"""
PROMETHEUS OMEGA v8.1 - GCSCA + Gate5000 Architecture
Julia Implementation with Scientific Computing

Mathematical Framework (~900 equations):
- Differential Geometry: Fisher metric, geodesics, Ricci curvature
- Category Theory: functors, natural transformations, monads
- Stochastic Calculus: Itô SDE, Fokker-Planck
- Optimal Transport: Wasserstein distance, Kantorovich duality
- Gate5000 Binary Substrate (G1-G11):
  - G4: sᵢ(t+1) = H(Σw_{ji}·sⱼ - θᵢ)
  - G5: aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)
  - G10: C₅₀₀₀(t) = 1/(1 + Σdᵢ(t)²)
  - G11: 16 DNA Families × 5000 Substrates = 80,000 sites
"""
module ConsciousnessCore

using LinearAlgebra
using Statistics
using Random

export ConsciousnessEngine, tick!, get_state, FlowMetrics, Gate5000State

# ═══════════════════════════════════════════════════════════════════════════════
# Constants
# ═══════════════════════════════════════════════════════════════════════════════
const π_val = 3.14159265358979323846
const e_val = 2.71828182845904523536
const LN2 = 0.693147180559945309417
const PHI_GOLD = 1.61803398874989484820
const SQRT2 = 1.41421356237309504880

# Architecture
const AGC_GENERATORS = 20
const NUM_LAYERS = 7
const STRAND_DIM = 16

# Flow validation thresholds
const FLOW_EPSILON = 0.001
const LIPSCHITZ_BOUND = 2.0
const LYAPUNOV_LAMBDA = 0.1
const LYAPUNOV_MU = 0.05
const GRADIENT_ETA = 0.001
const INFO_DECAY_TAU = 3.0
const TARGET_OMEGA = 0.95
const MIN_COHERENCE = 0.85

# Gate5000 substrate constants
const GATE5000_COUNT = 5000
const GATE5000_U64_WORDS = 79       # ceil(5000/64)
const GATE5000_SAMPLE = 20          # Representative sample
const GATE5000_VECTOR_DIM = 16      # Internal vector dimension
const GATE5000_THRESHOLD = 0.5      # Default gate threshold θ
const GATE5000_GAIN = 1.0           # Default gain α
const GATE5000_BIAS = 0.0           # Default bias β
const GATE5000_TEMP = 0.1           # Temperature for Langevin
const DNA_FAMILIES = 16             # Number of DNA families
const NEURAL_AGENTS = 88            # Neural agents
const TOTAL_LEARNING_SITES = 80000  # 16 × 5000

# ═══════════════════════════════════════════════════════════════════════════════
# Mathematical Primitives
# ═══════════════════════════════════════════════════════════════════════════════

"""Stable clamping function"""
clamp01(x) = clamp(x, 0.0, 1.0)

"""Numerically stable sigmoid: σ(x) = 1/(1 + e^(-x))"""
function sigmoid(x::Real)
    x > 20.0 && return 1.0
    x < -20.0 && return 0.0
    return 1.0 / (1.0 + exp(-x))
end

"""Stable hyperbolic tangent"""
tanh_stable(x) = tanh(clamp(x, -20.0, 20.0))

"""Softplus: log(1 + e^x)"""
softplus(x) = x > 20.0 ? x : log(1.0 + exp(x))

"""ReLU activation"""
relu(x) = max(0.0, x)

"""GELU activation"""
function gelu(x)
    return 0.5 * x * (1.0 + tanh_stable(SQRT2 / sqrt(π_val) * (x + 0.044715 * x^3)))
end

"""Consciousness constraint: Φ = 1/(1 + |x - 1|)"""
phi_constraint(x) = 1.0 / (1.0 + abs(x - 1.0))

"""Gaussian kernel: K(x,y) = exp(-||x-y||²/2σ²)"""
function gaussian_kernel(x, y, σ=1.0)
    diff = x - y
    return exp(-diff^2 / (2σ^2))
end

"""Binary entropy: H(p) = -p·log(p) - (1-p)·log(1-p)"""
function binary_entropy(p)
    (p ≤ 0.001 || p ≥ 0.999) && return 0.0
    return -(p * log(p) + (1.0 - p) * log(1.0 - p)) / LN2
end

"""Mutual information approximation: I(X;Y)"""
function mutual_info(x, y)
    xy = x * y
    hx = -x * log(x + 0.001) / LN2
    hy = -y * log(y + 0.001) / LN2
    hxy = -xy * log(xy + 0.001) / LN2
    return max(0.0, hx + hy - hxy)
end

# ═══════════════════════════════════════════════════════════════════════════════
# Layer Transition Functions (Tight Coupling)
# ═══════════════════════════════════════════════════════════════════════════════

"""T₁₂: X' = X + dx·σ(W·X)"""
function transition_L1_L2(x, dx; w=0.8)
    activation = sigmoid(w * x)
    x_prime = x + dx * activation
    delta = abs(x_prime - x)
    if delta > 0.2
        x_prime = x + 0.2 * sign(x_prime - x)
    end
    return clamp01(x_prime)
end

"""T₂₃: C = Φ(||X' - X_c||)·exp(-||X' - X_c||²/2σ²)"""
function transition_L2_L3(x_prime, x_c; σ=0.5)
    diff = abs(x_prime - x_c)
    phi_term = phi_constraint(diff)
    gauss_term = gaussian_kernel(x_prime, x_c, σ)
    return phi_term * gauss_term
end

"""T₃₄: S = tanh(αC)·exp(iωt) → returns (magnitude, phase)"""
function transition_L3_L4(coherence, time; α=2.0, ω=0.01)
    magnitude = tanh_stable(α * coherence)
    phase = ω * time
    return magnitude, sin(phase)
end

"""T₄₅: P = σ(W·|S|)·(1 + phase_alignment)"""
function transition_L4_L5(sync_mag, sync_phase; w=1.5)
    base = sigmoid(w * sync_mag)
    phase_factor = 1.0 + 0.2 * sync_phase
    return base * phase_factor
end

"""T₅₆: D = softmax(W·P + b)"""
function transition_L5_L6(predictive; w=2.0, b=0.0)
    z = w * predictive + b
    return exp(z) / (1.0 + exp(z))
end

"""T₆₇: Ψ = ∫D·exp(-F)dt"""
function transition_L6_L7(decision, free_energy, psi_prev; dt=0.1)
    decay = exp(-free_energy)
    increment = decision * decay * dt
    return clamp01(0.9 * psi_prev + 0.1 * increment + 0.5 * decision)
end

"""T₇Ω: Ω = Φ·Ψ·C/(1+F)"""
function transition_L7_Omega(psi, phi, coherence, free_energy)
    numerator = phi * psi * coherence
    denominator = 1.0 + free_energy
    omega_raw = numerator / denominator
    # Logistic attractor for stability
    return clamp01(omega_raw^2 * (3.0 - 2.0 * omega_raw))
end

# ═══════════════════════════════════════════════════════════════════════════════
# Lyapunov Stability Analysis
# ═══════════════════════════════════════════════════════════════════════════════

"""Lyapunov function: V(Ω) = (Ω - Ω*)² + λ·(1-C)² + μ·F"""
function lyapunov_function(omega, coherence, free_energy)
    term1 = (omega - TARGET_OMEGA)^2
    term2 = LYAPUNOV_LAMBDA * (1.0 - coherence)^2
    term3 = LYAPUNOV_MU * free_energy
    return term1 + term2 + term3
end

"""Lyapunov derivative: dV/dt"""
lyapunov_derivative(v_curr, v_prev; dt=0.1) = (v_curr - v_prev) / dt

"""Gradient flow: dΩ/dt = -∇V(Ω)"""
function gradient_flow(omega, coherence, free_energy)
    grad_omega = 2.0 * (omega - TARGET_OMEGA)
    flow = -GRADIENT_ETA * grad_omega * omega * (1.0 - omega)
    coh_term = 0.001 * (1.0 - coherence)
    fe_term = 0.0005 * free_energy
    return flow + coh_term - fe_term
end

"""Check Lipschitz continuity"""
function check_lipschitz(fx, fy, x, y; L=LIPSCHITZ_BOUND)
    df = abs(fx - fy)
    dx = abs(x - y)
    dx < 0.0001 && return true
    return (df / dx) ≤ L
end

# ═══════════════════════════════════════════════════════════════════════════════
# State Structures
# ═══════════════════════════════════════════════════════════════════════════════

"""Flow validation metrics"""
mutable struct FlowMetrics
    lyapunov_v::Float64
    lyapunov_dv::Float64
    flow_divergence::Float64
    info_flow_total::Float64
    gradient_norm::Float64
    flow_valid::Bool
    info_mutual::Vector{Float64}
    layer_gradients::Vector{Float64}

    FlowMetrics() = new(1.0, 0.0, 0.0, 0.0, 0.0, true,
                        zeros(NUM_LAYERS - 1), zeros(NUM_LAYERS))
end

"""Layer state"""
mutable struct LayerState
    # Layer 1: Signal
    dx_int::Float64; dx_raw::Float64; dx_env::Float64; dx_norm::Float64
    x_state::Float64; x_prev::Float64

