研究称原始对话 RAG 在多数长期记忆基准上优于复杂智能体记忆系统

RAG is so back!

This work shows that plain RAG over raw chat history beats complex agentic memory systems on most long-term memory benchmarks.

(bookmark it)

They tested Raw-RAG, which is just dense retrieval plus reranking over the raw conversation, feeding a reader LLM.

Across LoCoMo, LongMemEval, MEME and MemoryAgentBench, it ranks first in six of seven benchmark and model settings, ahead of the strongest agentic memory system by 0.13 to 5.53 points. Ingestion on BEAM-1M takes 0.3 hours, against 3.2 to 27 hours for systems like Mem0, MemOS and Memora.

Why does it hold up? Raw history keeps more of the evidence a question needs, including the relations and dependency paths behind an answer. Rewriting history into memory entries tends to drop those important bits.

Even with complete access to the transformed memory stores, models answer less accurately than with raw evidence.

Before adding a memory layer to your agent, at least run this baseline on your own data.

arxiv.org/abs/2610.30755

Chat with Paper: academy.dair.ai/papers/is-co…