An LLM-Agent Framework for Adaptive Task Decomposition and Continual Strategy Updating in Non-Stationary Environments

The study addresses the challenges faced by agents in dynamic and uncertain environments, where decision-making is easily disrupted, task structures are difficult to maintain, and strategies lack continuous adaptability. It proposes an adaptive task decomposition and strategy updating method grounded in large model reasoning. The approach first introduces state modeling and semantic context encoding mechanisms that capture environmental non-stationarity, allowing the agent to acquire and integrate temporal information throughout long-term interactions. Building on this foundation, an adaptive task decomposition module dynamically generates hierarchical task structures through semantic reasoning, enabling the agent to preserve coherent execution even when task goals change, disturbances intensify, or feedback becomes incomplete. In parallel, the strategy updating mechanism adjusts decision distributions based on real-time feedback, allowing rapid recovery and stable behavior when action deviations, sudden scene changes, or state noise occur. These components are integrated into a unified closed-loop reasoning framework that equips the agent with structural understanding, behavioral adjustment, and robust execution capabilities in complex scenarios. Systematic evaluation across multiple key metrics demonstrates that the method improves task completion, planning consistency, error recovery, and decision stability, highlighting its potential value for complex tasks in diverse application domains.