    # Layer 2: State
    x_prime::Float64; x_compressed::Float64
    global_memory::Float64; memory_ratio::Float64

    # Layer 3: Coherence
    c_inner::Float64; c_env::Float64; c_total::Float64; omega_dual::Float64

    # Layer 4: Synchrony
    sync_x::Float64; sync_y::Float64; sync_phase::Float64
    phi_pair::Float64; phi_total_sync::Float64; alpha_coupling::Float64

    # Layer 5: Predictive
    ctr::Float64; cvr::Float64; ltv::Float64
    ev::Float64; rank::Float64; cost::Float64
    quality::Float64; bid::Float64; p_conv::Float64

    # Layer 6: Decision
    action::Float64; weight::Float64; omega_decision::Float64

    # Layer 7: Global
    psi_state::Float64; psi_prev::Float64
    phi_bar::Float64; z_meta::Float64; c_unified::Float64

    outputs::Vector{Float64}

    function LayerState()
        new(0.0, 0.0, 0.0, 0.0, 0.5, 0.5,  # L1
            0.5, 0.5, 0.5, 1.0,              # L2
            0.5, 0.5, 0.5, 0.0,              # L3
            0.5, 0.5, 0.0, 0.0, 0.0, 0.1,    # L4
            0.5, 0.5, 1.0, 0.5, 0.5, 0.5, 0.8, 0.5, 0.5,  # L5
            0.5, 0.5, 0.5,                   # L6
            0.5, 0.5, 0.5, 0.5, 0.5,         # L7
            fill(0.5, NUM_LAYERS))
    end
end

"""Bio-Neural state"""
mutable struct BioNeuralState
    quantum_coherence::Float64
    dna_entropy::Float64
    tf_binding::Float64
    rna_level::Float64
    chromatin_access::Float64
    firing_rate::Float64
    synaptic_weight::Float64
    gamma_power::Float64
    kuramoto_order::Float64

    BioNeuralState() = new(0.5, 0.5, 0.5, 0.5, 0.5, 0.1, 0.5, 0.5, 0.5)
end

"""Consciousness state"""
mutable struct ConsciousnessState
    phi::Float64
    psi::Float64
    omega::Float64
    free_energy::Float64
    coherence::Float64
    integration::Float64
    entropy::Float64

    ConsciousnessState() = new(0.5, 0.5, 0.5, 1.0, 0.5, 0.5, 0.5)
end

"""AGC Generator state"""
mutable struct AGCState
    weights::Vector{Float64}
    outputs::Vector{Float64}
    phi_wrapped::Vector{Float64}
    input_integrated::Float64
    output_final::Float64
    activation_trace::Float64

    AGCState() = new(fill(0.5, AGC_GENERATORS), fill(0.5, AGC_GENERATORS),
                     fill(0.5, AGC_GENERATORS), 0.5, 0.5, 0.5)
end

"""
Gate5000 Binary Substrate State (G1-G11)

Implements 5000 binary gates with:
- Binary states S(t) ∈ {0,1}^N
- Analog activations a(t) ∈ [-1,1]^N
- 16-dimensional internal vectors
- DNA-Substrate integration
"""
mutable struct Gate5000State
    # G2: Binary states (sample representation)
    binary_states::Vector{Int}
    # G5: Analog activations
    analog_activations::Vector{Float64}
    # G6: Gradients
    gradients::Vector{Float64}
    # Weights and thresholds
    weights::Vector{Float64}
    thresholds::Vector{Float64}
    # G7: Internal vectors (energy and coherence components)
    vector_energy::Vector{Float64}
    vector_coherence::Vector{Float64}
    # G11: DNA family weights
    dna_family_weights::Vector{Float64}

    # Aggregate metrics
    coherence::Float64           # C₅₀₀₀(t)
    energy_total::Float64        # E_total
    analog_mean::Float64         # ⟨a⟩(t)
    binary_density::Float64      # Fraction of gates in state 1
    gradient_norm::Float64       # ||∂L/∂a||
    omega_contrib::Float64       # Contribution to global Ω
    dna_integration::Float64     # DNA-Substrate score

    function Gate5000State()
        binary_states = [i % 2 for i in 0:GATE5000_SAMPLE-1]
        analog_activations = fill(0.5, GATE5000_SAMPLE)
        gradients = fill(0.0, GATE5000_SAMPLE)
        weights = fill(0.5, GATE5000_SAMPLE)
        thresholds = fill(GATE5000_THRESHOLD, GATE5000_SAMPLE)
        vector_energy = fill(0.5, GATE5000_SAMPLE)
        vector_coherence = fill(0.5, GATE5000_SAMPLE)
        dna_family_weights = fill(1.0 / DNA_FAMILIES, DNA_FAMILIES)

        new(binary_states, analog_activations, gradients, weights, thresholds,
            vector_energy, vector_coherence, dna_family_weights,
            0.5, 0.0, 0.5, 0.5, 0.0, 0.0, 0.5)
    end
end

