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