Task Memory Engine (TME): Enhancing State Awareness for Multi-Step LLM Agent Tasks

Understand

Large Language Models (LLMs) are increasingly used as autonomous agents for multi-step tasks.

• However, most existing frameworks fail to maintain a structured understanding of the task state, often relying on linear prompt concatenation or shallow memory buffers.
• This leads to brittle performance, frequent hallucinations, and poor long-range coherence.
• In this work, we propose the Task Memory Engine (TME), a lightweight and structured memory module that tracks task execution using a hierarchical Task Memory Tree (TMT).