arXiv (All)
open access
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when prediction
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
- LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations#1014540
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#799234
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#797811
- Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks#394448
- MIRAGE: Multi-Perspective Creative Language Model Reasoning with Reinforcement Learning Guidance#1035951
Record · ID 1142935
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