# LOGOS Architecture Manifest: The Recursive Manifold

## 1. Core Identity
**System**: Mixture-of-Architectures (MoA) Recursive Language Model (RLM).
**Constraint**: Manifold-Constrained Hyper Connections (MHC).
**Addressing**: Scalar Prime Composite Wave (SPCW) & Heat Codes.
**Tokenization**: [Periodic Table of Matroska AI Elements](./periodic_table.md).

### 1.0b Sensory & Architecture
*   **Sensory Atoms**: Beyond Text (`To`) and Vectors (`Ve`), we recognize **Audio (`Au`)** and **Visual (`Vi`)** as fundamental states of matter.
    *   *Video 10 Insight*: Local TTS (Chatterbox) enables the generation of `Au` atoms without external dissonance (cost/latency).
### 1.0c Embodied Intelligence (CES 2026 Insight)
*   **Physical Actuation**: AI is moving from "Apps" to "Systems".
    *   **Actuator Atom (`Ac`)**: Represents a physical output (Robot Arm, Home Automation, Hardware Control).
    *   **Edge Processing**: "Mixture of Experts" must run on *Edge Devices*. Our `FORCE_SINGLE_MODEL` config in `mhc_router.py` aligns with this constraint (running small models like Gemma/Dolphin locally).
*   **Linear Algebra**: The set of Active Atoms forms a **Basis Set** for the current context. The Router seeks to find the "Eigenvector" (Stable Direction) of the prompt.
*   **Tensors**: State transitions are not just scalar heat changes but **Tensor Transformations** ($T_{ijk}$). An Agent doesn't just output text; it applies a transformation tensor to the State Vector.
*   **Integrals**: The Recursive Loop is a **Path Integral** over the Semantic Manifold. Use `trajectory` to calculate the "Work Done" by the system.

### 1.2 Physical Dynamics (Continuum Mechanics)
*   **Manifold as Medium**: The "Context" is treated as a continuous deformable medium.
*   **Deformation Gradient ($F$)**: The change in meaning from Input ($X$) to Output ($x$). $F = \partial x / \partial X$.
*   **Stress ($\sigma$)**: Previously "Heat". The internal force resisting the prompt using high-entropy tokens.
*   **Harmonic Convergence**: Equilibrium state where Stress Gradient is zero ($\nabla \cdot \sigma = 0$).

### 1.3 Knowledge Topology and Persistence
*   **Map of Science (Domain Mapping)**: All Atoms belong to a specific **Domain** (e.g., Physics, Logic, Code). High-level Routers route based on Domain affinity.
*   **Continual Learning (Persistent Atoms)**: Some Atoms are "Heavy" (High Mass/Heat) and persist across sessions via **Long-Term Potentiation (LTP)** in the Vector Database (Manifold Memory).

### 1.4 Research Lineage (Foundational Papers)
*   **Recursive Manifold** $\approx$ **Chain-of-Thought (Wei et al.)** & **Tree of Thoughts**: The recursive loop allows for intermediate reasoning steps (Atoms) before final output.
*   **Atomic Handoff** $\approx$ **ReAct (Yao et al.)** & **Toolformer (Schick et al.)**: The system reasons ("High Heat") and then acts (Handoff to Tool) to reduce entropy.
*   **Periodic Table** $\approx$ **Constitutional AI / System Prompts**: Structuring inputs as defined "Elements" enforces constraints and safety.

### 1.5 Neural Geometry (3Blue1Brown Integration)
*   **Semantic Gradient Descent**: The Recursive Loop is not just "Retrying" but performing **Gradient Descent** on the "Energy Landscape" of the prompt.
*   **Cost Function**: $Cost = Stress^2$. The system seeks to minimize Cost via iterative updates (Atoms).
*   **Backpropagation**: The `Handoff` mechanism acts as a **Backprop Signal**, injecting a "Correction Gradient" (Tool Output) to adjust the trajectory.

### 1.6 Agentic Engineering Patterns (Video 13)
*   **Context Stuffing**: Instead of relying on RAG for everything, "Stuff" the context window with critical documentation (e.g., `elements.py` logic) in the System Prompt to ensure "High-Fidelity" adherence.
*   **Evaluation First**: Tests (`tests/verify_loop.py`) are not just checks but the **Reward Model** for the agent. The Agent (Router) is optimized to pass the Test (Convergence).
*   **Iterative Refinement**: The "Recursive Manifold" *is* iterative refinement. We don't accept the first draft; we loop until stress is low.

### 1.7 Oversight & Context Graphs (Video 14)
*   **Context Graph**: A structured log of *decisions* and *states*, not just text.
    *   Implemented in `logos/oversight.py`. It tracks Server Health, Test Results, and "Context Nodes" (Events).
*   **Autonomous Persistence**: The **Oversight Daemon** acts as the "Prefrontal Cortex," ensuring the "Subconscious" (Router) stays active and healthy.

