neural_reversible: analog read-noise and conductance-mismatch sweeps on the reversible matrix stack. The permutation stays bit-exact through read noise sigma ~ 0.10 (errors at 0.15, where the 0.5-margin error model predicts) and conductance mismatch sigma_G ~ 0.10, the same tolerances neural_matrix8 measures; README states the confirmed tolerance rather than implying it.

neural_reversible: compile the reversible arithmetic to a ternary matrix stack (tools/reversible_matrix.py) and substantiate the no-erasure claim concretely. The 4-bit adder becomes 39 ternary matrices with a Heaviside step; the composed transition is verified a permutation of the state space over all 512 inputs, matches the gate circuit, and clears the analog threshold by the same 0.5 margin as neural_matrix8, so the crossbar realization is bit-exact and information-theoretically lossless. README updated from the by-analogy phrasing to the measured result.