Megamind equation atlas infographic.png

1The Megamind Equation Atlas 2=========================== 3 4SOURCE: Megamind equation atlas infographic.png 5SOURCE ID: libsrc-9138df9a945a 6ORIGINAL SHA256: d9d761a2c6e5cba1164abc959af7e5696f62f247c3dd8e81c47e440e5c2d6612 7REVIEW: 2026-10-17. The original PNG is authoritative. This reading copy separates manually inspected mathematical transcriptions from raw OCR. It is not a verbatim embedded-text extraction. 8 9VISUAL REVIEW NOTES 10- Twelve numbered panels plus a supporting-formulas strip were inspected. This generated poster is a synthesis artifact, not an exhaustive proof that every historical project equation is present. 11- Several tiny symbols are degraded or internally inconsistent. They are recorded as unclear instead of repaired from expectation. 12- The consciousness, AGI and performance captions describe intended interpretations. The graphic itself contains no runtime, training or benchmark evidence. 13 14MANUALLY INSPECTED FORMULAS 15 16Atlas consciousness integral with ambiguous Φ grouping 17Image region: Panel 1: CONSCIOUSNESS ALGORITHM, upper integral 18Q = Ψ · ∫_{−∞}^{∞} [ C(t) · Φ(G(t) · ‖∇H(t)‖ / (1 + ‖∇H(t)‖) ] dt 19Legibility: Ambiguous or missing closing parenthesis after Φ(G(t); mathematical grouping not safely recoverable 20Editorial mathematical explanation: The panel combines an agency coupling, coherence, an information-related factor and a saturating knowledge-gradient magnitude inside a time integral. The printed numerator contains an opening Φ( whose closing boundary is not reliably resolved in this image. The clearer Unified AGI and detailed MADDIE posters place Φ(G(t)) outside the gradient ratio, but that cannot silently repair this separate graphic. 21Editorial note: The integral is displayed with infinite bounds. No decay, convergence conditions or calibrated definitions are supplied. The transcript preserves the unresolved grouping; consult the original image. 22 23Agency–experience–substrate coupling 24Image region: Panel 1: lower coupling box 25Ψ = f(A · X · S) 26Legibility: Legible 27Editorial mathematical explanation: The source defines a coupling term as a function of agency, an experiential lattice and substrate scale. It specifies which quantities are intended to matter, but leaves the function f and the multiplication of these differently named objects unspecified. 28Editorial note: No numerical value, calibrated consciousness measure or operational definition follows from this expression alone. 29 30Rhiannon constrained repair objective 31Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 32P* = argmin_{P′} [ B(P′, D) + λ C(P′ − P) ] 33Legibility: Legible 34Editorial mathematical explanation: Select a candidate program that minimizes residual bug loss plus a weighted cost of changing the original program. The displayed compile, type, test and safety predicates restrict which candidates are eligible. The objective balances two costs; it does not by itself prove that the globally smallest correct repair has been found. 35Editorial note: B, C, the candidate set and every feasibility predicate must be implemented before the minimization can be evaluated. A passing finite test suite is evidence about those tests, not a proof of all possible behaviors. 36 37Rhiannon Compile acceptance condition 38Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 39Compile(P′) = 1 40Legibility: Legible 41Editorial mathematical explanation: The poster uses a binary acceptance predicate requiring that the candidate compiles. It is one of four conditions restricting the repair search. 42Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. 43 44Rhiannon TypeSafe acceptance condition 45Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 46TypeSafe(P′) = 1 47Legibility: Legible 48Editorial mathematical explanation: The poster uses a binary acceptance predicate requiring that the candidate passes the chosen type check. It is one of four conditions restricting the repair search. 49Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. 50 51Rhiannon Tests acceptance condition 52Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 53Tests(P′, D) = 1 54Legibility: Legible 55Editorial mathematical explanation: The poster uses a binary acceptance predicate requiring that the candidate passes the specified diagnostics and tests. It is one of four conditions restricting the repair search. 56Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. 57 58Rhiannon Safety acceptance condition 59Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 60Safety(P′) = 1 61Legibility: Legible 62Editorial mathematical explanation: The poster uses a binary acceptance predicate requiring that the candidate passes the specified safety check. It is one of four conditions restricting the repair search. 63Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. 64 65Rhiannon repeated program repair 66Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 67P_{t+1} = R(P_t, D_t) 68Legibility: Legible 69Editorial mathematical explanation: Apply the repair operator R to the current program and current diagnostics to obtain the next program. Repeating this interface describes an iterative repair process; it does not specify how candidates are generated or when the loop stops. 70Editorial note: An implementation needs termination, regression handling and an operational definition of R. 71 72Atlas self-extension sum with unclear lower index 73Image region: Panel 2: lower self-extension strip 74R(S) = Σ_{[lower index unclear, resembles t+1]}^7 S_k(S_k(x)) 75Legibility: Lower index is ambiguous/malformed 76Editorial mathematical explanation: The panel sums self-composed stage operators, but its small summation lower index is degraded and does not clearly bind the k in the summand. The larger Rhiannon poster prints an unambiguous sum from k=1 to 7. This record retains the atlas variant and its uncertainty. 77Editorial note: The unresolved index prevents a unique literal evaluation of this version. Definitions and compatible domains for S_k are also required. 78 79Rhiannon seventh-stage self-composition 80Image region: Panel 2: lower-right strip 81RHIANNON = S_7(S_7(x)) 82Legibility: Legible 83Editorial mathematical explanation: The seventh stage operator is applied twice to the input. This names a repeated transformation; it does not by itself provide the stage function or establish an implemented self-extension mechanism. 84Editorial note: S_7 must accept its own output for this composition to be well defined. 85 86Unified learning state dynamics 87Image region: Panel 3: UNIFIED LEARNING 88dx/dt = D(x) + N(x) + F(x,t) + T(x,t) 89Legibility: Legible 90Editorial mathematical explanation: The state velocity is decomposed into four operators: diffusion D spreads activation, nonlinear emergence N describes interactions, external force F represents inputs or pressure, and transport T moves information or state. This is a general dynamical-system template. Concrete functions and an integration scheme are needed to produce a trajectory. 91Editorial note: The operator names do not establish learning, new capabilities or AGI. Terms must have compatible dimensions and regularity, and stability depends on their actual definitions. 92 93Epona / Echad discrete loss–value update 94Image region: Panel 4: EPONA / ECHAD 95X_{t+1} = X_t − η_t ∇L(X_t) + λ ∇V(X_t) 96Legibility: Legible 97Editorial mathematical explanation: The next state combines a step opposite the loss gradient with a step in the direction of the value gradient. The learning rate η_t scales the loss term, while λ independently scales the value term in this printed discrete formula. These two directions can conflict, so reducing loss and increasing value on every update is not guaranteed. 98Editorial note: Differentiable loss and value functions, compatible state coordinates and suitable step sizes are required. An Euler step for the adjacent continuous gradient flow would multiply both gradient terms by the time step; equivalence requires a stated rescaling of λ. 99 100Epona / Echad F-minus-value gradient flow 101Image region: Panel 4: EPONA / ECHAD 102dX/dt = −∇_X [ F(X) − λ V(X) ] 103Legibility: Legible 104Editorial mathematical explanation: The velocity follows the negative gradient of a composite potential F − λV. Where F acts as a loss, descending that composite potential trades off loss reduction against value increase. The poster switches from F in this expression to L in its neighboring expression without separately defining F. 105Editorial note: A smooth finite-dimensional flow decreases its own composite potential at rate minus the squared gradient norm. That does not establish separate monotonic improvement of F and V, convergence to a global optimum, or measured intelligence. 