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[2602.22406] Towards Autonomous Memory Agents
Summary
The paper proposes autonomous memory agents that enhance LLMs by actively acquiring and curating knowledge, improving performance on benchmarks like HotpotQA and AIME25.
Why It Matters
As AI systems increasingly rely on memory to enhance their capabilities, this research addresses limitations in existing memory agents by introducing a proactive approach. This could lead to more efficient and intelligent AI applications, making it a significant contribution to the field of artificial intelligence.
Key Takeaways
- Autonomous memory agents can actively acquire and validate knowledge.
- The proposed U-Mem framework improves LLM performance on key benchmarks.
- Cost-aware knowledge extraction reduces the need for expensive expert feedback.
- Semantic-aware Thompson sampling balances exploration and exploitation.
- The approach addresses limitations of existing passive memory solutions.
Computer Science > Artificial Intelligence arXiv:2602.22406 (cs) [Submitted on 25 Feb 2026] Title:Towards Autonomous Memory Agents Authors:Xinle Wu, Rui Zhang, Mustafa Anis Hussain, Yao Lu View a PDF of the paper titled Towards Autonomous Memory Agents, by Xinle Wu and 3 other authors View PDF HTML (experimental) Abstract:Recent memory agents improve LLMs by extracting experiences and conversation history into an external storage. This enables low-overhead context assembly and online memory update without expensive LLM training. However, existing solutions remain passive and reactive; memory growth is bounded by information that happens to be available, while memory agents seldom seek external inputs in uncertainties. We propose autonomous memory agents that actively acquire, validate, and curate knowledge at a minimum cost. U-Mem materializes this idea via (i) a cost-aware knowledge-extraction cascade that escalates from cheap self/teacher signals to tool-verified research and, only when needed, expert feedback, and (ii) semantic-aware Thompson sampling to balance exploration and exploitation over memories and mitigate cold-start bias. On both verifiable and non-verifiable benchmarks, U-Mem consistently beats prior memory baselines and can surpass RL-based optimization, improving HotpotQA (Qwen2.5-7B) by 14.6 points and AIME25 (Gemini-2.5-flash) by 7.33 points. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22406 [cs.AI] (or arXiv:2602.22406v1 [cs.AI] for...
