Search: “framework”

Partial Effective Information Decomposition for Synergistic Causality

arXiv:2605.03267v1 Announce Type: cross Abstract: Causality is a central topic in scientific inquiry, yet for complex systems, the identification and analysis of synergistic causation remain a challen

6 May 2026

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Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation

DGX agent

arXiv:2605.02192v1 Announce Type: new Abstract: Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation,

agentsarxiv-cs-ro

5 May 2026

Efficient Reasoning with Hidden Thinking

model-releasesarxiv-cs-cl

arXiv:2501.19201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Mod

How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse

arXiv:2605.01897v1 Announce Type: new Abstract: This work investigates the phenomenon of Neural Collapse (NC) in multi-label classification, extending its conceptual framework from multi-class learnin

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk

arXiv:2511.19175v2 Announce Type: replace-cross Abstract: A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive te

MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding

model-releasesarxiv-cs-cl

arXiv:2510.08804v3 Announce Type: replace Abstract: We present MOSAIC, a multi-agent Large Language Model (LLM) framework for solving challenging scientific coding tasks. Unlike general-purpose coding

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

model-releasesarxiv-cs-lg

arXiv:2605.02524v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for integrating data-driven learning with physical knowledge. In

SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging

arXiv:2605.00887v1 Announce Type: new Abstract: We propose SparseContrast, a new framework that merges dynamic sparse attention with contrastive learning for medical imaging, with a focus on chest X-r

researcharxiv-cs-cv

Disentangled Control of Multi-Agent Systems

arXiv:2511.05900v3 Announce Type: replace-cross Abstract: This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarante

agentsarxiv-cs-ro

4 May 2026

Local Ai

E^2DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation

arXiv:2605.00159v1 Announce Type: new Abstract: In reinforcement learning (RL) for robotic manipulation, the Decision Transformer (DT) has emerged as an effective framework for addressing long-horizon

local-aiarxiv-cs-ro

4 May 2026

Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making

arXiv:2604.27753v1 Announce Type: new Abstract: This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed by agentic AI to ens

agentsarxiv-cs-ai

1 May 2026

Prediction-powered Inference by Mixture of Experts

arXiv:2604.27892v1 Announce Type: cross Abstract: The rapidly expanding artificial intelligence (AI) industry has produced diverse yet powerful prediction tools, each with its own network architecture

1 May 2026

CurEvo: Curriculum-Guided Self-Evolution for Video Understanding

model-releasesarxiv-cs-cv

arXiv:2604.26707v1 Announce Type: new Abstract: Recent advances in self-evolution video understanding frameworks have demonstrated the potential of autonomous learning without human annotations. Howev

Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training

arXiv:2604.26833v1 Announce Type: cross Abstract: This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery

arXiv:2502.14912v2 Announce Type: replace Abstract: We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framewor

researcharxiv-cs-cl

Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning

arXiv:2604.26873v1 Announce Type: new Abstract: We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty

applicationsarxiv-cs-cv

Local Ai

AgenticCache: Cache-Driven Asynchronous Planning for Embodied AI Agents

arXiv:2604.24039v1 Announce Type: cross Abstract: Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this pape

local-aiarxiv-cs-ai

ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering

arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import

researcharxiv-cs-cl

Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery

arXiv:2604.23940v1 Announce Type: cross Abstract: Decompilation -- recovering source code from compiled binaries -- is essential for security analysis, malware reverse engineering, and legacy software

agentsarxiv-cs-ai

Designing escalation criteria for international AI incident response: criteria, triggers, and thresholds

arXiv:2604.23183v1 Announce Type: cross Abstract: AI incident reporting requirements are emerging in regulation and policy, yet no operational criteria exist for determining when a detected AI inciden

Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding

arXiv:2604.24692v1 Announce Type: new Abstract: We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without gre

Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph

arXiv:2604.23494v1 Announce Type: new Abstract: Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor

GS-DOT: Gaussian splatting-based image reconstruction for diffuse optical tomography

arXiv:2604.23675v1 Announce Type: cross Abstract: This work presents GS-DOT, a novel image reconstruction framework based on Gaussian Splatting (GS) for diffuse optical tomography (DOT). Inspired by G

researcharxiv-cs-cv

GWT: Scalable Optimizer State Compression for Large Language Model Training

arXiv:2501.07237v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing benchmarks. However, the es

researcharxiv-cs-ai

Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms

arXiv:2604.22797v1 Announce Type: cross Abstract: Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework

safetyarxiv-cs-lg

Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

arXiv:2511.21213v2 Announce Type: replace-cross Abstract: Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, ZT. To accelerate the dis

applicationsarxiv-cs-lg

Learning from Demonstration with Failure Awareness for Safe Robot Navigation

arXiv:2604.23360v1 Announce Type: new Abstract: Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of s

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support

arXiv:2604.23902v1 Announce Type: new Abstract: Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to ad

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples

arXiv:2511.10047v2 Announce Type: replace Abstract: Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this p

researcharxiv-cs-cv

No Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows

model-releasesarxiv-cs-ai

arXiv:2604.23106v1 Announce Type: cross Abstract: Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Ou

Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation

arXiv:2604.23648v1 Announce Type: new Abstract: Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered

Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots

arXiv:2604.24518v1 Announce Type: cross Abstract: This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile rob

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks

arXiv:2505.07765v3 Announce Type: replace-cross Abstract: We propose a novel framework for solving nonlinear PDEs using sparse radial basis function (RBF) networks. Sparsity-promoting regularization i

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions

arXiv:2604.23528v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training o

Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners

arXiv:2604.22542v1 Announce Type: cross Abstract: Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. T

27 Apr 2026

FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

arXiv:2604.22586v1 Announce Type: new Abstract: We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have

safetyarxiv-cs-cv

27 Apr 2026

V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach

arXiv:2604.22196v1 Announce Type: new Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and t

27 Apr 2026

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming

model-releasesarxiv-cs-lg

arXiv:2505.05261v3 Announce Type: replace-cross Abstract: Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability remains a cha

Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications

arXiv:2604.21793v1 Announce Type: new Abstract: In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge

VFM^{4}SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection

arXiv:2604.21502v1 Announce Type: new Abstract: In real-world scenarios, continual changes in weather, illumination, and imaging conditions cause significant domain shifts, leading detectors trained o

safetyarxiv-cs-cv

Vista4D: Video Reshooting with 4D Point Clouds

arXiv:2604.21915v1 Announce Type: new Abstract: We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically,

applicationsarxiv-cs-cv

A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes

arXiv:2604.20799v1 Announce Type: new Abstract: This paper presents a framework for mapping unknown scalar fields using a sensor-equipped autonomous robot operating in unsafe environments. The unsafe

Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation

arXiv:2601.03396v2 Announce Type: replace Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underex

safetyarxiv-cs-cl

Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems

arXiv:2604.19980v1 Announce Type: new Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed appro

Federated Learning over Blockchain-Enabled Cloud Infrastructure

arXiv:2604.20062v1 Announce Type: new Abstract: The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning

applicationsarxiv-cs-lg

From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

arXiv:2604.19775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments.

agentsarxiv-cs-ai

What Makes a Good AI Review? Concern-Level Diagnostics for AI Peer Review

arXiv:2604.19998v1 Announce Type: new Abstract: Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system

BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps