Neagari patch fixes Bonsai extraction

Interesting technique, but the results read more like targeted behavioral memorization than a durable model repair.

• –The appeal is operational: it preserves the native 1-bit inference path and avoids retraining, adapters, dequantization, or FP16 fallback.
• –The held-out numbers are the real story: 7.7% copy-to-pass conversion on 100 probes, with all conversions confined to the two training-target domains.
• –That makes the method useful as a proof of concept for patch search in binary weight space, not as a general-purpose fix for Bonsai-style copying failures.
• –The repo is unusually reproducible for this kind of work, with code, patches, a paper, and a Colab demo that runs on a free T4.