arXiv (All)
open access
FACT: Failure-Aware Causal Training for World-Action Models
Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on the future-prediction ability of video models, many WAMs generate future videos and recover actions with inverse-dynamics models, or use these predic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Can World and Action Modeling Benefit Each Other under Unified Causal Training?#1356431
- I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?#1105719
- LPA-CWM: A Learned Physical Adjudicator for Motion Reasoning with Counterfactual World Models#1083549
- A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models#1110003
- Rethinking Representations for World-Action Modeling#1142458
Record · ID 1344678
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive