Parsing, Lexical Scoping and Incremental Development for a Dependently-Typed Programming Language
Bibliographic record
Abstract
Beluga is a functional programming language and proof assistant for specifying formal systems in contextual LF, an extension of the Edinburgh Logical Framework, and mechanically proving theorems about them using recursive programs.To facilitate the incremental development of proofs with commands and automation tactics, the Harpoon interactive proof environment is subsequently implemented as a read-eval-print loop with structural editing features over Beluga programs.Due to architectural limitations in the implementation of Beluga and Harpoon, top-down and out-of-order proof development sessions can lead to invalid proof states and unsound translated programs.This thesis reports on technical challenges and solutions to soundly implementing the structural editing of proofs, including the navigation between proof holes, with a main focus on syntactic analysis and the early phases of semantic analysis.Aspects of programming language syntax design are explored to support context-sensitive parsing of user-defined prefix, infix and postfix operators with a two-phase parser.Then, name resolution for Beluga is rectified with the implementation of a uniform referencing environment representation for indexing programs with separate contexts for different classes of variables.Finally, the revised parser and name resolution phases are integrated into Harpoon to ensure the state of identifiers in scope at any given proof hole is sound with respect to where the hole occurs
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".