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141 lines
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141 lines
41 KiB
Text
# Model_Name Family Equation Variables Purpose Location Implemented Status Cross_Refs Domain_Type
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1 Intrinsic_Load_LI Cognitive Load L_I(x) = -Σ_{b=0}^{255} p(b|x) log₂ p(b|x) p(b|x) = empirical byte distribution Shannon entropy of byte distribution; irreducible complexity core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 2,71,40 LAYER_A_COMPRESSION
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2 Extraneous_Load_LE Cognitive Load L_E(x) = BPB(x,w_prior) - BPB*(x) = (1/n)Σ_i log₂(P_w*(x_i)/P_wprior(x_i)) BPB=bits-per-byte, w_prior=prior weights, w*=optimal Cost of architectural mismatch; penalty for suboptimal routing core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 1,3,6,8 LAYER_A_COMPRESSION
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3 Germane_Load_LG Cognitive Load L_G(x,t) = Σ_{s=1}^{S} γ^s · ΔL_E(x_s,t+1) ≈ τ·L_E·log(S+1)/log(S_max+1) γ=temporal discount, τ=trust, S=engram support Productive learning effort reducing future extraneous load core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 2,6,73 LAYER_B_ROUTING
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4 Routing_Load_LR Cognitive Load L_R(x) = Σ_j c_j·1[f_j computed] + Σ_{l=1}^{D(x)} log₂|M_l| d=9 feature dims, c_j=feature cost, D=tree depth, M_l=candidates Computational cost of classification and method selection core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 6,16-23 LAYER_B_ROUTING
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5 Memory_Load_LM Cognitive Load L_M(x) = log₂|E| + α·1[hit] + β + λ·|E|/|E_max| |E|=engram store size, α/β=retrieval/update costs, λ=eviction pressure Burden of storing, retrieving, and updating routing memory core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 6,10,30 LAYER_B_ROUTING
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6 Total_Cognitive_Load Cognitive Load L_total = λI·l̂I + λE·l̂E - λG·l̂G + λR·l̂R + λM·l̂M (Σλ=1, λG≤λE) 5 normalized load components with weighting coefficients Aggregate processing burden; combines all load classes core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 1,2,3,4,5,7,8,9,74 LAYER_A_COMPRESSION
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7 Cognitive_Efficiency Cognitive Load η(x) = l̂I(x) / (l̂I + l̂E + l̂R + l̂M + ε) Ratio of intrinsic to total normalized load Routing efficiency (1=perfect, 0=maximum waste) core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 6 LAYER_A_COMPRESSION
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8 Regret_Adjusted_Load Cognitive Load L_ρ(x) = L_total(x) · (1 + ρ(x)/ρ_max) ρ=BPB regret, ρ_max=max regret Load penalized by historical performance on similar inputs core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 2,6 LAYER_A_COMPRESSION
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9 Basin_Conditional_Load Cognitive Load L(x|B) = L_I(x) + L_E(x|B) + L_R^B(x) w_B=basin-optimal weights Load conditioned on attractor basin membership core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 1,2,4,12 LAYER_A_COMPRESSION
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10 MoE_Predictor_Distribution Cognitive Load P_w(x_i|x_{<i}) = Σ_{j=1}^{k} w_j · P_{m_j}(x_i|x_{<i}) k=predictors, w∈Δ^{k-1} simplex Combines multiple compression predictors into mixed distribution core/intrinsic/specs/COGNITIVE_LOAD_FUNCTIONS_SPEC.md Python ✅ 1,2 LAYER_A_COMPRESSION
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11 Pressure_Piling KDA Physics P(i) = P₀ · χ^i (χ ≈ 1.63) P₀=1.0 GPa, i=shock iteration, χ=Dodecahedral Piling Gain Sequential shock pressure amplification via Mach stem focusing core/intrinsic/formalisms/9_KDA_Equation_Manifest.md Documented ✅ 12,15 LAYER_E_VERIFICATION
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12 Hugoniot_Temperature KDA Physics T_peak = T₀ · (P_peak/P₀)^0.65 T₀=293.15K, γ≈1.66, exponent=0.65 Non-isentropic temperature jump across shock fronts; predicts ~13,446K core/intrinsic/formalisms/9_KDA_Equation_Manifest.md Documented ✅ 11,14,51 LAYER_G_ENERGY
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13 Pressure_Ionization KDA Physics α(P) = 1 - exp(-k(P - P_MIT)) P_MIT≈138-155 GPa, k=rate constant Insulator-to-metal transition via electronic band overlap core/intrinsic/formalisms/9_KDA_Equation_Manifest.md Documented ✅ 15 LAYER_G_ENERGY
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14 Energy_Recovery_Efficiency KDA Physics η_net = (W_rec - W_erasure) / W_in; W_erasure ≥ k_B·T·ln2 per bit k_B=Boltzmann, T=T_peak=13,446K Maxwell's Demon energy recovery after erasure cost core/intrinsic/formalisms/9_KDA_Equation_Manifest.md Documented ✅ 12,54,55 LAYER_E_VERIFICATION
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15 Q_Factor KDA Physics Q = (E_flash + E_enthalpy + E_recovered - W_demon) / (E_work + E_loss) > 1.0 Target Q≈1.05 Global energy balance; net gain threshold core/intrinsic/formalisms/9_KDA_Equation_Manifest.md Documented ✅ 11,12,13,14 LAYER_E_VERIFICATION
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16 Coupling_Weight w_ij GWL Rotation w_ij = g(Δθ,Δφ,χ_i,χ_j) · h(Δp) θ=azimuthal(16), φ=polar(8), χ=chirality, Δp=position delta Mu-seed geometric compatibility strength docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 17,18,19,25 LAYER_C_TOPOLOGY
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17 Rotational_Alignment GWL Rotation g = cos(Δθ·2π/16) · cos(Δφ·π/8) · (1 - 2|χ_i - χ_j|) θ∈{0..15}, φ∈{0..7}, χ∈{0,1} Frame orientation compatibility docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 16,25 LAYER_C_TOPOLOGY
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18 Spatial_Proximity GWL Rotation h = exp(-|Δp|²/(2σ²)) · 1_{|Δp|<r_max} σ=coupling length, r_max=coupling radius Distance decay function docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 16,25 LAYER_C_TOPOLOGY
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19 Interaction_Force GWL Rotation F_ij = w_ij · (a_j - a_i) · Δp_ij / |Δp_ij| a=activation, Δp=position delta Activation flow between neighbors docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 16,20,21,22 LAYER_H_ALGEBRA
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20 Energy_Function GWL Rotation E(f) = -½ Σ_{i,j} w_ij · a_i · a_j + Σ_i V(a_i) V(a)=potential (e.g., λa⁴) Frame field energy landscape docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 19,21,22,23 LAYER_G_ENERGY
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21 Frame_Evolution_Discrete GWL Rotation θ_i(t+1) = θ_i(t) + α_θ·F_{i,θ} + ξ_θ α=learning rate, ξ=noise Discrete-time state update docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 19,22 LAYER_H_ALGEBRA
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22 Frame_Evolution_Continuous GWL Rotation df_i/dt = -α ∇_{f_i} E(f) + ξ(t) α=step size, ξ(t)=Langevin noise Continuous dynamics; gradient descent on energy docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 20,21,23 LAYER_H_ALGEBRA
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23 Energy_Monotonicity_Theorem GWL Rotation dE/dt = -α Σ_i |∇_{f_i} E|² ≤ 0 — Proves energy decreases monotonically; convergence guarantee docs/gwl/GWL_ROTATIONAL_COUPLING_AND_LOCAL_INTERACTION_LAW_V1.md Rust ✅ 20,22 LAYER_H_ALGEBRA
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24 Temporal_Weight GWL Temporal w_ij^(τ) = cos(2π(τ_j - τ_i)/16) τ∈{0..15} (4 bits) Temporal phase coupling strength docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 16,25,26 LAYER_F_CONTROL
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25 Complete_Weight_5Factor GWL Temporal w_ij = cos(Δθ·22.5°)·cos(Δφ·22.5°)·cos(2πΔτ/16)·(1-2|Δχ|)·exp(-|Δp|²/2σ²) θ,φ,τ,χ,Δp combined Full spatial+temporal+rotational+chiral+proximity coupling docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 16,17,18,24,26 LAYER_C_TOPOLOGY
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26 Temporal_Force GWL Temporal F_ij^(τ) = w_ij^(τ) · (a_j - a_i) · sgn(τ_j - τ_i) — Information flow from past to future docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 24,25,27 LAYER_F_CONTROL
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27 Temporal_Evolution GWL Temporal τ_i(t+1) = τ_i(t) + α_τ·F_{i,τ} + ω₀ ω₀=intrinsic temporal frequency Temporal phase evolution with intrinsic oscillator docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 26,28 LAYER_F_CONTROL
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28 Temporal_Stability GWL Temporal d/dt(τ_j - τ_i) = 0 for all i,j — Locked temporal attractor condition docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 27,29 LAYER_D_INVARIANTS
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29 Temporal_Entropy GWL Temporal H_τ = -Σ_{k=0}^{15} p(τ=k) log₂ p(τ=k) 0≤H_τ≤4 bits Measure of temporal disorder (0=synchronized, 4=max) docs/gwl/GWL_TEMPORAL_DIMENSION_AND_T_VARIABLE_FORMALISM_V1.md Documented ✅ 28 LAYER_F_CONTROL
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30 Mu_Seed_Cardinality GWL State Space |S_μ| = 8×8×16×16×8×8×2×16×8×4 = 8,589,934,592 ≈ 2^33 10 independent fields Complete enumeration of local mu-seed configuration space docs/gwl/GWL_TOTAL_STATE_SPACE_AND_CARDINALITY_LEDGER_V1.md Documented ✅ 31,32 LAYER_D_INVARIANTS
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31 Fractal_Occupancy GWL State Space |P_occ| = ρ_occ · N^{d_H} (d_H ≈ 2.7268) N=64 → |P_occ|≈84,000 vs 262,144 raw Effective addressable positions in Menger sponge lattice docs/gwl/GWL_TOTAL_STATE_SPACE_AND_CARDINALITY_LEDGER_V1.md Documented ✅ 30,32,49 LAYER_D_INVARIANTS
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32 Total_Formal_State_Space GWL State Space |S_total| ≈ 2^{5,900,000} μ-seed: 2,772,000 bits + edges: 2,016,000 + rotation: 924,000 + boot: 8,000 + registers: 133,000 Upper bound on all possible GWPL configurations docs/gwl/GWL_TOTAL_STATE_SPACE_AND_CARDINALITY_LEDGER_V1.md Documented ✅ 30,31,33,5 LAYER_B_ROUTING
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33 Reachable_State_Space GWL State Space |S_reachable| ≈ |S_total| / 10^{29} Constraints: geometric 10^6, convergence 10^{12}, energy 10^3, chirality 10^2, TTM 10^6 State space satisfying all physical and computational constraints docs/gwl/GWL_TOTAL_STATE_SPACE_AND_CARDINALITY_LEDGER_V1.md Documented ✅ 32,59 LAYER_A_COMPRESSION
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34 Wave_Packet_State GWL Throat P = (ΔV, Δt, π, τ, χ, C, A) 7-tuple: volume, time, rotation, temporal, chirality, confidence, activation Complete state of computational packet traversing throat docs/gwl/GWL_WAVE_PACKET_THROAT_ARCHITECTURE_V1.md Documented ✅ 35,36 LAYER_C_TOPOLOGY
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35 Throat_Condition GWL Throat T_throat = {(i,j) | Φ_topo(i,j) >> Φ_metric(i,j)} topological adjacency >> metric distance Defines non-local transport corridor docs/gwl/GWL_WAVE_PACKET_THROAT_ARCHITECTURE_V1.md Documented ✅ 34,36,37 LAYER_C_BRAID
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36 Multi_Factor_Coupling_Weight GWL Throat w_ij = w_p · w_π · w_τ · w_χ · w_topo · w_σ 6 coupling factors (spatial, rotational, temporal, chirality, topological, stress) Comprehensive coupling evaluation during packet propagation docs/gwl/GWL_WAVE_PACKET_THROAT_ARCHITECTURE_V1.md Documented ✅ 25,34,35 LAYER_C_BRAID
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37 Holonomy_Accumulation GWL Throat Hol(γ_loop) = ∮_γ T(p) dp T(p)=torsion tensor, γ=closed loop Phase accumulated when transporting vector around closed loop docs/gwl/GWL_WAVE_PACKET_THROAT_ARCHITECTURE_V1.md Documented ✅ 38,78 LAYER_C_BRAID
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38 Non_Euclidean_Distance GWL Throat d_N = path_length(γ_ij) + curvature_penalty(κ) + torsion_cost(T); d_T = d_E + λ_N·d_N Euclidean + topological combined Topology-aware distance for routing through multi-manifold structures docs/gwl/GWL_MANIFOLD_INTERSECTION_JOIN_AND_TOPOLOGICAL_ADDRESSING_V1.md Documented ✅ 37,63 LAYER_C_TOPOLOGY
