VLWM
Summary
VLWM is the Vision Language World Model introduced by Planning with Reasoning using Vision Language World Model. It predicts high-level goals and interleaved textual action/state-change trajectories from video context, then supports either direct System-1 decoding or System-2 candidate-plan selection with a learned semantic critic.
Model Interface
visual context
-> goal + initial/final-state interpretation
-> action / textual state-change trajectory
-> direct plan or critic-ranked candidate planVLWM should be treated as a high-level semantic world model and planner, not as a low-level robot controller. Its actions are natural-language procedural steps rather than continuous control inputs, and its world state is free-form text rather than a calibrated physical-state representation.
Official Artifacts
- Paper: https://arxiv.org/abs/2509.02722v2
- Author announcement: https://x.com/Delong0_0/status/1963883798841483688
- Hugging Face model page: https://huggingface.co/delong-chen/VLWM
- Hugging Face dataset placeholder: https://huggingface.co/datasets/delong-chen/VLWM
- Local data-release audit: VLWM training data
As checked on 2026-07-15, the model page is manually gated and exposes no public weight file in its public manifest; the dataset repository has zero payload bytes; and no official code repository could be verified. The ICLR 2026 submission is marked withdrawn.
Relationship To Action100M
Action100M is a later hierarchical action/caption release by an overlapping team whose paper says it extends and improves the VLWM data-generation pipeline. It is not the same dataset or target schema as VLWM’s unreleased goal/action/state-change corpus.
Role In The Wiki
VLWM is the main language-state branch of the world-model map. It provides a concrete alternative to both pixel rollout and opaque latent rollout: predict readable semantic state changes and use a semantic cost model for high-level planning.
Its main warning is equally important. Readability does not guarantee physical sufficiency. A textual state can omit geometry, timing, stochasticity, partial observability, constraints, and continuous control details.
Relation To Foundation TSFM Agenda
Use the source-level slot mapping in VLWM. At entity level, VLWM is an adjacent architecture pattern for separating high-level action/state reasoning from low-level signal and control layers; it is not numeric time-series evidence.