Naive RAG Outperforms Complex Agentic Memory on Conversation Benchmarks
READ THE ORIGINAL SOURCEOpen original sourcearxiv.org · Opens in a new tabThe paper studies whether complex agentic memory systems are necessary for long-term conversation understanding. It evaluates Raw-RAG, a naive baseline using dense retrieval and reranking over raw history. Across LoCoMo, LongMemEval, MEME, and MemoryAgentBench, Raw-RAG ranks first among compared memory systems in six of seven benchmark-model settings, outperforming the strongest compared agentic memory system by 0.13-5.53 points. Evidence analysis attributes its competitiveness to higher coverage of necessary evidence, and the authors argue raw information preservation matters.
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The paper studies whether complex agentic memory systems are necessary for long-term conversation understanding. It evaluates Raw-RAG, a naive baseline using dense retrieval and reranking over raw history. Across LoCoMo, LongMemEval, MEME, and MemoryAgentBench, Raw-RAG ranks first among compared memory systems in six of seven benchmark-model settings, outperforming the strongest compared agentic memory system by 0.13-5.53 points. Evidence analysis attributes its competitiveness to higher coverage of necessary evidence, and the authors argue raw information preservation matters.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
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- 03Where are the sources?
Original link: arxiv.org. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Findings are limited to the evaluated conversation tasks and benchmarks; source claims not independently checked.
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Key findings from the stored brief
- Raw-RAG ranks first in six of seven benchmark-model settings against compared memory systems.
- It outperforms the strongest compared agentic memory system by 0.13-5.53 points.
- Higher coverage of necessary evidence can offset its lower evidence utilization.
- Even complete access to transformed memory stores leaves an accuracy gap versus raw evidence.
Findings are limited to the evaluated conversation tasks and benchmarks; source claims not independently checked.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Related archive coverage
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Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
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Bao et al. (arXiv, Oct 22, 2026) test Raw-RAG, plain dense retrieval plus reranking over raw chat history, against complex agentic memory systems and argue the simple baseline is competitive. Preprint; evidence post is a third-party summary.
Findings are limited to the evaluated conversation tasks and benchmarks; source claims not independently checked.
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- x post ↗ · 7 likes, 0 reposts, 6 replies, 0 quotes · observed 10/24/2026, 10:02:28 PM
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Why this may matter
The results challenge assumptions that complex agentic memory operations are required for conversation tasks, suggesting naive RAG should be treated as an essential baseline when evaluating memory systems.
Limits and open questions
Findings are limited to the evaluated conversation tasks and benchmarks; source claims not independently checked.
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