Stage-3 applicants choose which specific streams to apply to. Some apply to 1–2; others apply to 20+. Does applying to more streams improve an applicant's chance of being ranked? And if so, is that effect driven by the strategy itself, or by the fact that strong applicants apply broadly (higher composite scorers naturally have more relevant streams)?
MATS (Machine Alignment, Transparency & Security) is an AI safety research fellowship that places ~120 fellows with ~100 mentors per cohort. Cohort 10.0 ran in summer 2026 and was the first cohort with a centralized application review instead of decentralized stream-specific review. This analysis is part of a broader effort to evaluate the 10.0 process and inform the design of 11.0 (autumn 2026).
The 10.0 pipeline in brief. ~2,200 people applied. Each applicant went through three stages:
For the empirical track, the composite formula is 0.50·RS + 0.35·TE + 0.15·SS, where TE = 0.50·MLE + 0.30·SWE + 0.20·Math. A "relevance multiplier" (Direct=1.0 / Adjacent=0.85 / Distant=0.60) is applied to Research Skills based on how the applicant's experience matches the streams they applied to.
Outcome definitions used throughout these analyses:
is_ranked (primary outcome) — applicant was ranked by ≥1 stream. This is the cleanest signal of "the selection process picked this person." Not the same as "received an offer" — offer count is bounded by cohort size (~120), but rank count reflects quality independently of capacity.is_invited_to_worktest (secondary outcome) — applicant was engaged by ≥1 stream in any way: invited to a work test, invited to an interview, ranked, or sent the Megastream takehome. Strict superset of is_ranked. One level above is_ranked in the funnel.passed_mentors_bar — applicant was offered or waitlisted. In 10.0, this equals is_ranked exactly (every ranked person got either an offer or a waitlist slot).Yes — but mostly because strong applicants apply broadly. The univariate effect of #streams → is_ranked is meaningful (AUC = 0.632 [0.590, 0.671]) but it's heavily confounded with composite (Spearman ρ between #streams and composite = +0.434).
In a joint model (n_streams + composite), composite carries most of the weight (standardized coefficient: composite = +0.19, n_streams = +0.28). The marginal effect of applying to more streams, after controlling for quality, is small.
| # streams | n | Mean # | P(ranked) | Mean composite |
|---|---|---|---|---|
| 1–2 | 219 | 1.4 | 5.5% | 1.29 |
| 3–5 | 246 | 3.9 | 18.7% | 1.37 |
| 6–10 | 292 | 7.8 | 17.5% | 2.07 |
| 11–15 | 142 | 12.3 | 26.8% | 2.32 |
| 16–20 | 78 | 18.1 | 29.5% | 2.67 |
| 20+ | 82 | 26.5 | 23.2% | 2.51 |
Composite rises monotonically with stream count — strong candidates apply broadly. The 1–2 bucket is mostly weaker candidates who didn't see many streams as relevant; the 20+ bucket is mostly strong empirical-track applicants who could reasonably make a case for many streams.
Green = ranked, gray = not. High-composite + high-#streams concentrate in the top-right.
Sample. Stage-3 applicants (n_s3_streams > 0): n=1,059. Joint logistic regression sample: n=1,059 (after dropping missing composite).
Outcome variable(s). is_ranked.
Predictor fields. n_s3_streams = len(Stage 3 streams actually applied to). composite_n = empirical composite (for the joint model).
Filters applied. Canonical dedup. Non-empty Stage-3 application list.
Missing-data handling. Listwise drop on composite for joint regression.
Key assumptions / caveats.