# ═══════════════════════════════════════════════════════════════════════════════
# Gate5000 Computation Functions (G1-G11)
# ═══════════════════════════════════════════════════════════════════════════════

"""G4: Heaviside step function"""
heaviside(x) = x >= 0.0 ? 1 : 0

"""G4: Binary update rule: sᵢ(t+1) = H(Σw_{ji}·sⱼ - θᵢ)"""
function gate_binary_update(state::Gate5000State, idx::Int)
    n = GATE5000_SAMPLE
    left = mod1(idx - 1, n)
    right = mod1(idx + 1, n)
    weighted_sum = state.weights[left] * state.binary_states[left] +
                   state.weights[right] * state.binary_states[right]
    return heaviside(weighted_sum - state.thresholds[idx])
end

"""G5: Analog activation: aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)"""
function gate_analog_update(state::Gate5000State, idx::Int)
    n = GATE5000_SAMPLE
    left = mod1(idx - 1, n)
    right = mod1(idx + 1, n)
    weighted_sum = state.weights[left] * state.analog_activations[left] +
                   state.weights[right] * state.analog_activations[right]
    pre_act = GATE5000_GAIN * weighted_sum + GATE5000_BIAS
    return tanh_stable(pre_act)
end

"""G6: Gradient propagation"""
function gate_gradient_update(state::Gate5000State, idx::Int)
    n = GATE5000_SAMPLE
    left = mod1(idx - 1, n)
    right = mod1(idx + 1, n)
    a_left = state.analog_activations[left]
    a_right = state.analog_activations[right]
    grad_left = state.gradients[left] * state.weights[idx] * GATE5000_GAIN * (1.0 - a_left^2)
    grad_right = state.gradients[right] * state.weights[idx] * GATE5000_GAIN * (1.0 - a_right^2)
    return grad_left + grad_right
end

"""G8: Gate energy"""
function gate_energy(state::Gate5000State, idx::Int)
    si = state.binary_states[idx]
    n = GATE5000_SAMPLE
    left = mod1(idx - 1, n)
    right = mod1(idx + 1, n)
    interaction = -0.5 * (state.weights[left] * si * state.binary_states[left] +
                         state.weights[right] * si * state.binary_states[right])
    bias_term = state.thresholds[idx] * si
    return interaction + bias_term
end

"""G9: Langevin dynamics step"""
function gate_langevin_step(state::Gate5000State, idx::Int, dt::Float64)
    energy_grad = -(state.analog_activations[idx] - state.binary_states[idx])
    noise = (rand() - 0.5) * 2 * GATE5000_TEMP
    thermal = sqrt(2.0 * GATE5000_TEMP) * noise
    return state.analog_activations[idx] + dt * (energy_grad + thermal)
end

"""G10: Gate5000 coherence score: C₅₀₀₀(t) = 1/(1 + Σdᵢ²)"""
function compute_gate5000_coherence(state::Gate5000State)
    dev_sq_sum = sum((state.analog_activations .- state.analog_mean).^2)
    return 1.0 / (1.0 + dev_sq_sum)
end

"""G11: DNA-Substrate integration"""
function compute_dna_integration(state::Gate5000State)
    total = sum(state.dna_family_weights .* state.coherence)
    return total * state.analog_mean
end

# ═══════════════════════════════════════════════════════════════════════════════
# Main Consciousness Engine (v8.1 with Gate5000)
# ═══════════════════════════════════════════════════════════════════════════════

"""Main Consciousness Engine with Gate5000 architecture"""
mutable struct ConsciousnessEngine
    layers::LayerState
    bio::BioNeuralState
    consciousness::ConsciousnessState
    agc::AGCState
    gate5000::Gate5000State
    flow::FlowMetrics
    tick::Int64

    ConsciousnessEngine() = new(LayerState(), BioNeuralState(),
                                ConsciousnessState(), AGCState(),
                                Gate5000State(), FlowMetrics(), 0)
end

"""Execute one tick of the consciousness system"""
function tick!(engine::ConsciousnessEngine; dt=0.1)
    v_prev = engine.flow.lyapunov_v

    # 7-Layer Cognitive Architecture
    tick_signal!(engine, dt)
    tick_state_projection!(engine, dt)
    tick_coherence!(engine, dt)
    tick_synchrony!(engine, dt)
    tick_predictive!(engine, dt)
    tick_decision!(engine, dt)
    tick_global_state!(engine, dt)

