Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought
Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought
Raw-RAG outperforms complex agent memory systems in benchmark tests such as LoCoMo, LongMemEval, MEME, and MemoryAgentBench, ranking first in all six tests with a maximum lead of 5.53 points, proving that retaining original dialogue history is more critical than complex memory transformations.
Published on Oct 22
Authors:
,
,
,
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.
View arXiv page View PDF GitHub 17 Add to collection
Get this paper in your agent:
hf papers read 2610.30755
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.30755 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2610.30755 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.30755 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co