Author

Pei Liu

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Preprint Sep 2026

ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving

In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents'behaviors. Existing World Action Models (WAMs) typically model other agents as components of the world model rather than as decision-makers that fundamentally shape the action of the ego agent,...

Ben-Shan Ma, Pei Liu, Rui-Guo Zhong et al. · 0 citations

Preprint Sep 2026

Where Do Embodied Decisions Come From? Rethinking Latent and Explicit Reasoning

Chain-of-Thought (CoT) reasoning is increasingly incorporated into Vision-Language-Action (VLA) models, yet it can degrade the performance of stronger embodied agents. We investigate this capability-dependent effect by distinguishing explicit reasoning from latent decision computation, i.e., perception-grounded computa...

Yuan-Peng Lin, Zi-Yu Zhou, Jin-Long Zhao et al. · 0 citations

#artificial intelligence Preprint Sep 2026

From Scene Graphs to Answers: Selective Neuro-Symbolic Reasoning for Autonomous Driving

This work introduces a query-adaptive neuro-symbolic reasoning framework that explicitly allocates computation according to the nature of the query and shifts the role of the LLM from a universal reasoning engine to a targeted semantic reasoner, while allowing deterministic computation to be handled exactly and efficie...

Yi-Yao Wang, Pei Liu, Fang Liu et al. · 0 citations

Preprint Sep 2026

Continuous Actions from Discrete Minds: Latent-Aligned Planning for End-to-End Autonomous Driving

LaPla is introduced, a unified Vision-Language-Action framework featuring latent-aligned planning to seamlessly ground semantic understanding in precise motion execution to bridge the modality gap between high-dimensional semantics and the raw action space.

Ruo-Yu Yao, Yu-Sen Xie, Qing-Zhao Liu et al. · 0 citations

Jul 2026

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments

LeapBot-WA establishes a novel Predictive-Latent paradigm for WAMs by operationalizing the Joint-Embedding Predictive Architecture (JEPA) as a World-Anchor and introduces the Isotropic Semantic Autoencoder (ISAE), which reshapes the anchor's latent space into a diffusion-friendly manifold to prevent off-manifold drift.

Pei Liu, Nan Zheng, Lang Zhang et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Aug 2026

DA-WAM: Decision-Aligned Future Latents for Driving World Models

DA-WAM is proposed, a framework that unifies predictive representation learning, action-conditioned future modeling, and trajectory scoring under a single decision-making objective and demonstrates state-of-the-art performance on NAVSIM-v1 and NAVSIM-v2.

Rui-Guo Zhong, Ben-Shan Ma, Xiao-Long Chen et al. · 4 citations