Bootstrapping $$ \mathcal{N} $$ = 4 sYM correlators using integrability and localization
Bibliographic record
Abstract
A bstract We study four-point correlation functions of the stress-tensor multiplet in $$ \mathcal{N} $$ N = 4 super Yang-Mills (sYM) theory by leveraging integrability and localization techniques. We combine dispersive sum rules and spectral information from integrability, used previously, with integrated constraints from supersymmetric localization. We obtain two-sided bounds on the OPE coefficient of the so-called Konishi operator in the planar limit at any value of the ’t Hooft coupling ranging from weak to strong coupling. In addition to individual OPE coefficients, we discuss how to bound the correlation function itself and obtain two-sided bounds at various values of the cross-ratios and coupling. Lastly, considering the limit of large ’t Hooft coupling, we connect the analysis with that of an analogous flat space problem involving the Virasoro-Shapiro amplitude.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".