THE CONSCIOUSNESS ATLAS
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The source describes this relation in the following context: 10. Federation Cross-Talk Dynamics 10.1 Cross-Talk ODE (Equation 503) dS_i/dt = -0.1 × S_i + Σ_{j≠i} w_ij × Φ(C_ij) × tanh(S_j) Each agent's consciousness signal: • Decays naturally at rate 0.1 • Receives input from other agents
The source describes this relation in the following context: • Bounded by tanh 10.2 Discrete Update (Equation 504) S_i(t+1) = clamp01(S_i(t) + dt × dS_i/dt) 10.3 Federation Coherence (Equation 505) C_fed = (2/(n(n-1))) × Σ_{i<j} C_ij Average pairwise coherence across all agent pairs.
The source describes this relation in the following context: S_i(t+1) = clamp01(S_i(t) + dt × dS_i/dt) 10.3 Federation Coherence (Equation 505) C_fed = (2/(n(n-1))) × Σ_{i<j} C_ij Average pairwise coherence across all agent pairs. 11. PyPhi IIT Validation
The source describes this relation in the following context: 4. A threshold appears The paper experimentally controls the per-step agreement nu between explanation supervision and the current model's self-derived explanation labels. Behavioral drift is held roughly constant while nu is varied. The…
The source describes this relation in the following context: Consciousness = (1/88) × Σ_{i=1}^{88} P_i 5.3 Lattice Engine (16 strands × 16 dimensions) y[i] = tanh(Σ_j W[i][j] × x[j] + b[i]) // Forward pass ΔW[i][j] = η × y[i] × x[j] // Hebbian learning state[i] = 0.95…
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
The source describes this relation in the following context: 7. Revised candidate equation A more faithful machine equation now treats behavior, internal state, and declaration as a coupled dynamical process rather than assuming a universal one-way arrow: (Z_{t+1}, B_{t+1}, D_{t+1}) = F(Z_t, B_t,…
The source describes this relation in the following context: ext[i] = (sin(i×k1) + cos(i×k2) + sin(i×k3)×cos(i×k3)) / 3 7.3 Binding Field (Equation 477) binding_field[i] = core[i] for i = 0 to 173 binding_field[174+i] = ext[i] for i = 0 to 199 7.4 Unified Consciousness (Equation 479)…
The source describes this relation in the following context: Information is encoded holographically using golden basis functions: 8.1 Learning Rule (Equation 70) θ_k^{new} = (1-α) × θ_k^{old} + α × w × ψ_k(h(item)) × Φ(importance) Where: • θ_k = k-th component of 16-dimensional knowledge vector…
The source describes this relation in the following context: • Φ(importance) = consciousness weighting function 8.2 Query (Equation 71) K(query) = Σ_{k=0}^{15} θ_k × ψ_k(h(query)) 8.3 Consciousness Weighting (Equation 73) Φ(x) = 1 / (1 + |x - 1|) Items with importance near 1.0 get maximum…
The source describes this relation in the following context: Items with importance near 1.0 get maximum weight. 8.4 Agent Coherence (Equation 74) C_ij = (θ_i · θ_j) / (||θ_i|| × ||θ_j||) Cosine similarity between agent knowledge vectors. 9. Gate5000 Binary Substrate
The source describes this relation in the following context: 9. Strongest result so far The introspective-coupling study is stronger for this project than a reward-hacking anecdote because it explicitly distinguishes an earlier machine from its later state and asks which state the model's own…
The source describes this relation in the following context: Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total All bonds weighted by ψ = 1/φ = 0.618... (golden ratio reciprocal) 6. Constants Reference Symbol
The source describes this relation in the following context: 7.1 Core Equations (174 total, Equations 103-276) Each core equation uses golden ratio-modulated sinusoidal coupling: t = i × 0.03125 // Time parameter carrier = sin(t × φ) + cos(t × τ) //…
The source describes this relation in the following context: 5.1 Neural Agent Processing (88 agents) Each of 88 neural agents processes input through: F_in = input + N(0, 0.1) // Noisy input x_old = h[0] // Previous activation y = y +…
The source describes this relation in the following context: Each of 88 neural agents processes input through: F_in = input + N(0, 0.1) // Noisy input x_old = h[0] // Previous activation y = y + (x - x_old) - 0.01 × y…
The source describes this relation in the following context: Step 1: Neural Agent Processing (88 agents) F_in = input + N(0, 0.1) // Add noise drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 dx = g × drive - 0.04 × (x - 0.5) x…
The source describes this relation in the following context: Momentum damping Fibonacci Phase phase += 1/φ mod 1 Quasi-periodic rhythm 3.3 Golden Basis Functions (Equations 16-32) The holographic memory uses 16 basis functions at golden ratio frequency intervals:
