Harpoon: Mechanizing Metatheory Interactively
Bibliographic record
Abstract
Abstract Beluga is a proof checker that provides sophisticated infrastructure for implementing formal systems with the logical framework LF and proving metatheoretic properties as total, recursive functions transforming LF derivations. In this paper, we describe Harpoon , an interactive proof engine built on top of Beluga . It allows users to develop proofs interactively using a small, fixed set of high-level actions that safely transform a subgoal. A sequence of actions elaborates into a (partial) proof script that serves as an intermediate representation describing an assertion-level proof. Last, a proof script translates into a Beluga program which can be type-checked independently. Harpoon is available on GitHub. We have used Harpoon to replay a wide array of examples covering all features supported by Beluga . In particular, we have used it for normalization proofs, including the recently proposed POPLMark reloaded challenge.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".