CARIBOO: A Multi-Strategy Termination Proof Tool Based on Induction
Bibliographic record
Abstract
1 A termination proof tool for rule-based programs CARIBOO is a termination proof tool for rule-based programming languages, where a program is a rewrite system and query evaluation consists in rewriting a ground expression [3]. It applies to languages such as ASF+SDF, Maude, Cafe-OBJ, or ELAN. By contrast with most of the existing tools, which prove in general termination of standard rewriting (rewriting without strategy) on the free term algebra, our proof tool, named CARIBOO (for Computing AbstRaction for Induction Based termination prOOfs), allows proving termination under specific reduction strategies, which becomes of special interest when the computations diverge for standard rewriting. It deals in particular with: the innermost strategy, specially useful when the rule-based formalism expresses functional programs, and central in the evaluation process of ELAN, local strategies on operators, provided in OBJ-like languages, and allowing to control evaluation strategies in a very fine local way, the outermost strategy, useful to avoid evaluations known to be non terminating for the standard strategy, to make strategy computations shorter, and used for interpreters and compilers using call by name. 2 Proving termination by explicit induction
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".