Predicting Diagonalizability of a Mean Matrix

Jinze Zhao

For fixed matrix dimension, whether the mean of i.i.d. binary random matrices is diagonalizable over R or C can be predicted with only finitely many errors almost surely. The argument uses eventual predictability of semialgebraic properties of bounded means.

Sources for this record

What was already known

Wu and Santhanam established eventual prediction for several matrix properties and asked the diagonalizability question. Their closed-set results alone do not settle it because diagonalizable matrices form neither an open nor a closed locus; the source cites a revised 2024 manuscript still listing it as open.

AI involvement AI-centered

Zhao chose the problem and directed the work. He credits GPT-5.6 Sol Ultra through Codex with the proof strategy and counterexample, then revised the manuscript.

The model supplied the central proof strategy and counterexample, while Zhao led problem selection and revision; the disclosure does not credit the model with every proof step.

Importance Notable

Answers the fixed-dimensional diagonalizability question with a broader semialgebraic prediction principle.

Limitations

The result is fixed-dimensional and does not claim an efficient predictor. A finite-r>1 moment extension and an integrable-case failure boundary are separate parts of the paper. No completed independent mathematical check is documented.

Technical verification Reported

The v1 paper gives a self-contained proof, but no completed human or machine check of the model-origin strategy is documented; Reported is the evidence default.

Human understanding Few

Default for undocumented human understanding: the paper explains the predictor, but model-assisted exposition and human revision do not identify a human explanation or grasp of the central model-origin reasoning.

Correction and publication history

2026-10-18 · publication

GPT 5.6 Sol Ultra supplied the strategy and counterexample; Zhao chose the problem and directed revisions.

Read this published revision