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WLA$^3$ (World Latent Action Modeling for Semantics, Dynamics, and Kinematics), a unified generalist policy model framework built around representations learned by a World Latent Action Model (WLAM), is introduced.
This paper argues that what a VLA needs is not the ability to generate language, but the ability to consume grounded language, and introduces a framework that endows a VLA with language competence through in-context post-training and an agentic tool-use interface.
Long-form article generation remains difficult for large language models because it combines long context, long instructions, and long outputs. Existing multi-agent pipelines such as STORM improve information coverage by simulating role-specialized agents, but their capabilities are often entangled in prompts and fixed...
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