docs: CRITICAL knowledge sync for signed session — 33% error, not cosmetic The signed session built i8 tables by converting u8 → i8 (subtract 128). This RELABELS ranks but does NOT recover gate sign information. The 33% scale error from SiLU gate nonlinearity is still baked in. MEASURED on real Qwopus BF16: 68.9% of gate weights near zero (decision boundary) 99.2% of table cells change with SiLU correction Mean change: 84.2 u8 levels (33% of 256 scale) Consistent across all 64 layers FIX: build i8 tables directly from BF16 → signed cosine → i8 NOT from u8 table → subtract 128 → i8 Also documents: - 7-lane calibration plan (u8/i8/γ+φ/signed/spiral) - BF16 bucket boundary awareness (5% rank flips) - Jina v5 as ONNX f32 ground truth (no API key) - Why CDF makes all models look identical (by design) https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp#118
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…metic The signed session built i8 tables by converting u8 → i8 (subtract 128). This RELABELS ranks but does NOT recover gate sign information. The 33% scale error from SiLU gate nonlinearity is still baked in. MEASURED on real Qwopus BF16: 68.9% of gate weights near zero (decision boundary) 99.2% of table cells change with SiLU correction Mean change: 84.2 u8 levels (33% of 256 scale) Consistent across all 64 layers FIX: build i8 tables directly from BF16 → signed cosine → i8 NOT from u8 table → subtract 128 → i8 Also documents: - 7-lane calibration plan (u8/i8/γ+φ/signed/spiral) - BF16 bucket boundary awareness (5% rank flips) - Jina v5 as ONNX f32 ground truth (no API key) - Why CDF makes all models look identical (by design) https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp
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