The September 2025 paper introduces **ReasoningBank**, a novel memory framework designed to enhance Large Language Model (LLM) agents by distilling and utilizing abstract reasoning patterns from both successful and failed task trajectories. Unlike previous approaches that focus only on raw interactions or successful workflows, ReasoningBank stores structured memory items—including titles, descriptions, and content—that capture generalizable strategies and lessons learned from mistakes. This framework is combined with **Memory-Aware Test-Time Scaling (MaTTS)**, which uses memory to guide efficient exploration, creating a positive feedback loop where diverse experiences generate stronger memories, leading to consistently improved success rates and reduced steps across various complex agent tasks like web browsing and software engineering.

Source:

https://arxiv.org/pdf/2509.25140