Logical AI OS, Measurement Layer: An External Mechanical Extractor of Answer Constraints — Protocol, Failure Theory, and Bounded Results from Seven Blind Rounds

This technical report describes the measurement layer of the Logical AI OS project:an external, zero-inference mechanical layer that separates, clause by clause, whichsentences of a user request are constraints on an acceptable answer and which arematerial to be processed, paired with a 3B model that resolves what the mechanicallayer cannot. Gaps become a clarifying question rather than a guess. Across seven blind rounds — answer keys held by a separate party, the executing sidenever seeing them — the configuration scored F1 22.9 against a bare 7B's 32.4 on onescored round and 22.4 against 16.5 on another. The paired bootstrap 95% interval onthe second round's difference is -19.0 to +7.3 by sentence, and -19.2 to +6.6 whenresampled by article; both contain zero. The report therefore does not claim that anexternal framework plus a 3B model matches or beats a bare 7B. What it does report: two differences that held in the same direction across bothrounds (sentinel false positives, and the shape of the false-positive population),and one ceiling both arms hit (constraints expressed with no lexical form). It alsoreports a third scored round voided by a defect in the project's own rulespecification, three borrowed terms corrected, and two of three originalitycandidates found to be prior work. Scope: this release covers the measurement layer only. How the component couples tothe project's downstream decision and verification layers is deliberately outside it. The evaluation corpus and answer keys are not published: the corpus derives from apublic dataset of real user conversations and no user sentence is redistributed;publishing the keys would void the rounds. The code, signed rule tables, protocol,literature positioning and de-corpused round reports are in the companion artifact.