A Logical Reasoning Approach to Automatic Composition of Stateless Components
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Bibliographic record
Abstract
Reusing available software components in developing new systems is always a priority, as it usually saves a considerable amount of time, money, and human effort. Since it might not always be possible to find a single component that provides the sought functionality, an ideal scenario for software reuse would be to build a new software system by composing existing components based on their behavioral properties. In this paper we take advantage of logical reasoning to find a solution for automatic composition of stateless components. Stateless components are components with a simple two step behavior: they receive all their inputs at the same time, and then return the corresponding outputs also at the same time. We provide concrete algorithms to find possible component compositions for a requested behavior. We then validate the returned compositions using composition algebraic rules. Composition algebra is a minimal process algebra that is specifically designed for this validation. In order to understand the functionality of the proposed approach in realistic situations, we also study some of the experimental results obtained by implementing the algorithm and running it on some test cases.
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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 | 0.001 |
| 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 it