Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents

[2608.15008] Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2608.15008 (cs) [Submitted on 15 Aug 2026] Title: Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents Authors: Wei-Chieh Huang , Weizhi Zhang , Yuchen Wu , Yankai Chen , Eric Hanchen Jiang , Wooseong Yang , Yiwei Yang , Henry Peng Zou , Hanrong Zhang , Ying Nian Wu , Haolun Wu , Kai-Wei Chang , Philip S. Yu , Xue Liu , Aylin Caliskan View a PDF of the paper titled Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents, by Wei-Chieh Huang and 14 other authors View PDF HTML (experimental) Abstract: Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms. Across three backbone models and four benchmark suites spanning user-centric question answering and agent-centric decision-making, we instrument 26 performance and efficiency metrics under a unified harness. Our results show that no single substrate consistently dominates: broad retrieval benefits long-context factual QA, while excessive retrieval can harm sequential decision-making by shifting attention away from action-critical context. Scalability introduces a further routing axis, as substrates that perform well at moderate history lengths can become costly or brittle at longer horizons. These findings motivate substrate routing as a necessary component of adaptive agent memory systems and provide empirical guidance for designing efficient, reliable, and regime-aware long-term memory for LLM agents. Code will be made available upon acceptance. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.15008 [cs.CL] (or arXiv:2608.15008v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.15008 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wei-Chieh Huang [ view email ] [v1] Sat, 15 Aug 2026 03:29:48 UTC (2,271 KB) Full-text links: Access Paper: View a PDF of the paper titled Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents, by Wei-Chieh Huang and 14 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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