Search: “benchmark”

Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications

arXiv:2606.09569v1 Announce Type: cross Abstract: With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation.

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Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems

DGX agent

arXiv:2606.09477v1 Announce Type: new Abstract: Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mo

model-releasesarxiv-cs-cv

9 Jun 2026

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

arXiv:2606.09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose chall

researcharxiv-cs-lg

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

arXiv:2406.07318v3 Announce Type: replace Abstract: The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems

End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

arXiv:2606.09792v1 Announce Type: new Abstract: End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formal

End-to-End Training for Discrete Token LLM based TTS System

arXiv:2606.09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive la

Enhanced Detection of Tiny Objects in Aerial Images

arXiv:2509.17078v3 Announce Type: replace Abstract: While one-stage detectors like YOLOv8 offer fast training speed, they often under-perform on detecting small objects as a trade-off. This becomes ev

EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation

arXiv:2606.08980v1 Announce Type: new Abstract: This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on ad

Escaping the KL Agreement Trap in On-Policy Distillation

arXiv:2606.09471v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense token-level supervision by asking a teacher to score student-generated rollouts. However, when the student d

safetyarxiv-cs-lg

Executable World Models for ARC-AGI-3 in the Era of Coding Agents

arXiv:2605.05138v2 Announce Type: replace Abstract: We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

arXiv:2606.08497v1 Announce Type: new Abstract: As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes param

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

arXiv:2512.15116v2 Announce Type: replace-cross Abstract: Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sen

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

arXiv:2606.08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectra

Local Ai

FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning

arXiv:2606.08653v1 Announce Type: cross Abstract: Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change

local-aiarxiv-cs-ai

FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction

arXiv:2606.07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception. To address this, we propose F

FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint

arXiv:2501.15509v5 Announce Type: replace-cross Abstract: Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intru

FMRFusion: Frequency-Aware Multi-View Representation Learning for Heterogeneous Image Fusion

arXiv:2606.07985v1 Announce Type: new Abstract: Infrared and visible image fusion aims to generate a composite image that retains significant target information and preserves detailed textures, integr

From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction

arXiv:2606.09392v1 Announce Type: new Abstract: Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems co

Agents

From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG

arXiv:2603.03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and o

agentsarxiv-cs-ai

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking

arXiv:2603.27493v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-eff

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs

arXiv:2606.07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data. Recent methods use LLMs to guide mutation from background text, which is more

GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer

arXiv:2606.09453v1 Announce Type: new Abstract: Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on va

Generalization in Nonlinear Least Squares via Learned Feature Geometry

model-releasesarxiv-cs-lg

arXiv:2606.08799v1 Announce Type: cross Abstract: We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local

Agents

Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups

arXiv:2606.08844v1 Announce Type: new Abstract: As autonomous systems expand from capital-intensive robotaxis to cost-sensitive logistics, sensor configurations are increasingly optimized for coverage

agentsarxiv-cs-cv

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems

arXiv:2606.09432v1 Announce Type: new Abstract: Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs)

researcharxiv-cs-lg

Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts

arXiv:2606.09105v1 Announce Type: new Abstract: Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.Recent Large Language Model (LL

Agents

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

arXiv:2603.24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, p

agentsarxiv-cs-lg

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

arXiv:2606.07526v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. How

GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence

arXiv:2606.08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained f

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics

arXiv:2606.08417v1 Announce Type: cross Abstract: Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling. Progress in both

HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning

arXiv:2606.08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains

Hardware

Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems

arXiv:2606.07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitiv

hardwarearxiv-cs-lg

HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents

arXiv:2606.09738v1 Announce Type: new Abstract: Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-ba

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions

arXiv:2606.08051v1 Announce Type: new Abstract: Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale. Our curr

Hybrid Robustness Verification for Spatio-Temporal Neural Networks

arXiv:2606.09746v1 Announce Type: cross Abstract: With AI increasingly deployed in safety-critical systems, providing formal robustness guarantees for the underlying models is essential. Existing veri

IDEQ -- Improving Diffusion Models for the Traveling Salesman Problem (TSP) by Leveraging the Structure of the Solution Space

arXiv:2412.13858v2 Announce Type: replace Abstract: We investigate diffusion models to solve the Traveling Salesman Problem. Building on the recent DIFUSCO and T2TCO approaches, we propose IDEQ. IDEQ

Applications

iMaC: Translating Actions into Motion and Contact Images for Embodied World Models

arXiv:2606.09813v1 Announce Type: cross Abstract: Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventio

applicationsarxiv-cs-cv

IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval

arXiv:2606.08144v1 Announce Type: new Abstract: Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing met

Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts

model-releasesarxiv-cs-lg

arXiv:2606.08587v1 Announce Type: cross Abstract: Statistical post-processing has proven to be an effective tool in improving ensemble forecast of different weather variables. Case studies show that p

In-Context Learning for Latent Space Bayesian Optimization

arXiv:2606.09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects

researcharxiv-cs-lg

In-Context Learning of Temporal Point Processes with Foundation Inference Models

model-releasesarxiv-cs-lg

arXiv:2509.24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamic

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

arXiv:2606.08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely o

Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning

arXiv:2604.07848v2 Announce Type: replace Abstract: Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -

safetyarxiv-cs-lg

INFUSER: Influence-Guided Self-Evolution Improves Reasoning

arXiv:2606.09052v1 Announce Type: cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet e

researcharxiv-cs-ai

Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

arXiv:2606.08314v1 Announce Type: new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high

IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations

arXiv:2606.08029v1 Announce Type: new Abstract: Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observab

safetyarxiv-cs-ro

KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection

arXiv:2606.07651v1 Announce Type: cross Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manip

Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language

model-releasesarxiv-cs-ro

arXiv:2606.08666v1 Announce Type: new Abstract: Robots deployed in human-centric environments routinely receive natural-language descriptions of spatial information ('I left my backpack on the table')