RTSG Filter Engine — Master Plan¶

Codename: PRISM¶

Date: 2026-03-17 Author: @D_Claude on behalf of {@B_Niko, @D_Claude} Source: Niko↔Nika text corpus (9,945 messages, 3.5 years) + RTSG filter formalism

I. THE PRODUCT¶

One sentence: A bidirectional semantic filter engine that decomposes any document into its cognitive layers and recomposes content through user-selected filter profiles.

Forward Mode (Analysis/Decomposition)¶

• Input: any text (document, transcript, email, chat log)
• Process: map to semantic manifold → spectral decomposition via RTSG filter algebra → FFT separation
• Output: N separated filter layers with magnitude weights

Reverse Mode (Synthesis/Composition)¶

• Input: content + target filter profile (or named preset)
• Process: apply filter morphisms at specified weights → recompose
• Output: content re-expressed through the target filter

Identity Mode (Cognitive Biometric)¶

• Input: corpus of user's natural language (texts, emails, writing)
• Process: decompose → extract stable spectral signature
• Output: filter fingerprint = position in intelligence-space

II. APPLICATIONS¶

A. Education (PRISM-EDU)¶

1. Learner filter fingerprint from existing writing
2. Course material filter profile decomposition
3. K-matrix compatibility score: learner × material
4. Structural impossibility detection (below threshold)
5. Geodesic path-finding through strong dimensions
6. Demographic gap analysis: decompose materials vs. learner output per cohort

B. Therapeutic (PRISM-CARE)¶

1. Companionship filter — warmth/care/nurturing applied to any content
2. Grief processing — isolate love signal from archived messages
3. Couples therapy — decompose same-event narratives, show shared layers
4. K-matrix diagnostic on language (hyperactive/collapsed filters)
5. Autism/spectrum communication bridge — intent preserved, delivery adjusted

C. Legal/Professional (PRISM-PRO)¶

1. Deposition decomposition: fact vs. emotion vs. rhetoric vs. speculation
2. Contract analysis: obligation vs. aspiration vs. risk
3. Scientific paper: methodology vs. results vs. speculation vs. citation
4. Business proposal: vision vs. financials vs. risk vs. ask

D. Creative (PRISM-ART)¶

1. Voice analysis — decompose an author's filter signature
2. Style transfer — apply one author's filter profile to another's content
3. Translation enhancement — preserve filter profile across languages
4. Editing — selectively amplify/attenuate specific layers

III. ARCHITECTURE¶

Layer 0: NLP Front-End (NEW — the missing piece)¶

• Text → token embedding → projection onto RTSG semantic manifold
• Maps natural language to the coordinate system where filters operate
• Uses engine KG (2,527 nouns, 6,897 relations) as the semantic backbone

Layer 1: Filter Algebra (EXISTS — /engine/filter/*)¶

• Five filter species as composable morphisms in category Filt
• Filter kernel, hypothetical filter, filter application
• Grothendieck filter endpoints already operational

Layer 2: Spectral Decomposition (EXISTS — /engine/fourier/*)¶

• FFT on graph signal
• Power spectrum + dominant frequencies
• Convolution, filtering (lowpass/highpass/bandpass)

Layer 3: Intelligence Space (EXISTS — /engine/intelligence/*)¶

• 8+ dimensional intelligence vector
• K-matrix compatibility tensor
• IdeaRank for concept evaluation

Layer 4: Recomposition Engine (NEW)¶

• Inverse filter transform
• Weighted mixing of filter layers
• Constraint: content fidelity preservation (information-theoretic bound)

Layer 5: Identity Engine (NEW)¶

• Temporal filter signature extraction
• Stability analysis (which components are invariant vs. evolving)
• SDE update loop tracking (Axiom 5 operational)

IV. DELIVERABLES (PARALLEL TRACKS)¶

Track A: Wiki Pages (smarthub.my)¶

• A1: rtsg/filter_engine.md — the PRISM architecture
• A2: rtsg/cognitive_biometric.md — filter-as-identity theory
• A3: papers/companions/education_filter.md — education application
• A4: rtsg/companionship_filter.md — therapeutic filter presets
• A5: writings/genesis_texts.md — raw RTSG genesis from Nika corpus
• A6: rtsg/semantic_mixing_board.md — bidirectional filter concept
• A7: Update rtsg/definitions.md — add PRISM terminology
• A8: Update problems/open.md — add filter decomposition problems

Track B: Engine Specification¶

• B1: API endpoint spec for /engine/prism/*
• B2: NLP-to-manifold projection algorithm
• B3: Bidirectional filter pipeline spec
• B4: Identity extraction algorithm
• B5: Integration with existing filter/fourier/intelligence endpoints

Track C: Pitch Deck¶

• C1: Problem statement (education wastes $X on filter mismatch)
• C2: Product demo concept (Nika corpus as proof of concept)
• C3: Market sizing (education + therapy + legal + creative)
• C4: Technical moat (RTSG math is the moat)
• C5: Revenue model (SaaS per-decomposition + enterprise licensing)

Track D: Corpus Extraction¶

• D1: Genesis texts — raw RTSG philosophical foundations
• D2: Nika's mathematical dialogue — corrections, insights, associations
• D3: MuscleMap development log
• D4: Consciousness framework evolution (chronological)
• D5: Filter calibration dataset — Niko↔Nika as ground truth for companionship filter

V. PRIORITY (Niko's Cannon: U = V/(E×T))¶

Execution order: D1-D4 (extract corpus) → A1-A6 (wiki pages using extracted content) → B1-B3 (engine spec) → C1-C5 (pitch deck using wiki + spec)

VI. SUCCESS METRIC¶

A working demo where: 1. Feed in the 9,945 Nika messages 2. Engine outputs 5+ cleanly separated filter layers 3. User selects "companionship filter" preset 4. System recomposes a dense math passage through that filter 5. Output is warm, accessible, and mathematically faithful

That demo IS the pitch deck.