AdMem: Advanced Memory for Task-solving Agents

2026-06-04

AI Summary

This work addresses the challenges large language models face in long-horizon tasks regarding knowledge retention, organization, and reuse. Existing approaches are often limited to static fact storage or replay of successful experiences, struggling to incorporate failure cases and lacking online extensibility. To overcome these limitations, the paper proposes a unified memory framework that, for the first time, integrates semantic, episodic, and procedural memory within a dual-layer short- and long-term storage architecture. A multi-agent design—comprising actor, memory, and critic components—enables automatic memory generation, reward-based annotation, and adaptive retrieval. A reward-driven long-term memory management strategy, including evaluation, consolidation, and pruning, facilitates continual learning and online evolution. Experiments demonstrate that the proposed method significantly improves success rates and robustness across diverse complex long-horizon tasks, outperforming current baselines.

Abstract

Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge. Prior memory approaches aim to resolve the situation, but mainly focus on storing factual information. Recent work on procedural memory improves task reuse, yet often reduces to replaying past successes without addressing failure cases or online scalability. We introduce a unified and automatic memory framework that integrates semantic, episodic, and procedural memory in a bi-level design combining short-term and long-term stores. A multi-agent architecture with actor, memory, and critic agents enables automatic memory generation, reward annotation, and adaptive retrieval. Long-term memory is managed through reward-based evaluation, merging, and pruning, ensuring scalability and continual improvement. Experiments across various environments show that our approach improves robustness and success on long multi-turn tasks compared to existing baselines. This work highlights the importance of comprehensive, adaptive memory for advancing LLM-based agents.

Problem

• long-horizon tasks
• memory framework
• procedural memory
• LLM-based agents
• online scalability

Innovation

• unified memory framework
• procedural memory
• multi-agent architecture
• reward-based memory management
• long-horizon task solving