Search: “papers”

Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency

arXiv:2609.36479v1 Announce Type: cross Abstract: Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-securit

researcharxiv-cs-ai

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RBF-GNN: Rational Basis Functions for Pseudo-Coordinate based Graph Convolutions

DGX agent

arXiv:2609.37015v1 Announce Type: new Abstract: We propose RBF-GNN, a new pseudo-coordinate based graph neural network architecture that takes into account Euclidean, spherical or angular coordinates

safetyarxiv-cs-cv

30 Sept 2026

Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment

arXiv:2609.36540v1 Announce Type: new Abstract: Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can conflict

safetyarxiv-cs-ro

Tutorials

RED: Reconstruction Evolution Dynamics for Generalizable AI-Generated Image Detection

arXiv:2609.36822v1 Announce Type: new Abstract: The rapid evolution of image generators calls for forensic cues that generalize beyond known generation mechanisms. Existing detectors often rely on sta

tutorialsarxiv-cs-cv

Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models

arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate i

Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning

arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructio

RLTL;DR: Self-improvement by Internalizing Self-generated Feedback

arXiv:2609.37633v1 Announce Type: cross Abstract: The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards t

RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts

arXiv:2609.36851v1 Announce Type: new Abstract: End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their

safetyarxiv-cs-cv

Safe-by-Design Learning via Energy-based Neural Networks

arXiv:2609.36942v1 Announce Type: cross Abstract: Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settin

SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models

arXiv:2603.04410v3 Announce Type: replace-cross Abstract: While different stakeholders are trying to leverage Arabic Language Models (ALMs), safety alignment in ALMs remains largely underexplored, hin

Sample Complexity of Equivariant Reinforcement Learning

arXiv:2609.36421v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. Th

safetyarxiv-cs-lg

Scene-Consistent Illumination Transfer for Inserted Advertising Graphics

arXiv:2609.37951v1 Announce Type: new Abstract: Replacing a visible advertisement in a broadcast frame is geometrically straightforward but photometrically delicate. A pasted graphic can have the corr

Tutorials

Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering

arXiv:2609.36873v1 Announce Type: new Abstract: Multivariate Time Series (MTS) clustering is an important tool in temporal data mining, aiming to discover latent group structures from complex observat

tutorialsarxiv-cs-lg

Simultaneous Neural Optimal Transport

arXiv:2609.37424v1 Announce Type: cross Abstract: Optimal Transport (OT) provides a principled framework for learning transformations between probability distributions from unpaired samples. In many a

SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging

arXiv:2609.36982v1 Announce Type: cross Abstract: Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners i

StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents

arXiv:2609.36319v1 Announce Type: new Abstract: Despite the recent success of coding agents built on large language models, it remains challenging to run them over long horizons, since every observati

Temporal-Aware Fusion for Robust Outdoor LiDAR Localization

arXiv:2609.36432v1 Announce Type: new Abstract: LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounte

Text2Sim: Agentic Physics-Based Simulation Generation with Distilled Expertise

arXiv:2609.36593v1 Announce Type: cross Abstract: Creating diverse physical simulations remains labor-intensive because assets, layout, physical parameters, motion, control, and rendering must be desi

agentsarxiv-cs-ai

Applications

The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations

arXiv:2609.36209v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While

applicationsarxiv-cs-cl

The finite-horizon five-expert prediction problem

arXiv:2609.38035v1 Announce Type: cross Abstract: We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting.

The Rashomon Wikipedia: A Data-Perspectivist Analysis of Divergent Historical Narratives

arXiv:2609.37498v1 Announce Type: new Abstract: Wikipedia aims to provide a unified, neutral record of history, yet its independent language editions often function as distinct epistemic communities,

safetyarxiv-cs-cl

The Universal Classifier for Graph Learning

arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learning

TopoEmbedX: A General Framework for Representation Learning on Topological Domains

arXiv:2609.37884v1 Announce Type: new Abstract: Topological structures such as simplicial complexes, hypergraphs, and cell complexes extend standard graph models by modeling higher-order relationships

Towards an AI Software Factory for Data Systems

arXiv:2609.36323v1 Announce Type: new Abstract: AI-assisted coding tools deliver significant acceleration of coding, but only limited impact across the end-to-end software development lifecycle (SDLC)

agentsarxiv-cs-ai

Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models

arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level superv

Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning

arXiv:2609.36530v1 Announce Type: new Abstract: Motion planning often admits multiple feasible solutions, making multimodal generation valuable, particularly for flexible multi-robot coordination. Dif

agentsarxiv-cs-ro

Understanding Decision-Making Mechanisms in Neural Routing Solvers

arXiv:2609.36063v1 Announce Type: cross Abstract: Neural Combinatorial Optimization (NCO) has achieved strong empirical success, yet the internal mechanisms driving model decisions remain largely unex

