Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought
The paper introduces Raw-RAG, which combines dense retrieval and reranking over raw conversation history, and argues that complex agentic memory is not always necessary for long-term conversation understanding. The authors report that Raw-RAG ranks first among compared memory systems in six of seven settings across four benchmarks. Their analyses point to evidence coverage and relational completeness as key drivers of performance, and they position naive RAG as an essential baseline. Discussion comments in the discovery channel mention embedding and reranker choices, but these details were not part of the supplied primary-source text.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper introduces Raw-RAG, which combines dense retrieval and reranking over raw conversation history, and argues that complex agentic memory is not always necessary for long-term conversation understanding. The authors report that Raw-RAG ranks first among compared memory systems in six of seven settings across four benchmarks. Their analyses point to evidence coverage and relational completeness as key drivers of performance, and they position naive RAG as an essential baseline. Discussion comments in the discovery channel mention embedding and reranker choices, but these details were not part of the supplied primary-source text.
Key findings from the stored brief
- Raw-RAG combines dense retrieval and reranking over raw conversation history.
- Raw-RAG ranks first among compared memory systems in six of seven settings across four benchmarks.
- Analyses highlight evidence coverage and relational completeness as key performance drivers.
- The authors position naive RAG as an essential baseline for long-term conversation memory.
Source claims not independently checked; the supplied text is an abstract-level summary without full benchmark details.
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Why this may matter
The result questions whether elaborate agentic memory architectures are needed for long-term conversational agents, and suggests simpler retrieval over raw history may be a strong baseline to beat.
Limits and open questions
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