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arXiv:2606.31755v1 Announce Type: cross Abstract: Research on artificial intelligence (AI) in the public sector often treats 'AI' as a single category, neglecting technical distinctions between differ

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Benchmarking Large Language Models on Floating-Point Error Classification

DGX agent

arXiv:2606.31308v1 Announce Type: new Abstract: This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We intr

model-releasesarxiv-cs-ai

1 Jul 2026

Local Ai

CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations

arXiv:2606.31033v1 Announce Type: new Abstract: In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallu

local-aiarxiv-cs-cl

Dataset Construction for Training LLM to Learn Analog Circuit Knowledge

arXiv:2508.10409v3 Announce Type: replace-cross Abstract: This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM trainin

Improving path-tracking performance of an articulated tractor-trailer system using a non-linear kinematic model

arXiv:2606.31889v1 Announce Type: new Abstract: This paper presents a novel non-linear mathematical model of an articulated tractor-trailer system that can be used, in combination with receding horizo

researcharxiv-cs-ro

Improving Survey Participation in Low-Literacy Populations Through Value-Sensitive Conversational AI

arXiv:2606.30660v1 Announce Type: cross Abstract: Collecting reliable social data from low-literacy populations remains a persistent challenge, particularly when surveys involve sensitive topics and m

Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition

arXiv:2606.31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.5-7B). Using historical pr

Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning

arXiv:2606.30857v1 Announce Type: new Abstract: This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all

researcharxiv-cs-cl

Online Generation of Collision-Free Trajectories in Dynamic Environments

arXiv:2603.00759v2 Announce Type: replace Abstract: In this paper, we present an online method for converting an arbitrary geometric path, represented by a sequence of states, and generated by any pla

safetyarxiv-cs-ro

Prompting Robot Teams with Natural Language

arXiv:2509.24575v2 Announce Type: replace-cross Abstract: This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use

Separation Capacity of Scattering Networks

arXiv:2606.30822v1 Announce Type: cross Abstract: In this paper, we attempt to enhance the theoretical understanding of convolutional neural networks (CNNs) as feature extractors in classification tas

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

arXiv:2606.23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In this tas

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG

arXiv:2606.30989v1 Announce Type: cross Abstract: Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs

safetyarxiv-cs-ai

A Posteriori Error Analysis for Decoupled Neural Approximations of Fully Coupled FBSDEs with Control Mismatch

arXiv:2606.29474v1 Announce Type: cross Abstract: This paper develops an a posteriori error analysis framework for decoupled neural approximations of fully coupled forward--backward stochastic differe

Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping

arXiv:2606.29715v1 Announce Type: new Abstract: This paper presents an automatic system for recognizing pulmonary diseases in chest X-rays using geometric normalization of the lung region. The method

safetyarxiv-cs-cv

Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDS

arXiv:2606.29665v1 Announce Type: cross Abstract: This paper examines how metric adjustments to Multidimensional Scaling (MDS) can enhance its effectiveness as a visual tool for pattern recognition. T

AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World

model-releasesarxiv-cs-cv

arXiv:2606.29716v1 Announce Type: new Abstract: This paper addresses the problem of monocular metric depth estimation in aerial UAV imagery. Although recent data-driven methods have achieved remarkabl

An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

arXiv:2606.28467v1 Announce Type: cross Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use. This paper proposes an

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

arXiv:2606.29117v1 Announce Type: cross Abstract: Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integ

Arbitrary Reduction of Validation Error for AI Decision Tests using Homomorphic AI and Repetition Codes

arXiv:2606.28994v1 Announce Type: cross Abstract: This paper presents new results and breakthrough obtained with the HbHAI techniques (Hash-based Homomorphic Artificial Intelligence) proposed in ite{f

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

arXiv:2606.29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, en

Applications

Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems

arXiv:2510.20640v2 Announce Type: replace Abstract: In this paper, we present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft. We provide ins

applicationsarxiv-cs-lg

Building Multi-Task Agentic LLMs via Two-Phase Distillation

arXiv:2606.30044v1 Announce Type: new Abstract: A key step toward artificial general intelligence is to train models that can perform multiple tasks. In this paper, we study how to build such models b

Tutorials

Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification System

arXiv:2606.28953v1 Announce Type: cross Abstract: Poisoning attacks entail attackers intentionally tampering with training data. In this paper, we consider a dirty-label poisoning attack scenario on a

tutorialsarxiv-cs-ai

Contact-Anchored Proprioceptive Odometry for Legged and Wheel-Legged Robots

arXiv:2602.17393v3 Announce Type: replace Abstract: Reliable odometry for legged robots without cameras or LiDAR remains challenging due to IMU drift and noisy joint velocity sensing. This paper prese

researcharxiv-cs-ro

Evaluating LLMs on Chinese Topic Constructions: A Research Proposal Inspired by Tian et al. (2024)

arXiv:2504.14969v2 Announce Type: replace Abstract: This paper proposes a framework for evaluating large language models (LLMs) on Chinese topic constructions, focusing on their sensitivity to island

researcharxiv-cs-cl

Agents

Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games

arXiv:2606.28824v1 Announce Type: cross Abstract: This paper develops a continuum theory of exit-and-join coalition dynamics in nonatomic cooperative games. We extend the Aumann-Shapley value and the

agentsarxiv-cs-ai

Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector

arXiv:2606.30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstr

Fractional Stochastic Neural Networks

arXiv:2606.29438v1 Announce Type: cross Abstract: In this paper, we develop a fractional stochastic neural network with residual dynamics driven by fractional Brownian motion. By introducing a discret

Generalization error of min-norm interpolators in transfer learning

arXiv:2406.13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-ell_2-norm interpolation in transfer learning, where data from diverse distribut

Hierarchical Policy Learning via Spectral Decomposition

arXiv:2606.29570v1 Announce Type: new Abstract: In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By

safetyarxiv-cs-ro

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position

arXiv:2606.15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models

HSAP: A Hierachical Sequence-aware Parallelism for Hybrid-Context Generative Models

arXiv:2606.30460v1 Announce Type: new Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is t

Tutorials

Interaction-Aware 4D Gaussian Splatting for Dynamic Hand-Object Interaction Reconstruction

arXiv:2511.14540v2 Announce Type: replace Abstract: This paper focuses on a challenging setting of simultaneously modeling geometry and appearance of hand-object interaction scenes without any object

tutorialsarxiv-cs-cv

Kriging and neural network models for pressure losses across perforated plates

arXiv:2606.29628v1 Announce Type: cross Abstract: In this paper, two novel data-driven models based on kriging and neural networks (NN) are proposed to predict pressure losses across perforated plates

KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability

arXiv:2606.30243v1 Announce Type: new Abstract: This paper presents KYON, a hybrid wheel-legged quadruped robot equipped with a bimanual upper body for loco-manipulation tasks. The platform features a

safetyarxiv-cs-ro

Tutorials

MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery

arXiv:2512.19612v2 Announce Type: replace Abstract: This paper introduces MauBERT, a multilingual extension of HuBERT that leverages articulatory features for robust cross-lingual phonetic representat

tutorialsarxiv-cs-cl

Agents

Normalizing Flow-Enhanced Message Passing for Multirobot Collaborative Localization

arXiv:2606.29868v1 Announce Type: new Abstract: Accurate, robust, and adaptive localization is essential for various robotic operations. This paper proposes a new message passing (MP) algorithm for re

agentsarxiv-cs-ro

Applications

Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning

arXiv:2606.29725v1 Announce Type: new Abstract: In this paper, we formulate a new vehicle dispatch optimization problem, called Nursing Care Taxi Dispatch, as a variant of the Vehicle Routing Problem,

applicationsarxiv-cs-lg

Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement

model-releasesarxiv-cs-lg

arXiv:2406.05830v2 Announce Type: replace-cross Abstract: This paper presents a fully probabilistic approach for solving optimal experimental design problems under budget constraints. The experimental

Research Entity Extraction and Topic Detection from UKRI Grant Proposals

arXiv:2606.30304v1 Announce Type: cross Abstract: This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke a

Sequential Hiring of Contingent Workers Through Learning-Based Optimization

model-releasesarxiv-cs-lg

arXiv:2606.18438v2 Announce Type: replace-cross Abstract: In this paper, we study a sequential workforce management problem in a contingent labor setting with uncertainty in both worker production and

Stability Boundaries and Motor Performance in Delayed Robot-Mediated Dyadic Interactions

arXiv:2510.14511v3 Announce Type: replace Abstract: This paper establishes analytical stability boundaries for robot-mediated human-human (dyadic) interaction systems, subject to haptic communication

researcharxiv-cs-ro

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots

arXiv:2508.12435v2 Announce Type: replace-cross Abstract: While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2606.30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance co

Virtual Ring Try-On

arXiv:2606.28792v1 Announce Type: new Abstract: This paper presents an innovative approach that enables the users to capture their hand and try the jewel ring on their hand. The user captures the imag

researcharxiv-cs-cv

Zero-Gated Language-conditioned Human Motion Prediction

model-releasesarxiv-cs-cv

arXiv:2606.29208v1 Announce Type: new Abstract: Pose histories provide the core kinematic evidence for 3D human motion prediction, but they lack explicit high-level semantic guidance. This paper intro

A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models

arXiv:2601.13602v3 Announce Type: replace-cross Abstract: This paper introduces an analytical approach to quantifying and optimizing the distributional discrepancy in generative diffusion models. For