Belief-Calibrated optimization: an explicit world model for agentic optimization
Read the original at arxiv.org→arXiv:2609.01861v1 Announce Type: new Abstract: The performance of an LLM agent depends on the scaffold around a frozen model. A common way to improve that scaffold is to use a coding agent as an optimizer: it reads...
Original headline: "Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization"
Coverage timeline
- Sep 3, 04:00 UTC arXiv cs.AI lead source Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
- Sep 3, 04:00 UTC arXiv cs.LG WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling