A brain-inspired agentic architecture to improve planning with LLMs.

ABSTRACT: Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed planning. To address this, we take inspiration from the human brain, in which planning is accomplished via component processes that are predominantly associated with specific brain regions. These processes include conflict monitoring, state prediction, state evaluation, task decomposition, and task coordination. We find that LLMs are often capable of carrying out these functions in isolation, but struggle to autonomously coordinate them in the service of a goal. Therefore, we propose a modular agentic architecture - the Modular Agentic Planner (MAP) - in which planning is performed via the interaction of specialize

SUBMITTER: Webb T 

PROVIDER: S-EPMC12485071 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature