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Record W2608463673

Eliminating Dependent Pattern-Matching in Coq

2015· preprint· en· W2608463673 on OpenAlexaff
Cyprien Mangin

Bibliographic record

VenueINRIA a CCSD electronic archive server · 2015
Typepreprint
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsPrevention of Organ Failure
Fundersnot available
KeywordsMatching (statistics)Computer scienceMathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

Coq [1] is a proof assistant which relies on the Curry-Howard isomorphism to construct certified proofs. Proving a theorem is the same thing as providing a term which inhabits a type corresponding to this theorem. In order to trust Coq, it is enough to trust its kernel, which is intentionally kept small enough for a motivated reader to understand and, hopefully, trust it. This approach can have some drawbacks, as high-level constructs have to be translated down to simpler constructs, by the user or by some part of code external to the kernel. For example, writing dependent pattern-matching in Coq can be complicated. Simpifying this task is one of the purposes of the Equations [2] plugin. Given a high-level specification of a function, which can use dependent pattern-matching and complex recursion schemes, it will compile it to pure Coq terms. Equations is the result of the work of Matthieu Sozeau[10], largely based on the research of Goguen et al[7]. This internship revolves around Equations as a tool to benchmark, improve and adapt to new settings. This in turn involves the study of dependent pattern-matching. Research problem The initial goal of this internship was to rewrite a part of Equations, in order to better control the use of the axiom K during the compilation phase. This axiom states that to prove a property depending on a proof of equality, it is enough to consider the case where this proof is the reflexivity. Equivalently, it says that any proof of equality is propositionally equal to the reflexivity. While it is useful in some cases, and even provable for a lot of types, it can be harmful when working in some contexts, like Homotopy Type Theory[12] – abbreviated HoTT. Another problem is that, as an axiom, it will block any computation in Coq that involves it.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.004
Research integrity0.0000.002
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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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