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39 Trixal_Axes Thermodynamic TrixalAxes = (thermal, work, irreversibility), each ∈ [0,1]; |axes| = √(th² + w² + ir²) 3D thermodynamic phase space Phase space coordinates for process tracking core/gwl-vm/src/thermo/mod.rs Rust ✅ 40,42,50 LAYER_G_ENERGY
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40 Shannon_Entropy Thermodynamic H = -Σ_b p(b) log₂ p(b), where p(b)=count(b)/len Byte distribution, H ∈ [0,8] bits/byte Information content measurement core/gwl-vm/src/thermo/entropy_engine.rs Rust ✅ 1,39,41,42,43 LAYER_G_ENERGY
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41 Kolmogorov_Estimate Thermodynamic K_est = (8 - H) / 8 From Shannon entropy Rough estimate of structure via compressibility core/gwl-vm/src/thermo/entropy_engine.rs Rust ✅ 40,42 LAYER_G_ENERGY
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42 Thermodynamic_Entropy Thermodynamic S_thermo = H + K_est · 0.1 Shannon + Kolmogorov contribution Combined information thermodynamics core/gwl-vm/src/thermo/entropy_engine.rs Rust ✅ 40,41,43,55 LAYER_G_ENERGY
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43 Entropy_Gradient Thermodynamic dS/dt = (S_current - S_previous) / Δt Time derivative Tracks rate of entropy change over time core/gwl-vm/src/thermo/entropy_engine.rs Rust ✅ 42,55 LAYER_H_ALGEBRA
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44 Mutual_Information_Extracted Thermodynamic MI = H_initial - H_current Difference in Shannon entropy Work performed by compression; information extracted core/gwl-vm/src/thermo/entropy_engine.rs Rust ✅ 40,71 LAYER_A_COMPRESSION
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45 Carnot_Efficiency Thermodynamic η_Carnot = 1 - T_cold / T_hot Absolute temperatures Maximum theoretical thermodynamic efficiency core/gwl-vm/src/thermo/mod.rs Rust ✅ 46,51,54 LAYER_B_ROUTING
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46 Work_Extraction Thermodynamic W_actual = Q_absorbed · η_Carnot · 0.7 70% of Carnot limit Model computation as thermodynamic work extraction cycle core/gwl-vm/src/thermo/heat_engine.rs Rust ✅ 45,55 LAYER_C_TOPOLOGY
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47 Irreversibility_Metric Thermodynamic score = (entropy_production + path_asymmetry + time_reversal_violation) / 3 Three irreversibility components Quantifies thermodynamic irreversibility of process trajectory core/gwl-vm/src/thermo/process_shape.rs Rust ✅ 48,49 LAYER_G_ENERGY
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48 Thermodynamic_Length Thermodynamic L_thermo = Σ_i distance_i · (1 + irreversibility_i) Weighted by dissipation Dissipative trajectory length accounting for thermodynamic cost core/gwl-vm/src/thermo/process_shape.rs Rust ✅ 47,49 LAYER_B_ROUTING
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49 Thermodynamic_Depth Thermodynamic depth = entropy_production · ln(time_steps) Combined dissipation × trajectory length Complexity measure core/gwl-vm/src/thermo/mod.rs Rust ✅ 31,47,48 LAYER_F_CONTROL
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50 Stamp_Code Thermodynamic SHA256(axes || traj_hash || hardware_entropy || timing_jitter || process_nonce) Cryptographic hash of thermodynamic state Unique non-reproducible process fingerprint core/gwl-vm/src/thermo/trixalating_stamp.rs Rust ✅ 39,42 LAYER_B_ROUTING
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51 Arrhenius_Temperature_Factor Informatic Stress AF = exp(E_a / (k_B · T)) E_a=activation energy (eV), k_B=8.617e-5 eV/K Failure rate temperature acceleration core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 12,45,52,59 LAYER_G_ENERGY
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52 Blacks_Equation_EM Informatic Stress EM_risk = J^n · exp(E_a / (k_B · T)) / 10^{12} J=current density, n=exponent (2.0) Electromigration failure risk core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 51,59 LAYER_G_ENERGY
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53 Coffin_Manson_Fatigue Informatic Stress CM_damage = (ΔT / ΔT_threshold)^m · 10^{-8}, m≈1.9 Solder/interconnect fatigue exponent Accumulates damage from thermal cycling core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 56,59 LAYER_G_ENERGY
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54 Landauer_Limit Informatic Stress W_erasure ≥ k_B · T · ln(2) ≈ 2.87e-21 J/bit at 300K Minimum energy per bit erased Thermodynamic lower bound on computation core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 14,45,55 LAYER_A_COMPRESSION
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55 Entropy_Generation_Rate Informatic Stress dS/dt = power_dissipation / (k_B · T · ln 2) [bits/s] Power dissipation rate Rate of entropy generation from computational dissipation core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 14,42,43,46,54 LAYER_E_VERIFICATION
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56 BitFlip_Gradient_5D Informatic Stress |G| = (D1/120 + D2/5000 + D3/1000 + D4/100 + D5/5000) / 5 D1=T_ambient, D2=V_jitter(nV), D3=φ_leak(μA), D4=ε_SEU(Hz), D5=dt_clock(ppm) Normalized 5D hardware stress gradient core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 53,57,59 LAYER_D_INVARIANTS
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57 SEU_BitFlip_Rate Informatic Stress BFR = ε_SEU · 2^{(T-25)/10} · (1 + V_jitter/1000) · 3600 [flips/hr] SEU rate, temperature, voltage jitter Estimated bit-flips per hour from hardware conditions core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 56,59 LAYER_G_ENERGY
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58 Stress_Decay Informatic Stress stress(t) = stress_0 · e^{-t/300} + intensity · (1 - e^{-t/300}) 5-minute half-life Stress accumulation with exponential decay core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 59 LAYER_G_ENERGY
