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arXiv:2510.18668v2 Announce Type: replace-cross Abstract: The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with b

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Model Releases

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks

DGX agent

arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot

model-releasesarxiv-cs-lg

27 May 2026

Provably Safe Motion Planning Under Unknown Disturbances

arXiv:2605.26625v1 Announce Type: new Abstract: We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We cons

safetyarxiv-cs-ro

Psychological Constructs in Shared Semantic Space

arXiv:2605.26801v1 Announce Type: new Abstract: Psychological constructs are often measured in separate instruments, datasets, and research traditions, which makes direct comparison difficult. This pa

Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation

arXiv:2507.16116v2 Announce Type: replace Abstract: The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchroniz

PyCAT4: A Hierarchical Vision Transformer-based Framework for 3D Human Pose Estimation

arXiv:2508.02806v3 Announce Type: replace Abstract: Recently, a significant improvement in the accuracy of 3D human pose estimation has been achieved by combining convolutional neural networks (CNNs)

safetyarxiv-cs-cv

Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning

arXiv:2605.27318v1 Announce Type: new Abstract: Video spatial reasoning requires accumulating viewpoint-dependent evidence over time while retaining information useful to the question being asked. Exi

QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling

arXiv:2605.26339v1 Announce Type: cross Abstract: Scalar post-training quantizers discard pairwise coordinate structure within weight rows. We introduce QAM-W (Quadrature Amplitude Modulation for Weig

Qiskit QuantumKatas: Adapting Microsoft's Quantum Computing exercises for LLM evaluation

arXiv:2605.27210v1 Announce Type: cross Abstract: We adapt Microsoft's QuantumKatas -- a well-established quantum computing curriculum -- from Q# to Qiskit, the most widely-adopted quantum computing f

Hardware

Qrita: High-performance Top-k and Top-p using Pivot-based Truncation and Selection

arXiv:2602.01518v2 Announce Type: replace Abstract: Despite their importance in model sampling, efficient implementation of Top-k and Top-p algorithms for large vocabularies remains a significant chal

hardwarearxiv-cs-ai

Agents

QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents

arXiv:2605.27068v1 Announce Type: cross Abstract: Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM)

agentsarxiv-cs-ai

Quadratic Term Correction on Heaps' Law

arXiv:2511.14683v2 Announce Type: replace Abstract: Heaps' or Herdan's law characterizes the word-type vs. word-token relation by a power-law function, which is concave in linear-linear scale but a st

Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids

arXiv:2605.26770v1 Announce Type: new Abstract: Prior work shows that Large Language Models (LLMs) can transform Explainable AI (XAI) outputs into Natural Language Explanations (NLEs) that score highl

Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion

arXiv:2605.26266v1 Announce Type: cross Abstract: Chunk-wise autoregressive video diffusion models rely on a KV cache of previously generated chunks to avoid redundant computation, but this cache quic

Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering

arXiv:2605.27164v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document ch

Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation

arXiv:2003.05746v4 Announce Type: replace-cross Abstract: In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of f

R^3: 3D Reconstruction via Relative Regression

arXiv:2605.26519v1 Announce Type: new Abstract: Recent feed-forward geometry foundation models have demonstrated impressive generalization by recovering depth and poses in a single forward pass. Howev

RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields

arXiv:2605.26328v1 Announce Type: new Abstract: Radars are an ideal complement to cameras: both are inexpensive, solid-state sensors, with cameras offering fine angular resolution, while radars provid

Tutorials

RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender

arXiv:2605.26819v1 Announce Type: cross Abstract: We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RA

tutorialsarxiv-cs-ai

Rank-Learner: Orthogonal Ranking of Treatment Effects

arXiv:2602.03517v2 Announce Type: replace Abstract: Many decision-making problems require ranking individuals by their treatment effects rather than estimating the exact effect magnitudes. Examples in

Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T

arXiv:2605.26127v1 Announce Type: cross Abstract: Purpose: Real-time (RT) bSSFP MRI enables fast free-breathing cardiovascular imaging but requires 10-16 slices for functional assessment, resulting in

RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections

arXiv:2605.26854v1 Announce Type: new Abstract: The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis. While algebraic multigrid (AMG) offers

Local Ai

Ratio-Variance Regularized Policy Optimization

arXiv:2605.26784v1 Announce Type: cross Abstract: Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscrim

local-aiarxiv-cs-ai

RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2605.26348v1 Announce Type: new Abstract: Mobile robots can fail before they collide: a velocity that is safe now may commit the robot to a passage that moving obstacles will soon close. We stud

