A Fixed-Depth Size-Hierarchy Theorem for AC$^0[\\oplus]$ via the Coin\n Problem
Bibliographic record
Abstract
We prove the first Fixed-depth Size-hierarchy Theorem for uniform\nAC$^0[\\oplus]$ circuits; in particular, for fixed $d$, the class\n$\\mathcal{C}_{d,k}$ of uniform AC$^0[\\oplus]$ formulas of depth $d$ and size\n$n^k$ form an infinite hierarchy. For this, we find the first class of explicit\nfunctions giving (up to polynomial factor) matching upper and lower bounds for\nAC$^0[\\oplus]$ formulas, derived from the $\\delta$-Coin Problem, the\ncomputational problem of distinguishing between coins that are heads with\nprobability $(1+\\delta)/2$ or $(1-\\delta)/2,$ where $\\delta$ is a parameter\ngoing to $0$. We study this problem's complexity and make progress on both\nupper bounds and lower bounds.\n Upper bounds. We find explicit monotone AC$^0$ formulas solving the\n$\\delta$-coin problem, having depth $d$, size $\\exp(O(d(1/\\delta)^{1/(d-1)}))$,\nand sample complexity poly$(1/\\delta)$, for constant $d\\ge2$. This matches\nprevious upper bounds of O'Donnell and Wimmer (ICALP 2007) and Amano (ICALP\n2009) in terms of size and improves the sample complexity.\n Lower bounds. The upper bounds are nearly tight even for the stronger model\nof AC$^0[\\oplus]$ formulas (which allow NOT and Parity gates): any\nAC$^0[\\oplus]$ formula solving the $\\delta$-coin problem must have size\n$\\exp(\\Omega(d(1/\\delta)^{1/(d-1)})).$ This strengthens a result of Cohen,\nGanor and Raz (APPROX-RANDOM 2014), who prove a similar result for AC$^0$, and\na result of Shaltiel and Viola (SICOMP 2010), who give a superpolynomially\nweaker (still exponential) lower bound.\n The upper bound is a derandomization involving a use of Janson's inequality\n(as far as we know, the first such use of the inequality) and classical\ncombinatorial designs. For the lower bound, we prove an optimal (up to constant\nfactor) degree lower bound for multivariate polynomials over $\\mathbb{F}_2$\nsolving the $\\delta$-coin problem, which may be of independent interest.\n
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".