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Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessity

arXiv:2604.04692v2 Announce Type: replace-cross Abstract: Automated fact-checking is a crucial task that supports a responsible information ecosystem. While recent research has progressed from text-on

researcharxiv-cs-ai

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Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models

DGX agent

arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen

safetyarxiv-cs-cv

14 May 2026

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

arXiv:2605.12768v1 Announce Type: cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce

'It became a self-fulfilling prophecy': How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

arXiv:2605.13261v1 Announce Type: cross Abstract: In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive p

researcharxiv-cs-ai

It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows

arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may

applicationsarxiv-cs-ai

IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning

arXiv:2605.12924v1 Announce Type: new Abstract: The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification

JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift

arXiv:2605.13813v1 Announce Type: new Abstract: Automated CT triage requires models that are simultaneously accurate across diverse pathologies and reliable under institutional shift. While Vision Tra

JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning

arXiv:2605.13013v1 Announce Type: new Abstract: Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel di

Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs

arXiv:2605.12772v1 Announce Type: new Abstract: Wu et al. (2026) showed that most frontier large language models (LLMs) recommend a sponsored, roughly twice-as-expensive flight when their system promp

KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.13322v1 Announce Type: new Abstract: Kamon (family crests) are an important part of Japanese culture and a natural test case for compositional visual recognition: each crest combines a smal

KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural Interpretation

arXiv:2605.13133v1 Announce Type: new Abstract: While EEG foundation models have shown significant potential in universal neural decoding across tasks, their advancement remains constrained by the ina

Kernel-based guarantees for nonlinear parametric models in Bayesian optimization

arXiv:2605.13160v1 Announce Type: cross Abstract: Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such model

Kernel Embeddings and the Separation of Measure Phenomenon

arXiv:2505.04613v4 Announce Type: replace-cross Abstract: We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct continuous probability distributio

Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance

arXiv:2603.02175v4 Announce Type: replace-cross Abstract: Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural langu

KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving

arXiv:2605.13734v1 Announce Type: cross Abstract: LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggre

applicationsarxiv-cs-ai

Agents

Language-Based Agent Control

arXiv:2605.12863v1 Announce Type: cross Abstract: This paper introduces language-based agent control (LBAC), a new programming model for agentic applications that brings techniques from programming la

agentsarxiv-cs-ai

Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task

arXiv:2603.03295v2 Announce Type: replace-cross Abstract: Whether in agentic workflows, social studies, or chat settings, large language models (LLMs) are increasingly being asked to replace humans in

Language Model Networks: Supervision-Efficient Learning through Dense Communication

arXiv:2505.12741v2 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time reasoning

applicationsarxiv-cs-ai

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ

safetyarxiv-cs-ai

Large Language Models Lack Temporal Awareness of Medical Knowledge

arXiv:2605.13045v1 Announce Type: new Abstract: The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, w

Latent-Augmented Discrete Diffusion Models

arXiv:2510.18114v3 Announce Type: replace-cross Abstract: Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implemen

researcharxiv-cs-ai

Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs

arXiv:2605.12714v1 Announce Type: new Abstract: Hidden states change substantially across the layers of modern language models, but most layer-wise analyses focus on one aspect of that change. We prop

LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving

arXiv:2605.13137v1 Announce Type: cross Abstract: Proving theorems in Lean 4 often requires identifying a scattered set of library lemmas whose joint use enables a concise proof -- a task we call glob

Learning a Continue-Thinking Token for Enhanced Test-Time Scaling

arXiv:2506.11274v2 Announce Type: replace-cross Abstract: Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference t

Tutorials

Learning Local Constraints for Reinforcement-Learned Content Generators

arXiv:2605.13570v1 Announce Type: new Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually

tutorialsarxiv-cs-ai

Learning Perturbations to Extrapolate Your LLM

arXiv:2605.13284v1 Announce Type: cross Abstract: Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, cu

applicationsarxiv-cs-lg

Agents

Learning POMDP World Models from Observations with Language-Model Priors

arXiv:2605.13740v1 Announce Type: new Abstract: Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model

agentsarxiv-cs-lg

Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles

model-releasesarxiv-cs-ro

arXiv:2605.13751v1 Announce Type: new Abstract: Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies

Learning to Approximate Uniform Facility Location via Graph Neural Networks

arXiv:2602.13155v2 Announce Type: replace Abstract: Neural networks, particularly message-passing neural networks (MPNNs), are increasingly used as heuristics for hard combinatorial optimization probl

Learning to Decide with AI Assistance under Human-Alignment

arXiv:2605.12646v1 Announce Type: cross Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate th

safetyarxiv-cs-ai

Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization

arXiv:2605.13713v1 Announce Type: new Abstract: Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue spa

Learning to See What You Need: Gaze Attention for Multimodal Large Language Models

arXiv:2605.13080v1 Announce Type: new Abstract: When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their inten

local-aiarxiv-cs-cv

Learning Transferable Latent User Preferences for Human-Aligned Decision Making

arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often stru

safetyarxiv-cs-ai

Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance

arXiv:2605.12561v1 Announce Type: new Abstract: Safe reinforcement learning (RL) typically asks extit{what} an agent should do. We ask extit{when} it needs to act, and show that a single policy can jo

safetyarxiv-cs-lg

Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation

arXiv:2605.12741v1 Announce Type: new Abstract: Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy s

safetyarxiv-cs-lg

LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models

model-releasesarxiv-cs-cv

arXiv:2505.15616v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to r

LEXI-SG: Monocular 3D Scene Graph Mapping with Room-Guided Feed-Forward Reconstruction

arXiv:2605.13741v1 Announce Type: cross Abstract: Scene graphs are becoming a standard representation for robot navigation, providing hierarchical geometric and semantic scene understanding. However,

Lifelong Learning in Vision-Language Models: Enhanced EWC with Cross-Modal Knowledge Retention

model-releasesarxiv-cs-ro

arXiv:2605.12789v1 Announce Type: new Abstract: Large language-vision models (LVLMs) such as CLIP, Flamingo, and BLIP have revolutionized AI by enabling understanding across textual and visual modalit

LIFT: Last-Mile Fine-Tuning for Table Explicitation

arXiv:2605.13424v1 Announce Type: new Abstract: We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard

LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections

arXiv:2605.13265v1 Announce Type: new Abstract: Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server, but the cut-layer interface int

Limitations of Quantum Advantage in Unsupervised Machine Learning

arXiv:2511.10709v2 Announce Type: replace-cross Abstract: Machine learning models are used for pattern recognition analysis of big data, without direct human intervention. The task of unsupervised lea

Limits of Personalizing Differential Privacy Budgets

arXiv:2605.13503v1 Announce Type: cross Abstract: A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natur

LLM Flow Processes for Text-Conditioned Regression

arXiv:2601.06147v2 Announce Type: replace Abstract: Recent work has demonstrated surprisingly good performance of pre-trained LLMs on regression tasks (for example, time-series prediction), with the a

LLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics

arXiv:2605.13412v1 Announce Type: cross Abstract: Off-the-shelf large language models (LLMs) are increasingly used to automate text annotation, yet their effectiveness remains underexplored for underr

LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information

arXiv:2605.13188v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generat

Agents

LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration

arXiv:2605.13782v1 Announce Type: cross Abstract: Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant

agentsarxiv-cs-ai

LMU-Based Sequential Learning and Posterior Ensemble Fusion for Cross-Domain Infant Cry Classification

arXiv:2603.02245v3 Announce Type: replace-cross Abstract: Decoding infant cry causes remains challenging for healthcare monitoring due to short nonstationary signals, limited annotations, and strong d

Local Conformal Calibration of Dynamics Uncertainty from Semantic Images

arXiv:2605.13028v1 Announce Type: new Abstract: We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR), a conformal prediction-based algorithm that uses perception information

local-aiarxiv-cs-ro