机器之心·2025-10-12 08:02Core Insights - The article discusses the concept of "Agentic Context Engineering," which allows language models to self-improve without the need for fine-tuning, drawing attention from the academic community [1] - Google's earlier work on "ReasoningBank" presents a similar idea, focusing on an innovative memory framework for agent systems that extracts and organizes memory items from the agent's own experiences [1][3] Summary by Sections ReasoningBank Overview - ReasoningBank captures effective strategies from successes and important lessons from failures, creating actionable principles in a closed-loop process [1][3] - The framework consists of structured memory items that include a title, description, and content, allowing agents to interact with their environment and build new memory items from past experiences [5][7] Key Components of ReasoningBank - Memory Structure: Memory items are designed from past experiences, abstracting low-level execution details while retaining transferable reasoning patterns [7] - Integration with Agents: Agents equipped with ReasoningBank can draw from a curated pool of transferable strategies to guide decision-making, enhancing adaptability to unseen queries [7] Memory-Aware Test-Time Expansion (MaTTS) - MaTTS integrates ReasoningBank with test-time expansion, generating diverse explorations to provide comparative signals for better memory synthesis [8][9] - Two complementary implementations of MaTTS are introduced: parallel expansion and sequential expansion, enhancing the effectiveness of memory planning [9] Experimental Results - Extensive experiments on challenging benchmarks, including WebArena and SWE-Bench-Verified tasks, show that ReasoningBank outperforms baseline methods with effectiveness improvements of up to 34.2% and a reduction of 16.0% in interaction steps [11] - The results indicate that ReasoningBank significantly enhances both the resolve rate and efficiency compared to models without memory [13][14] Overall Impact - The collaboration between ReasoningBank and MaTTS is highlighted as a key component for memory-based experience expansion, demonstrating superior performance in various tasks [14][15]