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The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content

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arXiv:2606. 11198v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content -- distinct from its semantic relevance -- can independently distort the model's attention distribution.

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Authors: Yuqi Zhang, Di Zhang
Released: 2026-06-11 (28d ago)
Topic: Related to current project
Relation to my work: Read next because The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, alignment, eval, source, token, rate, control, position. Source: arxiv cs.CL (NLP).
arXiv: 2606.11198

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