ROZA Graphs: Self-Improving Near-Deterministic RAG through Evidence-Centric Feedback
Language model agents reason from scratch on every query, discarding their chain of thought after each run. The result is lower accuracy and high run-to-run variance. We introduce reasoning graphs, which persist the per-evidence chain of thought as structured edges. Unlike prior memory that retrieves distilled strategies by query similarity, reasoning graphs enable evidence-centric feedback: for every candidate item, the system traverses all incoming evaluation edges across prior runs to surface how that specific item has been judged before. We further introduce retrieval graphs, which feed a planner that prunes consistently-rejected candidates over successive runs. Together they form a R...
| Authors | Matthew Penaroza |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 6 May 2026 |
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