On the large <i>D</i> expansion of Hermitian multi-matrix models
Bibliographic record
Abstract
We investigate the existence and properties of a double asymptotic expansion in 1/N2 and 1/D in U(N) × O(D) invariant Hermitian multi-matrix models, where the N × N matrices transform in the vector representation of O(D). The crucial point is to prove the existence of an upper bound η(h) on the maximum power D1+η(h) of D that can appear for the contribution at a given order N2−2h in the large N expansion. We conjecture that η(h) = h in a large class of models. In the case of traceless Hermitian matrices with the quartic tetrahedral interaction, we are able to prove that η(h) ≤ 2h; the sharper bound η(h) = h is proven for a complex bipartite version of the model, with no need to impose a tracelessness condition. We also prove that η(h) = h for the Hermitian model with the sextic wheel interaction, again with no need to impose a tracelessness condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".