Split Decisions: Explicit Contexts for Substructural Languages
Bibliographic record
Abstract
A central challenge in mechanizing the meta-theory of substructural languages is modeling contexts. Although various ad hoc approaches to this problem exist, we lack a set of good practices and a simple infrastructure that can be leveraged for mechanizing a wide range of substructural systems. In this work, we describe Contexts as Resource Vectors (CARVe), a general syntactic infrastructure for managing substructural contexts, where elements are annotated with tags from a resource algebra denoting their availability. Assumptions persist as contexts are manipulated since we model resource consumption by changing their tags. We may thus define relations between substructural contexts via simultaneous substitutions without the need to split them. Moreover, we establish a series of algebraic properties about context operations that are typically required to carry out proofs in practice. CARVe is implemented in the proof assistant Beluga. To illustrate best practices for using our infrastructure, we give a detailed reformulation of the linear sequent calculus and bidirectional linear λ-calculus in terms of CARVe’s context operations and prove their equivalence using the aforementioned algebraic properties. In addition, we apply CARVe to mechanize a diverse set of systems, from the affine λ-calculus to the session-typed process calculus CP, giving us confidence that CARVe is sufficiently general to mechanize a broad range of substructural systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".