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版本更新 · v2arXiv:2607.14203

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

NVIDIA, :, Jiahui Huang, Jiawei Ren, Michal Tyszkiewicz, Bjoern Haefner, Michael Shelley, Xin Kang 等 17 位作者

原文摘要

3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; however, these methods remain relatively slow and typically require per-scene tuning. In this work, we present Instant NuRec, a feed-forward neural reconstruction model that turns a short multi-view driving log into a fully simulatable 3D Gaussian Splatting (3DGS) world in a single forward pass. The model accepts multi-view input from a calibrated camera rig and emits a layered output consisting of static and dynamic 3DGS layers, a sky cubemap, and per-camera ISP corrections, while providing native support for non-pinhole camera models via 3DGUT. It reconstructs a 10-20-second multi-camera scene in roughly 1.5 seconds and achieves a PSNR on the Waymo Open Dataset that is 2.01 dB above the strongest evaluated baseline. Instant NuRec is deeply integrated into NuRec and is compatible with AlpaSim for closed-loop simulation.

cs.GR · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09355

MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry

Junye Zou, Huiyi Yan, Xinning Xu, Xiaolei Li, Pengkun Zhou, Jinhui Zhang, Ziyang Meng

原文摘要

Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry. MosaicIMU uses a prototype-based router to compose carrier-specific expert features, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domain adaptation, it freezes the pretrained base model and learns a new lightweight expert residual branch. For edge-deployment, it further reuses the router to select informative online samples for efficient incremental updates. Experiments show that MosaicIMU consistently outperforms learning-based baselines, reducing average ATE and RTE-10s by 40% and 34%, respectively. These results highlight that MosaicIMU provides a scalable pretraining-to-deployment paradigm for generalizable and adaptive neural inertial odometry.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05844

Sure Enough to Share? Overconfidence in GenAI Video Discernment is Associated with Sharing Intentions

Haoning Xue, Sijia Qian, Christopher Calabrese, Cuihua Shen, Laramie Taylor, Yixuan Wang

原文摘要

Generative Artificial Intelligence (GenAI) is blurring the line between synthesized and recorded videos, but less is known about people's awareness of their discernment abilities and the consequences of miscalibration. This study examines three forms of overconfidence in GenAI video discernment (i.e., overestimation, overplacement, and overprecision) and their associations with source judgments and sharing intentions in an online study (N = 973). Participants evaluated videos depicting everyday scenes and achieved 62.5% accuracy. Three forms of overconfidence showed distinct patterns: participants overplaced themselves by 12.8 percentile points, but overprecision showed consistent positive associations with sharing intentions and the tendency to classify videos as human-made. Age, GenAI usage, and AI literacy showed consistent positive associations with all three forms of overconfidence. These findings highlight the importance of distinguishing the three forms of overconfidence to better understand GenAI engagement and incorporating metacognition into interventions to support informed media consumption.

cs.HC · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05828

Autonomy and Coordination in Intelligent Manufacturing: A Recursive Autonomous Agent Framework

Yuning Chen, Hongda Zhang, Linqiang Hu, Ziqing Zhou, Yi Liu, Zhao Huang, Ding Zhou, Haining Xu

原文摘要

Intelligent production requires systems to make decisions and organize work according to production goals and operating conditions. It also requires continuous adjustment through feedback. This paper analyzes intelligence in human production organizations from a complex adaptive systems (CAS) perspective. The analysis identifies two coupled mechanisms: individual autonomy and collective coordination. Individual autonomy enables actors to form local decisions using available information. Collective coordination aligns these decisions with shared goals and joint constraints. Learning and adaptation improve both mechanisms over time. A production-system model formalizes how local decision rules and coordination jointly form system decisions. On this basis, we propose the Recursive Autonomous Agent (RAA) framework, which combines goal-driven autonomous agents with a recursive organizational structure. Goal contracts define local objectives and operating boundaries, while capability contracts support the selection and composition of production units. Common interfaces allow coordinated groups to participate as units in larger organizations, connecting equipment, production lines, and factories. RAA thus provides an engineering approach to next-generation intelligent manufacturing, translating the requirements of intelligence, openness, and system-wide integration into mechanisms for autonomous and coordinated production.

cs.AI · cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2019ACL Anthology · ACL 2026

PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation

Chenning Xu Tencent Hunyuan Team, Mao Zheng, Mingyu Zheng, Mingyang Song Tencent

原文摘要

Podcast script generation requires LLMs to synthesize structured, context-grounded dialogue from diverse inputs, yet systematic evaluation resources for this task remain limited. To bridge this gap, we introduce PodBench, a benchmark comprising 800 samples with inputs up to 21K tokens and complex multi-speaker instructions. We propose a multifaceted evaluation framework that integrates quantitative constraints with LLM-based quality assessment. Extensive experiments reveal that while proprietary models generally excel, open-source models equipped with explicit reasoning demonstrate superior robustness in handling long contexts and multi-speaker coordination compared to standard baselines. However, our analysis uncovers a persistent divergence where high instruction following does not guarantee high content substance. PodBench offers a reproducible testbed to address these challenges in long-form, audio-centric script generation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表