Harpoon: Mechanizing Metatheory Interactively
Bibliographic record
Abstract
Abstract Belugais 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 describeHarpoon, an interactive proof engine built on top ofBeluga. It allows users to develop proofs interactively using a small, fixed set of high-levelactionsthat safely transform a subgoal. A sequence of actions elaborates into a (partial)proof scriptthat serves as an intermediate representation describing an assertion-level proof. Last, a proof script translates into aBelugaprogram which can be type-checked independently.Harpoonis available on GitHub. We have usedHarpoonto replay a wide array of examples covering all features supported byBeluga. 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".