Modern Transformers Are Implicit Hybrids: From Functional Differentiation to Principled Hybrid Architecture Design
Hybrid architectures combining Full Attention (FA) and Linear Attention (LA) are increasingly prominent, yet their allocation remains heuristic. We seek an evidence-grounded basis in head-level functional organization learned by RoPE-based Transformers. Behavioral probes do not yield a complete taxonomy, so we propose two intervention metrics: RoPE Frequency Importance Score (RFIS), measuring how each frequency affects a head's attention distribution, and RoPE Positional Dependence (RPD), isolating dependence on rotary positional modulation. On Qwen3-series models and Llama3.1, RFIS suggests and RPD verifies a complete taxonomy of retrieval and positional heads separated by a salient mid-...
| Authors | Runlin Shi, Bojian Yin, Guoqi Li |
|---|---|
| Primary category | cs.LG |
| Published | 2 September 2026 |
| Last updated at source | 24 September 2026 |
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