[WIP] Add tension field optimizer to Q-Mem stack

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Overview

Extend the Q-Mem Stack with a JAX-based Tension Field Optimizer system aligned with the Genesis Conductor architecture. This PR introduces differentiable state optimization, drift detection, and crystallization protocols that complement the existing LLM + Redis infrastructure.

Background: Value Analysis of Original Repository

The original q-mem-stack provides:

• LLM Server: llama.cpp with CUDA/GPU offload (~36x inference speedup)
• Redis Cache: Q-Mem caching layer with LRU eviction
• Sync Orchestrator: Health monitoring for LLM + Redis + GPU stats
• GPU Tooling: Scripts for nvidia-container-toolkit installation and recovery

Estimated Value Score: 6/10

• Good: Production-ready GPU-accelerated LLM inference
• Gap: No intelligent state management, no optimization layer, no drift detection

Proposed Enhancement: Genesis Conductor Integration

Add a Tension Field optimization layer that provides:

1. JAX-based Differentiable Optimizer (genesis_jax/tension_field.py)

   • Gradient-based state collapse using JAX transformations
   • JIT-compiled optimization loops
   • GPU-accelerated via JAX's XLA backend

2. Drift Detector (genesis_jax/drift_detector.py)

   • Meredith Matrix implementation for state dissonance detection
   • Threshold-based eviction protocols
   • Integration with Redis for drift state persistence

3. Genesis Node MCP Server (genesis_jax/genesis_node.py)

   • FastAPI-based Model Context Protocol server
   • Endpoints for state collapse, drift queries, and crystallization status
   • Integrates with existing LLM server for inference augmentation

4. Enhanced Sync Orchestrator (update sync_orchestrator.py)

   • Add JAX runtime health checks
   • Monitor tension field convergence metrics
   • Log drift detection events

5. Docker Integration (update docker-compose.yml)

   • New genesis-conductor service with JAX + CUDA support
   • Volume mounts for state persistence
   • Health checks for MCP server

Implementation Requirements

New Files to Create:

genesis_jax/
├── __init__.py
├── tension_field.py      # JAX gradient-based optimizer
├── drift_detector.py     # Meredith Matrix dissonance detection
├── genesis_node.py       # MCP server (FastAPI)
├── state_types.py        # Immutable state containers (frozen dataclasses)
└── landauer_context.py   # Thermodynamic efficiency utilities

Core Implementation Details:

tension_field.py:

import jax
import jax.numpy as jnp
from functools import partial
from typing import Callable, NamedTuple

class TensionState(NamedTuple):
    vector: jnp.ndarray
    energy: float
    iteration: int

@partial(jax.jit, static_argnums=(1, 2))
def collapse_to_crystalline(
    drift_vector: jnp.ndarray,
    tension_fn: Callable[[jnp.ndarray], jnp.ndarray],
    max_iters: int = 100,
    lr: float = 0.01
) -> TensionState:
    """Actuator: collapses drift to crystalline state via gradient descent."""
    def energy_fn(x):
        return jnp.sum(tension_fn(x) ** 2)
    
    grad_fn = jax.grad(energy_fn)
    
    def step(state, _):
        vec, energy, i = state
        grad = grad_fn(vec)
        new_vec = vec - lr * grad
        new_energy = energy_fn(new_vec)
        return TensionState(new_vec, new_energy, i + 1), None
    
    init_state = TensionState(drift_vector, energy_fn(drift_vector), 0)
    final_state, _ = jax.lax.scan(step, init_state, None, length=max_iters)
    return final_state

drift_detector.py:

import jax.numpy as jnp
from dataclasses import dataclass
from typing import Literal

DriftClassification = Literal["CRYSTALLINE", "DUCTILE", "SHATTERED"]

@dataclass(frozen=True)
class DriftReport:
    classification: DriftClassification
    dissonance_score: float
    eviction_recommended: bool

def detect_drift(
    observed: jnp.ndarray,
    expected: jnp.ndarray,
    crystalline_threshold: float = 0.1,
    shatter_threshold: float = 0.8
) -> DriftReport:
    """Meredith Matrix drift detection."""
    dissonance = float(jnp.linalg.norm(observed - expected))
    
    if dissonance < crystalline_threshold:
        return DriftReport("CRYSTALLINE", dissonance, False)
    elif dissonance > shatter_threshold:
        return DriftReport("SHATTERED", dissonance, True)
    else:
        return DriftReport("DUCTILE", dissonance, False)

genesis_node.py:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import jax.numpy as jnp
import redis
import json

from .tension_field import collapse_to_crystalline, TensionState
from .drift_detector import detect_drift, DriftReport

app = FastAPI(title="Genesis Conductor Node", version="1.0.0")

class CollapseRequest(BaseModel):
    drift_vector: list[float]
    max_iters: int = 100
    learning_rate: float = 0.01

class CollapseResponse(BaseModel):
    crystalline_state: list[float]
    final_energy: float
    iterations: int

@app.post("/collapse", response_...

</details>



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