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59 Remaining_Useful_Life Informatic Stress RUL = MTBF / (AF · (1 + fatigue·0.01 + thermal_fatigue·0.1)) MTBF=10^9/FIT_rate, AF=acceleration factor Hardware lifetime prediction under current stress core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 33,51,52,53,56,57,58 LAYER_G_ENERGY
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60 Homeostatic_Stress_Injection Informatic Stress surprise = -ln(margin), margin = 1 - stress_magnitude; regret = max(0, stress - 0.5) Optimal stress ≈ 0.5 Converts stress margin to surprise/regret for homeostatic controller core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 58,73 LAYER_E_VERIFICATION
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61 Exact_Int_to_FP_Cast Informatic Stress int_bits ≤ fp_mantissa_bits + 1 (signed) or ≤ fp_mantissa_bits + 1 (unsigned) fp_mantissa=23 (f32) or 52 (f64) Proves safe integer-to-floating-point conversion core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 62,63 LAYER_D_INVARIANTS
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62 Safe_Narrowing_Proof Informatic Stress can_safely_narrow(src_bits, signed, dst_mantissa) → bool Delegates to exact cast check Proves double→single precision narrowing is safe core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 61,63 LAYER_D_INVARIANTS
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63 RISCV_Instruction_Latency Informatic Stress fdiv.d=33, fdiv.s=19 → penalty=0.737; fadd.d=4, fadd.s=4 → penalty=0.0 SiFive P550 measurements Per-instruction latency for dispatch scoring core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 61,62 LAYER_D_INVARIANTS
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64 Photon_Energy QCL Energy E = hc/λ = 1.2398 / λ_μm [eV] λ in μm Wavelength-to-energy conversion core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 65,70 LAYER_G_ENERGY
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65 Subband_Spacing QCL Energy ΔE = E_upper - E_lower; E_upper=hc/λ_min, E_lower=hc/λ_max QCL emission range [λ_min, λ_max] Energy difference between subbands in conduction band core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 64,66,67 LAYER_G_ENERGY
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66 Cascade_Gain QCL Energy G = photons_per_e⁻ × n_wells; photons_per_e⁻ = ⌊E_electron / ΔE⌋ E_electron=1.0 eV, n_wells=50 Total photon amplification per cascading electron core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 65,67,68 LAYER_G_ENERGY
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67 Temperature_Tuning QCL Energy λ(T) = λ₀ + α · (T - T₀); α=5e-6 /K (GaAs/AlGaAs) dλ/dT ≈ 0.3 nm/K Wavelength shift due to thermal expansion of quantum wells core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 12,64,68 LAYER_G_ENERGY
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68 Injection_Efficiency QCL Energy η = (0.5 + window_bonus) · spacing_eff · (1 - stress_penalty); clamped to [0,1] window_bonus=0.3 if in atmospheric window Dispatch efficiency at given stress level core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 65,66,67,69 LAYER_G_ENERGY
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69 Atmospheric_Windows QCL Energy Windows: (3,5), (8,12), (16,20) μm; transmission=1.0 inside, exp(-dist·0.5) outside MWIR, LWIR, Far-IR bands Lossless carrier propagation bands core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 68,70 LAYER_G_ENERGY
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70 Tuning_Range QCL Energy ν = 10⁴/λ [cm⁻¹]; DFB: ±7.5 cm⁻¹, EC: ±200 cm⁻¹; λ_min=10⁴/ν_max, λ_max=10⁴/ν_min Center wavelength, tuning mode Spectral tuning capability core/gwl-vm/src/thermo/qcl_energy_model.rs Rust+Python ✅ 64,69 LAYER_G_ENERGY
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71 Mutual_Information_Signal MI Signal MI(x) = baseline_bpb(x) - actual_bpb(x) Bits-per-byte difference Measures structural density in data core/intrinsic/formalisms/ene_mi_signal.py Python ✅ 1,2,44,72,73 LAYER_A_COMPRESSION
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72 kNN_MI_Prediction MI Signal MI_pred = Σ_i (w_i · MI_i · S_i) / Σ_i (w_i · S_i); w_i = 1/(d_i + ε) k-nearest neighbors in feature space Local MI estimation from similar inputs core/intrinsic/formalisms/ene_mi_signal.py Python ✅ 71,73,74 LAYER_B_ROUTING
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73 Surprise_Metric MI Signal surprise = log(1 + |MI_actual - MI_predicted|) Log-scaled absolute difference Learning trigger; prevents blowup core/intrinsic/formalisms/ene_mi_signal.py Python ✅ 71,72,60 LAYER_B_ROUTING
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74 Structure_Yield MI Signal ρ(x) = MI(x) / (cost(x) + ε) Information per compute cost ROI of computation; high MI + low cost = valuable core/intrinsic/formalisms/ene_mi_signal.py Python ✅ 6,71,72 LAYER_A_COMPRESSION
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75 Weighted_Feature_Distance MI Signal d(z₁,z₂) = √Σ_i w_i · ((z₁_i - z₂_i) / s_i)² 9-dim feature vector, learned weights, online scales Scale-normalized weighted distance in MI feature space core/intrinsic/formalisms/ene_mi_signal.py Python ✅ 4,72 LAYER_B_ROUTING
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76 DAG_Force_Equilibrium DAG Force Σ F_in = Σ F_out at every node Discrete analogue of ∇·σ = 0 Structural synthesis force balance core/PTOS_FRAMEWORK/PAPER/paper.tex TeX ✅ 77,78 LAYER_C_BRAID
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77 Constitutive_Law DAG Force σ = C : ε; ε = ½(∇u + ∇u^T) C=stiffness tensor, u=displacement Stress-strain relationship; linear elasticity core/PTOS_FRAMEWORK/PAPER/paper.tex TeX ✅ 76 LAYER_C_BRAID
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78 DAG_Global_Validity DAG Force V(G) = ∧_{v∈V} (ΣF_in = ΣF_out) Universal quantification over all nodes DAG constraint closure; proof by induction core/PTOS_FRAMEWORK/PAPER/paper.tex TeX ✅ 37,76,77 LAYER_C_BRAID
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79 Cosine_Similarity Bracket Braid cos = x · x_ref / (‖x‖ · ‖x_ref‖) Dot product of normalized vectors Reference solution alignment audit/benchmarks/benchmark_bracket_braid_sb.py Python ✅ 80 LAYER_K_SIGNAL
