that share a common goal. Each robot can only interact with a small subset o

agentsarxiv-cs-ro

MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology

arXiv:2602.02282v2 Announce Type: replace Abstract: Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-

Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimation

arXiv:2605.04731v1 Announce Type: new Abstract: Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs fo

researcharxiv-cs-cv

MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents

model-releasesarxiv-cs-ai

arXiv:2605.03952v1 Announce Type: cross Abstract: Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The chal

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning

arXiv:2605.04058v1 Announce Type: new Abstract: Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significa

MRI-Eval: A Tiered Benchmark for Evaluating LLM Performance on MRI Physics and GE Scanner Operations Knowledge

model-releasesarxiv-cs-cl

arXiv:2605.05175v1 Announce Type: cross Abstract: Background: Existing MRI LLM benchmarks rely mainly on review-book multiple-choice questions, where top proprietary models already score highly, limit

MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation

arXiv:2605.04108v1 Announce Type: new Abstract: Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning f

researcharxiv-cs-cv

Multi-Agent Strategic Games with LLMs

arXiv:2605.03604v1 Announce Type: cross Abstract: This paper asks whether large language models (LLMs) can be used to study the strategic foundations of conflict and cooperation. I introduce LLMs as e

agentsarxiv-cs-ai

Tutorials

Multi Language Models for On-the-Fly Syntax Highlighting

arXiv:2510.04166v2 Announce Type: replace-cross Abstract: Syntax highlighting is a critical feature in modern software development environments, enhancing code readability and developer productivity.

tutorialsarxiv-cs-ai

Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding

arXiv:2605.04680v1 Announce Type: new Abstract: EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and

safetyarxiv-cs-cv

Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems

arXiv:2602.01486v2 Announce Type: replace Abstract: Recent years have seen a surge in data-driven surrogates for dynamical systems that can be orders of magnitude faster than numerical solvers. Howeve

safetyarxiv-cs-lg

Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast

arXiv:2505.00835v2 Announce Type: replace-cross Abstract: Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew

Multi-Source Human-in-the-Loop Digital Twin Testbed for Connected and Autonomous Vehicles in Mixed Traffic Flow

arXiv:2603.17751v3 Announce Type: replace Abstract: In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human-driven vehicles (HDVs).

agentsarxiv-cs-ro

MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains

arXiv:2511.06452v3 Announce Type: replace Abstract: Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Curren

Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points

arXiv:2605.04589v1 Announce Type: cross Abstract: A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically id

Multivariate Time Series Data Imputation via Distributionally Robust Regularization

arXiv:2602.00844v2 Announce Type: replace-cross Abstract: Multivariate time series imputation is often compromised by mismatch between the observed and true data distributions, a bias induced by the c

safetyarxiv-cs-lg