Some Attention is All You Need for Retrieval

TL;DR

This paper shows that in hybrid SSM-Transformer models, retrieval relies solely on self-attention layers, and sparsifying attention can maintain performance, revealing functional specialization within the architecture.

Contribution

It demonstrates complete segregation of retrieval function to self-attention layers and identifies mechanistic requirements for retrieval in hybrid models.

Findings

Retrieval depends exclusively on self-attention layers.

Sparsifying attention to 15% of heads retains near-perfect retrieval.

Hybrid models operate as specialized modules rather than integrated systems.

Abstract

We demonstrate complete functional segregation in hybrid SSM-Transformer architectures: retrieval depends exclusively on self-attention layers. Across RecurrentGemma-2B/9B and Jamba-Mini-1.6, attention ablation causes catastrophic retrieval failure (0% accuracy), while SSM layers show no compensatory mechanisms even with improved prompting. Conversely, sparsifying attention to just 15% of heads maintains near-perfect retrieval while preserving 84% MMLU performance, suggesting self-attention specializes primarily for retrieval tasks. We identify precise mechanistic requirements for retrieval: needle tokens must be exposed during generation and sufficient context must be available during prefill or generation. This strict functional specialization challenges assumptions about redundancy in hybrid architectures and suggests these models operate as specialized modules rather than integrated systems, with immediate implications for architecture optimization and interpretability.