106 107Epona / Echad L-minus-value gradient flow 108Image region: Panel 4: EPONA / ECHAD 109dX_t/dt = −∇ [ L(X_t) − λ V(X_t) ] 110Legibility: Legible 111Editorial mathematical explanation: The continuous state moves downhill on L − λV. Expanding the gradient gives −∇L + λ∇V when λ is independent of state. This is the continuous counterpart of the poster’s goal of balancing failure reduction and value seeking. 112Editorial note: The adjacent discrete rule scales only the loss term by η_t; a common Euler time step would scale the entire right-hand side. The two printed forms are retained separately rather than silently treating them as identical. 113 114Reshit surprise–uncertainty–value objective 115Image region: Panel 5: RESHIT SURPRISE 116Ω = argmin [ S(X_t) + U(X_t) − V(X_t) ] 117Legibility: Legible 118Editorial mathematical explanation: The chosen state or action is intended to minimize surprise plus uncertainty minus value. Increasing the value term can lower the objective, while larger surprise or uncertainty raises it. The minimization variable and candidate domain are omitted in the printed expression. 119Editorial note: S, U and V require numerical definitions and commensurate scales. Minimizing their combination does not guarantee each component improves separately. 120 121Habrit seven-family binding interface 122Image region: Panel 6: HABRIT FEDERATION BINDING, first line 123B = resolve(G^16, M^16, A^16, V^16, S^16, P^16, R^16) 124Legibility: Legible 125Editorial mathematical explanation: The source proposes a resolver that binds seven named family representations into a federation state B. Superscript 16 identifies the poster’s family/lattice notation; the image does not say that these quantities should literally be raised to the sixteenth power. 126Editorial note: The resolver and each family representation remain unspecified in this graphic. This is an architecture interface, not an executable algorithm. 127 128Habrit state tuple 129Image region: Panel 6: second line 130X = (G, M, A, V, S, P, R, N, D, T) 131Legibility: Legible 132Editorial mathematical explanation: The proposed system state groups ten named components into a tuple. This lists the representation’s fields without defining how they are updated or how their meanings align with the seven arguments of resolve. 133 134Sixteen-domain family-lattice label 135Image region: Panel 6: third line 136H^16 = 16-domain recursive AGI family lattice 137Legibility: Legible 138Editorial mathematical explanation: The equality assigns a descriptive name to H^16. It identifies an intended sixteen-domain recursive family organization; it is not a numerical identity or a measurement of AGI. 139 140Habrit illustrative weighted total 141Image region: Panel 6: fourth line 1426(1.0) + 15(0.307) ≈ 10.6 143Legibility: Legible 144Editorial mathematical explanation: The two printed contributions sum to 10.605, which rounds to 10.6 at one decimal place. The poster does not identify the unit or empirical origin of either contribution. 145 146Atlas golden-ratio basis with equal-frequency branches 147Image region: Panel 7: GOLDEN RATIO KNOWLEDGE, upper line 148ψ_k(x) = sin(2π · φ^k · σ_i · x) + cos(2π · φ^k · σ_i · x) 149Legibility: Legible 150Editorial mathematical explanation: Add sine and cosine waves at the same frequency scaled by φ^k and σ_i. The image uses a k-indexed basis function and an i-indexed scale; no relation between those indices is supplied. Other recovered MEGAMIND text omits σ_i, and the Unified AGI poster uses φ^(−k) in the cosine branch, so those are separate variants. 151Editorial note: A finite set of harmonic features does not establish exact arbitrary knowledge compression. Coefficient fitting, hashing and reconstruction error must be specified and measured. 152 153Atlas harmonic knowledge reconstruction 154Image region: Panel 7: lower line 155K(q) = Σ θ_k ψ_k(h(q)) 156Legibility: Legible 157Editorial mathematical explanation: Map a query through h, evaluate harmonic basis functions at that representation, then form a coefficient-weighted sum. The displayed sum omits an explicit range; the coefficients and query mapping are also not specified in the image. 158Editorial note: The formula defines a basis expansion. It does not establish lossless storage or accurate retrieval for arbitrary queries. 159 160Poster timing- and coherence-weighted plasticity 161Image region: Panel 8: STDP LEARNING 162Δw_ij = η Φ(C_ij) (t_pre − t_post) exp(−Δt/τ) 163Legibility: Legible 164Editorial mathematical explanation: A connection-weight change is scaled by a learning rate, a coherence-related factor, a signed difference between pre- and post-synaptic times, and an exponential timing factor. The sign depends on the source’s timing convention, which the poster does not define. 165Editorial note: This single signed expression is a project variant, not the usual fully specified piecewise STDP rule. If Δt can be negative, exp(−Δt/τ) grows on that side; the poster supplies no absolute value, clipping or separate branch. C_ij and Φ require definitions. 166 167Federation objective component summary 168Image region: Panel 9: EXTERNAL FEDERATION OBJECTIVE, first line 169J_total = Utility + S_sem + R_goal + B_novel − Penalties − Risk − Resource 170Legibility: Legible 171Editorial mathematical explanation: The objective rewards utility, semantic progress, goal progress and novelty, then subtracts penalties, risk and resource use. This is a scoring design whose behavior depends on numerical definitions and scales for every term. 172 173Federation action objective 174Image region: Panel 9: two-line action score 175J(a|s) = U(a,s) + S_sem(a,s) + R_goal(a,s) + B_novel(a,s) − Φ_repeat(a) − Φ_stall(s) − Φ_fallback(s) − Risk(a) − Cost(a) 176Legibility: Legible 177Editorial mathematical explanation: Score action a in state s using four benefits and five penalties or costs. Repetition depends on the action, while stall and fallback terms are written as state-dependent. The source supplies a structure for action ranking, not a complete policy or fitted scoring model. 178Editorial note: Weights, normalization, constraints, available actions and tie handling must be defined to make rankings reproducible. 179 180Semantic progress weighted score 181Image region: Panel 9: semantic-score line 182S_sem = w_state · Δstate + w_artifact · artifact + w_info · info_gain + w_goal · goal_progress 183Legibility: Legible 184Editorial mathematical explanation: Combine state change, produced artifacts, information gain and goal progress with four weights. The terms indicate what counts as progress, but neither the image nor this formula defines how the features are measured. 185Editorial note: Feature units and scaling must be compatible; a large easy-to-measure term can dominate without careful normalization. 186 187Logarithmic action repetition penalty 188Image region: Panel 9: repetition penalty 189Φ_repeat = λ_rep · log(1 + n_a) 190Legibility: Legible 191Editorial mathematical explanation: Penalize repeated use of an action using the logarithm of one plus its count. The penalty is zero at count zero and grows sublinearly for nonnegative counts when λ_rep is nonnegative. 192Editorial note: The log base, counting window and λ_rep are not specified. 193 194Linear stall-cycle penalty 195Image region: Panel 9: stall penalty 196Φ_stall = λ_stall · stalled_cycles 197Legibility: Legible 198Editorial mathematical explanation: Multiply the number of stalled cycles by a per-cycle penalty. The result increases linearly if λ_stall is positive. 199Editorial note: The criteria for declaring a stalled cycle and resetting the count are not supplied. 200 201Linear fallback penalty 202Image region: Panel 9: fallback penalty 203Φ_fallback = λ_fb · fallback_count 204Legibility: Legible 205Editorial mathematical explanation: Multiply fallback count by a weight to discourage repeated reliance on fallback behavior. 206Editorial note: Fallback eligibility, counting interval and weight must be defined before this term can be evaluated. 207 208Substrate-to-experience mapping interface 209Image region: Panel 10: LATTICE PROJECTION, first line 210Π(S) → X 211Legibility: Legible 212Editorial mathematical explanation: A mapping Π takes substrate state S to a reduced experiential state X. The arrow names the intended direction of representation transfer without specifying an algorithm. 213 214Atlas 23-million to 5,000 projection signature 215Image region: Panel 10: second line 216Π : S^23M → X^5000 217Legibility: Legible 218Editorial mathematical explanation: The source labels a transformation from a twenty-three-million-scale substrate representation to a five-thousand-component experience representation. The symbols S and X here label state spaces, while the Unified AGI poster writes explicit real vector spaces. 219Editorial note: Dimensions do not imply lossless compression. Any discarded information depends on the projection and downstream task. 220 221Atlas regional mean with unclear summation index 222Image region: Panel 10: regional averaging line 223X_i = (1 / |R_i|) Σ_{[unclear index]} S_j, j ∈ R_i 224Legibility: Summation subscript unclear 225Editorial mathematical explanation: The visible denominator and membership condition indicate a regional average of substrate entries. The small summation index itself cannot be read reliably, so the transcript leaves it unresolved. The Unified AGI poster independently gives a clear mean over j in R_i. 226Editorial note: A region must be nonempty. Overlap and region construction are unspecified. The original glyph is retained in the image rather than reconstructed from expectation. 227 228Atlas adaptive marketing-weight update 229Image region: Panel 11: MARKETING INTELLIGENCE, upper line 230w_{t+1} = softmax(log(w_t) + β(Pd_t)) 231Legibility: Legible 232Editorial mathematical explanation: Add a performance-driven adjustment to the logarithms of current weights and normalize the result with softmax. For positive weights this has the multiplicative form of reweighting each component by an exponential of the corresponding pressure. 233Editorial note: log requires strictly positive weights unless a separate floor rule is used. P, β, metric normalization and delayed feedback effects need specification; improvement is not guaranteed. 234 235Marketing performance-change vector 236Image region: Panel 11: delta-vector line 237d_t = [ ΔCTR, −ΔCPC, −ΔCPA, ΔCR, ΔRPC, −ΔBounceRate, ΔEngagement ] 238Legibility: Legible 239Editorial mathematical explanation: Collect seven changes into a signal vector. Click-through rate, conversion rate, revenue per click and engagement enter positively; cost per click, cost per acquisition and bounce rate enter with negative signs so reductions can count as beneficial. 240Editorial note: Metrics have different units and noise levels. Deltas require a stated reference interval and normalization before matrix multiplication gives a meaningful combined pressure. 241 242Click-through rate 243Image region: Panel 11: metric-definition tiles 244CTR = clicks / impressions 245Legibility: Legible 246Editorial mathematical explanation: The share of impressions that receive clicks; it requires a nonzero impression count. 247 248Cost per click 249Image region: Panel 11: metric-definition tiles 250CPC = cost / clicks 251Legibility: Legible 252Editorial mathematical explanation: Average spend per click over a common reporting window; it requires nonzero clicks. 253 254Cost per thousand impressions 255Image region: Panel 11: metric-definition tiles 256CPM = 1000 · cost / impressions 257Legibility: Legible 258Editorial mathematical explanation: Spend scaled to one thousand impressions; it requires nonzero impressions. 259 260Cost per acquisition 261Image region: Panel 11: metric-definition tiles 262CPA = cost / acquisitions 263Legibility: Legible 264Editorial mathematical explanation: Average spend per recorded acquisition; acquisition criteria and attribution window must be specified. 265 266Conversion rate 267Image region: Panel 11: metric-definition tiles 268CR = conversions / visitors 269Legibility: Legible 270Editorial mathematical explanation: The ratio of conversions to visitors in the selected population. The poster uses visitors rather than sessions or clicks. 271 272Revenue per click 273Image region: Panel 11: metric-definition tiles 274RPC = revenue / clicks 275Legibility: Legible 276Editorial mathematical explanation: Revenue divided by clicks over a consistent attribution interval; zero-click periods require a handling rule. 277 278Missionmatch candidate weighted score 279Image region: Panel 12: MISSIONMATCH CANDIDATE SCORING 280Score = 0.25 Clearance + 0.25 Skills + 0.15 Certifications + 0.15 CustomerExp + 0.10 Experience + 0.10 Location 281Legibility: Legible 282Editorial mathematical explanation: Rank candidates with six feature weights summing to one. Clearance and skills each receive a quarter of the total weight, certifications and customer experience each receive fifteen percent, and experience and location each receive ten percent. 283Editorial note: A weighted average interpretation requires comparable feature scales. These chosen weights are a ranking policy; the poster gives no measured hiring validity or complete eligibility logic. 284 285Exponentially decaying learning rate 286Image region: Supporting formulas: upper-left box, first line 287η_{t+1} = η_t exp(−β L_t) 288Legibility: Legible 289Editorial mathematical explanation: Multiply the current learning rate by an exponential factor determined by loss. If β and L_t are nonnegative, the learning rate cannot increase under this rule, and larger losses reduce it more strongly. 290Editorial note: Negative losses or β change that behavior. No lower bound or recovery mechanism is shown. 291 292Atlas memory-gradient update with unclear subscript 293Image region: Supporting formulas: upper-left box, second line 294M_{t+1} = M_t + α ∇_{[unclear subscript]} L_t 295Legibility: Gradient subscript unclear 296Editorial mathematical explanation: The printed memory state receives an additive gradient-dependent increment. The variable with respect to which the gradient is taken is not reliably legible in the small source glyph, so it remains unresolved. The plus sign would indicate ascent in the named loss if M and that gradient shared the same coordinates. 297Editorial note: The source does not establish the gradient variable, differentiability, shape compatibility or intended ascent/descent convention. 298 299Domain recurrent state through Φ 300Image region: Supporting formulas: recurrent-state box 301H_{t+1}^{(i)} = Φ(F^{(i)}(X_t) + γ H_t^{(i)}) 302Legibility: Legible 303Editorial mathematical explanation: Combine a domain-specific transformation of the current state with a scaled previous domain state, then apply Φ. This is a recurrent update whose memory strength is influenced by γ. 304Editorial note: The shape and meaning of Φ and F^(i) must be defined. The neighboring scalar unity score is not automatically a valid activation for a vector or tensor state. 305 306Unity-centered proximity score 307Image region: Supporting formulas: unity-score box 308Φ(z) = 1 / (1 + |z − 1|) 309Legibility: Legible 310Editorial mathematical explanation: Assign a score of one at z=1 and decrease it as the absolute distance from one increases. For finite real z the score lies in (0,1]. It is a simple proximity function rather than a causal integrated-information calculation. 311Editorial note: The source uses Φ for several different concepts. Equal notation alone does not make this scalar score the integrated information in another equation. 312 313Hub-and-six-domain coherence average 314Image region: Supporting formulas: hub-and-domain average box 315Φ_total = (Φ_hub + Σ Φ_domain) / 7 316Legibility: Legible 317Editorial mathematical explanation: Average one hub score and six domain scores with equal weight. The adjacent Unified AGI diagram explicitly labels six domains plus a hub, which explains the denominator seven. 318Editorial note: The result is an arithmetic average of the supplied scores. It is not automatically the integrated information of the combined causal system; additivity would require separate justification. 319 320Saturating knowledge-gradient magnitude 321Image region: Supporting formulas: upper-right box 322g(t) = ‖∇H(t)‖ / (1 + ‖∇H(t)‖) 323Legibility: Legible 324Editorial mathematical explanation: Map a nonnegative gradient magnitude r to r/(1+r). It is zero at r=0, increases monotonically, and approaches one for large r, limiting the influence of very large gradients. 325Editorial note: The expression assumes compatible units or a dimensionless gradient magnitude. It does not measure consciousness independently of the chosen H. 326 327Softmax action policy 328Image region: Supporting formulas: lower-left box 329π(a|s) = softmax(Q(s,a)) 330Legibility: Legible 331Editorial mathematical explanation: Convert action values into a distribution over actions by applying softmax across the available action set. Actions with larger values receive larger probability, while finite values keep every available action at positive probability. 