AI News Archive: May 21, 2026 — Part 19

Sourced from 500+ daily AI sources, scored by relevance.

• Economic costing of evaluating, deploying and monitoring an artificial intelligence-based reconstruction for acceleration of rectal MRI examinations

  Objectives: AI-based reconstructions can reduce MRI acquisition times and/or improve image quality. Guidelines recommend clinical evaluations and post-deployment monitoring of these novel methods, however, there has been little investigation of the clinical resources required for such assessments. The aim of this study was to evaluate the healthcare resource utilisation and potential savings associated with AI-based reconstructions in rectal MRI. Methods: A retrospective economic costing analysis was conducted from the NHS healthcare perspective. Resource utilisation data were extracted from the Electronic Patient Records for 9 healthy volunteer scans and 104 rectal MRI examinations evaluating an AI-based reconstruction. The resource profile included the MRI scan and the staff time required for data acquisition and analysis. Results: The clinical evaluation of the AI-based reconstruction cost {pound}15,023. Deployment of the AI-based reconstruction reduced the length of an MRI rectum s

  📄 ResearchMay 21, 2026https://www.medrxiv.org/content/10.64898/2026.05.18.26353474v1?rss=1
• Developing Provider-Co-Created Prototypes Addressing Equity-Related Barriers in Liver Transplantation for Hepatocellular Carcinoma"

  Background: Black patients and individuals with low socioeconomic status (SES) face significant disparities in accessing curative therapies for hepatocellular carcinoma (HCC), including liver transplantation. This study aimed to develop provider-co-created intervention prototypes in response to patient-identified barriers and recommendations. Methods: A human-centered design session with hepatology and transplant providers at a large academic medical center was conducted. Prior to the session, participants were presented with barriers and preliminary solutions identified through an earlier human-centered design session with Black and low-SES patients. Using structured ideation methods, including brainwriting, challenge mapping, and concept voting, providers co-created intervention prototypes. Final concepts were synthesized from patient insights, provider input, and design methods using affinity diagramming and concept modeling. Results: Nine providers participated in the session. They

  📄 ResearchMay 21, 2026https://www.medrxiv.org/content/10.64898/2026.05.15.26353301v1?rss=1
• AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation

  Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit an...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22816v1
• GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations

  Vision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, existing VLA systems primarily rely on textual instructions and struggle to resolve spatial ambiguity in complex scenes with multiple similar objects. To...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22812v1
• DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders

  Representation Autoencoders (RAEs) leverage frozen vision foundation models (VFMs) as tokenizer encoders, providing robust high-level representations that facilitate fast convergence and high-quality generation in latent diffusion models. However, freezing the VFM inherently constrains its spatial r...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22777v1
• Spectral Tail Auxiliary Learning for AI-Generated Image Detection

  As generative image models evolve rapidly, the perceptual gap between generated and real images continues to narrow, making AI-generated image detection increasingly challenging. Many existing methods exploit frequency-domain cues for detection, typically described as frequency-domain artifacts or h...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22751v1
• WorldKV: Efficient World Memory with World Retrieval and Compression

  Autoregressive video diffusion models have enabled real-time, action-conditioned world generation. However, sustaining a persistent world, where revisiting a previously seen viewpoint yields consistent content, remains an open problem. Full KV-cache attention preserves this consistency but breaks re...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22718v1
• Swift Sampling: Selecting Temporal Surprises via Taylor Series

  While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted evolution. Inspired by the human brain's predictive coding, we introduce Swift Sampling, an elegant, training-free frame s...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22678v1
• Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment

  Deploying vision models across devices with varying resource constraints, or even on a single device where available compute fluctuates due to battery state, thermal throttling, or latency deadlines, typically requires training and maintaining separate models. Width-adaptive inference addresses this...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22677v1
• From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model

  Vision-Language-Action (VLA) models often suffer from performance degradation under distribution shifts, as they struggle to learn generalized behavior representations across varying environments. While existing approaches attempt to construct behavior representations through action-centric latent v...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22671v1
• SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers

  Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Ro...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22668v1
• SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation

  While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoning to visual perception. Current methods, such as latent query alignment, are end-to-end yet opaque "black boxes". Conversely, textual localization ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22658v1
• From Baseline to Follow-Up: Counterfactual Spine DXA Image Synthesis in UK Biobank Using a Causal Hierarchical Variational Autoencoder

  Dual-energy X-ray absorptiometry (DXA) is widely used for large-scale skeletal assessment, yet learning controllable and interpretable factor-specific anatomical variation remains challenging. We propose a metadata-conditioned causal hierarchical variational autoencoder (CHVAE) for causally consiste...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22649v1
• GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT

  Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-text narratives and volumetric anatomy. Existing report-assisted and vision-language grounding methods typically rely on ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22619v1
• Enhancing Gaze Reasoning in Vision Foundation Models for Gaze Following

  Gaze following requires both scene understanding and gaze reasoning to localize the gaze target of an in-scene person. Recently, vision foundation models (VFMs) have demonstrated strong performance on this task, enabling simpler architectures while outperforming prior methods. However, we observe a ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22607v1
• Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure

  Frozen Vision Foundation Models (VFMs) with lightweight classification heads are increasingly used in medical imaging because they offer efficient and reproducible deployment. Yet noisy-label learning methods for this frozen-feature regime remain poorly understood, and most existing methods still re...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22591v1
• SceneAligner: 3D-Grounded Floorplan Localization in the Wild

  Many public buildings provide floorplans with a "you are here" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22581v1
• AlliHat

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  🧰 ToolsMay 21, 2026https://www.producthunt.com/products/allihat?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation

  Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphological variability, class imbalance, and overlapping appearances of tumour regions across imaging sequences. We propose SegGuidedNet, a three-dimension...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22572v1
• FashionLens: Toward Versatile Fashion Image Retrieval via Task-Adaptive Learning

  Fashion image retrieval is a cornerstone of modern e-commerce systems. A unified framework that supports diverse query formats and search intentions is highly desired in practice. However, existing approaches focus on narrow retrieval tasks and do not fully capture such diversity. Therefore, in this...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22552v1
• Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking

  Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects and challenging scenarios with distractors, occlusion, and nonlinear motion. Recent vision foundation models, exemplified by SAM 2, learn strong video...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22538v1
• LACO: Adaptive Latent Communication for Collaborative Driving

  Collaborative driving aims to improve safety and efficiency by enabling connected vehicles to coordinate under partial observability. Recent approaches have evolved from sharing visual features for perception to exchanging language-based reasoning through foundation models for behavioral coordinatio...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22504v1
• Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline

  Fine-grained semantic segmentation requires both precise localization and discrimination between visually similar classes. In FungiTastic, this problem is further complicated by a long-tailed distribution and strong variation in image acquisition conditions. We propose a training-free two-stage fram...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22492v1
• Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling

  Vision-Language Models (VLMs) excel at tasks like zero-shot classification and cross-modal retrieval by mapping images and text to a shared space, but this requires expensive end-to-end training with massive paired datasets. Current post-hoc alignment methods reduce computational costs by connecting...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22484v1
• Matching with Deliberation: Test-Time Evolutionary Hierarchical Multi-Agents for Zero-Shot Compositional Image Retrieval

  Zero-Shot Compositional Image Retrieval (ZS-CIR) requires both preserving the visual continuity of the reference image and faithfully executing the semantic variables specified in the modification text, which constitutes the core challenge of the task. Existing methods often suffer from Perception M...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22478v1
• MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation

  Evaluating single-concept personalization in text-to-image diffusion requires measuring both concept preservation, which captures identity fidelity to a reference, and prompt following, which captures whether the generated scene matches the prompt. Existing metrics commonly compute these signals usi...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22469v1
• Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light

  Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and the low number of samples in low density regions makes it hard to run evaluatio...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22455v1
• Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts

  While large vision-language-action (VLA) models and generative world models (WM) have advanced long-horizon embodied intelligence, their practical deployment remains challenged by uncertainty in learning-based action generation. Low-quality actions may cause physical failures during execution or lea...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22446v1
• Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration

  Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality....

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22814v1
• The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning

  Robustness, domain adaptation, photometric and occlusion invariance, compositional generalisation, temporal robustness, alignment safety, and classical anisotropic regularisation are usually treated as separate problems with separate method families. This paper argues that much of their shared struc...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22800v1
• LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systems

  Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing systems communicate through natural language, recent work shows that latent communication, particularly through transformer key-value (KV) caches, can...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22786v1
• Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization

  Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and control, it also increases vulnerability to cyber-physical disruptions. Operators must distinguish physical incidents, such as faults or line distu...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22749v1
• Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning

  Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the dominant single-agent paradigm for physical applications, where other actors are ignored or treated as environmental noise, pr...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22748v1
• Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier

  Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (EDL) provides single-pass uncertainty estimation by modeling the class probabilities via Dirichlet distributions, where the Dirichlet parameters are ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22746v1
• SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation

  Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation interference. Existing modular methods either rely on expensive post-hoc fusion or freeze adaptation subspaces, which limit...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22743v1
• Proxy-Based Approximation of Shapley and Banzhaf Interactions

  Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these higher-order interactions trade off between speed and accuracy. To overcome this limitation, we introduce ProxySHAP. ProxySHAP reconciles the high sam...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22738v1
• Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation

  Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed through their loss functions: maximum likelihood, policy gradients, forward KL, reverse KL, or related objective-level variants. We study a complementary ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22731v1
• Reading Task Failure Off the Activations: A Sparse-Feature Audit of GPT-2 Small on Indirect Object Identification

  We report a small, reproducible audit of which sparse-autoencoder (SAE) features of GPT-2 small fire differently on failed versus successful trials of the Indirect Object Identification (IOI) task. On 300 prompts, GPT-2 small reaches 79.7% accuracy; 146 of the 24,576 features in the layer-8 residual...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22719v1
• Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators

  Interactive streaming music generation promises the use of generative models for live performance and co-creation that is impossible with offline models. However, SOTA models exist in the discrete-AR regime, requiring industrial levels of compute for both training and inference. In this work, we inv...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22717v1
• Abstraction for Offline Goal-Conditioned Reinforcement Learning

  Markov Decision Processes (MDPs) often exhibit significant redundancy due to symmetries and shared structure across state-goal pairs in real-world Goal-Conditioned Reinforcement Learning (GCRL). While hierarchical policies have been motivated for horizon reduction via temporal abstraction in offline...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22711v1
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals

  Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a central paradigm for scaling LLM reasoning, yet its optimization often suffers from training instability and suboptimal convergence. Through a systematic dissection of clipping-based GRPO-style objectives, we identify the rigid c...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22703v1
• ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification

  Time Series Foundation Models (TSFMs) have become a new component of the state-of-the-art in general time series forecasting. However, adapting them to specialized classification tasks remains constrained by two interconnected challenges: the quadratic cost of standard attention mechanisms and the i...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22684v1
• Holographic functions and neural networks

  A fuzzy Boolean function is a map $f:\cube^n\to [0,1]$, where $n\in\mathbb N$. We introduce and compare three ways of saying that such a function has bounded complexity. The first is a sampling property: the value $f(x)$ can be recovered, up to small error and with high probability, from the values ...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22666v1
• A note on convergence of Wasserstein policy optimization

  Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stochastic policies in continuous action spaces. Despite its empirical success, the theoretical convergence properties of WPO in environments with conti...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22622v1
• Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery

  Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structure. We introduce Evolutionary Multi-Task Optimization (EMO) for LLM-guided progr...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22613v1
• Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions

  Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation--deployment gap arises not because of poorly designed benchmarks, but from implicit assumptions about how users interact with models that cannot be su...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22612v1
• Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs

  Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibiotic use, increasing antimicrobial resistance and potential long-term harms. Machine learning offers a promising approach for identifying patient-leve...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22611v1
• Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms

  The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security and convenience. This paper outlines a comprehensive framework designed to enhance cybersecurity, introduce auto-generated virtual cards, and mitigat...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22604v1
• Novi Notes 1.1

  A local AI memory layer for your Mac

  🧰 ToolsMay 21, 2026https://www.producthunt.com/products/novi-notes?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

  While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply assumed based on successes in domains like computer vision. We investigate standard deep ensembles specifically for messa...

  📄 ResearchMay 21, 2026http://arxiv.org/abs/2605.22593v1