arXiv (All)
open access
Adapt for Planning, Not just Prediction: Optimistic Test-Time World Model Adaptation
Latent world models are typically pre-trained offline and kept frozen at test time, so planning with them can degrade under distribution shift. Test-time adaptation (TTA) can mitigate this degradation by updating the model during deployment. Unlike pre-training on fixed data, TTA for planning is clo
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
- Adapt for Planning, Not just Prediction: Optimistic Test-Time World Model Adaptation#1450801
- Representation Editing for Multimodal Test-Time Adaptation#1116594
- Test-Time Generalized Category Discovery#1119010
- LatentWave: JEPA Pretraining for Wireless Foundation Models#259516
- RSI-Anything: Unlocking Recursive Self-Improvement with World Models#1313565
Record · ID 1459106
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