EXAR: A Unified Experience-Grounded Agentic Reasoning Architecture. Bergmann, R., Brand, F., Lenz, M., & Malburg, L. In Bichindaritz, I. & López, B., editors, Case-Based Reasoning Research and Development, volume 15662, of Lecture Notes in Computer Science, pages 3–17, Cham, 2025. Springer Nature Switzerland.
EXAR: A Unified Experience-Grounded Agentic Reasoning Architecture [pdf]Paper  doi  abstract   bibtex   9 downloads  
Current AI reasoning often relies on static pipelines (like the 4R cycle from Case-Based Reasoning (CBR) or standard Retrieval-Augmented Generation (RAG)) that limit adaptability. We argue it is time for a shift towards dynamic, experience-grounded agentic reasoning. This paper proposes EXAR, a new unified, experience-grounded architecture, conceptualizing reasoning not as a fixed sequence, but as a collaborative process orchestrated among specialized agents. EXAR integrates data and knowledge sources into a persistent Long-Term Memory utilized by diverse reasoning agents, which coordinate themselves via a Short-Term Memory. Governed by an Orchestrator and Meta Learner, EXAR enables flexible, context-aware reasoning strategies that adapt and improve over time, offering a blueprint for next-generation AI.

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