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This paper proposes a reward-driven LLM agent workflow that integrates POMDP routing and self-correcting reward models, achieving a 24.5% improvement in task success rate on benchmarks like ALFWorld and WebShop.
An experiential framework for long-horizon image editing that couples planning with reward-driven execution to improve coherence and reliability of complex multi-step edits.