MemPro: Agentic Memory Systems as Evolvable Programs
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Computer Science > Computation and Language
arXiv:2606.00619 (cs)
[Submitted on 30 May 2026]
Title:MemPro: Agentic Memory Systems as Evolvable Programs
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Abstract:Long-horizon autonomous agents require memory systems to retain historical information, track evolving states, and reuse relevant knowledge beyond finite context windows. Existing agentic memory systems typically follow a memory construction-retrieval (MCR) pipeline, but often adapt mainly the memory bank while keeping the surrounding pipeline fixed after deployment. This fixed-pipeline design struggles to handle heterogeneous task-specific failure modes and can become misaligned with memory banks that evolve in scale and structure over time. To address these limitations, we propose MemPro, a system-level evolution framework that treats the entire MCR pipeline as an evolvable program rather than adapting only the memory bank or prompt text. MemPro maintains a version tree of runnable memory-system implementations, where an Evolving Agent iteratively selects promising versions, diagnoses recurring failures, and creates improved child versions through failure-mode-guided edit-debug refinement. Experiments on LongMemEval, LoCoMo, HotpotQA, and NarrativeQA show that MemPro consistently outperforms strong static and prompt-level evolving baselines within a few iterations, continues to improve with evolution, and achieves a favorable performance-cost trade-off. Code is available at this https URL.
| Comments: | 20 pages, 14 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.00619 [cs.CL] |
| (or arXiv:2606.00619v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.00619
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled MemPro: Agentic Memory Systems as Evolvable Programs, by Qingshan Liu and 6 other authors
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