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arXiv:2606.29212v1 Announce Type: new Abstract: Agent-based evacuation simulations are widely used to study crowd behavior during emergencies, but many models rely on assumptions such as perfect event

agentsarxiv-cs-ai

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A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories

DGX agent

arXiv:2606.29068v1 Announce Type: cross Abstract: Text encoders are known for their utility in natural language processing, as they are able to efficiently compress inputs into dense vectors while pre

model-releasesarxiv-cs-ai

30 Jun 2026

A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

arXiv:2508.00472v2 Announce Type: replace Abstract: The tabular form constitutes the standard way of representing data in relational database systems and spreadsheets. But, similarly to other forms, t

A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

arXiv:2606.29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNN

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel

arXiv:2111.10722v4 Announce Type: replace-cross Abstract: We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution rho^* by minimizing the kernel discrepancy, al

A Diagnostic Framework and Multi-Evaluator Audit of Evaluator-Driven Preference Dynamics in Self-Adapting LLM Agents

model-releasesarxiv-cs-cl

arXiv:2606.29719v1 Announce Type: cross Abstract: Measurements of proprietary LLM evaluators can become invalid within weeks -- we document one case and provide the diagnostic framework to detect it.

Tutorials

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems

arXiv:2606.30230v1 Announce Type: cross Abstract: Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution dur

tutorialsarxiv-cs-lg

A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

arXiv:2606.30159v1 Announce Type: new Abstract: Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in

A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training

arXiv:2606.28526v1 Announce Type: new Abstract: The clinical and communication skills of medical students are commonly assessed through Objective Structured Clinical Examinations (OSCEs), which consis

researcharxiv-cs-cl

Applications

A Good Talk Does not Look Like a Summary, It Teaches You! Measuring Takeaways from Paper-to-Video Talks

arXiv:2606.28531v1 Announce Type: cross Abstract: Automatically generated videos from scientific papers are increasingly used for education and research dissemination. However, existing evaluation met

applicationsarxiv-cs-cl

A Gravitational Interpretation of Fine-Tuning Reversion

arXiv:2606.28525v1 Announce Type: cross Abstract: Fine-tuning on harmless data can partially undo behaviors acquired earlier in training. Safety can erode under benign post-alignment updates, unlearne

Local Ai

A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage

arXiv:2606.30586v1 Announce Type: cross Abstract: Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequent

local-aiarxiv-cs-lg

A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

arXiv:2606.29273v1 Announce Type: cross Abstract: Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song. As a result, lyric

A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest

arXiv:2606.29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher

Applications

A Knowledge Theory of Capital:The Value of Natural and Artificial Intelligence, Volume 1

arXiv:2606.18288v2 Announce Type: replace-cross Abstract: This volume develops a knowledge theory of capital for economies in which productive capacity increasingly resides in software, data, models,

applicationsarxiv-cs-ai

A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming

arXiv:2606.29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots

A Machine-Verified Proof of a Quantum-Optimization Conjecture

arXiv:2606.29687v1 Announce Type: cross Abstract: We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture

A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection

arXiv:2606.29516v1 Announce Type: new Abstract: A central challenge in statistical modeling is identifying the subset of features that belong in the true regression model. The classical best subset se

A Mechanistic Study of Transformers Training Dynamics

arXiv:2410.24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models. However, the scale of those models limits our understa

A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP

arXiv:2508.02923v3 Announce Type: replace Abstract: Maximum A Posteriori (MAP) estimation is a cornerstone framework for blind inverse problems, where an image and a forward operator are jointly estim

A multi-architecture study of specificity refinement and false-positive mechanism analysis in prostate MRI

model-releasesarxiv-cs-cv

arXiv:2606.29977v1 Announce Type: cross Abstract: Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level

A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories

arXiv:2606.30209v1 Announce Type: cross Abstract: We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels

A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis

arXiv:2606.29193v1 Announce Type: cross Abstract: LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observa

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

arXiv:2606.30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution

A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI

arXiv:2606.28684v1 Announce Type: cross Abstract: Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing

A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization

arXiv:2606.29112v1 Announce Type: new Abstract: Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks

Local Ai

A Path-Space Formulation of Prediction in World Models: From a Single Action to Prediction, Planning, and Irreversibility

arXiv:2606.28751v1 Announce Type: new Abstract: We propose a path-space formulation of prediction in AI world models. Rather than sequences of one-step conditional distributions, we argue that a world

local-aiarxiv-cs-lg

A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

model-releasesarxiv-cs-cv

arXiv:2606.28757v1 Announce Type: new Abstract: Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi

A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises

arXiv:2606.30309v1 Announce Type: new Abstract: Rehabilitation exercises are essential in restoring lost physical functions of patients suffering from various diseases (e.g., Parkinson's, back pain).

A Posteriori Error Analysis for Decoupled Neural Approximations of Fully Coupled FBSDEs with Control Mismatch

arXiv:2606.29474v1 Announce Type: cross Abstract: This paper develops an a posteriori error analysis framework for decoupled neural approximations of fully coupled forward--backward stochastic differe

A Probabilistic Approach to Trajectory-Based Optimal Experimental Design

model-releasesarxiv-cs-lg

arXiv:2601.11473v2 Announce Type: replace-cross Abstract: We present a novel probabilistic approach for optimal experimental path design. In this approach a discrete path optimization problem is defin

Agents

A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving

arXiv:2606.28384v1 Announce Type: cross Abstract: Digital twins (DTs) have become a potential technology to perform risk-free simulation of physical entities for deterministic and high-reliability ser

agentsarxiv-cs-ai

A Self-Supervised Learning Framework for Video Encoding Complexity Clustering

arXiv:2606.29166v1 Announce Type: cross Abstract: Adaptive video streaming is a widely used technique for delivering video content over the internet. One of the key challenges is determining the optim

A Stochastic--Geometric Theory of Scaling Laws in Grokking

arXiv:2606.30388v1 Announce Type: cross Abstract: Delayed generalization (ie~grokking) refers to the phenomenon in which a neural network fits its training data early in training but only begins to ge

A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation

arXiv:2606.28337v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocess

Agents

A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation

arXiv:2606.28896v1 Announce Type: cross Abstract: Synthetic aperture radar (SAR) data augmentation is important for improving the generalization of data-driven SAR interpretation models, yet practical

agentsarxiv-cs-ai

A Theoretical Interpretation of In-Context Learning via Probabilistic Modeling

arXiv:2606.28926v1 Announce Type: cross Abstract: In-context learning (ICL) is an emerging paradigm that employs the semantic information inherent in large language models (LLMs) for generating answer

Hardware

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

arXiv:2606.28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensio

hardwarearxiv-cs-lg

A Unified Framework for Multi-Contact Path Planning in the Rolling Robot Systems

arXiv:2606.29065v1 Announce Type: new Abstract: Rolling motion planning is challenging because rolling contact imposes nonholonomic constraints and the configuration evolves on a curved manifold. The

researcharxiv-cs-ro

A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy

arXiv:2606.28431v1 Announce Type: cross Abstract: Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysi

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

arXiv:2606.28943v1 Announce Type: new Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge. Existing methods often rely on rigid explore-then-ex

safetyarxiv-cs-cl

AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering

arXiv:2606.29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fi

AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generation

arXiv:2603.12575v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) are a dominant backbone for high-fidelity text-to-image generation due to strong scalability and alignment at high res

safetyarxiv-cs-cv

Accelerating Q-learning through Efficient Value-Sharing across Actions

arXiv:2606.29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning. Therefore learning action-values efficiently is central to reinforcement

Accelerating scientific discovery with Co-Scientist

arXiv:2502.18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augm

Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping

arXiv:2606.29715v1 Announce Type: new Abstract: This paper presents an automatic system for recognizing pulmonary diseases in chest X-rays using geometric normalization of the lung region. The method

safetyarxiv-cs-cv

ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

arXiv:2606.30072v1 Announce Type: new Abstract: Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return. Under the Centralized Training w

Active Quantum Kernel Acquisition for Gaussian Process Regression

arXiv:2606.28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled