Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
Belief-Calibrated Optimization is introduced, a method that writes that belief down as a persistent in-context document and continually revises that document as new candidates are evaluated, and reaches a higher train passrate than a matched control that lacks only the world model.
Yu-Han Chen, Zhi-Hua Tian, Mahavir Dabas et al.
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