Hi Wai.

Given that Ternii translates standard FP16/BF16 weights down to variable trit densities (TR2.25 to TR3) without retraining, I wanted to reach out and share a highly relevant optimization from my pipeline that could directly address the multilingual and mathematical regressions noted in your model cards.

I introduced a Symbolic Lattice Algebraic Solver that uses an exact algebraic pinning strategy to drop net DC bias (mean error shift) within quantized blocks to absolute zero.

Because ternary integer codes are inherently symmetric, my DC pinning method can surely be used by your pipeline.

By forcing the residual error vector to be perfectly orthogonal to the intercept vector, you can eliminate the subtle, accumulating distortion that degrades deep reasoning tasks in low-bit states.