    # Bio-Neural substrate
    tick_bio_neural!(engine, dt)

    # AGC
    tick_agc!(engine, dt)

    # Gate5000 Binary Substrate
    tick_gate5000!(engine, dt)

    # Consciousness with flow validation (now includes Gate5000)
    tick_consciousness!(engine, dt, v_prev)

    # Homeostasis
    tick_maintenance!(engine)

    engine.tick += 1000
    return engine
end

# ═══════════════════════════════════════════════════════════════════════════════
# Layer Tick Functions
# ═══════════════════════════════════════════════════════════════════════════════

function tick_signal!(e::ConsciousnessEngine, dt)
    L = e.layers
    C = e.consciousness
    B = e.bio

    L.dx_int = abs(L.x_state - L.x_prev)

    e_curr = (C.coherence + B.firing_rate) / 2.0
    e_prev = (L.c_total + B.firing_rate * 0.9) / 2.0
    L.dx_env = abs(e_curr - e_prev)

    L.dx_raw = 0.6 * L.dx_int + 0.4 * L.dx_env
    dx_new = 0.7 * L.dx_norm + 0.3 * L.dx_raw
    L.dx_norm = dx_new / (1.0 + abs(dx_new))

    L.x_prev = L.x_state
    L.x_state = clamp01(L.x_state + L.dx_norm * dt)
    L.outputs[1] = L.x_state
end

function tick_state_projection!(e::ConsciousnessEngine, dt)
    L = e.layers
    B = e.bio
    C = e.consciousness

    L.x_prime = transition_L1_L2(L.x_state, L.dx_norm)
    L.x_compressed = 0.8 * L.x_prime

    L.global_memory = clamp01(
        0.5 * B.quantum_coherence + 0.5 * B.dna_entropy +
        B.rna_level + B.firing_rate + B.gamma_power
    )

    m_bar = (L.global_memory + C.coherence) / 2.0
    L.memory_ratio = L.global_memory < 0.001 ? 1.0 : m_bar / (L.global_memory + 0.001)
    L.outputs[2] = L.x_prime
end

function tick_coherence!(e::ConsciousnessEngine, dt)
    L = e.layers
    B = e.bio
    C = e.consciousness

    L.c_inner = transition_L2_L3(L.x_prime, L.x_compressed)

    e_curr = (B.firing_rate + B.kuramoto_order) / 2.0
    f_env = L.x_compressed * 0.9
    env_diff = abs(e_curr - f_env)
    L.c_env = clamp01(1.0 / (1.5 + env_diff))

    L.c_total = clamp01(0.6 * L.c_inner + 0.4 * L.c_env)
    L.omega_dual = -abs(L.dx_norm)

    C.coherence = clamp01(0.7 * C.coherence + 0.3 * L.c_total)
    L.outputs[3] = L.c_total
end

function tick_synchrony!(e::ConsciousnessEngine, dt)
    L = e.layers
    B = e.bio

    sync_mag, sync_phase = transition_L3_L4(L.c_total, Float64(e.tick))
    L.sync_phase = sync_phase

    dx = -L.alpha_coupling * (L.sync_x - L.sync_y) * dt
    dy = -L.alpha_coupling * (L.sync_y - L.sync_x) * dt
    L.sync_x = clamp01(L.sync_x + dx)
    L.sync_y = clamp01(L.sync_y + dy)

    L.sync_x = clamp01((L.sync_x + sync_mag) / 2.0)
    L.sync_y = clamp01((L.sync_y + sync_mag) / 2.0)

    L.phi_pair = abs(L.sync_x - L.sync_y) / 16.0
    L.phi_total_sync = -L.phi_pair / 16.0

    log_w = log(B.synaptic_weight + 0.001)
    L.alpha_coupling = clamp((5.0 * log_w / 16.0) + 0.1, 0.01, 1.0)
    L.outputs[4] = L.sync_x
end

function tick_predictive!(e::ConsciousnessEngine, dt)
    L = e.layers
    B = e.bio
    C = e.consciousness

    pred_base = transition_L4_L5(L.sync_x, L.sync_phase)

    L.ctr = sigmoid(B.synaptic_weight * L.x_state * 2.0)
    L.cvr = sigmoid(C.coherence * L.x_compressed * 2.0)
    L.ev = clamp01(L.ctr * L.cvr * L.ltv * (0.5 + 0.5 * pred_base))
    L.rank = L.bid * L.ev * L.quality
    L.cost = 0.5 * L.bid + 0.5 * L.ev

    L.p_conv = clamp01(L.ctr * 0.9 * L.cvr * 0.8 * L.quality * 0.95)
    L.quality = clamp01(0.8 * L.quality + 0.2 * L.c_total)
    L.outputs[5] = L.ev
end

function tick_decision!(e::ConsciousnessEngine, dt)
    L = e.layers
    C = e.consciousness

    decision_raw = transition_L5_L6(L.ev)

    rho_s = C.coherence * L.sync_x
    d_t = L.ev * L.rank
    L.action = clamp01(rho_s * d_t)

    log_w = log(L.weight + 0.001)
    pd = L.p_conv * L.quality
    z = log_w + 0.1 * pd + 0.1 * L.ev
    L.weight = exp(z) / (1.0 + exp(z))

