Search: “papers”

An Efficient Algorithm for Minimum-Pressure Growth Planning of Vine Robots

model-releasesarxiv-cs-ro

arXiv:2609.18070v1 Announce Type: new Abstract: Vine robots navigate cluttered environments by extending from their tip. Although their ability to operate in such environments has been extensively dem

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Anomaly Detection in General Ledger Data: Results from a Hybrid Approach

DGX agent

arXiv:2609.18228v1 Announce Type: new Abstract: Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both highrisk audit areas and potential material misstatements.

tutorialsarxiv-cs-lg

17 Sept 2026

Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models

arXiv:2609.18320v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit hallucinations, presenting a major barrier to reliability in complex reasoning tasks. While traditional

safetyarxiv-cs-cl

Autonomy in Check: Governor-Mediated Adaptive Security at the Edge

arXiv:2609.18338v1 Announce Type: cross Abstract: Adaptive security at the network edge increasingly relies on automated planners, including rule-based controllers, learned policies, and LLM-assisted

Local Ai

BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection

arXiv:2609.17562v1 Announce Type: cross Abstract: Hybrid Spiking Neural Network (SNN)-Artificial Neural Network (ANN) architectures combine the energy efficiency of SNNs with the superior detection ac

local-aiarxiv-cs-ai

Breaking the T^{2/3} Barrier for Sequential Calibration

arXiv:2406.13668v4 Announce Type: replace Abstract: A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on t

CLASP: A Cluster-Level Autonomous Selective Picking Robot with a Soft Rolling-Band Gripper for Fresh-Market Blueberry Harvesting

arXiv:2609.18051v1 Announce Type: new Abstract: Fresh-market blueberries require selective, gentle picking, which is labor-intensive and expensive. Over-the-row machine harvesters are fast but non-sel

agentsarxiv-cs-ro

Clueing up LLMs with Tool-Augmented Deductive Reasoning

model-releasesarxiv-cs-ai

arXiv:2609.18736v1 Announce Type: new Abstract: Despite recent advances in large language models (LLMs), performing logically consistent deductive reasoning over extended interactions remains challeng

Collaborative Memory for Multi-Agent VLM Systems

arXiv:2609.17921v1 Announce Type: new Abstract: Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, differ

agentsarxiv-cs-ai

Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control

arXiv:2609.17697v1 Announce Type: new Abstract: Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining

agentsarxiv-cs-lg

Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration

arXiv:2609.18836v1 Announce Type: new Abstract: A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly

researcharxiv-cs-cv

Divide and Conquer: A Hybrid Strategy Defeats Multimodal Large Language Models

model-releasesarxiv-cs-cl

arXiv:2412.16555v4 Announce Type: replace Abstract: Large language models (LLMs) are widely applied in various fields of society due to their powerful reasoning, understanding, and generation capabili

DRT&R: Direct Radar Teach & Repeat

arXiv:2609.17766v1 Announce Type: new Abstract: Radar-based navigation is appealing for its robustness to adverse conditions involving airborne particles, such as precipitation, dust, fog, and smoke,

researcharxiv-cs-ro

Efficient Robust Learning at the Information-Theoretic Limit

arXiv:2609.17655v1 Announce Type: cross Abstract: In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that ou

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Ra

researcharxiv-cs-cl

Examining the Difference in Human Behavior Between Virtual and Real-World Human-Robot Teaming

arXiv:2609.18900v1 Announce Type: new Abstract: Prototyping and evaluating human-robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an a

applicationsarxiv-cs-ro

FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment

arXiv:2609.17786v1 Announce Type: new Abstract: Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions bec

FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels

arXiv:2609.18089v1 Announce Type: new Abstract: Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication r

Finite-Sample Unbiased Variance of MMD under Unbalanced Sampling: Exact Estimation and Quasi-Linear Computation

arXiv:2601.13874v3 Announce Type: replace-cross Abstract: Accurately and efficiently estimating the variance of the Maximum Mean Discrepancy (MMD) remains challenging, particularly for unbalanced samp

Flexible-Region Based Adaptive In-Loop Filter for Video Coding

arXiv:2609.18983v1 Announce Type: cross Abstract: Adaptive loop filter (ALF) for video coding, which is designed to minimize the mean square error between original and reconstructed samples by using W

researcharxiv-cs-cv

Gradient Descent with Stochastic Subspaces via Persistence of Memory

arXiv:2609.18416v1 Announce Type: cross Abstract: Stochastic subspace methods have gained popularity as gradient descent based techniques for large scale optimisation problems, especially in distribut

safetyarxiv-cs-lg

Tutorials

IRIS: Implicit Rendering Matters for Pose-Free Novel View Synthesis

arXiv:2609.18034v1 Announce Type: new Abstract: Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters wit

tutorialsarxiv-cs-cv

Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

arXiv:2412.07255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities in generative tasks but pose potential risks due to their tendency to generat

