# ActionRAG preprint reports 5.4-point gain in agent memory benchmark

_Published Monday, October 26, 2026 at 6:56 AM EDT · AI, Science · Latest · Tier 2 — Notable_

A preprint reports that ActionRAG, a system for retrieving evidence from AI agents' interaction histories, achieved 55.1 average accuracy across eight deterministic-scoring columns in MemoryAgentBench. That exceeded the strongest retrieval-based memory baseline by 5.4 points.

ActionRAG reconciles entity identities when storing information, then uses relationships among retrieved facts to revise their relevance to a query. Mean synthesis cost stayed below $0.001 per query on four reported cost columns under the benchmark's query-time accounting. Separately measured retrieval-side p99 components summed to 440.7 ms on the authors' consumer Apple Silicon setup; that figure is not an end-to-end latency measurement.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2610.32752)

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Canonical: https://techandbusiness.org/newswire/r6Zpiah-denLX8ltuFRV9C
Published: 2026-10-26T10:56:36.297Z
Story chronology: 2026-10-26T04:00:00.000Z
Retrieved: 2026-10-26T13:41:34.215Z
Publisher: Tech & Business (techandbusiness.org)