LeCuration: A Tiny World Model as a Data Curation Multi-Tool
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arXiv:2610.09285v1 Announce Type: new Abstract: Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior. Examples in...
arXiv:2607. 05352v1 Announce Type: cross Abstract: We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions.
The paper introduces Kuration SDK, a toolkit for curating raw gameplay data to address the Virtual2Real gap in action‑conditioned world models. It demonstrates that conventional metrics like FVD, LPIPS, and JEDi do not reliably predict playability, using CounterStrike data to show divergent model performance despite similar scores. By providing curation strategies and diagnostic tools, the SDK helps uncover root causes of the gap and accelerate the creation of sample‑efficient training datasets.
The paper introduces CoDeR, a new paradigm for world modeling that explicitly builds an executable world using code rather than relying solely on visual observations. CoDeR translates high‑level concepts into structured world rules, executable dynamics, and perceptual observations through five complementary roles, enabling long‑term memory, open‑ended interactions, autonomous world evolution, and persistent multi‑agent dynamics. Experiments show that this framework extends the capabilities of existing world models and achieves state‑of‑the‑art performance across multiple evaluation settings.
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primarily rely on motion capture datasets, which are expensive to scale and limited in functional diversity.
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment...