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Preprint Sep 2026

HelloWorld: Towards Practical Applications of Generative Driving World Models

Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. Realizing this potential requires a system that can generalize across diverse scenes, respond faithfully to prescribed controls, generate coherent multi-sensor observati...

Fan Lu, Han-Shi Wang, Zi-Jing Wang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Seek Before You Move: Evidence Seeking for Progress Grounding in Vision-Language Navigation

Vision-Language Navigation (VLN) requires agents to continuously ground task progress from long-horizon instructions and partial egocentric observations. Existing VLM-based navigation agents typically reason only over available observations and may remain confident even when task-relevant evidence is missing. For examp...

Zhi-Min Wang, Mei-Yuan Zhu, Duo Wu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies

This work introduces eVTA, which learns success probabilities from mixed-quality policy rollouts through temporal-difference-style bootstrapping, without expert demonstrations or intermediate annotations, and introduces RL with Evolving Rewards (RLER), a closed-loop framework that adapts eVTA using newly collected roll...

Duo Wu, Hai-Feng Wang, Rongwei Lu et al. · 0 citations

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