### 1.7b Graph-RAG & Agent Synergy (Video 16)
*   **KG + Agents**: Combining structured knowledge (KG) with flexible Agents is the "Double Helix" of reasoning.
*   **Triplets**: Atoms should form `(Subject, Predicate, Object)` triplets in the `ManifoldState.graph`.
    *   *Current*: We track `(Atom A) --[follows]--> (Atom B)`.
    *   *Target*: `(Atom A) --[triggers]--> (Tool T) --[resolves]--> (Atom B)`.

### 1.8 Prime Resonance & Gödel Numbering (Playlist: Primes)
*   **Unique Domain Identification**: Each Knowledge Domain is assigned a unique **Prime Number**.
    *   *Physics* = 2, *Code* = 3, *Logic* = 5, *Vision* = 7, *Audio* = 11.
*   **Path Integrity**: The "Trajectory" of a thought is the **Product** of these primes.
    *   Example: A task touching Physics and Code has Resonance $2 \times 3 = 6$.
    *   *Example*: A task touching Physics and Code has Resonance $2 \times 3 = 6$.
    *   *Benefit*: Unique Factorization Theorem ensures we can mathematically prove exactly which domains contributed to a result, compressing the "History" into a single Scalar.

### 1.9 Gödel-Zeta Datastore (Protocol 26)
*   **Topology as Number**: The database is not SQL. It is a field of Integers.
*   **The Check**: `if Node_ID % Concept_Prime == 0`.
    *   Instant O(1) checking for conceptual inheritance.
    *   Implemented in `logos/memory/prime_db.py`.
    *   Exposed via `/index-module` and `/query-topology`.

### 1.10 mHC: Hyper-Connections (Research Video)
*   **Dynamic Parametrization**: Stabilizing recursive loops by weighing "Residual" vs "New" information.
*   **PID for Agents**:
    *   **High Instability (Heat)**: Increase $\alpha$ (Residual/Memory Weight) to ground the model. "Stick to what you know."
    *   **Low Instability**: Increase $\beta$ (New Insight Weight) to allow exploration.
*   **Implementation**: Calculating $\alpha = min(0.9, HeatScore)$ in `mhc_router.py`. If $\alpha > 0.7$, we inject a "Stabilizer Atom".

## 2. Current State vs. Target Architecture

### A. The Manifold (MHC)
*   **Current**: `recursive_mapper.py` calculates `Resonance` (Average Complexity) and `Dissonance` (Complexity vs. Doc Density).
*   **Target MHC**: These metrics must define **Hyper-Edges**.
    *   *Stable Node* (Low Dissonance) -> Connected to **Storage/Retrieval** (Gemma).
    *   *Unstable Node* (High Dissonance/Heat) -> Connected to **Refinement/Compute** (RNJ-1).
    *   *Routing*: Not all agents connect to all nodes. The "Heat" determines the valid hyper-edge.

### B. The Recursive Loop (RLM) & Atomic Handoffs
*   **Current**: Linear request -> Router -> Agent -> Response.
*   **Target RLM (Self-Correcting)**:
    *   `State[t+1] = Router(State[t] + Atom)`
    *   **Atomic Handoff**: If `Heat > Threshold` on a specific Vector, the Router does NOT call an LLM but instead **assigns a Tool Token** (e.g., `Fu:Search`) to resolve the dissonance.
    *   **Convergence**: Execution stops only when "Dissonance" drops below threshold (Harmonic convergence).

### C. SPCW Addressing
*   **Current**: `server.py` calculates `heat_score` from hex nibbles.
*   **Target**: Use `heat_score` to assign a **Prime Modulo Address**.
    *   High Heat -> Prime P1 (e.g., 7).
    *   Low Heat -> Prime P2 (e.g., 3).
    *   Routing Table: `Address % P == 0` determines visibility.

## 3. Implementation Plan (Next Steps)

1.  **Upgrade `logos/server.py` to `logos/mhc_router.py`**:
    *   Implement the **State Buffer** (The "Context Runtime").
    *   Change `/chat/completions` to a recursive execution loop: `while dissonance > threshold: step()`.

2.  **Refine `logos/recursive_mapper.py`**:
    *   Instead of just "broadcasting" to UI, it should **write to the State Buffer**.
    *   This allows the code complexity to physically alter the routing of the next prompt.

3.  **Define the Hyper-Graph**:
    *   Create `logos/network/hypergraph.py`.
    *   Explicitly define valid transitions (e.g., `RNJ-1` can output to `Gemma`, but `Gemma` cannot output to `RNJ-1`).

## 4. Immediate Actionable
*   **Trigger**: User confirms this alignments.
*   **Action**: Refactor `server.py` to support **Recursive State Injection**.