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Related Experiment Video

Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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

Taylor Webb^1,2, Shanka Subhra Mondal^3,4, Ida Momennejad⁵

¹Université de Montréal, Montreal, QC, Canada. taylor.w.webb@gmail.com.

Nature Communications

|October 1, 2025

Summary

Large language models (LLMs) struggle with planning. A new Modular Agentic Planner (MAP) architecture, inspired by the brain, improves multi-step reasoning and goal-directed planning in LLMs.

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Area of Science:

Artificial Intelligence
Cognitive Neuroscience

Background:

Large language models (LLMs) excel at many tasks but falter in multi-step reasoning and goal-directed planning.
Human planning involves specialized cognitive processes like conflict monitoring, state prediction, and task coordination, often linked to specific brain regions.

Purpose of the Study:

To develop an improved planning capability for LLMs by integrating brain-inspired modular processes.
To address the limitations of standard LLMs in autonomously coordinating planning functions.

Main Methods:

Proposed a Modular Agentic Planner (MAP) architecture, utilizing specialized, brain-inspired LLM modules.
Evaluated MAP on graph traversal, Tower of Hanoi, PlanBench benchmark, and the strategyQA NLP task.

Main Results:

MAP significantly outperformed standard LLMs and agentic baselines on planning tasks.
MAP demonstrated effective combination with smaller, cost-efficient LLMs and superior cross-task transfer.
The architecture showed improved multi-step reasoning and goal-directed planning.

Conclusions:

Integrating cognitive neuroscience principles into LLM architectures enhances planning abilities.
Modular, brain-inspired design is a promising direction for advancing LLM performance in complex reasoning tasks.