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Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets

model-releasesarxiv-cs-lg

arXiv:2605.19233v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS

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Human DGX agent

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Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees

DGX agent

arXiv:2605.18775v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retri

researcharxiv-cs-ai

20 May 2026

Local Ai

Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data

arXiv:2605.18763v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A

local-aiarxiv-cs-ai

Rapid patient-specific neural networks for intraoperative X-ray to volume registration

arXiv:2503.16309v2 Announce Type: replace-cross Abstract: Advanced navigation techniques in image-guided interventions and surgical robotics require the rapid and precise alignment of 3D preoperative

safetyarxiv-cs-cv

RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding

arXiv:2605.19329v1 Announce Type: cross Abstract: Conventional vision-language models (VLMs) struggle to interpret scenes captured under adverse conditions (e.g., low light, high dynamic range, or fas

ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking

arXiv:2605.19077v1 Announce Type: cross Abstract: Task-oriented dialogue systems -- handling transactions, reservations, and service requests -- require predictable behavior, yet the moderately-sized

Local Ai

Real-Time Parallel Counterfactual Regret Minimization

arXiv:2605.19928v1 Announce Type: cross Abstract: Counterfactual Regret Minimization (CFR) is the dominant algorithmic family for solving large imperfect-information games, underpinning breakthroughs

local-aiarxiv-cs-ai

Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2605.19744v1 Announce Type: new Abstract: Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challen

Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era

arXiv:2605.18903v1 Announce Type: cross Abstract: Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging

safetyarxiv-cs-cv

Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models

arXiv:2605.19398v1 Announce Type: cross Abstract: Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by

Receptogenesis in a Vascularized Robotic Embodiment

arXiv:2603.09473v2 Announce Type: replace Abstract: Equipping robotic systems with the capacity to generate extit{ex novo} hardware during operation extends control of physical adaptability. Unlike mo

researcharxiv-cs-ro

RECIPE: Procedural Planning via Grounding in Instructional Video

model-releasesarxiv-cs-cv

arXiv:2605.19976v1 Announce Type: new Abstract: Visual planning asks a model to generate the remaining steps of a procedure in natural language given a partial video context and a goal. Progress on th

RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents

arXiv:2605.18805v1 Announce Type: cross Abstract: LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet ex

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning

arXiv:2605.18799v1 Announce Type: cross Abstract: Large language models can fail in critic interaction not only by answering incorrectly, but also by abandoning an initially correct scientific solutio

Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model

arXiv:2506.00286v3 Announce Type: replace-cross Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs with recursive entropic risk measures (ERM), where the risk parameter

Applications

Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics

arXiv:2605.19170v1 Announce Type: cross Abstract: Diffusion/score-based models have emerged as powerful generative models, capable of generating high-quality samples that mimic the training data distr

applicationsarxiv-cs-lg

Reflection-Based Relative Localization for Cooperative UAV Teams Using Active Markers

arXiv:2511.17166v2 Announce Type: replace Abstract: Reflections of active markers in the environment are a common source of ambiguity in onboard visual relative localization. This work presents a nove

researcharxiv-cs-ro

Replacement Learning: Training Neural Networks with Fewer Parameters

model-releasesarxiv-cs-cv

arXiv:2605.19533v1 Announce Type: new Abstract: End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates a

Resilient Byzantine Agreement with Predictions

arXiv:2605.19452v1 Announce Type: cross Abstract: This paper studies the Byzantine Agreement problem where the nodes have access to a predictor that flags nodes for suspicion of faulty (Byzantine) beh

Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory

model-releasesarxiv-cs-cl

arXiv:2605.19952v1 Announce Type: new Abstract: To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over acc

Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR

arXiv:2605.19282v1 Announce Type: new Abstract: Muon is a matrix-aware optimizer that leverages Newton-Schulz (NS) iterations to enforce spectral gradient orthogonalization by driving all singular val

researcharxiv-cs-lg

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design

arXiv:2602.04663v2 Announce Type: replace-cross Abstract: Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these

Local Ai

Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models

arXiv:2605.20158v1 Announce Type: cross Abstract: Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raise

local-aiarxiv-cs-ai

Retrieval-Augmented Generation for Natural Language Processing: A Survey

model-releasesarxiv-cs-cl

arXiv:2407.13193v4 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that stor

Retrieval-Augmented Linguistic Calibration

arXiv:2605.19344v1 Announce Type: new Abstract: Linguistic cues such as 'I believe' and 'probably' offer an intuitive interface for communicating confidence, yet a generalisable, principled calibratio

researcharxiv-cs-cl

Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method

arXiv:2605.18766v1 Announce Type: cross Abstract: Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such a

Return of Frustratingly Easy Unsupervised Video Domain Adaptation

arXiv:2605.19510v1 Announce Type: new Abstract: Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, calle

researcharxiv-cs-cv

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents

arXiv:2605.20061v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactiv

safetyarxiv-cs-cl

Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior

model-releasesarxiv-cs-cl

arXiv:2510.14261v2 Announce Type: replace Abstract: We present an experimental recipe for studying the relationship between training data and language model (LM) behavior. We outline steps for interve

Riemannian Networks over Full-Rank Correlation Matrices

arXiv:2605.19073v1 Announce Type: cross Abstract: Representations on the Symmetric Positive Definite (SPD) manifold have garnered significant attention across different applications. In contrast, the

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning

arXiv:2605.19033v1 Announce Type: cross Abstract: Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, mul

RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents

model-releasesarxiv-cs-ro

arXiv:2605.19328v1 Announce Type: cross Abstract: Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical platfo

RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields

arXiv:2412.02818v4 Announce Type: replace-cross Abstract: Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the

safetyarxiv-cs-lg

Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains

arXiv:2605.19940v1 Announce Type: new Abstract: Foundation models are increasingly deployed in socially sensitive domains such as education, mental health, and caregiving, where failures are often cum

Robots that learn to evaluate models of collective behavior

model-releasesarxiv-cs-ro

arXiv:2604.07303v2 Announce Type: replace Abstract: Understanding and modeling animal behavior is essential for studying collective motion, decision-making, and bio-inspired robotics. Yet, evaluating

Robust Basis Spline Decoupling for the Compression of Transformer Models

arXiv:2605.18794v1 Announce Type: cross Abstract: Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear

Agents

Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking

arXiv:2605.18852v1 Announce Type: cross Abstract: Checkpoint selection for multimodal large language models (MLLMs) presents significant challenges when performance differentials are marginal and eval

agentsarxiv-cs-ai

Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation

arXiv:2605.19230v1 Announce Type: new Abstract: Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with dise

researcharxiv-cs-cv

Robustness and Regularization in Hierarchical Re-Basin

arXiv:2510.09174v3 Announce Type: replace Abstract: This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme

researcharxiv-cs-lg

RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations

arXiv:2605.19924v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robo

safetyarxiv-cs-ro

RoomPilot: Controllable Indoor Scene Synthesis via Multimodal Semantic Parsing

arXiv:2512.11234v2 Announce Type: replace Abstract: Generating controllable indoor scenes is fundamental to applications in game development, architectural visualization, and embodied AI. However, exi

researcharxiv-cs-cv

Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference

arXiv:2605.19218v1 Announce Type: cross Abstract: Vision-Language Models suffer severe KV cache pressure at inference, as a single image often encodes into thousands of tokens. Most existing methods e

RoVLA: Multi-Consistency Constraints for Robust Vision-Language-Action Models

arXiv:2605.19678v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance on embodied manipulation, yet they remain brittle under visual observation changes, pa

safetyarxiv-cs-ro

Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints

arXiv:2605.18842v1 Announce Type: new Abstract: Safe reinforcement learning in nonstationary environments requires safety mechanisms that adapt as environmental conditions change. Standard safe reinfo

safetyarxiv-cs-lg

SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving

arXiv:2605.19524v1 Announce Type: cross Abstract: End-to-end autonomous driving systems excel in common scenarios but struggle with safety-critical long-tail cases. Vision-Language-Action (VLA) models

safetyarxiv-cs-cv

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction

model-releasesarxiv-cs-lg

arXiv:2605.19014v1 Announce Type: new Abstract: Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and se

SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection

arXiv:2605.20157v1 Announce Type: new Abstract: Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat

safetyarxiv-cs-lg

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs

arXiv:2605.18864v1 Announce Type: cross Abstract: Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yiel