Verifying Safety-Critical Meaning, Recipient-Bound Delivery and Corrections in Multilingual Emergency Instructions Produced or Relayed by AI

Emergency and industrial operations increasingly rely on speech recognition and machine translation to pass spoken instructions between people who do not share a language. A single dropped “not”, a lost condition (“only after pressure is below…”), a changed number or unit, or a message delivered to last shift’s radio can turn a correct instruction into a dangerous one, while every conventional signal still looks healthy: the speaker was authenticated, recognition confidence was high, the translation was fluent, the gateway reported success and the device acknowledged receipt. This disclosure describes a vendor-neutral, non-actuating method for reconstructing whether amultilingual instruction remained faithful from source to effect. The original audio or message is preserved as evidence and registered as non-effective; speaker identity, operational authority, integrity and sequence are evaluated as separate propositions. The instruction’s meaning is captured as a contract of typed, source-linked ropositions — action, prohibition (polarity), target, condition, quantity with unit, and sequence — and every translated candidate is checked proposition by proposition, with dimensional unit conversion and without treating agreement between correlated models or back-translation as independent proof. Dispatch is bound to the actual outgoing payload, recipient, endpoint, channel, version and deadline. Submission, delivery, receipt, readback, comprehension and physical response are kept as distinct evidence, and corrections are tracked per recipient and per version without erasing earlier deliveries or effects. Under network degradation the method requires approved fallback and escalation rather than either suppressing the warning or fabricating certainty. Results are combined in three-valued logic per scope: a proven contradiction yields INCONSISTENT, full support yields CONSISTENT, and missing evidence yields UNRESOLVED; an absent acknowledgement is never proof of non-receipt. The ocument provides a 48-stage, six-phase engineering workflow with flowcharts, formal definitions, six detailed embodiments, a 21-page mathematical supplement (H-M01–H-M36), 40 engineering checkpoints, a comparison with existing approaches (translation-quality metrics, ASR confidence, back-translation and LLM judges, controlled language, OASIS CAP and wireless emergency alerts, NG9-1-1 data exchange, closed-loop readback, delivery receipts, authenticated calling and 3GPP mission-critical push-to-talk) and a reference evaluator in Python, JavaScript and Go with 93 shared synthetic test vectors, all passing. The evaluator checks supplied structured evidence only; it does not transcribe, translate, dispatch or certify emergency communication safety. The laboratory is self-authored and not peer reviewed. The author isopen to acquihire or strategic collaboration. Keywords: AI safety; multilingual communication; emergency instructions; machine translation verification; speech recognition uncertainty; negation; semantic integrity; source provenance; speaker authority; recipient binding; delivery receipts; readback; corrections and supersession; degraded networks; three-valued logic; human-in-the-loop; OASIS CAP; NG9-1-1; MCPTT