Understanding LLM Parameter Update Sparsity through the Lens of Fisher

arXiv:2609.36262v1 Announce Type: new Abstract: Recent studies have observed that parameter changes during language-model post-training can be concentrated in a small subset of coordinates. This pheno

VLM Fine-Tuning for End-to-End Combinatorial Optimization

arXiv:2609.37175v1 Announce Type: new Abstract: Large language models (LLMs) have provided a unified interface for end-to-end combinatorial optimization (CO), but textual serialization alone may obscu

researcharxiv-cs-cl

Waggle-Dance-Inspired Multi-UAV Recruitment with RGB and Synthetic Event Vision

arXiv:2609.37464v1 Announce Type: new Abstract: The honeybee waggle dance motivates a communication mechanism in which one agent's motion guides the subsequent work of other agents. For aerial robots,

agentsarxiv-cs-ro

Wrench-ACT: Enhancing Robot Policies for Contact Rich Behavior Using Direct Wrench Control

arXiv:2609.37552v1 Announce Type: new Abstract: While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly rep

safetyarxiv-cs-ro

A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards

arXiv:2609.33467v1 Announce Type: new Abstract: When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order,

A Comparative Analysis of Attention versus State-Space Models for In-Context Learning

arXiv:2609.32341v1 Announce Type: new Abstract: Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and exist

A Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset

arXiv:2609.31677v1 Announce Type: new Abstract: Face recognition is widely deployed in surveillance, access control, and forensic workflows, yet accuracy degrades sharply once the face is occluded by

A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture

model-releasesarxiv-cs-cv

arXiv:2609.33991v1 Announce Type: new Abstract: Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Althou

A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

arXiv:2404.14019v3 Announce Type: replace Abstract: Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentatio

A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149v1 Announce Type: cross Abstract: Linear bandits model sequential decision-making problems with noisy rewards that are linear in the decision variable, where an agent must simultaneous

ABO-Med: Accelerated Bilevel Optimization for Few-Shot Medical Image Classification

arXiv:2609.33176v1 Announce Type: new Abstract: In recent years, bilevel optimization has been widely used in a variety of machine learning tasks. However, prior bilevel optimization algorithms genera

Action-Driven Processes for Continuous-Time Control

arXiv:2510.26672v2 Announce Type: replace-cross Abstract: At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally funda

safetyarxiv-cs-lg

Agentic Multi-Turn Reasoning: A Fairness Approach

arXiv:2609.33323v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems capable of solving complex tasks through multi-turn planning, tool use, v

safetyarxiv-cs-lg

AgentWare: Automating the Lifecycle of Agentic Applications across the Edge-to-Cloud Continuum

arXiv:2609.34586v1 Announce Type: cross Abstract: Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity

agentsarxiv-cs-lg

AHMAD: Adaptive Hybrid Multi-task Vision Learning with Assisted Distillation for Keypoint Detection

arXiv:2609.35490v1 Announce Type: new Abstract: Generalist multitasking vision models aim to unify multiple vision tasks within a single framework, enabling more efficient and versatile learning. Howe

Local Ai

AI-Driven Collaborative Assembly Line Inspection: System Integration and Deployment Challenges

arXiv:2609.33522v1 Announce Type: cross Abstract: Manual visual inspection on assembly lines is a persistent manufacturing bottleneck: operator fatigue over extended shifts lowers defect-detection rat

local-aiarxiv-cs-cv

AIM-ZO: Activation-Informed Subspace Maintenance for Zeroth-Order LLM Fine-Tuning

arXiv:2609.35257v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization offers a memory-efficient alternative for LLM fine-tuning by estimating updates only from forward evaluations of perturbe

All-Atom GPCR-Ligand Dynamics Simulation via a Residual Latent Flow Mode

arXiv:2602.03902v2 Announce Type: replace-cross Abstract: G-protein-coupled receptors (GPCRs), which are targeted by over one-third of approved drugs, undergo intricate conformational transitions to t

Anytime-Valid LLM Leaderboards via Benchmark-weighted and Block-Factorized e-Processes

arXiv:2609.32248v1 Announce Type: new Abstract: Large language model (LLM) leaderboards compare model capabilities by ranking models according to their mean performance on fixed benchmarks. However, v

API Secrets Should Never Become Tokens in the LLM's Vocabulary: A Threat Analysis of API Credential Handling in LLM Agent Systems and an Empirical Evaluation of a Vault-Mediated Execution Boundary

arXiv:2609.33371v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a pr

agentsarxiv-cs-lg

AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents

model-releasesarxiv-cs-ro

arXiv:2609.33299v1 Announce Type: new Abstract: World Action Models (WAMs) are becoming increasingly important and useful for embodied intelligence, as they enable robots to anticipate the consequence