原始 RAG 在多个基准测试中优于复杂智能体记忆系统
Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought
Raw-RAG在LoCoMo、LongMemEval、MEME和MemoryAgentBench等基准测试中表现优于复杂智能体记忆系统，六项测试中排名第一，最高领先5.53分，证明保留原始对话历史比复杂的记忆转换更关键。
Published on Oct 22
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Abstract
Raw retrieval-augmented generation over unprocessed conversation history outperforms complex agentic memory systems across multiple benchmarks, indicating that preserving raw evidence is more critical than elaborate memory transformations.
LLM memory is widely used in long-term conversation understanding. Agentic memory systems often use mutation, structural organization, learned retrieval strategies, or other complex operations. Is this complexity always necessary? We study Raw-RAG, a naive RAG baseline using dense retrieval and reranking over raw history to supply evidence to a reader LLM. Across LoCoMo, LongMemEval, MEME, and MemoryAgentBench, Raw-RAG ranks first among the compared memory systems in six of seven benchmark-model settings, outperforming the strongest compared agentic memory system by 0.13-5.53 points. We investigate why raw-history retrieval remains competitive. Evidence analysis shows that its higher coverage of necessary evidence can offset lower utilization. Useful evidence includes both answer values and the relations and dependency paths needed to derive them. Even complete access to transformed memory stores leaves an accuracy gap relative to raw evidence, highlighting the importance of preserving raw information. These findings challenge the necessity of complex agentic memory for the evaluated conversation tasks and establish naive RAG as an essential baseline.
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来源: Hugging Face Daily Papers · huggingface.co