Search: “framework”

New universal operator approximation theorem for encoder-decoder architectures

arXiv:2503.24092v2 Announce Type: replace-cross Abstract: Motivated by the rapidly growing field of mathematics for operator approximation with neural networks, we present a novel universal operator a

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PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

DGX agent

arXiv:2607.13428v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones.

safetyarxiv-cs-lg

16 Jul 2026

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

arXiv:2508.16403v3 Announce Type: replace Abstract: Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due t

Self-Improvements in Modern Agentic Systems: A Survey

arXiv:2607.13104v1 Announce Type: new Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, fro

Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving

arXiv:2607.13576v1 Announce Type: new Abstract: Interactive driving, wherein an intelligent lead vehicle equipped with real-time traffic data coordinates route choices of connected vehicles, offers a

safetyarxiv-cs-lg

Tabular Foundation Models for Discrete Choice Estimation

arXiv:2607.13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation. We ask whether TFM

researcharxiv-cs-ai

Local Ai

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

arXiv:2607.13880v1 Announce Type: new Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete infor

local-aiarxiv-cs-lg

The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce

arXiv:2607.13998v1 Announce Type: cross Abstract: The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive r

Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems

arXiv:2607.13048v1 Announce Type: cross Abstract: Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (LLMs) that provide rich semantic understanding at

VGIF-Score: Interpretable and Diagnostic Evaluation of Spatio-Temporal Instruction Following in Video Generation

arXiv:2607.13527v1 Announce Type: new Abstract: Recent video generation models (VGMs) have made substantial progress in visual fidelity, yet their ability to follow long, compositional instructions re

What Models Express, Suppress, and Resist: Auditing Open-Weight LLMs with Persona Vectors

arXiv:2607.13162v1 Announce Type: cross Abstract: What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or resists is not revealed by

A Shortcut to Statistically Steady-State Turbulence with Flow Matching

arXiv:2607.13022v1 Announce Type: cross Abstract: Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically s

Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques

arXiv:2607.12829v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models. However, parallel ge

researcharxiv-cs-ai

An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering

arXiv:2607.12048v1 Announce Type: cross Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without

applicationsarxiv-cs-ai

ANGLE: Angular Neural Generative Learning via Engression

arXiv:2607.12833v1 Announce Type: cross Abstract: Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regres

agentsarxiv-cs-lg

Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation

arXiv:2607.12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabili

Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses

arXiv:2607.11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone. For smar

Causal Supervision of Attention for Affective Behaviour Analysis

arXiv:2607.12091v1 Announce Type: new Abstract: Affective Behaviour Analysis aims to enable machines to infer human affective states from behavioural signals, particularly facial expressions, in real-

applicationsarxiv-cs-cv

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

model-releasesarxiv-cs-lg

arXiv:2607.12916v1 Announce Type: new Abstract: In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-cl

Local Ai

Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI

arXiv:2607.11962v1 Announce Type: new Abstract: Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high. We present COJE

local-aiarxiv-cs-cv

Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification

arXiv:2607.12987v1 Announce Type: new Abstract: Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated i

DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter

arXiv:2602.05513v3 Announce Type: replace-cross Abstract: Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks. To address the challenges of effe

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

arXiv:2603.25112v2 Announce Type: replace-cross Abstract: Standard evaluation of LLM confidence relies on calibration metrics (ECE, Brier score) that conflate how much a model knows (Type-1 accuracy)

Dynamic Resource Allocation for Ensemble Determinization MCTS

arXiv:2607.13007v1 Announce Type: new Abstract: Simulation-based algorithms are especially suited for high-uncertainty environments such as adversarial board games with significant elements of randomn

Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning

arXiv:2512.01113v2 Announce Type: replace-cross Abstract: Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning

Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation

arXiv:2607.12065v1 Announce Type: cross Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions. In fact, deploying aut

Energy-Based Physics-Informed Form Finding for Clustered Tensegrity Structures

arXiv:2607.12888v1 Announce Type: new Abstract: Tensegrity form-finding and physical property prediction are fundamental inverse problems in structural mechanics, which aim to determine equilibrium co

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

arXiv:2607.11963v1 Announce Type: cross Abstract: The early detection of Chronic Kidney Disease using machine learning has attracted significant interest in healthcare-related computer science. Despit

applicationsarxiv-cs-ai

Evidence-Grounded AI for Musculoskeletal Care

arXiv:2607.12527v1 Announce Type: new Abstract: Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation. Because recovery,

Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs

arXiv:2607.12650v1 Announce Type: cross Abstract: Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions

ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning

arXiv:2607.12931v1 Announce Type: new Abstract: Reinforcement Learning (RL) has demonstrated significant potential for improving Vision-Language-Action (VLA) models on complex manipulation tasks. Howe

safetyarxiv-cs-ro

FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

arXiv:2607.11464v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions.

applicationsarxiv-cs-ai

Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals

arXiv:2607.12233v1 Announce Type: cross Abstract: Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidenc

Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight

arXiv:2607.12275v1 Announce Type: new Abstract: Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisi

researcharxiv-cs-ro

Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning

model-releasesarxiv-cs-lg

arXiv:2607.12204v1 Announce Type: new Abstract: Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged

GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures

arXiv:2512.09925v2 Announce Type: replace Abstract: Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light trans

Tutorials

GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

arXiv:2607.11941v1 Announce Type: new Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and struc

tutorialsarxiv-cs-cv

GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions

arXiv:2607.12284v1 Announce Type: new Abstract: Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where cou

researcharxiv-cs-cv

Good Benchmarks

arXiv:2607.12217v1 Announce Type: new Abstract: Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced pr

researcharxiv-cs-ai

IdeaTrail: Full-Process Agent Trajectories for Scientific Ideation

arXiv:2607.10144v2 Announce Type: replace Abstract: Scientific ideation unfolds over multiple stages, including literature search, paper reading, tool use, claim checking, cross-paper synthesis, brain

Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs

arXiv:2607.12593v1 Announce Type: cross Abstract: The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the

agentsarxiv-cs-ro

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606.19387v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success in software development. However, they are susceptible to hallucinations, meanin

Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions

arXiv:2607.12406v1 Announce Type: new Abstract: The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model. Consequentl

Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification

arXiv:2607.12704v1 Announce Type: new Abstract: Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different fr

LapSurgie: Humanoid Robots Performing Surgery via Teleoperated Handheld Laparoscopy

arXiv:2510.03529v3 Announce Type: replace Abstract: Robotic laparoscopic surgery has gained increasing attention in recent years for its potential to deliver more efficient and precise minimally invas

applicationsarxiv-cs-ro

LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning

arXiv:2607.12858v1 Announce Type: new Abstract: Accurate open-world obstacle detection is critical for autonomous driving. Current anomaly segmentation methods suffer from a fundamental blind spot: th

agentsarxiv-cs-cv

Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit

arXiv:2604.04241v2 Announce Type: replace Abstract: Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predic

Learning the Graphical Nature of Symmetries

arXiv:2607.12026v1 Announce Type: cross Abstract: Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured,