An elementary algorithm for the automatic derivation and proof of tensor product identities via computer algebra
Bibliographic record
Abstract
Tensor product identities in two variables are quite common in mathematics: Exponential, logarithmic, trigonometric, and hyperbolic functions all satisfy tensor product identities, and the binomial theorem is a familiar example of a tensor product identity for polynomial functions. This article presents a new elementary technique which can derive and prove all of these identities---automatically! This unified approach is based on the author's recent research on uniqueness theory for dual asymptotic expansions and remainder theory for Taylor interpolation on two lines. These results provide a simple iterative algorithm which derives a tensor product from a closed-form expression using the author's asymptotic splitting operator, and a simple hyperbolic eigenfunction criterion which proves that the two forms are identically equal. The author has implemented these methods as a complete derivation and proof system in the Maple 8 computer algebra system. The Maple code, which is surprisingly brief, is included in its entirety. The article also includes numerous examples which illustrate a variety of novel techniques for deriving and proving tensor product identities using this simple but effective system. (Maple is a registered trademark of Waterloo Maple Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".