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Pope Leo XIV today issued an encyclical that contains an extensive discussion of artificial intelligence and its risks. The approximately 43,000-word document is entitled “Magnifica Humanitas,” which

industrysiliconangle

Pope Leo XIV presents Magnifica humanitas, his encyclical on AI, calling for AI regulation, protection for children against hypersexualized AI images, and more (New York Times)

DGX agent

New York Times: Pope Leo XIV presents Magnifica humanitas, his encyclical on AI, calling for AI regulation, protection for children against hypersexualized AI images, and more — The document marks a p

industrytechmeme

25 May 2026

Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks

arXiv:2605.23170v1 Announce Type: cross Abstract: Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not cont

Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models

arXiv:2605.23522v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators.

PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations

arXiv:2605.22855v1 Announce Type: cross Abstract: Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision makin

Local Ai

Preisach Attention: A Hysteretic Model of Sequential Memory

arXiv:2605.23603v1 Announce Type: cross Abstract: We introduce the Preisach Attention Layer (PAL), a novel sequence modelling architecture grounded in the classical Preisach hysteresis operator from m

local-aiarxiv-cs-ai

Industry

Product >> Brand

Product >> Brand 2 years ago: 'Elon destroyed the Tesla brand globally! It'll never recover!' Today: #1 selling EV in California: Tesla Model Y #1 selling EV in the U.S.: Tesla Model Y #1 selling EV i

industryelon-musk--x

ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization

arXiv:2603.02897v2 Announce Type: replace Abstract: Recent advances in generative image compression (GIC) have delivered remarkable improvements in perceptual quality. However, many GICs rely on large

researcharxiv-cs-cv

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2601.15224v2 Announce Type: replace-cross Abstract: Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Lang

ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation

arXiv:2605.04118v2 Announce Type: replace-cross Abstract: Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain diffic

Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays

arXiv:2605.23351v1 Announce Type: new Abstract: We study adversarial multi-armed bandits with and without delayed feedback under a safety-aware goal: achieving minimax-optimal worst-case regret while

safetyarxiv-cs-lg

Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback

arXiv:2605.23182v1 Announce Type: new Abstract: Pure exploration in episodic Reinforcement Learning has primarily focused on Best Policy Identification (BPI), which seeks to identify a (near)-optimal

safetyarxiv-cs-lg

Push Your Agent: Measuring and Enforcing Quantitative Goal Persistence in Long-Horizon LLM Agents

model-releasesarxiv-cs-lg

arXiv:2605.23574v1 Announce Type: new Abstract: Long-horizon language agents can make many plausible local tool calls yet fail to persist until a requested count is actually complete. We study this ga

Query-Adaptive Semantic Chunking for Retrieval-Augmented Generation: A Dynamic Strategy with Contextual Window Expansion

arXiv:2605.22834v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems depend critically on document chunking quality for retrieving relevant context. Fixed chunking segments doc

agentsarxiv-cs-cl

R^3L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification

arXiv:2601.03715v2 Announce Type: replace-cross Abstract: Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration

RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs

arXiv:2605.23550v1 Announce Type: cross Abstract: We study nonsmooth difference-of-convex programs whose subtracted convex term is a finite maximum of smooth convex functions. In this setting, standar

researcharxiv-cs-ai

RADAR: Relative Angular Divergence Across Representations

arXiv:2605.23028v1 Announce Type: cross Abstract: Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for doma

researcharxiv-cs-cl

RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations

arXiv:2510.11195v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminatin

RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis

arXiv:2605.22833v1 Announce Type: cross Abstract: Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative recovery trajectories. Trad

Ralph Loops are powerful, but wrapping a naive loop in a shell script is a total token burner in production. Pinecone Principal Engineer Jen…

Ralph Loops are powerful, but wrapping a naive loop in a shell script is a total token burner in production. Pinecone Principal Engineer Jen Hamon breaks down why standard loops collapse: ❌ The Bug: P

agentspinecone--x

RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation

arXiv:2605.22937v1 Announce Type: new Abstract: Inference-time scaling can reduce errors in structured query generation, but methods to allocate the compute for query code generation remains underexpl

researcharxiv-cs-cl

Reading Calibrated Uncertainty from Language Model Trajectories

arXiv:2605.22864v1 Announce Type: new Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with struct

Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia

arXiv:2605.22836v1 Announce Type: cross Abstract: Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload

arXiv:2605.11215v2 Announce Type: replace-cross Abstract: Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient t

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models

model-releasesarxiv-cs-cv

arXiv:2605.23656v1 Announce Type: new Abstract: Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, e

Redrawing the AI Map: A Theory of Accountability Boundaries in Agentic Ecosystems

arXiv:2605.23179v1 Announce Type: new Abstract: Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seeming

agentsarxiv-cs-ai

Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control

arXiv:2605.23415v1 Announce Type: cross Abstract: Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant M

Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints

arXiv:2605.23285v1 Announce Type: cross Abstract: How network structure determines function is a fundamental question, and it can be investigated by graph ensembles with precisely controlled structura

Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers

arXiv:2510.00915v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, man

Relevant Walk Search for Explaining Graph Neural Networks

arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, an

safetyarxiv-cs-lg

Applications

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

arXiv:2605.23241v1 Announce Type: new Abstract: Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) me

applicationsarxiv-cs-lg

Remote Teleoperation of Endovascular Intervention Robots: A Systematic Review

arXiv:2605.22889v1 Announce Type: new Abstract: Remote robotic-assisted endovascular intervention offers a promising approach to reduce clinician radiation exposure and physical strain, while extendin

safetyarxiv-cs-ro

Industry

Report: the EU plans to fine Google a high triple-digit million euro amount as part of a 2025 probe over concerns it favors its own services in search results (Reuters)

Reuters: Report: the EU plans to fine Google a high triple-digit million euro amount as part of a 2025 probe over concerns it favors its own services in search results — The European Union is planning

industrytechmeme

Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology

model-releasesarxiv-cs-lg

arXiv:2605.22886v1 Announce Type: cross Abstract: AI-native wireless receivers based on deep learning exhibit remarkable performance under stationary channel conditions, yet their resilience to distri

Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks

arXiv:2605.23472v1 Announce Type: new Abstract: Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness

researcharxiv-cs-cv

Revitalizing Dense Material Segmentation: Stabilized Vision Transformers and the Generalization Paradox

model-releasesarxiv-cs-cv

arXiv:2605.23747v1 Announce Type: new Abstract: Material segmentation, the pixel-wise classification of physical surface properties, remains a challenging problem in computer vision, requiring physico

Tutorials

RiGS: Rigid-aware 4D Gaussian Splatting from a Single Monocular Video

arXiv:2605.23672v1 Announce Type: new Abstract: Reconstructing dynamic 3D scenes from monocular videos is a fundamental yet highly challenging task, as real-world motions often involve both long-term

tutorialsarxiv-cs-cv

RMA: an Agentic System for Research-Level Mathematical Problems

arXiv:2605.22875v1 Announce Type: new Abstract: We present extbf{Research Math Agents (RMA)}, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies

RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering

model-releasesarxiv-cs-cv

arXiv:2605.23068v1 Announce Type: new Abstract: Reliable visual understanding in robot-assisted and minimally invasive surgery (RMIS/MIS) demands more than accurate masks: in clinical practice, clinic

Robotic Strawberry Harvesting with Robust Vision and Deep Reinforcement Learning based Sim-to-Real Control

arXiv:2605.23863v1 Announce Type: new Abstract: This study presents a closed-loop robotic strawberry harvesting system that combines a robust vision module, simulation-trained deep reinforcement learn

safetyarxiv-cs-ro

Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations

arXiv:2605.22986v1 Announce Type: cross Abstract: Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects o

researcharxiv-cs-ai

Robust Counterfactual Inference in Markov Decision Processes

arXiv:2502.13731v5 Announce Type: replace Abstract: This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a

researcharxiv-cs-ai

Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection

arXiv:2605.23175v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-o

researcharxiv-cs-cl

Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift

arXiv:2605.23115v1 Announce Type: new Abstract: Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This pape

RS2AD-LiDAR: End-to-End Autonomous Driving LiDAR Data Generation from Roadside Sensor Observations

arXiv:2605.23406v1 Announce Type: new Abstract: End-to-end autonomous driving solutions, which directly process multimodal sensory data and output fine-grained control commands, have gradually become

agentsarxiv-cs-cv

RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization

arXiv:2403.12401v2 Announce Type: replace Abstract: Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks

researcharxiv-cs-cv

S^3GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning

arXiv:2605.23467v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often suffer from an information bottleneck when capturing long-range dependencies, leading to the oversquashing

Safe Reinforcement Learning with Preference-based Constraint Inference

arXiv:2603.23565v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be com