Bankai (卍解) — the first post-training adaptation method for true 1-bit LLMs.

I've been experimenting with Bonsai 8B — PrismML's true 1-bit model (every weight is literally 0 or 1, not ternary like BitNet). I realized that since weights are bits, the diff between two model behaviors is just a XOR mask. So I built a tool that searches for sparse XOR patches that modify model behavior. The basic idea: flip a row of weights, check if the model got better at the target task without breaking anything else, keep or revert. The set of accepted flips is the patch. What it does on held-out prompts the search never saw: Without patch: d/dx [x^7 + x] = 0 ✗ With patch: d/dx [x^7 + x] = 7x^6 + 1 ✓ Without patch: Is 113 prime? No, 113 is not prime ✗ With patch: Is 113 prime? Yes, 113 is a prime number ✓ 93 row flips. 0.007% of weights. ~1 KB. Zero inference overhead — the patched