3 citations · 4 across the 12 of their papers we have counts for
15 papers
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency
Jack Lu, Yurong You, Boyi Li +8
Textual reasoning before acting has improved vision-language-action (VLA) models, but at the cost of latency: every action conditioned on the reasoning text must wait for autoregre…
VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning
Zewei Zhou, Rachel Luo, Yulong Cao +10
Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constrain both optimization feedba…
Grounding What Shapes the Plan: Rethinking Groundedness for Physical Intelligence in Autonomous Driving
Minkyoung Cho, Zewei Zhou, Wenhao Ding +11
Driving models increasingly ground reasoning in causal relations, spatial structure, perceptual evidence, and predicted futures. These advances make reasoning more faithful to the…
One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context
Yingzi Ma, Zichen Zhu, Ming Jiang +1
On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts. Existing teachers…
GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving
Yingzi Ma, Chaowei Xiao, Ming Jiang
Vision-language models (VLMs) for autonomous driving have shown promising performance, but their ability to handle region-specific traffic rules remains underexplored, raising unce…
SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation
Yingzi Ma, Xiaogeng Liu, Yawen Zheng +1
With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos from a text prompt or an initia…
dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
Yingzi Ma, Yulong Cao, Wenhao Ding +6
The autonomous driving community is increasingly focused on addressing the challenges posed by out-of-distribution (OOD) driving scenarios. A dominant research trend seeks to enhan…
ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning
Zhengyue Zhao, Yingzi Ma, Somesh Jha +3
Large Language Models have shown impressive generative capabilities across diverse tasks, but their safety remains a critical concern. Existing post-training alignment methods, suc…
RealDrive: Retrieval-Augmented Driving with Diffusion Models
Wenhao Ding, Sushant Veer, Yuxiao Chen +3
Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rul…
DreamDrive: Generative 4D Scene Modeling from Street View Images
Jiageng Mao, Boyi Li, Boris Ivanovic +7
Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based…
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Wenhao Ding, Yulong Cao, Ding Zhao +2
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has bee…
Dolphins: Multimodal Language Model for Driving
Yingzi Ma, Yulong Cao, Jiachen Sun +2
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce Dolph…
CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption
Zhuoyuan Wu, Jiachen Sun, Chaowei Xiao
Despite recent advancements in deep neural networks for point cloud recognition, real-world safety-critical applications present challenges due to unavoidable data corruption. Curr…
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
Haizhong Zheng, Jiachen Sun, Shutong Wu +4
Given a real-world dataset, data condensation (DC) aims to synthesize a small synthetic dataset that captures the knowledge of a natural dataset while being usable for training mod…
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
Yulong Cao, Boris Ivanovic, Chaowei Xiao +1
In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of…