Search: “papers”

The Normalization of Deviance in AI Development

arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,

safetyarxiv-cs-ai

10 Sept 2026

Towards Automatic Evolution Tree Generation from Citation Graphs

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arXiv:2609.09561v1 Announce Type: new Abstract: Surveys remain the primary way researchers grasp the lineage of methods within an AI subfield, but they scale poorly against the current rate of publica

model-releasesarxiv-cs-cl

10 Sept 2026

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions

arXiv:2603.15576v2 Announce Type: replace Abstract: This paper develops new variance-reduction techniques for the forward-reflected-backward splitting (FRBS) method to solve a class of possibly nonmon

10 Sept 2026

X-CoSD: Communication-Efficient Cross-Vocabulary Collaborative Speculative Decoding

arXiv:2609.09166v1 Announce Type: new Abstract: This paper investigates collaborative speculative decoding (CoSD), a distributed large language model (LLM) inference framework in which an on-device sm

local-aiarxiv-cs-cl

10 Sept 2026

A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management

model-releasesarxiv-cs-lg

arXiv:2609.08078v1 Announce Type: new Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an explor

Agents

A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction

arXiv:2609.07125v1 Announce Type: new Abstract: This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either de

agentsarxiv-cs-cv

Analysis of Respiratory Sinus Arrhythmia with Neural Networks

arXiv:2609.05698v1 Announce Type: new Abstract: The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phe- nomenon of Respirator

applicationsarxiv-cs-lg

CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards

arXiv:2503.15895v2 Announce Type: replace Abstract: This paper investigates whether sequential context improves goal-conditioned Reinforcement Learning in sparse-reward manipulation tasks. While Hinds

safetyarxiv-cs-ro

Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

arXiv:2609.05520v1 Announce Type: new Abstract: In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are

EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan

arXiv:2609.06195v1 Announce Type: new Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds

Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data

arXiv:2609.07893v1 Announce Type: cross Abstract: Deep learning is an efficient technique to monitor the real time welding process, reducing post-welding repairs and production delays. This paper leve

applicationsarxiv-cs-lg

FreeTransformSR: Efficient Lightweight Image Super-Resolution via Free Low-Rank Learnable Transform

model-releasesarxiv-cs-cv

arXiv:2609.05912v1 Announce Type: new Abstract: Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightw

GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra

arXiv:2609.08136v1 Announce Type: new Abstract: This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (R

hardwarearxiv-cs-lg

Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology

arXiv:2609.08802v1 Announce Type: new Abstract: This paper presents a graph-based safe multi-agent reinforcement learning (MARL) framework for cooperative navigation with time-varying topology. To add

safetyarxiv-cs-ro

Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation

arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying

Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation

arXiv:2609.05523v1 Announce Type: new Abstract: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate fr

Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning

arXiv:2609.05946v1 Announce Type: new Abstract: Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have

Inverse Digital Marbling: Recovering Gesture Programs with a Replay Adjoint

arXiv:2609.08722v1 Announce Type: cross Abstract: Pigment deposition in paper marbling displaces the pattern already present, coupling the appearance of each gesture to later actions. We recover execu

hardwarearxiv-cs-cv

Is Single-View Mesh Reconstruction Ready for Robotics?

arXiv:2505.17966v3 Announce Type: replace-cross Abstract: This paper evaluates single-view mesh reconstruction models for their potential in enabling instant digital twin creation for real-time planni

Iterative Audio Separation with Mixture Consistency via MIMO Model Extension

arXiv:2609.07226v1 Announce Type: cross Abstract: This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation mo

LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation

arXiv:2602.12351v2 Announce Type: replace-cross Abstract: This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VL

safetyarxiv-cs-cv

Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

arXiv:2609.06042v1 Announce Type: new Abstract: This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and im

mjorbit: A Simulation Framework for Space Robotics

arXiv:2609.08010v1 Announce Type: new Abstract: This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-spac

hardwarearxiv-cs-ro

Multi-bounce Drum Roll with Optimized Active Tricks to Leverage Soft Embodiment

arXiv:2609.08490v1 Announce Type: new Abstract: This paper presents a soft robotic drummer for accurate and efficient drum rolls. High-frequency drum rolls require the 'multi-bounce technique,' where

Multi-Level-Set-Based Physics-Driven Neural Network to Solve 3-D Inverse Scattering Problems

arXiv:2609.08594v1 Announce Type: new Abstract: This paper proposes a level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering. To mitigate boundary blu

local-aiarxiv-cs-lg

Observation Design for Certified Control Authority: Projection--Estimability Separation and Active-Face Equivalence

arXiv:2609.05962v1 Announce Type: new Abstract: A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations

On Generalisation Error Bounds for Transformers

arXiv:2410.11500v2 Announce Type: replace-cross Abstract: In this paper, we establish a collection of covering number bounds for linear function classes under various norm constraints on the inputs an

Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer

arXiv:2609.07440v1 Announce Type: new Abstract: This paper proposes an open-set ego-noise separation framework for legged-robot audition via annotation-free adaptation and pretrained-model transfer. T

Optimal estimation for Functional Linear Regression with Noisy Discretized Data

arXiv:2609.08671v1 Announce Type: cross Abstract: In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are obs

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

arXiv:2609.07941v1 Announce Type: new Abstract: In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely

Recovering linear images of sparse signals from indirect observations

arXiv:2609.06182v1 Announce Type: cross Abstract: In this paper, we develop and analyze techniques for recovering a linear image Bx of an unknown signal x from indirect noisy observation omega=Ax+xi.

Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration

arXiv:2609.07230v1 Announce Type: new Abstract: This paper propose a robust decentralized federated distillation method that enables clients with heterogeneous models to collaborate through prediction

applicationsarxiv-cs-lg

Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

arXiv:2609.07312v1 Announce Type: new Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks

local-aiarxiv-cs-lg

Robust Immersive Bilateral Teleoperation of Beyond-Human-Scale Systems with Enhanced Transparency and Sense of Embodiment

arXiv:2505.14486v3 Announce Type: replace Abstract: This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enh

Singularity-Free Guiding Vector Fields on SO(3) with Designer-Specified Progression Behavior

arXiv:2609.07269v1 Announce Type: new Abstract: This paper develops a singularity-free guiding vector field (SF-GVF) for path following on the special orthogonal group SO(3). First, we lift the Euclid

Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection

model-releasesarxiv-cs-cv

arXiv:2609.06232v1 Announce Type: new Abstract: Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision sig

Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection

arXiv:2609.07100v1 Announce Type: new Abstract: Credit card fraud detection typically relies on tabular features, while repeated attributes can also provide useful relational signals. This paper propo

Toward Sustainable Distributed LLM Inference: A Systems Synthesis and Research Agenda for an Energy-, Carbon-, and Cache-Aware llm-d Control Plane

model-releasesarxiv-cs-lg

arXiv:2609.05565v1 Announce Type: cross Abstract: Large language model (LLM) sustainability is increasingly a serving-systems problem, not only a training problem. In production, energy and carbon imp

UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment

arXiv:2609.05888v1 Announce Type: new Abstract: In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation

safetyarxiv-cs-cv

A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning

arXiv:2609.05133v1 Announce Type: cross Abstract: This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reasoning and local control operate at differe

local-aiarxiv-cs-ai

Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM

model-releasesarxiv-cs-ai

arXiv:2609.04738v1 Announce Type: new Abstract: In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique cha

Automatic Speech Recognition for Multilingual Oral History Research

arXiv:2609.04232v1 Announce Type: cross Abstract: This paper offers a unique perspective on how speech technologies are being adopted by community-led heritage language preservation and revitalisation

researcharxiv-cs-cl

Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

arXiv:2609.05150v1 Announce Type: cross Abstract: This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorit

applicationsarxiv-cs-ai

BIT.UA at BioASQ 14B: Modular Retrieval with pg_textsearch and Qdrant, and Agent-Based Answer Generation

arXiv:2609.04999v1 Announce Type: new Abstract: This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical

hardwarearxiv-cs-cl

Blockchain-Enabled Secure Logging for Fiscal Electronic Mechanisms: Evaluation of the Greek eSEND and myDATA Tax Systems

arXiv:2609.04356v1 Announce Type: cross Abstract: This paper analyzes the implementation of blockchain-based integrity mechanisms in Greek Fiscal Electronic Mechanisms (FEMs) and the central tax infor

researcharxiv-cs-ai

CPR-IE:A Compression-Prediction-Resource Intelligence Efficiency Metric

arXiv:2609.04809v1 Announce Type: new Abstract: Comparing intelligent systems under deployment constraints requires more than predictiveaccuracy.This paper develops Compression-Prediction-Resource Int

researcharxiv-cs-ai

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Data-Driven Learning of Unknown Nonlinear Differential Equations Using Functional Analysis

arXiv:2609.04329v1 Announce Type: cross Abstract: In this paper, the problem of data-driven discovery of nonlinear ordinary differential equations (ODEs) is recast, and a new interpretable machine lea

agentsarxiv-cs-ai

Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers

arXiv:2609.05081v1 Announce Type: new Abstract: This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates