Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization

arXiv:2609.01861 · cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Terminology

• Benchmark — 34× in this paper · explained in 2 episodes
• Trace — 24× in this paper · explained in 2 episodes
• World Model — 23× in this paper · explained in 7 episodes
• Calibration — 16× in this paper · explained in 7 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Agentic Optimization — 4× in this paper · explained in 1 episode
• Frozen model — 4× in this paper · explained in 3 episodes
• Bayesian Optimization — 3× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Agentic System — 2× in this paper · explained in 2 episodes
• Amortization — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Agent Harness — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Self-Evolving Agents — 1× in this paper · explained in 2 episodes

Sources

• HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
• Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
• CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
• EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
• Automated Design of Agentic Systems
• FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• Meta-Harness: End-to-End Optimization of Model Harnesses
• Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
• Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams
• Self-Refine: Iterative Refinement with Self-Feedback
• Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
• Generative Agents: Interactive Simulacra of Human Behavior
• AgentSquare: Automatic LLM Agent Search in Modular Design Space
• Reflexion: Language Agents with Verbal Reinforcement Learning
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• An Explanation of In-context Learning as Implicit Bayesian Inference
• Large Language Models as Optimizers
• G\"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
• Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

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