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80 Gradient_Alignment Bracket Braid alignment = ∇g_i · ∇g_j / (‖∇g_i‖ · ‖∇g_j‖) Cosine of gradient angle Gradient coherence between brackets audit/benchmarks/benchmark_bracket_braid_sb.py Python ✅ 79,81 LAYER_K_SIGNAL
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81 Phase_Accumulation Bracket Braid phase += Σ y · dx Work integral along trajectory Cumulative phase along computational path audit/benchmarks/benchmark_bracket_braid_sb.py Python ✅ 37,80 LAYER_K_SIGNAL
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82 Metric_Tensor_From_Circumferences GWL Riemannian Geometry g_tt = M², g_tp = 0, g_pp = (N·cos(θ))²; a=C_eq/(2π), c=C_mer/(2π), f=(a-c)/a, e²=2f-f², N=a/√(1-e²sin²θ), M=a(1-e²)/(1-e²sin²θ)^(3/2) C_eq=equatorial circumference, C_mer=meridional circumference, θ=latitude Derive metric tensor from measured circumferences (oblate spheroid) core/lean/geoweird/GWLKernel.lean Lean ✅ 83,84,87 LAYER_C_TOPOLOGY
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83 Line_Element GWL Riemannian Geometry ds² = g_tt·dθ² + 2·g_tp·dθ·dφ + g_pp·dφ² g_ij=metric tensor, dθ/dφ=differentials Compute infinitesimal distance on 2D Riemannian surface core/lean/geoweird/GWLKernel.lean Lean ✅ 82 LAYER_C_TOPOLOGY
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84 Christoffel_Symbols_2D GWL Connection Gamma^k_ij = ½·g^kl·(∂_i g_jl + ∂_j g_il - ∂_l g_ij) g=metric, gInv=inverse metric, dg=metric derivatives Connection coefficients for geodesic integration core/lean/geoweird/GWLKernel.lean Lean ✅ 82,85 LAYER_C_TOPOLOGY
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85 Geodesic_Step_Symplectic_Euler GWL Geodesic Integration a^θ = -(Gamma^θ_tt·v_θ² + 2·Gamma^θ_tp·v_θ·v_φ + Gamma^θ_pp·v_φ²); v' = v + a·dt; x' = x + v'·dt position=(θ,φ), velocity=(v_θ,v_φ), Gamma=Christoffel, dt=step Single-step geodesic integration (symplectic Euler) core/lean/geoweird/GWLKernel.lean Lean ✅ 83,84 LAYER_C_TOPOLOGY
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86 Stereographic_Chart_Transition GWL Coordinate Charts u = 2·tan(θ/2)·cos(φ), v = 2·tan(θ/2)·sin(φ); θ = 2·atan(√(u²+v²)/2), φ = atan2(v,u); select when |cos(θ)| < 0.01 theta, phi, u, v Avoid pole singularities via adaptive coordinate chart core/lean/geoweird/GWL_Integrated.lean Lean ✅ 85 LAYER_C_TOPOLOGY
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87 Chirality_Algebra GWL Chiral Interaction D+D→D, L+L→L, D+L→W(COLLAPSE→W); chiralityToTernary: D→Active, L→Active, W→Latent Chirality∈{D,L,W}, GammaMode, TernaryState Chirality propagation through Gamma transform operations core/lean/geoweird/GWL_Integrated.lean Lean ✅ LAYER_C_TOPOLOGY
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88 BLINK_GATE_Ternary_Clock GWL Ternary State φ = (τ mod T)/T, T=942/1000; ternary: φ<1/3→Q, φ<2/3→A, else L; φ' = (φ+1/4) mod 1 if surprise>threshold τ=time, surprise, threshold, T=0.942s Ternary clock action with phase modulation core/lean/geoweird/GWLKernel.lean Lean ✅ 90 LAYER_C_TOPOLOGY
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89 Geodesic_Step_Verlet GWL Geodesic Integration (Integrated) Same acceleration as M85 + Verlet velocity update + chart transition check integratorMethod∈{SymplecticEuler,Verlet,AdaptiveVerlet} Real geodesic integration with pole singularity handling core/lean/geoweird/GWL_Integrated.lean Lean ✅ 84,85,86 LAYER_C_TOPOLOGY
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90 Waveprobe_Risk_Function Waveprobe Control risk(s) = (1 + γ·(1-cos(θ)))/d² + η·h; γ=3, η=0.8 d=distance, θ=torsion angle, h=heat Combined risk metric for hysteretic control core/lean/geoweird/WaveprobeKernel.lean Lean ✅ 91,92 LAYER_F_CONTROL
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91 Waveprobe_Heat_Evolution Waveprobe Control h' = α·h + β·a; α=0.95, β=0.2 h=heat, a=excitation value (E1=1,E2=2,E3=3,E4=4) Heat accumulation with exponential decay core/lean/geoweird/WaveprobeKernel.lean Lean ✅ 90 LAYER_F_CONTROL
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92 Waveprobe_Mode_Hysteresis Waveprobe Control locked→caution if risk<B_recover=2; caution→locked if risk≥B_lock=5; normal→caution if risk≥B_warn=3 B_recover=2, B_warn=3, B_lock=5 Hysteretic mode transitions with risk barriers core/lean/geoweird/WaveprobeKernel.lean Lean ✅ 90 LAYER_F_CONTROL
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93 Phase_Transition_Derivative Emergence System diminishingRatio = |secondDeriv|/(|firstDeriv|+ε); transition when firstDeriv>ε AND |secondDeriv|<δ sustained N windows ε=0.001, δ=0.0001, sustainedWindows=10, entropyFloor=0.5 Detect optimization plateau to trigger emergence phase core/lean/geoweird/EmergenceSystem.lean Lean ✅ 94 LAYER_F_CONTROL
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94 Universe_Collision_Consensus Emergence System canCollide: dim_a==dim_b AND type_a!=type_b; strength=min(vol_a,vol_b)/vol_intersection; invariant: κ_a·κ_b>0→SphereLike, <0→HyperbolicLike two universes with manifolds, types, axioms Compute intersection of computational universes for witness generation core/lean/geoweird/EmergenceSystem.lean Lean ✅ 93 LAYER_E_VERIFICATION
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95 Shannon_Type_Entropy_Swarm Swarm Coordination H = -Σ(p_i·log₂(p_i)) where p_i=w_i/Σw; confidence update: reinforced=min(conf+0.1,1.0), decayed=conf·0.95 learned types, weights, confidence threshold=0.8 Measure uncertainty in agent's universe type assignment core/lean/geoweird/SwarmCoordination.lean Lean ✅ 94 LAYER_B_ROUTING
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96 Mean_Curvature_From_Constraints Constraint Geometry kappa = (cyclicRatio - treeRatio)·2.0; range [-2,2] cyclicRatio=count(Cyclic)/total, treeRatio=count(Hierarchical)/total Estimate curvature from constraint topology core/lean/geoweird/ConstraintGeometry.lean Lean ✅ 97 LAYER_C_TOPOLOGY
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97 Universe_Type_Scoring Constraint Geometry scoreEuclidean=flatness+translation+noTime+linearGrowth; scoreHyperbolic=negativeCurve+exponential+treeLike; scoreSpherical=positiveCurve+compact+rotation; scoreLorentzian=time+causal+mixedMetric; each capped at 1.0 constraint features (curvature, symmetry, causality, etc.) Score how well each universe type fits domain constraints core/lean/geoweird/ConstraintGeometry.lean Lean ✅ 96 LAYER_C_TOPOLOGY
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98 Homeostatic_Pressure_Dynamics Homeostatic Control s_t = α·surprise_t + β·regret_t; p_{t+1} = γ·p_t + s_t; surprise=-log(P_actual); regret=max(0,log(P_optimal)-log(P_actual)) p=pressure, λ=canal width, s=stress, γ=decay∈(0,1), α,β weights Pressure-driven exploration-exploitation control docs/HOMEOSTATIC_CONTROL_SPEC.tex LaTeX ✅ 99,100 LAYER_B_ROUTING
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99 Canal_Deformation Homeostatic Control λ_t = λ₀·(σ + (1-σ)·e^{-ξ·p_t}); λ_t ∈ [λ₀·σ, λ₀] λ₀=base canal width, σ∈(0,1)=min fraction, ξ=sensitivity Adaptive selectivity controlled by pressure docs/HOMEOSTATIC_CONTROL_SPEC.tex LaTeX ✅ 98 LAYER_B_ROUTING
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100 Decision_Reweighting_Homeostatic Homeostatic Control score_t = log(P_expected) - λ_t·s_t λ_t from canal deformation, s_t=stress Reduce penalty on high-stress paths when pressure rises docs/HOMEOSTATIC_CONTROL_SPEC.tex LaTeX ✅ 98,99 LAYER_B_ROUTING
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101 Homeostatic_Equilibrium Homeostatic Control (1-γ)·p* = s(p*); stability: |γ + s'(p*)| < 1 p*=fixed point, γ=decay, s'=stress derivative Stability condition for homeostatic fixed point docs/HOMEOSTATIC_CONTROL_SPEC.tex LaTeX ✅ 98 LAYER_B_ROUTING
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102 Hutter_Shape_Equation Information Theory H(W) = H₀ - I_local - I_structural - I_semantic - I_syntactic - I_cross; H₀=8.0, H(W)≈0.92 bpb (115 MB); I(W)=ΣI_k≈7.08 bpb H₀=uniform baseline (8 bpb), I_k=mutual information wedges per layer Hierarchical entropy decomposition for compression docs/geometry/HUTTER_SHAPE_EQUATION.md Documented ✅ 103 LAYER_A_COMPRESSION
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103 Hutter_Manifold_Deformation Information Theory Δ_t = M_{t+1} - M_t; v(x,t) = Φ_{t+1}(x) - Φ_t(x); H(x,t)=‖v(x,t)‖²; ε=½(∇v+(∇v)^T) M_t=manifold state at release t, Φ_t=embedding, v=velocity, ε=strain Model Hutter corpus evolution as tensor field deformation docs/roadmap/HUTTER_MANIFOLD_DYNAMICS.md Documented ✅ 102,104 LAYER_J_DYNAMICS
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104 SHA256_Field_Equations Information Theory Δ_manifold = μ_{t+1} - μ_t; ΔΣ = Σ_{t+1} - Σ_t; SHA256(μ_t‖Σ_t) → chain_hash; chunk_residual = f - μ_t μ_t=manifold mean, Σ_t=covariance, f=chunk feature Track Wikipedia knowledge evolution as hash-chained manifold deltas docs/roadmap/SHA256_FIELD_EQUATIONS.md Documented ✅ 102,103 LAYER_J_DYNAMICS
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105 Dyson_Swarm_Geodesic_Equation Dyson Swarm Geodesics d²x^μ/dτ² + Gamma^μ_νλ(dx^ν/dτ)(dx^λ/dτ) = η^μ(τ); <η^μ(τ)η^ν(τ')> = 2D·δ^μν·δ(τ-τ') x^μ=position in data space, Q=QUBO matrix, Gamma=Christoffel, η=thermal noise Model carrier state waves as geodesics on QUBO-derived data manifold docs/geometry/DYSON_SWARM_GEODESIC_EQUATIONS_2026-04-09.md Documented ✅ 84,85,106 LAYER_C_TOPOLOGY
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106 Metric_From_QUBO_Hessian Dyson Swarm Geodesics g_μν(x) = δ_μν + α·∂²E/∂x^μ∂x^ν = δ_μν + α·Q_μν Q=QUBO matrix, α=scaling, δ=Kronecker delta Define Riemannian metric from QUBO energy landscape docs/geometry/DYSON_SWARM_GEODESIC_EQUATIONS_2026-04-09.md Documented ✅ 105 LAYER_C_TOPOLOGY
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107 Langevin_Geodesic_Damping Dyson Swarm Geodesics d²x^μ/dτ² + Gamma^μ_νλ(dx^ν/dτ)(dx^λ/dτ) = -γ(dx^μ/dτ) + √(2D)·ξ^μ(τ) γ=damping coefficient, D=diffusion, ξ=Gaussian white noise Damped stochastic geodesic with thermal friction docs/geometry/DYSON_SWARM_GEODESIC_EQUATIONS_2026-04-09.md Documented ✅ 105 LAYER_C_TOPOLOGY
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108 Alcubierre_Information_Metric Virtual Alcubierre dI² = -dτ² + (dH - β·dτ)²; β = v_eff·f·Ω; v_eff = v_local/(1-φ) τ=proper time, H=entropy, β=shift vector, φ=foam score, Ω=opcode coupling Information-theoretic model for search acceleration via negative curvature docs/geometry/Virtual_Alcubierre_Information_Metric_Proof.md Documented ✅ 83,98 LAYER_G_ENERGY
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109 Alcubierre_Null_Geodesics Virtual Alcubierre dH/dτ = β ± 1; superluminal when β>1; collapse when φ ≥ 1 - κ_critical/v_local κ=thermodynamic load, bandwidth, τ_coherence Event horizon collapse condition regulating search acceleration docs/geometry/Virtual_Alcubierre_Information_Metric_Proof.md Documented ✅ 108 LAYER_G_ENERGY
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110 Ray_Casting_Braid_Step Braid Topology R(s) = G_new + s·d⃗; collision: ‖R(s) - P_j‖ < T; D_topo = ∫_L σ(s)·P(s) ds G_new=new point, d⃗=direction, P_j=existing peak, T=threshold, σ=coherence, P=purity Geometric parent selection for Merkle braiding via raycasting docs/formal_spec/RAYCAST_MMR_BRAID_TOPOLOGY.md Documented ✅ 111,112 LAYER_C_BRAID
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111 Fast_Inverse_Square_Root Braid Topology 0x5f3759df bit-hack for 1/d calculation; DIAT constraint: y_inv·y_sqrt ≈ C² where C=2^{16} k=index, y_inv=1/√k, y_sqrt=√k, 16.16 fixed-point Near-constant-time intersection calculation docs/formal_spec/THETA_TAN_RAYCAST_AMMR.md Documented ✅ 110 LAYER_C_BRAID
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112 UVMAP_Projection Braid Topology 32-bit packed texel: (v<<16)|u; U-axis: distance-based albedo (t×1000); V-axis: spectral frequency index (k from DIAT) t=distance, k=frequency index Encode raycast intersections for stochastic UV map rendering docs/formal_spec/THETA_TAN_RAYCAST_AMMR.md Documented ✅ 110 LAYER_C_BRAID
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113 Boltzmann_Transport_Equation_ThetaTaN Theta-TaN Phonon Physics κ_ph^{αβ} = (1/V)·Σ_{pq} C_V(pq)·v_{pq}^α·F_{pq}^β; C_V(pq)=ℏω·∂n₀/∂T; v_{pq}^α=∂ω/∂q_α C_V=mode heat capacity, v=phonon group velocity, F=mean free displacement Model lattice thermal conductivity in θ-phase TaN via phonon BTE docs/formal_spec/THETA_TAN_RAYCAST_AMMR.md Documented ✅ 114 LAYER_G_ENERGY
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114 Phonon_Scattering_Matrix Theta-TaN Phonon Physics τ_{pq}^{-1} = τ_{3ph}^{-1} + τ_{4ph}^{-1} + τ_{ph-iso}^{-1} + τ_{ph-el}^{-1} 3-phonon, 4-phonon, isotope, electron-phonon scattering channels Total phonon scattering rate as sum of independent channels docs/formal_spec/THETA_TAN_RAYCAST_AMMR.md Documented ✅ 113 LAYER_G_ENERGY
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115 Tensor_Field_QUBO Non-Euclidean UV QUBO F_u = -stress_xx·du - stress_xy·dv, F_v = -stress_yx·du - stress_yy·dv; wave = amplitude·e^{-dist·0.1}·cos(phase+dist); spin_i = 1 if wave>0 else 0 tensor=[stress_xx,stress_xy,stress_yx,stress_yy] per grid point Map QUBO to tensor field with wave-like carrier propagation audit/benchmarks/benchmark_tensor_carrier.rs Rust ✅ 116,117 LAYER_C_TOPOLOGY
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116 Non_Euclidean_UV_Distance Non-Euclidean UV QUBO Hyperbolic (Poincare): d = acosh(1 + 2·|p1-p2|²/((1-|p1|²)(1-|p2|²))); Spherical: d = acos(dot(p1,p2)); energy uses non-Euclidean distance UV points, geometry type∈{Euclidean,Hyperbolic,Spherical} Map QUBO to non-Euclidean surface where frustration=curvature audit/benchmarks/benchmark_non_euclidean_uv.rs Rust ✅ 15,115 LAYER_C_TOPOLOGY
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117 PGA_Cl301_NFold_Mechanics Geometric Algebra 16D algebra: scalar(1)+vector(4)+bivector(6)+trivector(4)+pseudoscalar(1); B_i=I·e_i; SE(3) motor: M=R+ε(½·t·R); CFA: h_i^{l+1}=h_i^l+Σ_j M_{ij}^l·h_j^l·M_{ji}^l PGA multivectors, motors, curvature κ, torsion τ Unified framework for protein structures using projective geometric algebra docs/geometry/GEOMETRIC_FIRST_LANGUAGE_Protein_NFold_Mechanics.md Documented ✅ 84,96,105 LAYER_H_ALGEBRA
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118 Sine_Gordon_Equation Geometric Algebra d²θ/dt² - c²·d²θ/ds² + (m²c⁴/ℏ²)·sin(θ) = 0 θ=phase, c=wave speed, m=mass, ℏ=reduced Planck constant Soliton dynamics in protein backbone docs/geometry/GEOMETRIC_FIRST_LANGUAGE_Protein_NFold_Mechanics.md Documented ✅ 117 LAYER_H_ALGEBRA
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119 Curvature_Torsion_Coupling_NFold Geometric Algebra κ_n = f(κ_{n-1}, τ_{n-1}, S_n), τ_n = g(κ_n, τ_{n-1}, S_n); Γ^λ_{μν} = {Levi-Civita}^λ_{μν} + K^λ_{μν} (contortion) κ=curvature, τ=torsion, S_n=substrate properties, K=contortion tensor Curvature-torsion coupling in N-fold mechanics docs/geometry/GEOMETRIC_FIRST_LANGUAGE_Protein_NFold_Mechanics.md Documented ✅ 117 LAYER_H_ALGEBRA
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120 Hormone_Half_Life_to_Decay_Rate Hormone Derivation remaining = 2^{-t_sec/(T_half_min·60)}; decay_rate = 1 - remaining; Ebbinghaus: R(t) = e^{-t/S} where S = T_half/ln(2) T_half=biological half-life (min), pulse_interval_sec=10.0 Convert biological half-life to computational decay rate lab/hormone_derivation.py Python ✅ 98,59 LAYER_B_ROUTING
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121 Logit_Z_Normalization Hormone Derivation logit(x) = log(x/(1-x)); z = (logit(x) - mean_logit) / std_logit x∈[0,1], mean_logit, std_logit from calibration Transform bounded concentration to z-score lab/hormone_derivation.py Python ✅ 120 LAYER_B_ROUTING
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122 Dynamic_Amplification_Factor FEA Semi-Truck DAF = 1.0 + braking_factor·0.5 (1.0-1.5); load_steer = base_load·DAF; load_drive = base_load·(1-braking·0.2); patch area = load/tire_pressure GVWR, axle loads, tire pressure, braking factor Moving load simulation with dynamic amplification lab/physics_fea/semi_truck_physics.py Python ✅ LAYER_L_APPLICATION
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123 Voxel_Key_Encoding Heerich Voxel key = ((x+512)&0x3FF)<<20 | ((y+512)&0x3FF)<<10 | ((z+512)&0x3FF); inverse: decode bit fields; range [-512,511] per axis, 30-bit x,y,z integers Fast 30-bit integer address for n-space positions tools/heerich_model.py Python ✅ 124 LAYER_I_ENCODING
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124 Microvoxel_Seed_4Byte_Encoding Microvoxel Seed 32-bit: delta_p[9:0]|region[13:10]|gamma[18:14]|activation[22:19]|polarity[26:23]|confidence[30:27]|flag[31]; switch: eff<0.8→EXCLUDE, <1.2→EXPLORE, ≥1.2→PROMOTE delta_p, region, gamma, activation, polarity, confidence Compressed self-evolving program via ternary switch encoding lab/microvoxel_seed_self_evolving_encoder.py Python ✅ 1,123 LAYER_I_ENCODING
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125 DCVN_Verification_Invariant_Survival DCVN Verification 4 invariants: completeness(c), consistency(s), freshness(f), provenance(p); survival mask: bit set if value≥threshold; participation: 4→FULL(1.0), ≥2→PARTIAL(0.5), ≥1→OBSERVER(0.1), 0→ABSENT(0.0); variance=var([c,s,f,p])/4 c,s,f,p∈[0,1], thresholds Graduated participation based on invariant health in verification swarm lab/consensus_net/verification_core.py Python ✅ 10,11 LAYER_E_VERIFICATION
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126 Watanabe_Total_Correlation Tensor Field Analysis TC = ΣH(X_i) - H(X_1,...,X_n); KSG estimator for MI; Kolmogorov: len(compressed)/len(original); formula complexity: 0.4·kolmogorov + 0.4·entropy/8 + 0.2·CV feature vectors, variant formulas, Hutter submission data Rigorous tensor field analysis of Hutter variants lab/hutter/tensor_field_analysis_rigorous.py Python ✅ 102,103 LAYER_A_COMPRESSION
