Search: “models”

SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images

arXiv:2607.03872v1 Announce Type: new Abstract: Reconstructing high-fidelity 3D building models from UAV imagery is essential for large-scale digital twin development. However, existing 3D Gaussian Sp

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Agents

SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production

DGX agent

arXiv:2607.02731v1 Announce Type: cross Abstract: Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities. However, deployi

agentsarxiv-cs-ai

7 Jul 2026

Spatial Attention: Adapting Execution Horizons for Diffusion Policies via Observation Sensitivity

arXiv:2607.04739v1 Announce Type: new Abstract: Sampling action chunks via generative models has become a widely adopted methodology for robotic learning from demonstration. However, existing methods

safetyarxiv-cs-ro

Spatial Graph Representation and Morphometric Analysis of the Pulmonary Vascular Tree From Computed Tomography Using Multi-Scale Hessian-Based Filter Fusion and TEASAR Skeletonization

arXiv:2607.04457v1 Announce Type: new Abstract: Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessm

Spinning Straw into Gold: Relabeling LLM Agent Trajectories in Hindsight for Successful Demonstrations

arXiv:2607.04235v1 Announce Type: new Abstract: Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address th

agentsarxiv-cs-cl

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training

arXiv:2607.04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed r

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, wh

TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving

arXiv:2607.04812v1 Announce Type: new Abstract: Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the erro

agentsarxiv-cs-cv

The agent creates, we validate: A Lightweight Framework for Agentic Artifact Generation

arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straight

Towards Digital Preservation of Efik: TTS for a Low-Resource African Language

arXiv:2607.04515v1 Announce Type: new Abstract: Efik, a tonal language spoken by about 3 million second language speakers and 1.5 million native speakers in Southeastern Nigeria, remains underrepresen

researcharxiv-cs-cl

Tutorials

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

arXiv:2607.02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market i

tutorialsarxiv-cs-lg

Trajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot Recovery

arXiv:2607.04745v1 Announce Type: cross Abstract: Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an over

safetyarxiv-cs-cv

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int

UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks

arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversa

Applications

Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning

arXiv:2310.00505v3 Announce Type: replace-cross Abstract: Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. Howev

applicationsarxiv-cs-ai

USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning

arXiv:2607.03900v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. A

Video-based detection of cessation of breathing in pre-term infants using machine learning

arXiv:2607.05230v1 Announce Type: new Abstract: Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable re

Vision Pretraining for Dense Spatial Perception

arXiv:2607.05247v1 Announce Type: new Abstract: Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable represe

Applications

VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild

arXiv:2410.00296v2 Announce Type: replace Abstract: Vision-language Models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adv

applicationsarxiv-cs-lg

Wasserstein Residuals: Learning Gradient Flows from Population Dynamics

arXiv:2607.04738v1 Announce Type: cross Abstract: Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient f

When Does Resolution Help a Frozen Backbone? Global Attention at Resolution Predicts Scalable Adaptation for Camouflaged and Marine Animal Segmentation

arXiv:2607.02708v1 Announce Type: new Abstract: Adapting frozen vision foundation models to fine-grained segmentation now largely depends on backbone selection. Whether the backbone applies global att

A Convex Obstacle Avoidance Formulation

arXiv:2512.13836v2 Announce Type: replace-cross Abstract: Autonomous driving requires reliable collision avoidance in dynamic environments. Nonlinear Model Predictive Controllers (NMPCs) are suitable

agentsarxiv-cs-ro

A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention

arXiv:2601.04509v2 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard

A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification

arXiv:2607.01974v1 Announce Type: cross Abstract: This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to

Actual causality in fault trees

arXiv:2607.01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question 'what can go wrong?', especially through minimal cut

Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

arXiv:2607.02205v1 Announce Type: new Abstract: Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonl

safetyarxiv-cs-ro

Aggregation with Exponential Weights is Optimal in Expectation

arXiv:2607.02247v1 Announce Type: cross Abstract: The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss

Auto-FL-Research: Agentic Search for Federated Learning Algorithms

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local

Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring

arXiv:2607.02121v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Gu

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

arXiv:2607.02262v1 Announce Type: new Abstract: Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are pr

local-aiarxiv-cs-cl

Hardware

DeadPool: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint

arXiv:2607.01646v1 Announce Type: new Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures acro

hardwarearxiv-cs-lg

DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

arXiv:2607.01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios. People's individual personalities and concerns require tailored

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

arXiv:2607.01800v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete se

researcharxiv-cs-cl

Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

arXiv:2607.01852v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside the

ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues

arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

arXiv:2603.13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundr

Geometry-Aware R-Structured Kolmogorov-Arnold Networks

arXiv:2607.01449v1 Announce Type: new Abstract: We propose a novel hybrid neural architecture, the Geometry-aware R-Structured Kolmogorov-Arnold Network (GRS-KAN), which integrates V.L.Rvachev's R-fun

Tutorials

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size

arXiv:2607.01487v1 Announce Type: new Abstract: We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training steps and batch size (

tutorialsarxiv-cs-lg

Human Supervisor Workload Prediction: Lag Horizon Selection

arXiv:2505.15939v2 Announce Type: replace Abstract: Teleoperation systems must be aware of the human's workload during missions to maintain operator performance. Prior work employed wearable physiolog

researcharxiv-cs-ro

Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow

arXiv:2606.20101v2 Announce Type: replace-cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remai

Hybrid quantum-classical neural network for sentiment analysis

arXiv:2607.01943v1 Announce Type: new Abstract: Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learnin

Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

arXiv:2607.01237v1 Announce Type: cross Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high d

kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail

arXiv:2607.02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing g

Tutorials

Learning Consumer Preferences from Bundle Sales Data

arXiv:2209.04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data. Product bundling is a widely use

tutorialsarxiv-cs-lg

Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning

arXiv:2503.21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-s

MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

arXiv:2607.01539v1 Announce Type: cross Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection. W

agentsarxiv-cs-lg

Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing

arXiv:2607.01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) wi

NeoMap: Training-free Novel-View Synthesis from Single Images and Videos

arXiv:2607.01962v1 Announce Type: cross Abstract: We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assum