Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits

원문 초록

When a language model answers, we can usually only estimate why. We build a transformer in which every nonlinearity is a quadratic detection (an interference, a balanced interferometric reading or a camera) and the output is a Born probability: attention $|\langle q|k\rangle|^2$, the form of the Born rule, normalised over each row, a measured product in place of the feed-forward nonlinearity, and a Born readout; between detections a classical controller squares, divides, rescales and re-encodes. With its denominators held at their values for the input, each class probability is a polynomial in the word amplitudes, so its integrated-gradient evidence is computed by a finite quadrature with no quadrature error: the words' contributions sum to the class probability above its no-information share, to $10^{-13}$ or better. On e-SNLI natural language inference the model reaches 77.45 +- 0.91 % test accuracy, level within the spread of three seeds with a softmax transformer of the same size (76.62 +- 0.36 %). Deleting the word its evidence ranks first changes the answer in 44 % of test pairs, out of the 53 % any single deletion can change, against 32 % for the softmax model's integrated gradients and 31 % for the Born Transformer's own attention. The same weights compile exactly to optoelectronic optics (simulated), and its attention overlaps to Hadamard tests on qubits; a separate coherent attention layer becomes one post-selected circuit (852 CNOTs on 23 qubits). On IBM's ibm_kingston, reading only the attention overlaps, the device gave the model's answers on the two shortest test pairs, also when the one word that decides one of them was deleted; with calibration circuits as deep as the overlaps, each word's evidence with the device's attention row sums followed the exact evidence on one pair (correlation 0.997) and on the other less closely (0.962).