An Algorithm to compute the Kronecker cone and other moment cones
Bibliographic record
Abstract
We describe a new algorithm that computes the minimal list of inequalities for the moment cone of any representation of a complex reductive group, with implementation details for two fundamental cases: the Kronecker cone (governing the asymptotic support of Kronecker coefficients) and the fermionic cone. These correspond to the actions of ${\mathrm GL}\_{d\_1}({\mathbb C})\times\cdots\times {\mathrm GL}\_{d\_s}({\mathbb C})$ on ${\mathbb C}^{d\_1}\otimes\cdots\otimes {\mathbb C}^{d\_s}$ and ${\mathrm GL}\_d({\mathbb C})$ on $\bigwedge^r{\mathbb C}^d$, respectively. An implementation for these two cases in Python-Sage is available at https://ea-icj.github.io/. Our work overcomes the fundamental limitations that previously restricted such computations to cases like ${\mathbb C}^4\otimes{\mathbb C}^4\otimes{\mathbb C}^4$. The state-of-the-art method by Vergne-Walter faced two major bottlenecks: one from combinatorial geometry in finite-dimensional vector spaces, and another from deciding whether certain dominant morphisms are birational - a problem in effective algebraic geometry that lacked a direct algorithmic solution. We surmount these obstacles by: a novel use of Weyl group actions to master combinatorial complexity, and an original algorithm for deciding birationality that replaces previous workarounds relying on convex geometry. Our approach allow us to tackle problems at a new scale. We compute the minimal list of 5,333 (up to $\mathfrak S\_3$) inequalities for the Kronecker cone ${\mathbb C}^6\otimes{\mathbb C}^6\otimes{\mathbb C}^6$ in 2 hours. Furthermore, a parallel implementation computes the 64,792 (up to $\mathfrak S\_3$) inequalities for ${\mathbb C}^7\otimes{\mathbb C}^7\otimes{\mathbb C}^7$ in 188 hours.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".