Search: “framework”

Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

arXiv:2607.22667v1 Announce Type: new Abstract: This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime se

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Applications

Semantic Semi-Incremental Data-Association-Free Object SLAM

DGX agent

arXiv:2607.23384v1 Announce Type: cross Abstract: Data association between landmark measurements and landmark variables has long been a central challenge in SLAM, as estimation accuracy depends critic

applicationsarxiv-cs-ai

28 Jul 2026

Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning

arXiv:2607.22574v1 Announce Type: new Abstract: Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed search

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex

arXiv:2607.23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al. (2025), which uses decision-theoretic regret as a direct loss function for training mod

xMIx: High-Performance Serving-Time Platform for Mechanistic Interpretability Apps

arXiv:2607.22595v1 Announce Type: new Abstract: Mechanistic interpretability (MI) has emerged as a powerful approach for analyzing and intervening in inference computations, with a growing number of a

Action-Conditioned World Model for Goal Plane Probe Guidance in Robotic Ultrasound

arXiv:2607.21918v1 Announce Type: new Abstract: We present an action-conditioned world model framework for goal plane probe guidance in robotic ultrasound, with a focus on neck ultrasound scanning. Au

agentsarxiv-cs-ro

27 Jul 2026

Approximate Quantum State Preparation Through Proximal Policy Optimization

arXiv:2607.21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since th

safetyarxiv-cs-lg

Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants

model-releasesarxiv-cs-ai

arXiv:2607.20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time. When an individual agent becomes overloaded at runtime, for example by mixing

Hardware-Software Co-Design for Float16 On-Device Training on RISC-V Single-Core

arXiv:2607.21130v1 Announce Type: cross Abstract: By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framework to enable

local-aiarxiv-cs-ai

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries

arXiv:2607.20890v1 Announce Type: new Abstract: On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT

local-aiarxiv-cs-lg

Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery

arXiv:2607.20857v1 Announce Type: cross Abstract: Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse p

Non-Stationary Functional Bilevel Optimization

model-releasesarxiv-cs-lg

arXiv:2601.15363v2 Announce Type: replace-cross Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limi

Structure-Preserving Physics-Informed Neural Network for the Korteweg--de Vries (KdV) Equation

arXiv:2511.00418v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) offer a flexible framework for solving nonlinear partial differential equations (PDEs), yet conventional im

Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

arXiv:2510.14854v4 Announce Type: cross Abstract: Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellent penetrati

Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana

arXiv:2607.21559v1 Announce Type: new Abstract: A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patter

Hardware

Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography

arXiv:2607.19210v1 Announce Type: new Abstract: Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most chal

hardwarearxiv-cs-cv

CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation

arXiv:2607.16577v2 Announce Type: replace Abstract: This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume opt

local-aiarxiv-cs-cv

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

arXiv:2602.16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation

local-aiarxiv-cs-lg

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

model-releasesarxiv-cs-cv

arXiv:2607.18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnorm

Guardrails as Scapegoats: Auditing Unfaithful Safety Refusals in Tool-Augmented LLM Agents

arXiv:2607.19449v1 Announce Type: cross Abstract: Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or explicit tool crashes, leaving silent infrastructure

Reducing Learner Redundancy in Boosting via Residual Orthogonalization

model-releasesarxiv-cs-lg

arXiv:2606.17567v2 Announce Type: replace Abstract: While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting c

Tutorials

SCPP: A Unified Python Library for Soft Clustering

arXiv:2607.19620v1 Announce Type: cross Abstract: In this paper, we present SCPP (Soft Clustering Python Package), an open-source Python framework for soft clustering. SCPP establishes a canonical, sc

tutorialsarxiv-cs-ai

SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets

arXiv:2607.19943v1 Announce Type: new Abstract: Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low out-

researcharxiv-cs-cv

StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

model-releasesarxiv-cs-cv

arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research

Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations

arXiv:2607.19503v1 Announce Type: cross Abstract: A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embe

Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion

arXiv:2607.18365v1 Announce Type: cross Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain. Traditionall

safetyarxiv-cs-lg

Applications

AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling

arXiv:2607.13877v1 Announce Type: new Abstract: Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy,

applicationsarxiv-cs-lg

ClusIR: Towards Cluster-Guided All-in-One Image Restoration

arXiv:2512.10948v2 Announce Type: replace Abstract: All-in-One Image Restoration (AiOIR) aims to recover high-quality images from diverse degradations within a unified framework. However, existing met

researcharxiv-cs-cv

Introducing Human-Centeredness in AI-Assisted Lexicography

arXiv:2607.11808v2 Announce Type: replace-cross Abstract: This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography. While generative AI offers signifi

Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

arXiv:2607.13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati

Applications

NodeImport: Imbalanced Node Classification with Node Importance Assessment

arXiv:2607.13837v1 Announce Type: cross Abstract: In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resu

applicationsarxiv-cs-ai

Parsimonious disturbance-aware minimum-time planning with parametric uncertainty

model-releasesarxiv-cs-ro

arXiv:2607.13312v1 Announce Type: new Abstract: This study presents and validates a minimum-lap-time planning (MLTP) framework for motorsport applications that embeds robustness against both state dis

Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery

arXiv:2507.16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery,

Privacy Preserving Recommender Systems Balancing Personalization with Privacy

arXiv:2607.13328v1 Announce Type: cross Abstract: Personalized recommendation systems are central to modern e-commerce and retail platforms, but they typically rely on centralized storage of detailed

Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields

arXiv:2607.13626v1 Announce Type: cross Abstract: This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersec

researcharxiv-cs-ro

What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry

arXiv:2607.13046v1 Announce Type: new Abstract: We develop a framework for the information discarded by machine learning models whose inputs carry a Lie group action. Given a representation pi of a Li

BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification

arXiv:2607.11943v1 Announce Type: cross Abstract: Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for

CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform

arXiv:2607.12086v1 Announce Type: new Abstract: Recent LLM-based multi-agent urban simulators can generate semantically rich city routines, but they remain costly to scale and are often weakly validat

safetyarxiv-cs-cl

Do You Remember? Toward Memory-Centric Multimodal AI

arXiv:2607.11919v1 Announce Type: cross Abstract: Human memory is reconstructive, not a faithful recording. Current multimodal LLMs (MLLMs) lack this capability: they process images through a frozen v

HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning

model-releasesarxiv-cs-ai

arXiv:2607.11998v1 Announce Type: cross Abstract: High deployment cost, poor spatial coverage and susceptibility to storm conditions are all challenges faced by traditional in-situ methods. This paper

Human-AI Agent Interaction as a Neuroplastic Training Environment

arXiv:2607.12823v1 Announce Type: new Abstract: Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a

agentsarxiv-cs-ai

Infra-Swarm: Robust Vision-Based Multi-Robot Swarming via Near-Infrared Spectral Vision

arXiv:2607.12489v1 Announce Type: new Abstract: Distributed swarms typically rely on either active wireless communication or passive vision, and they are frequently hindered by bandwidth constraints o

agentsarxiv-cs-ro

Tutorials

Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks

arXiv:2607.11875v2 Announce Type: replace-cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous wo

tutorialsarxiv-cs-ai

Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking

model-releasesarxiv-cs-cl

arXiv:2607.11933v1 Announce Type: new Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-ti

Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents

arXiv:2306.02704v2 Announce Type: replace-cross Abstract: We introduce Calibrated Stackelberg Games (CSGs), a generalization of the standard Stackelberg Games (SGs) framework. In CSGs, a principal rep

agentsarxiv-cs-lg

Tutorials

Enhancing In-context Panoramic Generation via Geometric-aware Pretraining

arXiv:2607.08765v1 Announce Type: new Abstract: In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream t

tutorialsarxiv-cs-cv

fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code

arXiv:2607.07952v1 Announce Type: cross Abstract: Motion and emotion are core parts of intelligent, expressive behavior. In this paper, we introduce fog, a function composition framework for implement

researcharxiv-cs-cl

Psychological Competence as a Missing Dimension in AI Evaluation

model-releasesarxiv-cs-ai

arXiv:2607.08285v1 Announce Type: new Abstract: Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance. The