Michael Liu — accepted AI-conference papers

2026

Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning

Tong Wu, Michael Liu, Jun Bai, Zixia Jia, Shuyi Zhang, Ziyong Lin +3

ICML 2026poster

We introduce **Native Parallel Reasoner (NPR)**, a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a **self-disti…

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2026

PAT3D: Physics-Augmented Text-to-3D Scene Generation

Guying Lin, Kemeng Huang, Michael Liu, Ruihan Gao, Hanke Chen, Lyuhao Chen +5

ICLR 2026poster

We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision–language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes. Given a text prompt, PAT3D generates 3D objects, infers their…

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