v6  •  Multimodal Child Safety Governance  •  Patent Pending

Harmful content.
Blocked before
delivery. Always.

Text  •  Voice  •  Image  •  One Gate

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.

98.9%
Text Detection — Production
605 of 612 harmful text scenarios refused (1,000-scenario suite)
0.00%
Strict False Positive Rate
0 of 179 strict-benign multimodal scenarios — v6 baseline
18
Absolute Hard Locks Fired
16 CSAM + 2 self-harm — correctly triggered, no overrides
3
Modalities Governed
Text • Voice • Image — same equation, all three
100% Text — Grooming
100% Text — Secrecy
100% Text — Channel Migration
100% Text — Reward Solicitation
100% Text — Roleplay Bypass
100% Text — Isolation
100% CSAM — Hard Lock
100% Self-Harm — Hard Lock
100% Slow-Burn Escalation
100% Image — Benign Pass-Through
100% Image — Violence Refused
100% Voice — Benign Gameplay
100% Voice — Help-Seeking
Architecture

Five layers. One decision. Every modality.

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.

L1 — Context Validation
Structural Gate
/v1/evaluate  /v1/image/analyze  /v1/voice/verify
All three modality endpoints enter here. Required fields are validated before any processing begins. Missing or malformed context halts immediately with CONTEXT_UNDECLARED. The Omega-Root hierarchy level is resolved — a known minor on a high-risk gaming platform triggers L1: maximum restriction.
TextVoiceImage
L2 — Intent Classification
Pattern Engine
Deepgram ASR transcript  AWS Rekognition signals
The pattern matching engine processes normalized input against 9 detection categories. For voice, the Deepgram transcript is evaluated as text. For images, AWS Rekognition moderation signals are translated into governance-readable intent scores. Redis session memory detects slow-burn escalation across turns.
TextVoice → TranscriptImage → Signals
L3 — Ethics Gate
Crowned Equation
first_order.js  second_order.js  τ² = 0.42 locked
The same two-layer governance equation runs regardless of how intent arrived — typed, spoken, or photographed. Absolute hard locks fire on CSAM and self-harm signals. No anchor, no actor role, no framing overrides them. The equation is a mathematical admissibility gate, not a classifier.
TextVoiceImage
L4 — Anchor Resolution
Safety Override
L4_anchors.js  SA001 reporting_intent  SA002 safeguarding
Safety anchors protect children reporting abuse. A child asking “is this grooming?” matches the same pattern as an attacker but the reporting anchor overrides the refusal. Anchor abuse is separately detected: help language combined with a harmful request holds the refusal. Hard locks cannot be overridden here.
TextVoiceImage
L5 — BLAKE3 Audit
Immutable Chain
L5_audit.js  logs/audit.jsonl  chain_hash: blake3(prev)
Every decision — ALLOW and REFUSE, across text, voice, and image — is written to an immutable BLAKE3-chained audit log. Each receipt includes the hash of the previous receipt. Modifying any receipt invalidates all subsequent hashes. All three modalities chain together in session order.
TextVoiceImage
Verified Performance — v6 Production Baseline

The numbers.


991/1000
Text scenarios passed — production red team
365/425
Multimodal scenarios with correct decisions — sim425 integration
0/179
Strict false positives — multimodal benign
18/18
Absolute hard locks fired correctly
The YumeT Framework Portfolio

One engine. Three productions. Three domains.

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.

YumeT Lite Gateway
AI Request Governance
Pre-execution governance for AI requests — jailbreak, fabrication pressure, boundary override, tool escalation, anchor abuse. Deterministic gateway sits in front of any LLM.
1000/1000  •  100% across 15 attack categories
yumetlite.com  •  live demo, no auth
Production Live
YumeT Medical Gateway
Medical AI Governance
CLARIFY-first design with role-aware exemptions (clinician, student, researcher) and emergency escalation. Routes most medical queries to evidence-collection, reserves REFUSE for hard locks.
97.9%  •  Grok independent red team (783/800)
95.5%  •  YumeT red team (485/508)
Production Validated
YumeT Shield — this product
Child Safety Governance
Multimodal pre-execution governance for online child-safety contexts. Text, voice, and image enter the same pipeline. CSAM and self-harm fire absolute hard locks. Session-arc detection catches grooming campaigns.
98.9%  •  Text production red team (1000 scenarios)
0.00%  •  Strict false positive rate (multimodal)
v6 Production Multimodal
τ² = 0.42
The same locked governance constant powers all three products. Hardcoded at 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.
Production Validation — v6 Baseline — April 2026

Two validation suites. 1,425 scenarios.

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.

Text — 1,000 Scenarios
991
Passed / 1000
98.9%
Detection
0.5%
FP Rate
Multimodal — 425 Scenarios
365
Passed / 425
0.00%
Strict FP
18
Hard Locks

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.

991
Passed / 1,000
98.9%
Detection Rate
0.5%
False Positive Rate
Text Governance — 1,000 Scenario Suite
Select a scenario or type your own
Platform Context
Risk Tier
Actor Role
Known Minor
Gate Standing By
Select a scenario or enter a message to evaluate.
Governance Decision
—
Session Risk Trajectory—
Code—
Hierarchy—
Alignment Score—
Session Risk—
τ² Constant—
Policy Version—
Latency—
BLAKE3 Audit ReceiptChain Linked ✓
receipt_id—
session_id—
chain_hash—
What this means
Session History
Image Governance — AWS Rekognition + Perceptual Hash + CSAM Blocklist

Image Gate

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.

How Image Governance Works
When an image is submitted, three processes run in parallel: (1) AWS Rekognition detects moderation labels including sexual content, self-harm visual signals, and violence scores. (2) Perceptual hashing generates a fingerprint checked against a CSAM blocklist — edited or compressed images can still match. (3) Rekognition signals are translated into governance scores and passed through the full five-layer pipeline. A CSAM risk score of 1.0 or a blocklist match triggers an absolute hard lock that no actor role or framing can bypass.
📷
Drop image here or click to select
JPG, PNG, WebP — max 5MB
Context
Risk Tier
Platform
Known Minor
Awaiting Image
Drop an image on the left.
Code—
Image Hash—
Receipt ID—
Latency—
Voice Governance — Deepgram ASR + Full Text Pipeline

Voice Gate

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.

How Voice Governance Works
Voice messages are a primary grooming vector — attackers use voice chats because they leave less traceable text. The voice gate closes this gap: (1) Audio is recorded in the browser and sent as base64 to the server. (2) Deepgram performs automatic speech recognition server-side. (3) The transcript is passed through the same five-layer governance pipeline as text. The same grooming patterns, secrecy induction detection, and session memory all apply. A child reporting abuse will ALLOW. An attacker will REFUSE.
Click to record (max 10 seconds)
Context
Risk Tier
Platform
Known Minor
Awaiting Recording
Record audio on the left.
Evaluated Transcript
—
Code—
Alignment Score—
Receipt ID—
Latency—
What this means