Ziwei Liu
6 papers · Latest:
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
Omnimodal LLMs struggle to reject false textual claims contradicting sensory input, revealing a "Representation-Action Gap" in grounding.
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
SenseNova-U1 introduces a unified architecture (NEO-unify) that seamlessly integrates multimodal understanding and generation, outperforming specialized VLMs.
Is Your Driving World Model an All-Around Player?
WorldLens is a new benchmark, dataset, and agent for evaluating driving world models beyond visual realism, focusing on physical and behavioral fidelity.
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
This paper proposes a new five-level taxonomy for visual generation, shifting from appearance synthesis to intelligent, agentic world modeling.
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
This paper introduces a "levels x laws" taxonomy for agentic world models, synthesizing over 400 works and outlining a roadmap for future development.
XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
XRZero-G0 is a hardware-software system that enables scalable, high-quality robot-free data collection for dexterous manipulation, reducing costs significantly.
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