DAIS‑10 + SFPM‑10 Semantic Architecture for Present‑to‑Future Meaning Governance DAIS‑10 is a semantic governance framework that transforms raw data into structured, tiered, influence‑weighted, drift‑aware, and diagnostically validated meaning.

SFPM‑10 extends DAIS‑10 by forecasting future semantic states, enabling systems to anticipate how meaning will evolve under changing context, drift, and governance rules.

Together, they form a unified architecture for present‑to‑future semantic intelligence.

1. High‑Level Architecture

flowchart TD

    RAW["Raw Data / Sensors / Attributes"]

    subgraph DAIS10["DAIS‑10 Present Semantics"]
        SIS10["SIS‑10\nSemantic Interpretation"]
        MCM10["MCM‑10\nMeaning Roles"]
        TIER10["TIER‑10\nTier Assignment"]
        SICM10["SICM‑10\nSemantic Intensity"]
        SIF10["SIF‑10\nInfluence Weights"]
        DIFS10["DIFS‑10\nDrift & Subzones"]
        QFIM10["QFIM‑10\nQualified Interpretation"]
        AMD10["AMD‑10\nDiagnostics"]
    end

    subgraph SFPM10["SFPM‑10 Semantic Futures"]
        SFPM_S10["SFPM‑S10\nIntensity Futures"]
        SFPM_T10["SFPM‑T10\nTier Futures"]
        SFPM_W10["SFPM‑W10\nInfluence Futures"]
        SFPM_Z10["SFPM‑Z10\nDrift/Subzone Futures"]
        SFPM_Q10["SFPM‑Q10\nQualified Futures"]
    end

    subgraph PLANCTRL["Semantic‑Aware Planning & Control"]
        FUSION["Fusion / Aggregation"]
        PLANNER["Planner"]
        CONTROL["Control / Execution"]
    end

    RAW --> SIS10
    SIS10 --> MCM10 --> TIER10 --> SICM10 --> DIFS10 --> QFIM10
    SICM10 --> SIF10

    SICM10 --> SFPM_S10
    TIER10 --> SFPM_T10
    SIF10 --> SFPM_W10
    DIFS10 --> SFPM_Z10
    QFIM10 --> SFPM_Q10

    SFPM_S10 --> FUSION
    SFPM_T10 --> FUSION
    SFPM_W10 --> FUSION
    SFPM_Z10 --> FUSION
    SFPM_Q10 --> FUSION

    FUSION --> PLANNER --> CONTROL
    CONTROL --> AMD10

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2. DAIS‑10: Present Semantic Engine DAIS‑10 transforms raw data into a structured semantic state.

flowchart LR
    RAW["Raw Attribute"]
    SIS10["SIS‑10\nInterpretation"]
    MCM10["MCM‑10\nRole Assignment"]
    TIER10["TIER‑10\nTier Mapping"]
    SICM10["SICM‑10\nIntensity Score"]
    SIF10["SIF‑10\nInfluence Weight"]
    DIFS10["DIFS‑10\nDrift/Subzone"]
    QFIM10["QFIM‑10\nQualified Level"]
    AMD10["AMD‑10\nDiagnostics"]

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    RAW --> SIS10 --> MCM10 --> TIER10 --> SICM10 --> DIFS10 --> QFIM10 --> AMD10
    SICM10 --> SIF10

DAIS‑10 Output (per attribute)

x_i(t) = ( s_i(t), t_i(t), w_i(t), z_i(t), q_i(t) )
Where:

s_i(t) = semantic intensity

t_i(t) = tier

w_i(t) = influence weight

z_i(t) = drift/fading subzone

q_i(t) = qualified interpretation

3. SFPM‑10: Semantic Futures Engine SFPM‑10 forecasts how meaning will evolve over time.

flowchart LR

    PRESENT["Present Semantic State\nx_i(t)"]

    subgraph FUTURES["SFPM‑10 Sub‑Engines"]
        S10["SFPM‑S10\nIntensity Futures"]
        T10["SFPM‑T10\nTier Futures"]
        W10["SFPM‑W10\nInfluence Futures"]
        Z10["SFPM‑Z10\nDrift/Subzone Futures"]
        Q10["SFPM‑Q10\nQualified Futures"]
    end

    FUTURE["Future Semantic State\nx_i(t + dt)"]

    PRESENT --> S10 --> FUTURE
    PRESENT --> T10 --> FUTURE
    PRESENT --> W10 --> FUTURE
    PRESENT --> Z10 --> FUTURE
    PRESENT --> Q10 --> FUTURE

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SFPM‑10 Output (per attribute)

x_i(t + dt) = ( s_i(t+dt), t_i(t+dt), w_i(t+dt), z_i(t+dt), q_i(t+dt) )

4. Combined Semantic State Flow

sequenceDiagram
    participant Raw as Raw Data
    participant DAIS as DAIS‑10
    participant SFPM as SFPM‑10
    participant Plan as Planning/Control

    Raw->>DAIS: Provide attributes
    DAIS->>DAIS: Interpret, classify, tier, score, weight, drift, qualify
    DAIS->>SFPM: Present semantic state x_i(t)
    SFPM->>SFPM: Forecast semantic futures
    SFPM->>Plan: Future semantic state x_i(t + dt)
    DAIS->>Plan: Present semantic state x_i(t)
    Plan->>Plan: Risk‑aware, drift‑aware decisions

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5. Repository Structure

/dais10/
    sis10/
    sif10/
    mcm10/
    tier10/
    sicm10/
    difs10/
    qfim10/
    amd10/

/sfpm10/
    sfpm_s10/
    sfpm_t10/
    sfpm_w10/
    sfpm_z10/
    sfpm_q10/

/docs/
    diagrams/
    formulas/
    architecture/

/examples/ robotics/ semantic-futures/ data-governance/ 6. Key Features Present Semantics (DAIS‑10) Role classification

Tier governance

Influence weighting

Semantic intensity scoring

Drift & fading detection

Qualified interpretation

Semantic diagnostics

Future Semantics (SFPM‑10) Intensity forecasting

Tier transition prediction

Influence evolution

Drift/subzone projection

Qualified interpretation futures

Planning & Control Drift‑aware decisions

Pre‑emptive risk detection

Semantic stability management

Governance‑aligned planning

7. Why This Architecture Matters DAIS‑10 + SFPM‑10 gives systems the ability to:

understand meaning now

anticipate meaning next

act with semantic foresight

maintain governance stability

detect drift before failure

prioritize based on future influence

This is semantic intelligence that spans present → future, not just static interpretation.