Bibliographic record
Abstract
We study the soft limit of a recently proposed generalization of the biadjoint scalar amplitudes $m^{(k)}_{n}$, which have been conjectured to have a relation to the tropical Grassmannian $\text{Tr G}(k,n)$. Using the CHY formulation along with the Global Residue Theorem, we prove the soft factorization for $m^{(k)}_{n}$ amplitudes for arbitrary $k$ and $n$. We find that the soft factors are in direct correspondence to vertices of the associahedron $\mathcal{A}_{k-1}$, and hence take the form of $m^{(2)}_{n}$ amplitudes. This entails that all scattering amplitudes of the ordinary biadjoint scalar theory can be interpreted as an infinite family of soft factors. Additionally, Grassmannian duality reveals that generalized amplitudes $m^{(k)}_{n}$ with $k>2$ satisfy not only a soft theorem, but also a non-trivial "hard" theorem. We perform numerical checks of our theorems against previous results for $\text{Tr G}(4,7)$ and $\text{Tr G}(5,8)$, thereby providing strong evidence of their relation with the CHY formulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".