Adjacency matrices of random digraphs: singularity and\n anti-concentration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Let ${\\mathcal D}_{n,d}$ be the set of all $d$-regular directed graphs on $n$\nvertices. Let $G$ be a graph chosen uniformly at random from ${\\mathcal\nD}_{n,d}$ and $M$ be its adjacency matrix. We show that $M$ is invertible with\nprobability at least $1-C\\ln^{3} d/\\sqrt{d}$ for $C\\leq d\\leq cn/\\ln^2 n$,\nwhere $c, C$ are positive absolute constants. To this end, we establish a few\nproperties of $d$-regular directed graphs. One of them, a Littlewood-Offord\ntype anti-concentration property, is of independent interest. Let $J$ be a\nsubset of vertices of $G$ with $|J|\\approx n/d$. Let $\\delta_i$ be the\nindicator of the event that the vertex $i$ is connected to $J$ and define\n$\\delta = (\\delta_1, \\delta_2, ..., \\delta_n)\\in \\{0, 1\\}^n$. Then for every\n$v\\in\\{0,1\\}^n$ the probability that $\\delta=v$ is exponentially small. This\nproperty holds even if a part of the graph is "frozen".\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it