

Reasoning Graphs: Self-Learning Deterministic RAGLanguage model agents reason from scratch on every query, then discard everything they learned. Same question, different answer, no improvement over time.Reasoning graphs change this by persisting each chain of thought as structured edges tied to the evidence items they evaluated. On the next run, the system surfaces how each piece of evidence has been judged before, giving the model a memory that compounds with use. A companion retrieval graph tightens candidate selection over successive runs, and together both structures form a self-learning feedback loop. No retraining required.In this session, Matthew Penaroza walks through the architecture, the results (half the errors, 11-point accuracy gain on hard questions, perfect consistency, 47% lower cost, 46% lower latency), and how to apply reasoning graphs to your own RAG pipelines.Based on the paper: https://arxiv.org/abs/2604.07595
Speaker
Matthew Penaroza(Head of AI Solution Architecture, SurrealDB)
Matthew Penaroza is Head of Solution Architecture at SurrealDB, where he helps design and scale distributed AI systems for global enterprises. As the solutions architect behind the core database systems at companies such as Plaid, Uber, and Atlassian, he brings firsthand insight into how the world’s largest organizations design data infrastructure and how those same architectural principles can be applied to AI systems that demand context precision and intelligent data management.
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Webinar: Reasoning Graphs - Self-Learning Deterministic RAG is a free independent taking place on Thursday, May 7, 2026 at a venue to be announced. This independent is organised by SurrealDB Events. Attendance is free — register to secure your spot. Currently 105 people have registered out of 105 spots. The event runs for approximately 1 hour.
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