Аңдатпа
Global attention combines long-range lexical access with semantic-relation retrieval, making whole-layer replacement lossy. We present Strata-HeadQuotient, which statically assigns each key–value (KV) head to full-history attention (GLOBAL), 1024-token attention (LOCAL), or local attention plus an event-scoped typed graph read (LOCAL_GRAPH). For a 24-layer, 16-head decoder, we audited 384 heads and all 6903 pairs among 118 candidates before constrained subset selection. The Q25 export localized 96/384 heads: 81 were LOCAL and 15 were LOCAL_GRAPH. On 470 document-disjoint 8192-token windows (3.85 million tokens), graph-disabled Q25 perplexity was 1.00146× dense (95% document-bootstrap interval: 1.00131–1.00161); the 4096–8191-token ratio was 1.00201. On an untouched 2000-case semantic test, typed execution was 1998/2000, versus 374/2000 for a capacity-matched untyped graph. Two fresh adaptation/selection runs reproduced both directions, although every matched-capacity marginal graph-effect interval included zero. Q25 retained 78.125% of token-KV bytes and 98.44% of dense full-sequence throughput; a tested Q30 export failed. Thus, interaction-aware localization preserved 8192-token language-model quality, while a separate typed path controlled event-role behavior. The experiment does not establish that graph computation caused perplexity preservation.