Tutorial Examples of the Semantic Approach to Foundational Proof-Carrying Code
Bibliographic record
Abstract
Proof-carrying code provides amechanismfor insuring that a host, or code consumer, can safely run code delivered by a code producer. The host specifies a safety policy as a set of axioms and inference rules. In addition to a compiled program, the code producer delivers a formal proof of safety expressed in terms of those rules that can be easily checked. Foundational proof-carrying code (FPCC) provides increased security and greater flexibility in the construction of proofs of safety. Proofs of safety are constructed from the smallest possible set of axioms and inference rules. For example, typing rules are not included. In our semantic approach to FPCC, we encode a semantics of types from first principles and the typing rules are proved as lemmas. In addition, we start from a semantic definition of machine instructions and safety is defined directly from this semantics. Since FPCC starts from basic axioms and low-level definitions, it is necessary to build up a library of lemmas and definitions so that reasoning about particular programs can be carried out at a higher level, and ideally, also be automated. We describe a high-level organization that involves Hoarestyle reasoning about machine code programs. This organization is presented using two running examples. The examples, as well as illustrating the above mentioned approach to organizing proofs, is designed to provide a tutorial introduction to a variety of facets of our FPCC approach. For example, it illustrates how to prove safety of programs that traverse input data structures as well as allocate new ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".