Learning Fractional-Order Dynamics from a Single Trajectory

arXiv:2609.18127v1 Announce Type: new Abstract: Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the mo

applicationsarxiv-cs-lg

Learning Lyapunov Operators for Nonlinear Systems

arXiv:2609.18894v1 Announce Type: cross Abstract: Constructing Lyapunov functions for nonlinear dynamical systems is a central problem in stability analysis, yet remains challenging. Lyapunov function

Tutorials

LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era

arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,

tutorialsarxiv-cs-lg

LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging

model-releasesarxiv-cs-lg

arXiv:2609.19062v1 Announce Type: new Abstract: Automatic sleep staging is fundamental to personal health monitoring, yet many existing approaches are ill-suited for real-world applications. Tradition

Long-Context Demonstration Selection Using State Space Models

arXiv:2609.17888v1 Announce Type: cross Abstract: We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This pro

applicationsarxiv-cs-cl

Made in Hungary: Comments on the performance of generative language models

arXiv:2609.18284v1 Announce Type: new Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian,

researcharxiv-cs-cl

Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms

arXiv:2609.17602v1 Announce Type: new Abstract: Computational text-based ideal point estimation (CT-IPE) methods are usually compared as named algorithms, yet applying them involves numerous researche

researcharxiv-cs-cl

Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking

model-releasesarxiv-cs-cv

arXiv:2609.18716v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) match the event-driven nature of event cameras and naturally extract spatiotemporal features. These properties have motiv

Maximum Strong Independent Sets in Hypergraphs: Reductions, Bounds, and Greedy Certificates

arXiv:2609.17951v1 Announce Type: new Abstract: We study the maximum strong independent set problem in a finite hypergraph: find the largest vertex set that intersects every hyperedge in at most one v

Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation

arXiv:2502.14254v3 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, en

agentsarxiv-cs-ai

MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories

arXiv:2609.18260v1 Announce Type: new Abstract: Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and

applicationsarxiv-cs-cv

Multi-Session Multimodal Underwater Mapping with Acoustic and Optical Imaging

arXiv:2609.17929v1 Announce Type: new Abstract: Accurate seafloor mapping is essential for marine science, archaeology, and environmental monitoring. However, integrating data from different sensors,

safetyarxiv-cs-ro

Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations

arXiv:2609.18007v1 Announce Type: cross Abstract: Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations -- and whether

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

model-releasesarxiv-cs-lg

arXiv:2609.18084v1 Announce Type: cross Abstract: Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to ev

Online Multimodal Workload Assessment in Contact-Rich Physical Human-Robot Interaction

arXiv:2609.18031v1 Announce Type: new Abstract: Contact-rich physical human--robot interaction (pHRI) imposes time-varying demands associated with physical interaction, motor regulation, and physiolog

researcharxiv-cs-ro

Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

arXiv:2609.18588v1 Announce Type: new Abstract: For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existi

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

model-releasesarxiv-cs-ai

arXiv:2609.18120v1 Announce Type: cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, au

Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers

model-releasesarxiv-cs-cv

arXiv:2609.18056v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has recently emerged as a pivotal research direction for adapting pre-trained point cloud transformers to diverse

Predictive Varanus: Combining CSP Conformance Monitoring with Predictive LTL Runtime Verification

arXiv:2609.17625v1 Announce Type: new Abstract: Runtime Verification is well suited to autonomous and robotic systems because it checks the behaviour that is actually observed during execution. Its ma

agentsarxiv-cs-ro

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

arXiv:2609.18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data. In various fields, exploring causal relationships among variables remains an i

Provable Guarantees for Spectral Structured Prediction

arXiv:2609.18527v1 Announce Type: new Abstract: Structured prediction is the simultaneous prediction of multiple labels, and is widely used in various fields, such as natural language processing and c

Real-Time Maneuver Planning for Fixed-Wing UAVs in Unsteady Flows Using a GPU-Accelerated Vortex Particle Model

arXiv:2509.16079v2 Announce Type: replace Abstract: Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerod

safetyarxiv-cs-ro

ReFigBench: Benchmarking Scientific Figure Reconstruction as Editable PowerPoint Artifacts

model-releasesarxiv-cs-cl

arXiv:2609.18844v1 Announce Type: new Abstract: Multimodal coding agents are expected to turn visual inputs into usable artifacts, and they act through a harness, the layer of tools, context managemen

Regional Explanations via Causal Sufficiency and Necessity

arXiv:2609.18049v1 Announce Type: new Abstract: Model explainability is essential for understanding and trusting machine learning models. Existing explainable AI methods often explain predictions thro

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

arXiv:2609.19093v1 Announce Type: cross Abstract: Radiologists follow heterogeneous reporting practices. Two radiologists examining the same image and identifying the same clinical findings might neve

researcharxiv-cs-ai