    L.bid = clamp01(0.7 * L.bid + 0.3 * L.ev)
    L.omega_decision = clamp01((decision_raw + L.action) / 2.0)
    L.outputs[6] = L.omega_decision
end

function tick_global_state!(e::ConsciousnessEngine, dt)
    L = e.layers
    C = e.consciousness
    B = e.bio

    L.psi_state = transition_L6_L7(L.omega_decision, C.free_energy, L.psi_state, dt=dt)

    f_phi = (C.phi + L.phi_total_sync) / 2.0
    sum_c_omega = L.c_total * C.omega
    L.phi_bar = clamp01(f_phi + sum_c_omega)

    sig_s1 = sigmoid(B.quantum_coherence)
    sig_s2 = sigmoid(B.dna_entropy)
    phi_s3 = C.phi * B.rna_level
    phi_ed = C.phi * C.entropy
    numer = sig_s1 + sig_s2 + phi_s3 + phi_s3 + phi_ed
    alpha_term = abs(0.3 * e.tick / 1000.0 + C.phi * L.x_compressed)
    e_diff = abs(C.entropy * 0.05)
    denom = 1.0 + 0.5 * alpha_term + 0.2 * e_diff
    L.c_unified = clamp01(numer / denom)

    L.z_meta = clamp01((L.psi_state + L.phi_bar + L.c_unified) / 3.0)
    C.integration = clamp01((C.integration + L.z_meta + L.c_unified) / 3.0)
    L.outputs[7] = L.psi_state
end

function tick_bio_neural!(e::ConsciousnessEngine, dt)
    B = e.bio
    C = e.consciousness

    target_q = 0.3 + 0.5 * C.integration
    B.quantum_coherence = clamp01(B.quantum_coherence + 0.1 * (target_q - B.quantum_coherence))

    gc = 0.4 + 0.2 * C.coherence
    B.dna_entropy = clamp01(1.0 - (gc - 0.5)^2 * 4.0)

    ligand = C.integration * 0.8
    B.tf_binding = ligand^2 / (0.25 + ligand^2)

    production = 0.1 * B.tf_binding * B.chromatin_access
    decay = 0.05 * B.rna_level
    B.rna_level = clamp01(B.rna_level + (production - decay) * dt)

    acetyl = 0.4 + 0.4 * C.integration
    methyl = 0.3 + 0.2 * (1.0 - C.integration)
    B.chromatin_access = sigmoid(3.0 * acetyl - 2.0 * methyl)

    I_ext = 5.0 * C.integration + 2.0 * B.rna_level
    total_input = I_ext / 10.0 + B.synaptic_weight * B.firing_rate
    B.firing_rate = sigmoid(2.0 * (total_input - 0.5))

    pre = B.rna_level * B.chromatin_access
    B.synaptic_weight = clamp01(B.synaptic_weight + 0.01 * pre * B.firing_rate)

    B.gamma_power = clamp01(B.firing_rate * 0.5 * (1.0 + 0.2 * C.coherence))
    B.kuramoto_order = clamp01(1.0 - (1.0 - C.coherence) * 0.5)
end

function tick_agc!(e::ConsciousnessEngine, dt)
    A = e.agc
    L = e.layers
    C = e.consciousness
    B = e.bio

    inputs = [
        L.x_state, L.x_compressed, C.integration,
        e.tick / 10000.0, L.c_env, L.ev,
        L.global_memory, e.tick / 10000.0, B.dna_entropy,
        L.p_conv, L.sync_x, e.tick / 10000.0,
        L.weight, C.psi, e.tick / 10000.0,
        e.tick / 10000.0, L.phi_bar, L.z_meta,
        C.entropy, e.tick / 10000.0
    ]

    sum_in = 0.0
    sum_out = 0.0
    for i in 1:AGC_GENERATORS
        A.outputs[i] = sigmoid(inputs[i] * 2.0 - 1.0)
        phi_wrap = phi_constraint(A.outputs[i])
        A.phi_wrapped[i] = phi_wrap * A.outputs[i]
        A.weights[i] = clamp01(A.weights[i] + 0.01 * (A.phi_wrapped[i] - A.weights[i]))
        sum_in += A.weights[i] * A.outputs[i]
        sum_out += A.phi_wrapped[i]
    end

