CAIM: A Cognitive AI Memory Framework for Long-term Interaction with LLMs
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent research highlights the potential of combining LLMs with cognitive architectures to create more robust, efficient, and adaptable AI systems.
“To achieve this, recent research highlights the potential of combining LLMs with cognitive architectures to deepen the understanding of human cognition to create more robust, efficient, and adaptable AI systems [41, 54].”
From CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents (2026) · cites this paper as [54]
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References 11 · Cited by 2
- Authors
- Westhäußer, R., Zepf, S., & Minker, W.
- Affiliations
- Mercedes-Benz (Germany) (DE)
- Human-Computer Interaction, Mercedes-Benz AG, Boeblingen, Baden-Wuerttemberg, Germany
- Universität Ulm (DE)
- Venue
- Companion Proceedings of the 30th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2025-03-24
- Pages
- 22-25
- OpenAlex
- W4408550901
- Cited by
- 2
- References
- 11
- Citations by year
- 2026: 2
- FWCI
- 1.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- long-term interaction, large language models, chatbots
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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