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This paper proposes a neuro-symbolic agentic AI (NSAAI) framework for networked low-altitude UAVs to support reliable and adaptive autonomous decision-making under uncertainty, with a reference architecture and a case study in urban fire inspection.
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.
This paper introduces Experience Memory Graph (EMG), a framework that reformulates agent failure recovery as a graph matching problem to enable one-shot error correction for LLM agents without test-time trial-and-error.
This paper introduces the concept and architecture of a Business World Model (BWM), a specialized world model for business environments that encodes states, dynamics, constraints, and objectives to support autonomous decision-making and goal-driven planning.