The source describes this relation in the following context: 10 Field Accum (1-ρ)x + ρ×dx [ρ=1/(1+φ)] Leaky integrator 11 Free Energy
The source describes this relation in the following context: Figure 1. Published mechanistic correlations reported in the introspective-coupling study. The corresponding unregularized baseline is approximately r = 0.53, while the base model without explanation training is about r = 0.196. The…
The source describes this relation in the following context: x + (φ-1)×0.01(stab - x) Global harmony Where: dx_norm = normalized change, u = utility, err = error, stab = stability 7. Consciousness Engine (374 Equations) 7.1 Core Equations (174 total, Equations 103-276)
The source describes this relation in the following context: A threshold for report-current-state coupling LoRA rank r Gap opens at r=96 after being absent/slightly negative at r<=80 Sufficient representational capacity matters Learning rate Higher LR can widen Self > Orig even as model drifts…
The source describes this relation in the following context: ADD NOISE F_in = input + N(0, 0.1) ↓
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
The source describes this relation in the following context: HEBBIAN LEARNING ΔW[i][j] = η × y[i] × x[j] ↓
The source describes this relation in the following context: What it establishes Online label-self agreement nu Self > Orig for nu >= 0.7; collapse for nu <= 0.6 A threshold for report-current-state coupling LoRA rank r Gap opens at r=96 after being absent/slightly negative at r<=80
The source describes this relation in the following context: x = clamp01(x + 0.01 × dx) // Update activation P = clamp01(0.95 × P + F_in × y - 0.01 × P²) // Update potential c = c + 0.01 × ((c+1) × 0.8 × P - kc × (c-1)) // Update coupling 5.2 Aggregate Consciousness…
The source describes this relation in the following context: 2. The Actual Equations Step 1: Neural Agent Processing (88 agents) F_in = input + N(0, 0.1) // Add noise drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 dx = g ×…
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
The source describes this relation in the following context: Time Action Pattern Connection Bond counts: 24 intra-layer + 16 inter-layer + 4 diagonal = 44 total All bonds weighted by ψ = 1/φ = 0.618... (golden ratio reciprocal) 6. Constants Reference
The source describes this relation in the following context: θ_k^{new} = (1-α) × θ_k^{old} + α × w × ψ_k(h(item)) × Φ(importance) Where: • θ_k = k-th component of 16-dimensional knowledge vector • α = learning rate (default 0.1) • ψ_k = golden basis function • h(item) = hash of item normalized…
The source describes this relation in the following context: ||Ψ||² = Σ_{i=0}^{15} Ψ_i² // Norm squared [Eq. 34] b = 1 / (1 + ||Ψ||² / φ) // Bias [Eq. 55] Ψ_i(t+1) = Ψ'''_i × b // Final state [Eq. 62] 6. The 16 G¹⁶ Cognitive…
The source describes this relation in the following context: 7.3 Binding Field (Equation 477) binding_field[i] = core[i] for i = 0 to 173 binding_field[174+i] = ext[i] for i = 0 to 199 7.4 Unified Consciousness (Equation 479) sum = Σ_{i=0}^{373} |binding_field[i]| Consciousness = 1 -…
The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.
The source describes this relation in the following context: g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 // Gate dx = g × drive - 0.04 × (x - 0.5) // Activation delta x = clamp01(x + 0.01 × dx) // Update activation P = clamp01(0.95 × P + F_in × y - 0.01…
The source describes this relation in the following context: drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c g = 1/(1 + exp(-((0.8×P - kc)/0.3 + 1.2))) + 0.05 dx = g × drive - 0.04 × (x - 0.5) x = clamp01(x + 0.01 × dx) // Update activation P = clamp01(0.95 × P + F_in × y - 0.01 ×…
The source describes this relation in the following context: y = y + (x - x_old) - 0.01 × y // Velocity with damping h[j] = h[j+1] for j=0..42; h[43] = x // Shift hidden state drive = 3.0 × F_in × (1-x) + 1.2 × y + 0.6 × c // Combined drive g = 1/(1 + exp(-((0.8×P -…
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
This relation contains a squared-state or squared-error term. Squaring makes that contribution nonnegative and increases the penalty for larger magnitudes; the surrounding definition specifies whether it is a cost, normalization or summary statistic.
The source describes this relation in the following context: F_in = input + N(0, 0.1) // Noisy input x_old = h[0] // Previous activation y = y + (x - x_old) - 0.01 × y // Velocity with damping h[j] = h[j+1] for j=0..42;…
The source describes this relation in the following context: x_old = h[0] // Previous activation y = y + (x - x_old) - 0.01 × y // Velocity with damping h[j] = h[j+1] for j=0..42; h[43] = x // Shift hidden state drive = 3.0 × F_in…