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Record W4407904935 · doi:10.1214/25-ecp663

Equivalence of starting point cutoff and the concentration of hitting times on a general state space

2025· article· en· W4407904935 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronic Communications in Probability · 2025
Typearticle
Languageen
FieldMathematics
TopicMarkov Chains and Monte Carlo Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematicsCutoffEquivalence (formal languages)State (computer science)Space (punctuation)State spacePoint (geometry)Applied mathematicsMathematical analysisCombinatoricsCalculus (dental)Pure mathematicsStatisticsGeometryAlgorithmComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this article we extend a result of Martinez and Ycart from their paper “Decay Rates and Cutoff for Convergence and Hitting Times of Markov Chains with Countably Infinite State Space” [7] to show that for a regular Markov chain on a general state space, the existence of a cutoff phenomenon for total variation distance to stationarity as the starting point tends to infinity is equivalent to the concentration of hitting times for any fixed regular set as the starting points tend to infinity. We apply this result to show that all random walks on the half-line with bounded steps exhibit starting point cutoff.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.369
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it