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127 Relation_Sieve_5_Symbol Cache Sieve pack 5 2-bit symbols into 10-bit: sig=(T<<8)|(D<<6)|(C<<4)|(A<<2)|R; torsion: value<t0→00,<t1→01,<t2→10,else 11; coherence inverted: ≥c2→00,≥c1→01,≥c0→10,else 11; classify: REJECT if T=11,A=11,C=11,(T≥10&C≥10),(D=11&A≥10),(R=11&C≥10); HOLD if any=10 or C∈{01,10}; else PASS T=torsion, D=drift, C=coherence, A=angular momentum, R=radius Structural classification of cache manifolds into PASS/HOLD/REJECT tools/cache_sieve_exploration.py Python ✅ 115,128 LAYER_D_INVARIANTS
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128 Proxy_Extraction_From_Manifold Cache Sieve torsion = Σ|τ_i|(i<32)·100; drift = |φ_corr|·255; coherence = 255-torsion; angmom from geometry plugin; radius = |radius-1.0|·255 τ=torsion gradient samples, φ_corr=correlation Manifold features to discrete scalars tools/cache_sieve_exploration.py Python ✅ 127 LAYER_D_INVARIANTS
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129 SEISMIC_Shell_Detection Topological Encoder 0.35 ≤ φ_corr < 0.47; PHI-offset retry: φ_offset = φ_corr·PHI mod 1.0; SHA256 pattern hash: phi_weighted_profile[i]=τ_i·PHI^{-i/100}; hash=SHA256(φ_corr:path_length:Σphi_weighted_profile); orientation=(φ_corr·PHI) mod 2π φ_corr, τ=torsion, PHI=golden ratio "Detect SEISMIC torsion shells for ""The Wall"" construction" tools/topological_encoder.py Python ✅ 19,37 LAYER_I_ENCODING
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130 Half_Mobius_Closure_Integral Topological Encoder ∮τ·ds = π; solve: accumulated = Σ(τ_i·step_size); return path_length s when accumulated ≥ π τ=torsion gradient samples, step_size, φ_corr=correlation Half-twist closure condition for topologically stable wall tools/topological_encoder.py Python ✅ 41,129 LAYER_C_BRAID
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131 Regret_Field_Blink_Cycle KDA Control R_magnitude = surprise·(predicted_bpb - actual_bpb)/actual_bpb; blink_duration = baseline_ms + (regret_ms - baseline_ms)·R_magnitude ∈ [500ms,700ms]; decay: R *= e^{-λ·dt}, λ=2.0 surprise, predicted/actual bpb, blink baseline/regret, decay lambda Adaptive blink cycle control via regret field magnitude lab/CONCEPT_STAGE/2_KDA_Control_Kernel_Source.py Python ✅ 88,90,25 LAYER_F_CONTROL
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132 Hugoniot_Shock_Physics KDA Control Rankine-Hugoniot relations: T-P relationship for shock compression; transient vs equilibrium temperature; E_kinetic = ½·I·ω² (plasma flywheel); E_harvested = E_stored·efficiency Pressure(GPa), temperature(K), moment of inertia, RPM Thermodynamic state computation for plasma substrate lab/CONCEPT_STAGE/2_KDA_Control_Kernel_Source.py Python ✅ 25,98 LAYER_F_CONTROL
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133 Waveprobe_Binning_Functions Waveprobe Control binDistance: d<0.5→near(0), d<1→mid(1), else far(2); binTorsion: |sin(θ)|<1/3→aligned(0), <2/3→skewed(1), else twisted(2); binHeat: h<2→cool(0), h<5→warm(1), else hot(2) d=distance, θ=torsion angle, h=heat 3-category binning for LUT policy indexing core/lean/geoweird/WaveprobeKernel.lean Lean ✅ 90 LAYER_F_CONTROL
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134 Waveprobe_LUT_Policy Waveprobe Control baseLUT: 3×3×3 lookup mapping (distBin,torsionBin,heatBin)→Excitation; applyMode: normal=pass, caution=cap E2, locked=force E1; nextMode: hysteretic transitions by risk vs B_recover/B_warn/B_lock 3 bins × 3 bins × 3 bins = 27 entries Base control policy with mode override core/lean/geoweird/WaveprobeKernel.lean Lean ✅ 90,133 LAYER_F_CONTROL
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135 Parallel_Transport_Writhe Non-Euclidean Geometry project to oblique 2D: (x+z·cos(π/4)·0.5, y+z·cos(π/4)·0.5); deltas=consecutive differences; wr = Σ(ax·by - ay·bx)/(n-1) history of nd_points, window W=16, dox=doy=cos(π/4)·0.5 Parallel-transport writhe in curved PCA manifold tools/geometry_noneuclidean.py Python ✅ 35,51 LAYER_C_TOPOLOGY
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136 NE_Path_Validation Geometry Verifier PHI-weighted distance: d = √(Σ w_i·(a_i-b_i)²) where w_i=PHI^{-i}; metric continuity: max_jump>5.0→fail; writhe bound: |writhe|>2.0→fail; parallel transport: |sim-prev_sim|>1.5→instability 14D concept vectors, PHI weights, path points Validate paths through non-Euclidean concept space tools/geometry_verifier.py Python ✅ 37,51,135 LAYER_C_TOPOLOGY
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137 Hormone_Concentration_Normalization Hormone Derivation normalized = (value - species_min)/(species_max - species_min); modulation = effect_size·(1 - CV_species); CV = std/mean across species concentration values, species min/max, effect sizes, cross-species variance Normalize biological concentrations to computational 0-1 range lab/hormone_derivation.py Python ✅ 120 LAYER_B_ROUTING
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138 Thermal_Finality_BEA BEA Thermo Bridge confidence = min(bea_confidence + avg_irreversibility/n, 1.0); time_to_reverse = exp(total_irreversibility·100); weight = work_done/(1+entropy)·(1-0.5·irreversibility) bea_confidence, irreversibility array, work_done, entropy Integrate thermodynamic process tracking with BEA consensus core/gwl-vm/src/thermo/bea_thermo_bridge.rs Rust ✅ 21,11,27 LAYER_E_VERIFICATION
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139 Entropy_Generation_Rate_Informatic Informatic Stress dS/dt = power_dissipation/(k_B·T·ln(2)) [bits/s]; k_B=8.617e-5 eV/K; LANDAUER_LIMIT_300K=2.87e-21 J/bit power_dissipation, temperature, Boltzmann constant Rate of entropy generation from computational dissipation core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 55,14,54 LAYER_G_ENERGY
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140 Remaining_Useful_Life_Prediction Informatic Stress RUL = MTBF/(AF·(1+fatigue·0.01+thermal_fatigue·0.1)); MTBF=10^9/FIT_rate; AF=Arrhenius acceleration factor MTBF, acceleration factor, fatigue, thermal fatigue Hardware lifetime prediction under current stress core/gwl-vm/src/thermo/informatic_stress.rs Rust ✅ 51,52,53,56,57,58 LAYER_G_ENERGY
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