Search: “framework”

A Principled Approach to Unsupervised Anomaly Detection

arXiv:2609.21800v1 Announce Type: new Abstract: Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underly

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Analysing the Linearity of Linguistic Relations in Language Model Embedding Spaces

DGX agent

arXiv:2609.21655v1 Announce Type: new Abstract: We propose a framework to analyse how strongly different linguistic relations are linearly encoded in language model embedding spaces. We formalise line

researcharxiv-cs-cl

21 Sept 2026

Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection

arXiv:2609.21334v1 Announce Type: cross Abstract: Effective evaluation of zero-day jamming detectors requires robust adversarial models. However, existing attack models often assume prior knowledge of

LLMs as Feature Engineers for Text-and-Tabular Prediction

arXiv:2609.21894v1 Announce Type: new Abstract: We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular

Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support

arXiv:2609.21866v1 Announce Type: new Abstract: Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However

tutorialsarxiv-cs-cv

ProTracer: Proprioception-Guided Failure Diagnosis in Robot Manipulation

arXiv:2609.21369v1 Announce Type: cross Abstract: This paper presents a comprehensive framework for robot manipulation failure analysis that includes binary failure detection, failure categorization,

Uncertainty-driven training for three-dimensional calibrated lung nodule classification

arXiv:2609.20905v1 Announce Type: cross Abstract: In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where val

Ambient Dataloops: Generative Models for Dataset Refinement

arXiv:2601.15417v2 Announce Type: replace-cross Abstract: We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying d

tutorialsarxiv-cs-ai

Efficiently Distributed Federated Learning

arXiv:2609.19972v1 Announce Type: cross Abstract: Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federati

Applications

PART: Learning 3D Part Assembly and Retrieval with Transformers

arXiv:2609.19872v1 Announce Type: new Abstract: 3D assembly is fundamental to modern manufacturing and digital content creation. In this paper, we present PART, a unified transformer-based framework f

applicationsarxiv-cs-cv

Scientific Image Quality Assessment via Multi-modal Retrieval-Augmented Generation

arXiv:2609.19634v1 Announce Type: cross Abstract: This paper proposes a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, designed to simultaneously address both

researcharxiv-cs-cl

Safety

StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation

arXiv:2609.20791v1 Announce Type: new Abstract: Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminat

safetyarxiv-cs-ro

TorchCraft: Unified binder design by inverting an all-atom structure predictor

arXiv:2609.19770v1 Announce Type: new Abstract: All-atom structure predictors model diverse molecular interactions, but using their learned structural priors for binder design remains challenging. Her

2Fast-2Lamaa: Large-Scale Lidar-Inertial Localization and Mapping with Continuous Distance Fields

model-releasesarxiv-cs-ro

arXiv:2410.05433v3 Announce Type: replace Abstract: This paper introduces 2Fast-2Lamaa, a lidar-inertial state estimation framework for odometry, mapping, and localization. Its first key component is

Agents

A Study of the Reliability of Agentic AI-Generated Programs

arXiv:2609.18298v1 Announce Type: cross Abstract: Agentic-AI based software development offers the promise of faster completion of the software, greater programmer efficiency, and more reliable code.

agentsarxiv-cs-ai

Local Ai

Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning

arXiv:2609.18952v1 Announce Type: cross Abstract: Early detection of dental caries remains challenging due to limitations in traditional diagnostic methods, particularly for proximal lesions in poster

local-aiarxiv-cs-ai

Deep learning emergent spacetime from fermionic spectral functions in holography

arXiv:2609.18566v1 Announce Type: cross Abstract: We present a physics-informed machine learning framework based on Neural Ordinary Differential Equations that solves the holographic inverse problem:

Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results

arXiv:2603.02948v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) incorporate physics into neural networks by embedding partial differential equations (PDEs) into thei

Matching Multi-Loop Complexities with a Single Loop: Optimal Optimization Stationarity and Best-Known Game Stationarity in Nonconvex--Concave Minimax Optimization

arXiv:2609.17973v1 Announce Type: cross Abstract: We introduce a new single-loop algorithmic framework for smooth nonconvex--concave minimax optimization. The resulting projected damped extragradient

REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration

arXiv:2609.17555v1 Announce Type: cross Abstract: Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-

Track, Articulate, Act: Generating Articulation from Casual Human Videos

arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes i

researcharxiv-cs-cv

Hardware

Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks

arXiv:2508.11584v3 Announce Type: replace-cross Abstract: Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant

hardwarearxiv-cs-ai

CAD-Based Relation Learning and Geometric-Symbolic Planning for Robotic Assembly

arXiv:2609.17263v1 Announce Type: new Abstract: Assembly Sequence Planning (ASP) remains a challenging problem due to its combinatorial nature, making exhaustive planning approaches impractical for co

researcharxiv-cs-ro

Comparative Characterization of KV Cache Management Strategies for LLM Inference

arXiv:2604.05012v2 Announce Type: replace-cross Abstract: Efficient inference with Large Language Models (LLMs) increasingly relies on Key-Value (KV) caches to store previously computed key and value

Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition

arXiv:2609.16768v1 Announce Type: new Abstract: IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building

tutorialsarxiv-cs-ai

LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing

arXiv:2609.16155v1 Announce Type: cross Abstract: This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Moti

tutorialsarxiv-cs-ai

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems

arXiv:2603.11515v2 Announce Type: replace Abstract: Today's scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design s

OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

arXiv:2609.16898v1 Announce Type: cross Abstract: Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a

Single Document Extractive Summarization using Domination in Hypergraph

arXiv:2609.15993v1 Announce Type: new Abstract: Automatic Text Summarization (ATS) in Natural Language Processing has been an important task in Information Retrieval. It compresses a document to creat

researcharxiv-cs-cl

A Voxel-Spacing-Aware Extension of PyRadiomics for Anisotropic Texture Analysis

arXiv:2609.14103v1 Announce Type: cross Abstract: Radiomic texture features are commonly extracted from anisotropic CT and MRI acquisitions, where identical voxel offsets may represent different physi

Adaptive Phase-Switching for Communication-Efficient Federated LoRA Fine-Tuning

arXiv:2609.13512v1 Announce Type: cross Abstract: Federated fine-tuning of large language models with low-rank adaptation reduces per-client trainable parameters, but client-to-server communication re

Safety

AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems

arXiv:2609.14592v1 Announce Type: new Abstract: Artificial intelligence systems are rapidly becoming critical components in healthcare, finance, public services, and other safety-critical domains. Yet

safetyarxiv-cs-ai

AI Persuasion as a Threat to Human Control

arXiv:2609.14796v1 Announce Type: new Abstract: The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion at

AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery

arXiv:2609.15820v1 Announce Type: new Abstract: Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pip

Agents

Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments t

agentsarxiv-cs-ai

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

EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

arXiv:2609.15687v1 Announce Type: new Abstract: EEG foundation models such as BIOT, LaBraM, and EEGMamba have achieved remarkable performance in neural signal decoding, but their black-box nature limi

Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return

model-releasesarxiv-cs-cl

arXiv:2609.14758v1 Announce Type: cross Abstract: Tool-augmented language models are evaluated on whether they reach the right answer, not on whether they report honestly when a tool fails to supply o

Fault Diagnosis for Underwater Vehicles using Moving Horizon Estimation and Gaussian Processes

arXiv:2609.14539v1 Announce Type: new Abstract: This work proposes a model-based fault detection and diagnosis framework for underwater vehicles subject to actuator faults that explicitly accounts for

researcharxiv-cs-ro

Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

model-releasesarxiv-cs-ro

arXiv:2609.14087v1 Announce Type: new Abstract: Hamilton-Jacobi (HJ) reachability provides a principled framework for synthesizing safety certificates and robust controllers for safety-critical roboti

Hardware

GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo

arXiv:2609.13243v1 Announce Type: cross Abstract: We present GzDRL, a novel single-process reinforcement learning (RL) framework for Gazebo that overcomes longstanding bottlenecks in scalable, reprodu

hardwarearxiv-cs-lg

LLaTSA: Large Language Model-Aligned General-Purpose Transient Stability Analysis

arXiv:2609.14374v1 Announce Type: cross Abstract: Dynamic trajectory prediction has become an important paradigm for data-driven transient stability analysis (TSA), yet most existing predictors remain

Multi-View Structure-from-Motion Enables Oriented Projective Shape Analysis in Three Dimensions

arXiv:2609.13263v1 Announce Type: new Abstract: Projective shape analysis provides a geometric framework for studying landmark configurations in digital images acquired by pinhole cameras. In the clas

researcharxiv-cs-cv

One Example Is Enough to Pass Fairness Benchmarks: Rethinking Fairness Evaluation for Aligned LLMs

arXiv:2609.14860v1 Announce Type: cross Abstract: Warning: This submission studies stereotypes and biases, and contains toxic and offensive examples, used for illustration purposes only. Fairness benc

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

model-releasesarxiv-cs-lg

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based fr

Rethinking Camouflage Image Generation towards a Training-Free Paradigm

arXiv:2609.14377v1 Announce Type: new Abstract: Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background

SALUTE: Benchmarking and Adapting LLMs for the Defense Domain

model-releasesarxiv-cs-cl

arXiv:2609.15022v1 Announce Type: new Abstract: Defense is a knowledge-intensive domain that requires precise understanding of specialized terminology, doctrinal concepts, operational procedures, and

Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design

arXiv:2609.13468v1 Announce Type: cross Abstract: The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a fa