Search: “papers”

Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?

arXiv:2609.14718v1 Announce Type: new Abstract: Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory mana

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Are LLMs Good Financial User Simulators? A Preliminary Study

DGX agent

arXiv:2609.15727v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as user simulators, but their ability to reproduce evolving individual financial decisions remains un

researcharxiv-cs-ai

15 Sept 2026

Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation

model-releasesarxiv-cs-lg

arXiv:2609.15721v1 Announce Type: new Abstract: Automatic Related Work Generation (RWG) significantly reduces the human time and effort required to author the Related Work Section (RWS) of a research

Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments

arXiv:2609.15352v1 Announce Type: new Abstract: Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expe

ATTRICITE: Training an Open 4B Model for Citation Recovery toward Faithful Attribution

arXiv:2609.14248v1 Announce Type: cross Abstract: Faithful citation attribution begins with identifying the intended source for a scientific claim. We study this source-identification capability throu

Bangla Sentence Function Classification: Corpus Development, Model Benchmarking, and Interpretability

arXiv:2609.13869v1 Announce Type: cross Abstract: Automatic sentence function identification is important for many downstream natural language processing (NLP) applications such as dialogue systems, t

Local Ai

Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration

arXiv:2609.15555v1 Announce Type: new Abstract: Bayesian optimisation (BO) typically relies on a single global Gaussian process (GP) model as its surrogate model. However, GP regression has cubic comp

local-aiarxiv-cs-lg

Belief-Adaptive Online Autonomy for Quadrotor UAV Navigation under GNSS Degradation in Urban Environments

arXiv:2609.14806v1 Announce Type: new Abstract: Reliable online autonomy is critical for quadrotor operation in urban airspaces, where global navigation satellite systems (GNSS) measurements suffer fr

Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context

arXiv:2609.15087v1 Announce Type: cross Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-wo

Beyond Scene Description: Multi-Agent Orchestration for Non-visual Access to Virtual Worlds

arXiv:2609.14512v1 Announce Type: new Abstract: Virtual worlds now host classrooms, meetings, conferences, shops, and social venues, and nearly every interaction they expose assumes a user who can sca

safetyarxiv-cs-ai

Bi-Level Routing and Sparse Spatial Attention based Multi-View BEV 3D Object Detection for Autonomous Driving

arXiv:2609.14185v1 Announce Type: cross Abstract: Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and ef

agentsarxiv-cs-ai

BIG-CBF: Behavior-Imagination-Guided Control Barrier Function with Shared Uncertainty for Mobile Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2609.14343v1 Announce Type: new Abstract: Control barrier functions (CBFs) provide a mathematically grounded framework for enforcing local collision-avoidance constraints in autonomous mobile ro

Hardware

BOOST: Concurrent Access to Host Memory and HBM to Accelerate LLM Inference

arXiv:2609.13592v1 Announce Type: cross Abstract: GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference. The GPU memory system consists of a primary tier of high-b

hardwarearxiv-cs-lg

Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework

arXiv:2609.13335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models often exhibit unstable long-horizon reasoning a

agentsarxiv-cs-ai

Calibrating Interpretability Instruments Before Trusting Their Verdicts

arXiv:2609.14754v1 Announce Type: cross Abstract: Causal claims about large language model (LLM) internals rest on measurements. Those might include a projection, a cosine, an ablation delta, or an in

CALICO: A Human-Centered, Codebook-Aligned System for Annotation

model-releasesarxiv-cs-cl

arXiv:2609.14726v1 Announce Type: cross Abstract: Large language models are increasingly used to scale codebook-based annotation in scientific research, but existing workflows provide limited support

Can a Neural Encoding Model Replicate an fMRI Visualization Study?

arXiv:2609.15685v1 Announce Type: cross Abstract: Most knowledge of graphical perception comes from behavioral studies. Understanding from a neural perspective is much more limited due in part to neur

safetyarxiv-cs-cv

Can We Trust the Judges? Validation of Factuality Evaluation Methods via Answer Perturbation

arXiv:2609.15561v1 Announce Type: new Abstract: Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy?

researcharxiv-cs-cl

CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering

arXiv:2609.15830v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are gr

safetyarxiv-cs-ai

ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents

arXiv:2601.00290v3 Announce Type: replace Abstract: Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (2.6B per drug), where protocols are encode

safetyarxiv-cs-ai

Tutorials

CodeTS: Verifiable Text-to-Time Series Generation via Executable Code

arXiv:2609.15393v1 Announce Type: cross Abstract: Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specif

tutorialsarxiv-cs-ai

Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric

arXiv:2609.14936v1 Announce Type: new Abstract: In field robotics, acquiring independent large-scale reference trajectories more accurate than the evaluated estimates remains an open challenge. The do

Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MO

Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

arXiv:2311.03154v3 Announce Type: replace Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients trai

Convergence rates for generative drifting flows: fixed-scale obstructions and multihead acceleration

arXiv:2609.15193v1 Announce Type: new Abstract: Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a singl

Covariance-Regulated Recursive Koopman Learning for Nonlinear Systems with Uncertain Time-Varying Dynamics

model-releasesarxiv-cs-ro

arXiv:2606.15317v2 Announce Type: replace Abstract: Offline models for autonomous robots often fail under time-varying dynamics outside their training distribution. While Koopman operator theory offer

Criticality in Dissimilar Decomposition and Undersampling of Random Datasets with Anomalies

arXiv:2609.13201v1 Announce Type: new Abstract: Training datasets for upcoming LLMs would include a significant amount of AI text/image data generated from current LLMs. In such a scenario, it is impo

Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details

arXiv:2609.15722v1 Announce Type: new Abstract: AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) p

applicationsarxiv-cs-ai

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals

arXiv:2512.08379v3 Announce Type: replace Abstract: Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often

applicationsarxiv-cs-ai

Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

arXiv:2609.15744v1 Announce Type: new Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early

Discovering and Preserving Category Correlation Knowledge via Adaptive Reciprocal Knowledge Distillation

arXiv:2609.13199v1 Announce Type: cross Abstract: Knowledge distillation aims to improve the performance of lightweight student models by transferring knowledge from larger and more powerful teacher m

safetyarxiv-cs-cv

Distributed Safe Cooperative Vector Field for Trajectory Curvature Constrained Multi-Robot Systems

arXiv:2609.15266v1 Announce Type: new Abstract: Trajectory curvature constraints are inherent in practical multi-robot systems due to the limited turning capabilities of the robots. Without properly a

DuctAM: A Duct-Assisted Quadrotor-Based Aerial Manipulator Enabling High-Force Push-and-Pull Interactions

arXiv:2609.15861v1 Announce Type: new Abstract: Uncrewed Aerial Manipulators (UAMs) extend the capabilities of Uncrewed Aerial Vehicles (UAVs) from perception to physical interaction. Among various ae

applicationsarxiv-cs-ro

Dynamic Semantic Compression for Efficient Latent-Space Inference in Large Language Models

arXiv:2609.15338v1 Announce Type: cross Abstract: Large Language Models (LLMs) primarily perform inference at the token level, resulting in substantial memory overhead and compromised computational ef

EB-gMCR: Energy-Based Generative Modeling for Signal Unmixing and Multivariate Curve Resolution

arXiv:2507.23600v5 Announce Type: replace Abstract: A single measurement of a chemical mixture, a reaction mixture, a natural extract, or a tissue, records the sum of the profiles of the few component

EcoBoat: Design and Experimental Validation of an Autonomous Body-Board Boat For Cleaning Water Bodies

arXiv:2609.14450v1 Announce Type: new Abstract: Cleaning water bodies such as swimming pools and lakes typically demands significant manual effort or reliance on costly, sensor-intensive robotic syste

agentsarxiv-cs-ro

Empirical Evaluation of Open-Source Large Language Models for Retrieval-Augmented Generation in ESG Domain

arXiv:2609.15242v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reporting is critical for corporate accountability, with Large Language Models (LLMs) and Retrieval-Augmente

Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents

arXiv:2609.15422v1 Announce Type: new Abstract: AI agents are provisioned the same as employee-owned hosts in many enterprise settings with a static credential set fixed at deployment which includes a

End-to-End Cell Detection via Instance-aware Graph Modeling

arXiv:2609.15354v1 Announce Type: cross Abstract: Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To ca

Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving

arXiv:2512.10159v3 Announce Type: replace-cross Abstract: LLMs have shown strong performance in data-rich domains such as programming, but their reliability in engineering tasks remains limited. Circu

Event-Level Emotion Recognition in the Wild Using Deep Facial Expression Analysis

arXiv:2609.13854v1 Announce Type: new Abstract: Facial emotion recognition (FER) in real-world environments remains challenging due to unconstrained imaging conditions, including multiple faces, occlu

applicationsarxiv-cs-cv

EvoOntology: A Self-Evolving Ontology Layer for Data Agents

arXiv:2609.15779v1 Announce Type: new Abstract: Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a ch

agentsarxiv-cs-ai

Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models

arXiv:2609.13816v1 Announce Type: cross Abstract: JavaScript powers approximately 98.8% of all websites, making vulnerabilities in its code a significant security risk, yet existing detection approach

Exploring Multimodal Turn-Taking Cues in Face-to-Face Conversation using Voice Activity Projection

arXiv:2609.14666v1 Announce Type: cross Abstract: Turn-taking is a fundamental component of spoken interaction, and while humans naturally rely on both verbal and non-verbal signals, dialogue systems

researcharxiv-cs-cl

Exploring napping paradigm for Recurrent Spiking Neural Networks

arXiv:2609.13927v1 Announce Type: new Abstract: Biological organisms minimize free energy by balancing two competing demands on their internal world model: it must be accurate enough to predict sensor

FedV-KGQA in Practice: Design Lessons and an Interactive Prototype

arXiv:2609.13661v1 Announce Type: new Abstract: Knowledge graph question answering usually assumes that one system can reach the whole graph. In practice, facts are often held by organizations that sh

Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations

arXiv:2609.13920v1 Announce Type: cross Abstract: Persistent Gaussian perturbations have been shown to prevent asymptotic oversmoothing in recurrent Graph Neural Networks (GNNs) by ensuring a positive

Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation

arXiv:2609.13779v1 Announce Type: new Abstract: Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces