Search: “framework”

The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality

arXiv:2607.08495v1 Announce Type: cross Abstract: Sharp et al. (2025) introduce 'agentic inequality' as a framework for analyzing disparities in access to AI agents across three dimensions: availabili

10 Jul 2026

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UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks

DGX agent

arXiv:2607.08768v1 Announce Type: new Abstract: The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operati

model-releasesarxiv-cs-cl

10 Jul 2026

Approximate full conformal prediction in an RKHS

arXiv:2601.13102v3 Announce Type: replace-cross Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estima

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning

arXiv:2512.02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions. Extracting

applicationsarxiv-cs-ai

From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks

GeoGS-SLAM: Geometry-Only Gaussian Splatting for Dense Monocular SLAM

arXiv:2607.07452v1 Announce Type: new Abstract: Dense visual SLAM is a fundamental problem in robotics. Recent advances in 3DGS have demonstrated its potential for dense SLAM. Existing 3DGS frameworks

applicationsarxiv-cs-ro

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies

arXiv:2607.07029v1 Announce Type: cross Abstract: Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated t

Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models

arXiv:2507.14159v2 Announce Type: replace-cross Abstract: Predicting critical phenomena from limited labeled data remains a challenging task in statistical physics. As percolation theory provides a ca

Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch

arXiv:2607.05830v1 Announce Type: cross Abstract: The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for rob

From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations

arXiv:2607.06080v1 Announce Type: cross Abstract: Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity. However, traditional empirical methods face

safetyarxiv-cs-ai

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

arXiv:2607.06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidenc

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

model-releasesarxiv-cs-cv

arXiv:2112.02353v3 Announce Type: replace Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories. For example, a bird can be categorized according to

Learning The Minimum Action Distance

arXiv:2506.09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories,

Local Ai

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

arXiv:2409.06067v3 Announce Type: replace Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of t

local-aiarxiv-cs-ai

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control

arXiv:2607.06535v1 Announce Type: new Abstract: A learning-enabled disturbance-rejection framework based on a Neural Extended State Observer (Neural-ESO) is presented in this letter. Unlike existing l

researcharxiv-cs-ro

Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability

model-releasesarxiv-cs-cl

arXiv:2607.06196v1 Announce Type: new Abstract: Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional law

Prior-First, Condition-Second: Scalable and Controllable Hand Motion Completion

arXiv:2607.05938v1 Announce Type: cross Abstract: Synthesizing hand motion that matches the full body motion and the semantic labels is a difficult task due to their high degrees of freedom and the la

applicationsarxiv-cs-ro

SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models

arXiv:2512.18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45% of security-relevant scenarios~ite{veracode2025}, yet no public training dataset teaches b

WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation

model-releasesarxiv-cs-cv

arXiv:2607.06118v1 Announce Type: new Abstract: As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their nav

Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images

arXiv:2607.03073v1 Announce Type: cross Abstract: Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasi

Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer

arXiv:2607.05306v1 Announce Type: new Abstract: Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and rep

FM-ChangeNet: Learning Change through Pathwise Feature Transport

arXiv:2607.04750v1 Announce Type: new Abstract: We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature

safetyarxiv-cs-ai

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

arXiv:2607.03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning. Unlike co

Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions

model-releasesarxiv-cs-ai

arXiv:2607.03935v1 Announce Type: new Abstract: Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE

Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics

arXiv:2411.00214v2 Announce Type: replace-cross Abstract: Otto's Wasserstein gradient flow of the inclusive (forward) Kullback--Leibler (KL) divergence offers a principled framework for analyzing stat

Hardware

Lights, Camera, Carbon: Architectural Scaling Laws for Video Generation Energy Consumption

arXiv:2607.04553v1 Announce Type: cross Abstract: We present a bidirectional framework for estimating the energy consumption of text-to-video (T2V) and text-to-video-audio (T2VA) models from architect

hardwarearxiv-cs-ai

LiNO: Lifting based multiresolution neural operator

arXiv:2607.02715v1 Announce Type: new Abstract: Recently, neural operators have shown promising outcomes for learning solution operators of differential equations directly from data. This framework le

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection

arXiv:2607.03350v1 Announce Type: cross Abstract: Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories su

applicationsarxiv-cs-ai

Machine Unlearning via Information Theoretic Regularization

arXiv:2502.05684v5 Announce Type: replace-cross Abstract: How can we effectively remove or ``unlearn'' undesirable information, such as specific features or the influence of individual data points, fr

MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams

arXiv:2601.23038v3 Announce Type: replace Abstract: Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited

researcharxiv-cs-ro

On a Geometry of Interbrain Networks

arXiv:2509.10650v4 Announce Type: replace-cross Abstract: Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in so

OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-r

PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference

model-releasesarxiv-cs-ai

arXiv:2607.05134v1 Announce Type: cross Abstract: We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The w

Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

model-releasesarxiv-cs-cv

arXiv:2505.09484v2 Announce Type: replace Abstract: Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillanc

QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification

arXiv:2511.05730v2 Announce Type: replace Abstract: In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-p

Semi-parametric Functional Classification via Path Signatures Logistic Regression with Adaptive Order Selection

arXiv:2507.06637v2 Announce Type: replace-cross Abstract: We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar c

applicationsarxiv-cs-lg

Where do LLMs Fall Short in CBT-Guided Affective Reasoning?

arXiv:2607.02885v1 Announce Type: cross Abstract: Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user's mental state by examining the interaction between cognit

HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum

arXiv:2607.02332v1 Announce Type: new Abstract: General motion tracking and teleoperation offer a promising path to scalable humanoid skill acquisition, yet most existing frameworks are validated on c

applicationsarxiv-cs-ro

3 Jul 2026

MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding

arXiv:2607.01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in

safetyarxiv-cs-ai

3 Jul 2026

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

arXiv:2607.02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI syste

3 Jul 2026

AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing

model-releasesarxiv-cs-ro

arXiv:2607.00141v1 Announce Type: new Abstract: This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentia

Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios

arXiv:2607.00874v1 Announce Type: new Abstract: This paper introduces a probabilistic framework and hybrid validation methodology for V2X-enabled Collective Perception (CP) in complex traffic scenario

agentsarxiv-cs-ro

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

arXiv:2607.00889v1 Announce Type: cross Abstract: We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often

Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication

arXiv:2607.00609v1 Announce Type: new Abstract: Reconstruction-based generative models offer a natural framework for unsupervised out-of-distribution (OOD) detection, but multi-class normality modelli

researcharxiv-cs-cv

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing

arXiv:2602.02157v2 Announce Type: replace Abstract: Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the

Local Ai

EFlow: Learning Evidence Flow for Long-Video Reasoning with Adaptive Reflection

arXiv:2607.00867v1 Announce Type: new Abstract: Long-video reasoning is fundamentally constrained by how models acquire and utilize visual evidence. Existing tool-augmented video frameworks often inte

local-aiarxiv-cs-cv

EPO: Boosting 3D Foundation Models with Edge-based Pose Optimization

arXiv:2607.00579v1 Announce Type: new Abstract: We introduce extbf{Edge-based Pose Optimization (EPO)}, a trackless geometric optimization framework specifically designed to boost the Structure-from-M

safetyarxiv-cs-cv

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

arXiv:2603.10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a 'distribution c

safetyarxiv-cs-lg