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arXiv:2605.26509v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalabil

hardwarearxiv-cs-lg

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Applications

The Kalman Evolve: Closing the Gap in Kalman Filtering via Interpretable Algorithm Discovery

DGX agent

arXiv:2605.26830v1 Announce Type: cross Abstract: State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dyna

applicationsarxiv-cs-ai

27 May 2026

The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection

arXiv:2605.26872v1 Announce Type: cross Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current p

agentsarxiv-cs-ai

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

arXiv:2605.26759v1 Announce Type: new Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typica

applicationsarxiv-cs-lg

Towards Real-World Identification of Fatigued Muscle Groups via Musculoskeletal Simulation

arXiv:2605.26151v1 Announce Type: cross Abstract: Contactless diagnosis of musculoskeletal disorders can potentially improve population health as well as robot behaviours in collaborative settings. Ho

applicationsarxiv-cs-ro

Towards Shared Embodied Intelligence in Humanoid Robots through Optimization Development and Testing of the Human Aware ergoCub Robot

arXiv:2605.26991v1 Announce Type: new Abstract: Collaboration is central to human behavior, enabling tasks beyond individual capability. This ability arises from coordinating actions through internal

researcharxiv-cs-ro

VesselSim: learning 3D blood vessel segmentation without expert annotations

arXiv:2605.26277v1 Announce Type: cross Abstract: Blood vessel segmentation is a core task in medical image analysis for the care of vascular diseases and surgical planning, yet the challenges of prov

applicationsarxiv-cs-ai

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning

arXiv:2605.26350v1 Announce Type: cross Abstract: In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples. However, we

When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study

arXiv:2605.26929v1 Announce Type: new Abstract: Adversarial training (AT) remains one of the most reliable empirical defenses against adversarial attacks. Its robustness critically depends on how the

Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection

arXiv:2605.24906v2 Announce Type: replace Abstract: Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods

researcharxiv-cs-cv

YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting

arXiv:2605.27129v1 Announce Type: new Abstract: In greenhouse tomato production, automated harvesting requires accurate detection of ripe tomatoes, ripeness classification, and precise picking-point l

local-aiarxiv-cs-cv

Tutorials

A Context Augmented Multi-Play Multi-Armed Bandit Algorithm for Fast Channel Allocation in Opportunistic Spectrum Access

arXiv:2605.25391v1 Announce Type: new Abstract: We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario

tutorialsarxiv-cs-lg

A Sober Look at Agentic Misalignment in Automated Workflows

arXiv:2605.24197v1 Announce Type: new Abstract: We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Alt

Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators

arXiv:2605.25499v1 Announce Type: new Abstract: Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To s

Accelerating Bayesian inverse design in computational fluid dynamics using neural operators

arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain

Agent-Facing Information Design in LLM Tool Registries

arXiv:2605.23916v1 Announce Type: cross Abstract: LLM tool registries function as unregulated advertising platforms: providers write free-text descriptions that agents use for selection, yet no measur

Agent Learning via Early Experience

arXiv:2510.08558v3 Announce Type: replace Abstract: A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tas

Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems

arXiv:2602.03695v2 Announce Type: replace-cross Abstract: While existing multi-agent systems (MAS) can handle complex problems by enabling collaboration among multiple agents, they are often highly ta

agentsarxiv-cs-ai

Batch Normalization Amplifies Memorization and Privacy Risks

arXiv:2605.24420v1 Announce Type: cross Abstract: Batch Normalization (BN) is widely adopted to enable faster convergence and more stable training of deep neural networks. However, its impact on priva

Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion

arXiv:2605.24631v1 Announce Type: cross Abstract: Minority sampling aims to generate low-density instances on a data manifold and is of central importance in applications such as medical diagnosis, an

applicationsarxiv-cs-ai

Tutorials

BoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization

arXiv:2605.23937v1 Announce Type: new Abstract: Knowledge base (KB) embeddings aim at combining the capability of classical knowledge graph embeddings to generalize the information present in facts, t

tutorialsarxiv-cs-ai

CLiViS: Unleashing Cognitive Map through Linguistic-Visual Synergy for Embodied Visual Reasoning

arXiv:2506.17629v2 Announce Type: replace-cross Abstract: Embodied Visual Reasoning (EVR) seeks to follow complex, free-form instructions based on egocentric video, enabling semantic understanding and

Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies

arXiv:2605.25235v1 Announce Type: cross Abstract: We give an attribution method for neural combinatorial-optimisation (CO) policies that (i) decomposes a decision by constraint families via LP-relaxat

Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning

arXiv:2605.25977v1 Announce Type: cross Abstract: This paper provides an empirical implementation of the creative quality metric proposed in Calibrated Surprise (Zou & Xu, 2026a). The question this pa

CRPO: Character-centric Group Relative Policy Optimization for Role-aware Reasoning in Role-playing Agents

arXiv:2605.25511v1 Announce Type: new Abstract: Recent advancements in Reinforcement Learning (RL), particularly Group Relative Policy Optimization (GRPO), have significantly enhanced the reasoning ca

safetyarxiv-cs-cl

D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation

arXiv:2605.25022v1 Announce Type: cross Abstract: Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing stud

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

arXiv:2602.18640v2 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product r

agentsarxiv-cs-ai

DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment

arXiv:2410.01648v2 Announce Type: replace Abstract: The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sha

applicationsarxiv-cs-cl

Delayed Assignments in Online Non-Centroid Clustering with Stochastic Arrivals

arXiv:2601.16091v2 Announce Type: replace-cross Abstract: Clustering is a fundamental problem, aiming to partition a set of elements, like agents or data points, into clusters such that elements in th

Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks

arXiv:2605.25452v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various f

DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation

arXiv:2605.24841v1 Announce Type: new Abstract: Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We

Dynamic Dual-Granularity Skill Bank for Agentic RL

arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often l

Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations

arXiv:2605.25305v1 Announce Type: new Abstract: Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the dem

agentsarxiv-cs-lg

Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion

arXiv:2605.24495v1 Announce Type: new Abstract: This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby

local-aiarxiv-cs-ro

Emergent Analogical Reasoning in Transformers

arXiv:2602.01992v4 Announce Type: replace Abstract: Analogy is a central faculty of human intelligence, enabling abstract patterns discovered in one domain to be applied to another. Despite its centra

Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study

arXiv:2605.24913v1 Announce Type: cross Abstract: Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. Howe

local-aiarxiv-cs-ai

Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme

arXiv:2601.03327v3 Announce Type: replace-cross Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging.

Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette

arXiv:2605.24062v1 Announce Type: cross Abstract: Human-body communication (HBC) is a promising physical substrate for wearable body-area networks because it can localize communication around the body

local-aiarxiv-cs-ai

Fine-Tuning Over Architectural Complexity: Broad-Coverage PII Detection on PIIBench with DeBERTa

arXiv:2605.25816v1 Announce Type: cross Abstract: Personally identifiable information (PII) detection systems are frequently trained within narrow source or domain boundaries, limiting coverage when d

First, do no harm: Breaking suicidogenic echo chambers in media recommendation

arXiv:2605.25258v1 Announce Type: cross Abstract: Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of

From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas

arXiv:2602.00491v2 Announce Type: replace Abstract: Public health reasoning requires population level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it r

safetyarxiv-cs-cl

Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications

arXiv:2605.23936v1 Announce Type: new Abstract: This book presents a comprehensive and systematic survey of graph theory under uncertainty, with particular emphasis on the unifying role of the uncerta

Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion

arXiv:2605.24064v1 Announce Type: cross Abstract: Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current met

local-aiarxiv-cs-ai

Hierarchical Local-Global Transformer for Temporal Sentence Grounding

arXiv:2208.14882v2 Announce Type: replace-cross Abstract: This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment

local-aiarxiv-cs-cl

HumanFlow -- Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control

arXiv:2605.25685v1 Announce Type: new Abstract: Robust and accurate perception of humans in their 3D scene context is essential for integrating robots into everyday environments. Existing approaches,

safetyarxiv-cs-ro

Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization

arXiv:2605.24502v1 Announce Type: cross Abstract: We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization pr

Improving Labeling Consistency with Detailed Constitutional Definitions and AI-Driven Evaluation

arXiv:2605.24247v1 Announce Type: cross Abstract: Many automated labeling pipelines classify inputs into categories defined by a written specification, content moderation being a prominent use case. S

Hardware

Inference Time Context Sparsity: Illusion or Opportunity?

arXiv:2605.24168v1 Announce Type: new Abstract: Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer co

hardwarearxiv-cs-ai