Wenxiao Ma

Pei Liu, Songtao Wang, Lang Zhang et al.

Synthesizing high-fidelity and controllable 4D LiDAR data is crucial for creating scalable simulation environments for autonomous driving. This task is inherently challenging due to the sensor's unique spherical geometry, the temporal sparsity of point clouds, and the complexity of dynamic scenes. To address these challenges, we present LiSTAR, a novel generative world model that operates directly on the sensor's native geometry. LiSTAR introduces a Hybrid-Cylindrical-Spherical (HCS) representation to preserve data fidelity by mitigating quantization artifacts common in Cartesian grids. To capture complex dynamics from sparse temporal data, it utilizes a Spatio-Temporal Attention with Ray-Centric Transformer (START) that explicitly models feature evolution along individual sensor rays for robust temporal coherence. Furthermore, for controllable synthesis, we propose a novel 4D point cloud-aligned voxel layout for conditioning and a corresponding discrete Masked Generative START (MaskSTART) framework, which learns a compact, tokenized representation of the scene, enabling efficient, high-resolution, and layout-guided compositional generation. Comprehensive experiments validate LiSTAR's state-of-the-art performance across 4D LiDAR reconstruction, prediction, and conditional generation, with substantial quantitative gains: reducing generation MMD by a massive 76%, improving reconstruction IoU by 32%, and lowering prediction L1 Med by 50%. This level of performance provides a powerful new foundation for creating realistic and controllable autonomous systems simulations. Project link: https://ocean-luna.github.io/LiSTAR.gitub.io.

Pei Liu, Qingtian Ning, Xinyan Lu et al.

Recent advances in vision-language models (VLMs) have demonstrated impressive spatial reasoning capabilities for autonomous driving, yet existing methods predominantly focus on static scene understanding while neglecting the essential temporal dimension of real-world driving scenarios. To address this critical limitation, we propose the OmniReason framework, which establishes robust spatiotemporal reasoning by jointly modeling dynamic 3D environments and their underlying decision-making processes. Our work makes two fundamental advances: (1) We introduce OmniReason-Data, two large-scale vision-language-action (VLA) datasets with dense spatiotemporal annotations and natural language explanations, generated through a novel hallucination-mitigated auto-labeling pipeline that ensures both physical plausibility and temporal coherence; (2) We develop the OmniReason-Agent architecture, which integrates a sparse temporal memory module for persistent scene context modeling and an explanation generator that produces human-interpretable decision rationales, facilitated by our spatiotemporal knowledge distillation approach that effectively captures spatiotemporal causal reasoning patterns. Comprehensive experiments demonstrate state-of-the-art performance, where OmniReason-Agent achieves significant improvements in both open-loop planning tasks and visual question answering (VQA) benchmarks, while establishing new capabilities for interpretable, temporally-aware autonomous vehicles operating in complex, dynamic environments.

Junjie Yu, Wenxiao Ma, Jianyu Zhang et al.

Despite variations in architecture and pretraining strategies, recent studies indicate that large-scale AI models often converge toward similar internal representations that also align with neural activity. We propose that scale-invariance, a fundamental structural principle in natural systems, is a key driver of this convergence. In this work, we propose a multi-scale analytical framework to quantify two core aspects of scale-invariance in AI representations: dimensional stability and structural similarity across scales. We further investigate whether these properties can predict alignment performance with functional Magnetic Resonance Imaging (fMRI) responses in the visual cortex. Our analysis reveals that embeddings with more consistent dimension and higher structural similarity across scales align better with fMRI data. Furthermore, we find that the manifold structure of fMRI data is more concentrated, with most features dissipating at smaller scales. Embeddings with similar scale patterns align more closely with fMRI data. We also show that larger pretraining datasets and the inclusion of language modalities enhance the scale-invariance properties of embeddings, further improving neural alignment. Our findings indicate that scale-invariance is a fundamental structural principle that bridges artificial and biological representations, providing a new framework for evaluating the structural quality of human-like AI systems.