Search: “models”

CoArena: Evaluating Computer-Use and Multi-Agent Systems in Real Time

arXiv:2609.14239v1 Announce Type: new Abstract: Static benchmarks for computer-use agents fix a task set at release and score every system against it once. That makes them reproducible, and it lets th

agentsarxiv-cs-lg

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Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

DGX agent

arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MO

researcharxiv-cs-ai

15 Sept 2026

Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

arXiv:2311.03154v3 Announce Type: replace Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients trai

Cross-Block Conditioning in Deep Boltzmann Machines for Statistical Data Fusion

arXiv:2609.14934v1 Announce Type: cross Abstract: Statistical data fusion combines two panels that share a block of covariates but observe disjoint outcome blocks, and in its traditional form no row o

DARE: Dialectical Agentic Reasoning for Structured Knowledge Fact Checking

arXiv:2609.13808v1 Announce Type: new Abstract: Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-g

agentsarxiv-cs-cl

Decoupling Error Attribution in Cloud-Native Graph-RAG: A Data Integrity Diagnostic Framework

arXiv:2609.13324v1 Announce Type: cross Abstract: Graph-RAG systems often assume pristine data quality, overlooking the severe impact of perturbations in cloud-native databases. This paper proposes a

Tutorials

Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound

arXiv:2609.15225v1 Announce Type: new Abstract: Objective: To develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA) without requiring

tutorialsarxiv-cs-cv

Applications

Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization

arXiv:2506.06977v4 Announce Type: replace-cross Abstract: Domain generalization has become a critical challenge in predictive healthcare, where different patient groups exhibit shifting data distribut

applicationsarxiv-cs-ai

Disparate Impact in Synthetic Data Generation

arXiv:2606.13105v2 Announce Type: replace Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is th

safetyarxiv-cs-lg

Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation

arXiv:2512.09944v4 Announce Type: replace Abstract: Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, y

Echo-E^3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation

arXiv:2503.17543v4 Announce Type: replace-cross Abstract: Left ventricular ejection fraction (LVEF) is a primary marker of cardiac function. However, routine estimation from endocardial measurements r

researcharxiv-cs-cv

Enhancing Event Candidate Acquisition for Event Linking

arXiv:2609.13670v1 Announce Type: new Abstract: Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods

Ensemble-Conditioned Molecular Design

arXiv:2609.15077v1 Announce Type: new Abstract: Molecular design is typically approached as a problem of finding molecules which can adopt a single bioactive conformation. In reality, molecules occupy

Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting

arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exhi

researcharxiv-cs-cv

Formal Properties of Language as Constraints on Neural Dynamics

arXiv:2609.14384v1 Announce Type: new Abstract: What must a neural system be capable of to implement language? Current research annotates stimuli with linguistic variables and tests which electrodes,

Forward-Facing Near-Infrared Adds Little to Colour for Farm-Machinery Traversability: A Site-Disjoint Evaluation of Sensor-Dependent Spatial Leakage

arXiv:2609.13265v1 Announce Type: cross Abstract: Near-infrared (NIR) imaging does not consistently outperform standard color cameras for daytime agricultural traversability once spatial data leakage

From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements

arXiv:2609.13535v1 Announce Type: new Abstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy

From Visual Feedback to Textual Reviews: A Multi-Agent Vision-Language Framework for Image-Grounded Review Assistance

arXiv:2609.14761v1 Announce Type: cross Abstract: Visual feedback in the form of user-uploaded images and videos is becoming increasingly common in e-commerce platforms because it provides authentic e

Local Ai

G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity

arXiv:2609.15018v1 Announce Type: new Abstract: We study relative position encoding for multi-view vision Transformers under camera heterogeneity, including varying fields of view (FoVs) or projection

local-aiarxiv-cs-cv

Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions

arXiv:2609.14917v1 Announce Type: cross Abstract: We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for

GradRepair-ODE: Certified Gradient Repair for Neural ODE Training

arXiv:2609.13204v1 Announce Type: cross Abstract: Neural ordinary differential equations use numerical solvers inside the training loop. The solver determines the forward trajectory and also affects t

Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision

arXiv:2609.13947v1 Announce Type: cross Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the log

Local Ai

HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud

arXiv:2609.14968v1 Announce Type: new Abstract: Online scheduling of dependency-aware tasks in heterogeneous cloud clusters is a fundamental yet challenging problem due to the complex interplay betwee

local-aiarxiv-cs-lg

Homeostatic Continual Learning

arXiv:2609.13771v1 Announce Type: new Abstract: In this paper, I formulate a Continual Learning problem and propose a method named 'Homeostatic Continual Learning' that enables an AI agent to learn co

Hardware

Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum

arXiv:2601.14172v4 Announce Type: replace-cross Abstract: We study neural multi-label classification under severe label imbalance through sentence-level detection of the 19 refined Schwartz human valu

hardwarearxiv-cs-ai

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

arXiv:2609.15938v1 Announce Type: new Abstract: Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems comb

agentsarxiv-cs-cl

IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies

arXiv:2609.15005v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies perform robot manipulation tasks using multimodal inputs such as visual observations, proprioceptive states, and

Kaininja: Extending Native 3D Generators to the Part Level

arXiv:2609.15659v1 Announce Type: cross Abstract: Native 3D generators turn one image into a single mesh. TRELLIS.2 and its peers deliver high-fidelity non-watertight geometry with materials, but the

L-Lipschitz Gershgorin ResNet Network

arXiv:2502.21279v2 Announce Type: replace-cross Abstract: Deep residual networks (ResNets) have demonstrated outstanding success in computer vision tasks, attributed to their ability to maintain gradi

Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings

arXiv:2609.13281v1 Announce Type: cross Abstract: Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broa

Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors

arXiv:2209.11097v2 Announce Type: replace Abstract: Agile flights of autonomous quadrotors in cluttered environments require constrained motion planning and control subject to translational and rotati

safetyarxiv-cs-ro

Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding

arXiv:2609.15307v1 Announce Type: new Abstract: Graphical User Interface (GUI) grounding is essential for autonomous agents to map natural language instructions to precise screen coordinates. However,

agentsarxiv-cs-cv

Learning Steerable Clarification Policies with Collaborative Self-play

arXiv:2512.04068v3 Announce Type: replace Abstract: To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user in

safetyarxiv-cs-lg

LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents

arXiv:2609.14138v1 Announce Type: cross Abstract: As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on prev

Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination

arXiv:2609.14767v1 Announce Type: cross Abstract: Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in p

MedVA: An End-to-End Neuro-Symbolic Agentic System for Medical Volume Visualization

arXiv:2609.14874v1 Announce Type: cross Abstract: Medical volume visualization requires selecting regions of interest (ROIs) and carefully controlling their relative visual emphasis according to a giv

agentsarxiv-cs-cv

Local Ai

Multi-Objective Hyperparameter Search via Damped Gauss--Newton Optimization

arXiv:2401.03580v2 Announce Type: replace-cross Abstract: We study hyperparameter optimization (HPO) from a numerical-optimization perspective and propose a multi-objective, damped Gauss--Newton searc

local-aiarxiv-cs-lg

Multi-Stage NeRF for Efficient 3D Coronary Artery Reconstruction from Two Narrow-Angle Angiographic Projections

arXiv:2609.15550v1 Announce Type: cross Abstract: X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D proj

researcharxiv-cs-cv

Tutorials

Multi4D: an end-to-end neural network for structural determination at complex material interfaces

arXiv:2609.14348v1 Announce Type: cross Abstract: Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations with

tutorialsarxiv-cs-cv

MuPlon: Multi-Path Causal Optimization for Claim Verification through Controlling Confounding

arXiv:2509.25715v2 Announce Type: replace-cross Abstract: As a critical task in data quality control, claim verification aims to curb the spread of misinformation by assessing the truthfulness of clai

Not All Prompts Are Equal: Exploration-Guided Prompt Scaffolding for Multimodal Reinforcement Post-Training

arXiv:2609.15051v1 Announce Type: cross Abstract: Training prompts in online reinforcement learning (RL) differ substantially in how informative they are for the current policy: some are already satur

One Feedback System Does Not Fit All: Localising Data-to-Text Driver Coaching for the United Kingdom and Nigeria

arXiv:2609.14687v1 Announce Type: new Abstract: Data-to-text driver coaching is often presented as a generic pipeline from telematics events to advice. This paper argues that its content requires loca

safetyarxiv-cs-cl

Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and ca

safetyarxiv-cs-lg

Online local learning for generative thermodynamic computing

arXiv:2609.15439v1 Announce Type: cross Abstract: Generative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at ea

Optimizing Sparse Outcomes Through Dense Behavioral Signals via Value-Guided Preference Distillation

arXiv:2609.14648v1 Announce Type: new Abstract: Aligning multi-turn dialogue agents is usually framed as matching turn-level human preferences, yet direct optimization of long-term outcomes is often i

safetyarxiv-cs-cl

Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection

arXiv:2609.13785v1 Announce Type: new Abstract: Oracle-style quantities, including virtual best solvers, selected-portfolio VBS, virtual-best encodings, and best-in-family summaries, are widely report

Personalizing Personal Health Interfaces: Co-Design with Generative AI

arXiv:2609.15046v1 Announce Type: cross Abstract: Personal health interfaces present wellbeing data through standardized dashboards that rarely fit how people interpret or act on it. Personalizing the

Applications

PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning

arXiv:2609.13789v1 Announce Type: new Abstract: In multi-channel paid user acquisition, early and accurate prediction of user retention at the channel level is crucial for optimizing budget allocation

applicationsarxiv-cs-lg