Dual-Frontier: When Can an Agent Trust Its World Model?
- Published
- Source
- arXiv
- Paper number
- 1106
- Field
- AI Agents
- arXiv ID
- 2609.26293
Key points
- Proposes a dual-frontier framework separating world model errors from policy failures
- Provides principled criteria for when world model predictions can be trusted
- Directly tackles reliability of model-based planning in high-stakes settings
- Essential reading for general-purpose agent architecture and planning research
Paper links
External research summaries. These are not HDATF publications or measured product results.