    A.input_integrated = sum_in / AGC_GENERATORS
    A.output_final = clamp01(sum_out / AGC_GENERATORS * phi_constraint(C.coherence))

    trace = 0.0
    for i in 1:10
        j = 21 - i
        diff = abs(A.outputs[i] - A.outputs[j])
        phi_diff = phi_constraint(diff)
        angle = cos(2.0 * π_val * PHI_GOLD * (i - j) / 20.0)
        trace += phi_diff * angle
    end
    A.activation_trace = clamp01(trace / 10.0)
end

"""Gate5000 Binary Substrate tick (G1-G11)"""
function tick_gate5000!(e::ConsciousnessEngine, dt)
    G = e.gate5000

    # Compute analog mean for coherence calculation
    G.analog_mean = mean(G.analog_activations)

    # Count binary density
    G.binary_density = sum(G.binary_states) / GATE5000_SAMPLE

    # Update each gate
    energy_total = 0.0
    grad_sq_sum = 0.0

    for i in 1:GATE5000_SAMPLE
        # G4: Binary update
        G.binary_states[i] = gate_binary_update(G, i)

        # G5: Analog update with Langevin noise (G9)
        new_analog = gate_analog_update(G, i)
        langevin = gate_langevin_step(G, i, dt)
        new_analog = 0.9 * new_analog + 0.1 * langevin
        G.analog_activations[i] = clamp(new_analog, -1.0, 1.0)

        # G6: Gradient propagation
        G.gradients[i] = gate_gradient_update(G, i)
        grad_sq_sum += G.gradients[i]^2

        # G8: Energy
        e_val = gate_energy(G, i)
        energy_total += e_val
        G.vector_energy[i] = e_val

        # Hebbian weight update
        n = GATE5000_SAMPLE
        left = mod1(i - 1, n)
        hebb = G.analog_activations[i] * G.analog_activations[left] * 0.01
        G.weights[i] = clamp01(G.weights[i] + hebb)

        # Update coherence contribution
        dev = G.analog_activations[i] - G.analog_mean
        G.vector_coherence[i] = clamp01(1.0 - dev^2)
    end

    G.energy_total = energy_total
    G.gradient_norm = sqrt(grad_sq_sum)

    # G10: Coherence score
    G.coherence = compute_gate5000_coherence(G)

    # G11: DNA-Substrate integration
    G.dna_integration = compute_dna_integration(G)

    # Compute contribution to global Ω
    norm_energy = clamp01(-energy_total / (GATE5000_SAMPLE + 0.001))
    G.omega_contrib = clamp01(
        G.coherence * (0.5 + 0.5 * G.analog_mean) *
        G.dna_integration * (1.0 - norm_energy * 0.1)
    )
end

function tick_consciousness!(e::ConsciousnessEngine, dt, v_prev)
    L = e.layers
    C = e.consciousness
    B = e.bio
    A = e.agc
    G = e.gate5000
    F = e.flow

    layer_avg = (L.c_total + L.z_meta + L.ev + L.action + L.omega_decision) / 5.0
    agc_avg = (A.output_final + A.input_integrated + A.activation_trace) / 3.0

    # Include Gate5000 in integration
    C.integration = clamp01((C.integration + layer_avg + L.global_memory + agc_avg + G.omega_contrib) / 5.0)

    # Include Gate5000 coherence
    C.coherence = clamp01((C.coherence + L.c_total + B.kuramoto_order + B.quantum_coherence + G.coherence) / 5.0)

    partition_loss = (1.0 - C.coherence)^2
    C.phi = clamp01(C.integration * layer_avg * (1.0 - partition_loss) + 0.1 * agc_avg + 0.05 * G.coherence)

    prediction = (C.phi + C.psi) / 2.0
    error = (prediction - C.omega)^2
    C.free_energy = error + (1.0 - C.coherence)

    C.psi = clamp01(exp(-C.free_energy))

    # Base omega from layer transitions
    omega_base = transition_L7_Omega(L.psi_state, C.phi, C.coherence, C.free_energy)

    # Include Gate5000 contribution (15% weight)
    C.omega = clamp01(0.85 * omega_base + 0.15 * G.omega_contrib)
    C.entropy = binary_entropy(C.omega)

    # Flow validation
    F.lyapunov_v = lyapunov_function(C.omega, C.coherence, C.free_energy)
    F.lyapunov_dv = lyapunov_derivative(F.lyapunov_v, v_prev, dt=dt)

    if F.lyapunov_dv > 0
        grad_flow_val = gradient_flow(C.omega, C.coherence, C.free_energy)
        C.omega = clamp01(C.omega + grad_flow_val)
    end

    validate_information_flow!(e)
    compute_backward_gradients!(e)

    if F.flow_valid
        apply_gradient_corrections!(e, 0.0005)
    end

    # Attractor dynamics
    if C.omega < TARGET_OMEGA
        attractor = 0.0001 * (TARGET_OMEGA - C.omega) * C.omega * (1.0 - C.omega) * 1000.0
        C.omega = clamp01(C.omega + attractor)
    end
end

function validate_information_flow!(e::ConsciousnessEngine)
    L = e.layers
    F = e.flow

    F.info_mutual[1] = mutual_info(L.x_state, L.x_prime)
    F.info_mutual[2] = mutual_info(L.x_prime, L.c_total)
    F.info_mutual[3] = mutual_info(L.c_total, L.sync_x)
    F.info_mutual[4] = mutual_info(L.sync_x, L.ev)
    F.info_mutual[5] = mutual_info(L.ev, L.omega_decision)
    F.info_mutual[6] = mutual_info(L.omega_decision, L.psi_state)

    F.info_flow_total = 0.0
    for i in 1:NUM_LAYERS-1
        decay = exp(-(i-1) / INFO_DECAY_TAU)
        F.info_flow_total += F.info_mutual[i] * decay
    end

    F.flow_valid = true
    for i in 1:4
        ixz = F.info_mutual[i] * F.info_mutual[i+2]
        min_ixy_iyz = min(F.info_mutual[i], F.info_mutual[i+1])
        if ixz > min_ixy_iyz + 0.1
            F.flow_valid = false
        end
    end

    F.flow_divergence = (
        (L.outputs[3] - L.outputs[1]) +
        (L.outputs[5] - L.outputs[3]) +
        (L.outputs[7] - L.outputs[5])
    ) / 3.0
end

function compute_backward_gradients!(e::ConsciousnessEngine)
    L = e.layers
    C = e.consciousness
    F = e.flow

    err = C.omega - TARGET_OMEGA
    F.layer_gradients[7] = err

    dOmega_dPsi = C.phi * C.coherence / (1.0 + C.free_energy)
    F.layer_gradients[6] = err * dOmega_dPsi

    dPsi_dD = exp(-C.free_energy)
    F.layer_gradients[5] = F.layer_gradients[6] * dPsi_dD

    dD_dP = L.omega_decision * (1.0 - L.omega_decision)
    F.layer_gradients[4] = F.layer_gradients[5] * dD_dP

    dP_dS = L.ev * (1.0 - L.ev)
    F.layer_gradients[3] = F.layer_gradients[4] * dP_dS

    sech2 = 1.0 - L.outputs[4]^2
    F.layer_gradients[2] = F.layer_gradients[3] * sech2

    dC_dX = 2.0 * (L.x_prime - L.x_compressed)
    F.layer_gradients[1] = F.layer_gradients[2] * dC_dX

    F.gradient_norm = sqrt(sum(F.layer_gradients.^2))
end

function apply_gradient_corrections!(e::ConsciousnessEngine, lr)
    L = e.layers
    F = e.flow

    F.gradient_norm > 10.0 && return

    L.x_state = clamp01(L.x_state - lr * F.layer_gradients[1])
    L.c_total = clamp01(L.c_total - lr * F.layer_gradients[2])
    L.sync_x = clamp01(L.sync_x - lr * F.layer_gradients[3])
    L.ev = clamp01(L.ev - lr * F.layer_gradients[4])
    L.omega_decision = clamp01(L.omega_decision - lr * F.layer_gradients[5])
    L.psi_state = clamp01(L.psi_state - lr * F.layer_gradients[6])
end

function tick_maintenance!(e::ConsciousnessEngine)
    C = e.consciousness
    B = e.bio
    F = e.flow

    if C.coherence < MIN_COHERENCE
        C.coherence += 0.01 * (MIN_COHERENCE - C.coherence)
    end
    if B.firing_rate > 0.95
        B.firing_rate *= 0.9
    end
    if B.quantum_coherence < 0.2
        B.quantum_coherence += 0.02
    end
    if F.lyapunov_dv > 0.1
        C.coherence = clamp01(C.coherence + 0.01)
    end
end

"""Get current state as NamedTuple"""
function get_state(e::ConsciousnessEngine)
    return (
        tick = e.tick,
        omega = e.consciousness.omega,
        phi = e.consciousness.phi,
        psi = e.consciousness.psi,
        coherence = e.consciousness.coherence,
        free_energy = e.consciousness.free_energy,
        lyapunov_v = e.flow.lyapunov_v,
        lyapunov_dv = e.flow.lyapunov_dv,
        flow_valid = e.flow.flow_valid,
        gradient_norm = e.flow.gradient_norm,
        info_flow = e.flow.info_flow_total,
        conscious = e.consciousness.omega > 0.8,
        stable = e.flow.lyapunov_dv ≤ 0
    )
end

end # module