3 citations · 3 across the 16 of their papers we have counts for
19 papers
The -Derivative Associated with Linear Recurrence Sequences
Ronald Orozco López
We associate a canonical operator to every linear recurrence sequence admitting a distinct-root Binet representation \(\mathrm{U}_n=\sum_{j=1}^{k}A_jλ_j^n.\) The construction is ba…
GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
Qize Yu, Lianrui Fan, Bowen Ping +19
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidenc…
VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis
Kangjie Chen, Xiangyu Li, Dongbin Zhang +12
We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods face a fundamental trade-off: r…
X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation
Haojun Zhang, Yi Zou, Min Chen +7
Reducing audio-encoder depth lowers the inference cost of speech large language models, but removing complete blocks perturbs the embeddings consumed by the decoder and can cause d…
TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI
Yuheng Zhang, Yizhao Wang, Da Zhu +19
We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-context and latency-sensitive p…
SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control
Rulin Zhou, Qiujie Song, Yujie Ma +10
Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a st…
Unbiased Alignment for Large Language Models with Noisy Preferences
Jialiang Wang, Xianming Liu, Xiong Zhou +2
The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization. However, th…
X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving
Bohao Zhao, Chengrui Wei, Guangfeng Jiang +17
Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive percept…
TuringViT: Making SOTA Vision Transformers Accessible to All
Qiman Wu, Hanlin Chen, Lyujie Chen +19
Modern VLMs and VLA systems commonly adopt off-the-shelf ViTs such as SigLIP2 as visual encoders, but diverse downstream requirements in latency, temporal modeling, and VLM integra…
X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference
Yixiao Zeng, Jianlei Zheng, Chaoda Zheng +10
Real-time world simulation is becoming a key infrastructure for scalable evaluation and online reinforcement learning of autonomous driving systems. Recent driving world models bui…
X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
Baolu Li, Jingyu Qian, Rui Guo +17
Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive…
X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving
Chaoda Zheng, Sean Li, Jinhao Deng +9
Scalable and reliable evaluation is increasingly critical in the end-to-end era of autonomous driving, where vision--language--action (VLA) policies directly map raw sensor streams…
CorrectionPlanner: Self-Correction Planner with Reinforcement Learning in Autonomous Driving
Yihong Guo, Dongqiangzi Ye, Sijia Chen +2
Autonomous driving requires safe planning, but most learning-based planners lack explicit self-correction ability: once an unsafe action is proposed, there is no mechanism to corre…
EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation
Jiajun Cao, Xiaoan Zhang, Xiaobao Wei +10
Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the visual encoder and struggle with accumu…
FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
Hongbin Lin, Yiming Yang, Yifan Zhang +10
In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive relian…
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Qiang Li, Yingwenqi Jiang, Tuoxi Li +17
Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large vi…
FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning
Jiajun Cao, Qizhe Zhang, Peidong Jia +11
Vision-Language-Action (VLA) models have demonstrated significant potential in complex scene understanding and action reasoning, leading to their increasing adoption in end-to-end…
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction
Jian Liu, Sipeng Zhang, Chuixin Kong +7
This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the str…