Characterizing provability in BI’s pointer logic through resource graphs
Bibliographic record
Abstract
Abstract We propose a characterization of provability in BI's Pointer Logic (PL)that is based on semantic structures called resource graphs. This logic has been defined for reasoning about mutable data structures and results about models andverification have been already provided. Here, we define resource graphs that capture PL models by considering heaps as resources and by using a labellingprocess. We study provability in PL from a new calculus that builds such graphs from which proofs or countermodels can be generated. Properties of soundnessand completeness are proved and the countermodel generation is studied. 1 Introduction Separation logics are logics for reasoning about mutable data structures in which thepre- and postconditions are written in a logic enriched with specific forms of conjunction or implication. In this context, Reynolds has proposed an intuitionistic logic ex-tended with a separation connective \\Lambda [12] and Ishtiaq and O'Hearn have investigatedthe same approach from the point of view of the logic of Bunched Implications (BI)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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".