5 papers
What Generation Knows: Unlocking Fine-Grained Understanding in Unified Multimodal Models
Tengju Ru, Weitong Lian, Zecong Tang +6
Fine-grained visual understanding requires distinguishing subtle differences in local attributes and spatial relations, yet remains challenging for existing vision-language models.…
ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation
Boni Hu, Xiong Wei, Haoming Huang +20
Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three…
Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs
Zixuan Huang, Yang Zhou, Kaixuan Wang +7
Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision…
IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
Lingyi Meng, Zecong Tang, Haoran Li +12
In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved ima…
Drive-P2D: A Progressive Perception-to-Decision Benchmark for VLMs in Autonomous Driving
Zecong Tang, Zixu Wang, Yifei Wang +10
Autonomous driving requires reliable perception and safe decision-making in complex scenarios. Recent vision-language models (VLMs) demonstrate reasoning and generalization abiliti…