arXiv (All)
open access
World Potential Model: Pretrained World Knowledge as Progress Potentials
Long-horizon language agents often receive supervision only from terminal task outcomes, leaving little signal for distinguishing productive intermediate behavior from stagnation or even regression. Rather than learning a separate value function or process reward model for every task, we ask whether
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
- COUNTERMEM: World-Model Verified Counter-Factual Memory for Language Agents#1115830
- Working Memory, Attention Control, and Vocabulary Retention in AI (ChatGPT)-Assisted Foreign Language Learning: A Structural Cognitive Modelling Approach.#140521
- A Finger on the Scale: Covert Policy Steering through Agentic Skills#924721
- A Finger on the Scale: Covert Policy Steering through Agentic Skills#920487
- Heavy-Tailed Memory Traces in Long-Horizon Language Agents#1154633
Record · ID 1233599
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