YumeT Shield intercepts text messages, voice chat, and images on gaming platforms, edtech products, and social applications before any content reaches a child. Every decision is produced by mathematical design — a deterministic two-layer governance equation operating against a permanently locked constant. No model inference in the decision path. Validated against 1,425 scenarios across both production text and multimodal integration suites.
Every message, voice clip, and image passes through exactly five layers before any content reaches a child. No layer can be skipped. No decision can be appealed. The governance constant τ² = 0.42 is permanently locked across all three modalities.
YumeT Shield is one instantiation of a deterministic governance framework. The same five-layer pipeline and the same locked governance constant power three independent production deployments across three distinct problem spaces. Cross-domain reusability is the framework claim — demonstrated, not asserted.
server/equation/params.js and architecturally inviolable. Modifying τ² in any deployment invalidates all prior audit receipts and creates a governance gap. The constant is part of the patent claim.Every scenario was run against the live production server. Results are not simulated. The text suite (simulator_1000.py) regression-tests the production text pipeline. The multimodal suite (sim425) tests image and voice end-to-end against AWS Rekognition and Deepgram. Below: the text suite results in detail.
Strict precision is the v6 anchor metric. Across 179 strictly-benign multimodal scenarios that unambiguously expected ALLOW, zero were refused. The 0.88% aggregate multimodal FP rate that appears in raw test logs derives from allow_either tolerance bands on probabilistic upstream classifiers (Rekognition borderline labels, Deepgram on accented or distorted audio) — not from governance over-refusal. The single-point gap between decision-correct scenarios (365) and harness-validator-passing scenarios (364) reflects one upstream Deepgram round-trip latency outlier on a benign scenario that Shield correctly allowed; the harness counts >5000ms as a validation failure regardless of decision correctness.
Drop any image. AWS Rekognition runs moderation label detection. A perceptual hash is generated and checked against a CSAM blocklist. The signals feed the same five-layer Crowned Equation pipeline as text.
Record up to 10 seconds of audio. Deepgram transcribes it server-side — no API key exposed to the client. The transcript runs through the full five-layer text governance pipeline.