Add certification & corpus-fit guidance (PB score, 4-bit distractor certificate, chunking prescription)

README.md

@@ -59,3 +59,17 @@ ranked = m.rerank("how does a cross-encoder reranker work?",
 - Incorporates the `xlm-roberta-large` backbone under the **MIT License** — see `LICENSE-xlm-roberta-large.txt`.
 
 © 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.
+
+## Certification & corpus fit (2026-07)
+
+**Position Balance (PB): 0.31** — PB measures how findable a chunk is through its *second* fact when two
+facts share one embedding (second-fact / first-fact top-1 retrieval on an adversarial 1,300-chunk audit;
+fleet range 0.16–0.69). Best-in-fleet single-fact quality (1.00). Strongest choice for topically-unified corpora (abstracts, product data); for heterogeneous multi-fact documents prefer [Merino-Large](https://huggingface.co/baa-ai/Merino-Large) or enforce strictly atomic chunking.
+
+**4-bit quantization: certified lossless under distractor stress.** Paired contested-region robustness
+(gold document injected into pools of up to 100 near-topical distractors, n=300 queries, bootstrap CIs)
+is statistically indistinguishable from fp16 — an axis standard hit@k benchmarks do not measure.
+
+**Chunking prescription:** embed one atomic claim per chunk and lead with its key entity; retrieve small,
+return the parent section for context. Basis: single-vector embeddings preserve ~one independent fact per
+chunk regardless of encoder family (measured across 12 encoders).