Re-M3Dr: Rebalanced MultiModal Mean Deviation Regression

arXiv:2605.26513v1 Announce Type: new Abstract: Mean Deviation (MD) is a critical metric for assessing visual field loss in ophthalmology. While previous work has focused solely on predicting MD from

Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery

arXiv:2605.27315v1 Announce Type: new Abstract: Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language mod

safetyarxiv-cs-cl

Agents

Real-Time Progress Prediction in Reasoning Language Models

arXiv:2506.23274v4 Announce Type: replace-cross Abstract: Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic

agentsarxiv-cs-ai

Reasoning, Code, or Both? How Large Language Models Handle Variations in Math Questions

arXiv:2605.26414v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve impressive accuracy on mathematical reasoning benchmarks, yet their performance drops when problems are modified wi

Applications

Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks

arXiv:2605.26934v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to post-training reasoning models, yet a key limitation of existing studies i

applicationsarxiv-cs-ai

ReasonOps: A Unified Operational Paradigm for Trustworthy Verified LLM Reasoning

arXiv:2605.27014v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence from primarily generative systems into increasingly capable reasoning agents. Re

ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation

arXiv:2605.26525v1 Announce Type: cross Abstract: Minute-scale cinematic video generation is a central challenge for generative video models. Existing paradigms address only fragments of this challeng

Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift

model-releasesarxiv-cs-cv

arXiv:2605.26855v1 Announce Type: new Abstract: Public datasets such as DLC-2021, SynID, and KID34K have significantly contributed to research on presentation attack detection for identity documents,

Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling

arXiv:2605.26969v1 Announce Type: cross Abstract: User modeling aims to use language models (LMs) to mimic an individual's behavior from a corpus of past context-action pairs (e.g., conversation turns

Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade

arXiv:2512.01572v3 Announce Type: replace-cross Abstract: Extreme sensor sparsity makes full-field reconstruction a fundamentally ill-posed problem in scientific sensing,where the goal is to infer phy

Recursive Flow Matching

arXiv:2605.26535v1 Announce Type: cross Abstract: Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving hig

Reformulation of RBM to Unify Linear and Nonlinear Dimensionality Reduction

arXiv:1909.08210v4 Announce Type: replace Abstract: A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction

Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

arXiv:2605.26560v1 Announce Type: cross Abstract: Objective. Outpatient notes carry follow-up instructions pairing actions with future times ('MRI brain in two weeks'). Extracting (action, date) pairs

ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference

arXiv:2605.27081v1 Announce Type: cross Abstract: Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining hi

Reparametrizing Shampoo and SOAP for Subspace Basis Updates and BFloat16 Storage

arXiv:2605.26327v1 Announce Type: new Abstract: Shampoo-based methods, such as KL-Shampoo and SOAP, have demonstrated strong performance in training neural networks and rely on QR decomposition. Becau

RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations

arXiv:2605.26177v1 Announce Type: cross Abstract: Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on e

Representation-Aware Unlearning via Activation Signatures: From Suppression to Entity-Signature Erasure

model-releasesarxiv-cs-cl

arXiv:2601.10566v5 Announce Type: replace Abstract: Entity-level unlearning is usually evaluated by what a model says: whether it stops naming the target, refuses a query, or shifts a Truth Ratio dist

Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction

model-releasesarxiv-cs-cv

arXiv:2605.24634v2 Announce Type: replace Abstract: Composed image retrieval (CIR) searches a corpus with a reference image and a text describing how to modify it. Despite rapid progress from triplet-

Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models

arXiv:2605.26661v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected

Rethinking the Multilingual Reasoning Gap with Layer Swap

model-releasesarxiv-cs-cl

arXiv:2605.26735v1 Announce Type: new Abstract: Recent reasoning Large Language Models produce a chain-of-thought (CoT) predominantly in English, even when prompted in non-English languages. Prior wor

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602.04879v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) ser

Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective

arXiv:2605.26441v1 Announce Type: cross Abstract: This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposa

Revealing the core dimensions underlying representations in brains, behavior and AI

arXiv:2605.26921v1 Announce Type: new Abstract: The study of representations is widespread across fields, including neuroscience, psychology, and artificial intelligence. While representations are oft

Local Ai

ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback

arXiv:2604.04940v2 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise.

local-aiarxiv-cs-ai