Xmemory: Benchmarking Structured AI Memory Against RAG and Hybrid RAG
The paper introduces xmemory, a schema-grounded AI memory system that shifts memory processing from retrieval-based recall to a structured, iterative write process for improved accuracy and reliability. It evaluates xmemory on extraction and end-to-end memory benchmarks, showing superior performance compared to existing RAG and hybrid RAG approaches. The results emphasize that architectural design is more critical than model scale or retrieval capacity for tasks requiring stable facts and stateful computation.
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Computer Science > Artificial Intelligence arXiv:2604.27906 (cs) [Submitted on 30 Apr 2026] Title:From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction Authors:Alex Petrov, Alexander Gusak, Denis Mukha, Dima Korolev View a PDF of the paper titled From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction, by Alex Petrov and 3 other authors View PDF HTML (experimental) Abstract:Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant context later.
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