McTT: A Verified Kernel for a Proof Assistant
Why this work is in the frame
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Bibliographic record
Abstract
Proof assistants based on type theories have been widely successful from verifying safety-critical software to establishing a new standard of rigour by formalizing mathematics. But these proof assistants and even their type-checking kernels are also complex pieces of software, and software invariably has bugs, so why should we trust such proof assistants? In this paper, we describe the McTT (Mechanized Type Theory) infrastructure to build a verified implementation of a kernel for a core Martin-Löf type theory (MLTT). McTT is implementation in Rocq and consists of two main components: In the theoretical component, we specify the type theory and prove theorems such as normalization, consistency and injectivity of type constructors of MLTT using an untyped domain model. In the algorithmic component, we relate the declarative specification of typing and the model of normalization in the theoretical component with a functional implementation within Rocq . From this algorithmic component, we extract an OCaml implementation and couple it with a front-end parser for execution. This extracted OCaml code is comparable to what a skilled human programmer would have written and we have successfully used it to type-check a series of small-scale examples. McTT provides a fully verified kernel for a core MLTT with a full cumulative universe hierarchy. Every step in the compilation pipeline is verified except for the lexer and pretty-printer. As a result, McTT serves both as a framework to explore the meta-theory of advanced type theories and to investigate optimizations of and extensions to the type-checking kernel.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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