🔬Deep Fusion Memory Beats RAG Baselines by 12.7%
TL;DR
Deep Fusion Memory predicts memory chunks inside a frozen LLM, so inference needs no retrieval. It improves six QA tasks by up to 12.71% on Mistral-7B-v0.3 and 11.19% on Qwen3-8B-Base.
Deep Fusion Memory predicts memory chunks inside a frozen LLM, so inference needs no retrieval. It improves six QA tasks by up to 12.71% on Mistral-7B-v0.3 and 11.19% on Qwen3-8B-Base.
Key Points
Causal memory predictor fuses into hidden states via gated cross-attention
32x fewer retrieval queries and 2048x fewer retrieved items when building training data
Domain adaptation gains average 6.51 points on Qwen3-4B-Base and 4.38 on Qwen3-8B-Base
Backbone stays frozen; no online retrieval at inference
Why It Matters
If it holds up, you could drop the vector database from some knowledge-heavy apps. Baselines are research-grade, so test on your own data.
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