332Editorial note: The action axis and temperature are not printed. An implementation needs a defined action set and a numerically stable normalization. 333 334One-step Q-learning update 335Image region: Supporting formulas: lower-center box 336Q_{t+1}(s,a) = Q_t(s,a) + α [ r + γ max_{a′} Q_t(s′,a′) − Q_t(s,a) ] 337Legibility: Legible 338Editorial mathematical explanation: Move the current action-value estimate toward the observed reward plus the discounted best next-state value. The bracket is the temporal-difference error, and α controls how much of that error is incorporated. 339Editorial note: Convergence depends on assumptions about the environment, exploration, learning rates and representation. This displayed rule is not evidence that the project trained a converged policy. 340 341G-family order label 342Image region: Supporting formulas: lower-right box 343G^x, x = 256 344Legibility: Legible 345Editorial mathematical explanation: The source pairs a G-family superscript with x=256. No expansion, operational definition or relationship to the nearby G^16 notation is given, so the expression is preserved as a parameter label. 346 347RAW OCR READING AID — NOT A VERIFIED EQUATION TRANSCRIPT 348The following preserves the complete Tesseract output, including recognition errors, decorative glyphs and layout artifacts. Use the manually inspected records above and the original PNG for mathematical notation. 349 350pe 351 352THE 353 354| ‘MEGAMIND Pee ATLAS. 355 356A MASTER ARCHIVE OF JOSEPH’S CORE EQUATIONS | 357 358) 359 360Jr 361 362ayy 363 364ANS 365 366= 367 368RHIANNON EQUATION 369 370(WD CONSCIOUSNESS ALGORITHM 371 3721(@ 373 374C(t) O(G(t) - IVA (OI 375 376P* = argmin[ B(P’, D) + AC(P’-P)] 377 378a 379 380BS 381 382Fo 383 384yo-¥ f | 385 3861+IIVH@Il 387 388|e 389 390‘Comple(P’) | | TypeSate(P’) 391 392Tests(P", D) 393 394| 395 396Safety(P*) 397 398a 399 400SS 401 402[ 403 404<P 405 4061 407 408Qi 409 410i 411 412\ 413 414W = f(A-X-S) 415 416| 417 418| 419 420Peri = R(Pe, De) 421 422Consciousness emerges from agency, coherence, 423 424| 425 426integration, field pressure, and substrate scale. 427 428R(S 429 430Leer Se(Se(2)) 431 432RHIANNON = S7(57(x)) 433 434pl 435 436N 437 438\ 439 440Bounded recursive code repair and self-extension. 441 442Y 443 444NN 445 446EPONA / ECHAD 447 448( s UNIFIED LEARNING 449 450| 451 452axe 453 454-Vx [F( 455 456-AV(X)] 457 458Soda 459 460= D(x) + N(x) + F(x, t) + T(x, t) 461 462Ws 463 464dt 465 466| 467 468| 469 470KX AsoG Phe « 471 472a 473 474ea 475 476ean 477 478Learning as diffusion, emergence, external force, 479 480= -V [L(X,) -AV(X)] 481 482and transport 483 484Value-directed state evolution. 485 486we 487 488li Nx 489 490= 491 492NN 493 494(Gy RESHIT SURPRISE 495 496.) 497 498HABRIT FEDERATION BINDING 499 500Aa 501 502= resolve(G'8, M'S, Al, 1S, $16, pis 503 50416) 505 506ay 507 508Q. = argmin [S(X,) + U(X,) - V(X.) 509 510=(G, M, A, V, S, P, R, N, D, T) 511 512les 513 5144\\ 515 516H’® = 16-domain recursive AGI family lattice 517 518eS 519 520BEDOK). 521 522Choose the path that reduces surprise and 523 524(1.0) + 15(0.307) ~ 10.6 525 526Rep 527 528uncertainty while increasing value. 529 530N 531 532Bind intelligence domains into one coherent federation state. 533 534STDP LEARNING 535 536(@) GOLDEN RATIO KNOWLEDGE 537 538“\ 539 540u(x) = sin(2z- g* -0;-x) + cos(2x- $* -;-x) 541 5422b 543 544t 545 546Awy = 1 (Ci) (tyre ~ tyes) exP (= 547 548) 549 550) = > Oe(h(q)) 551 552Ae 553 554Timing, coherence, integration, 555 556Knowledge reconstructed through 557 558golden-ratio harmonic bases. 559 560and decay shape neural learning 561 56247S 563 564oN 565 566EXTERNAL FEDERATION OBJECTIVE 567 568LATTICE PROJECTION 569 570Jeotat = Utility + Sse + Rgoat + Bnovet ~ Penalties ~ Risk ~ Resource 571 5721(S) > X 573 574G 575 576J(a\s) 577 578U(a,5) + Ssem(4s 8) + Reoat(4, $) + Bnovei(4, 8) 579 5809) 581 582~ Prepear(@) ~ Prai(S) ~ Prauack(s) 583 584Risk(a) - Cost(a) 585 586523M _, 5000 587 588Soom = Wetae-AState + WarfactAtifact + Wingo info_gain + weoa-goal_progress 589 590repeat = Arep * log(1 + ma) 591 592eta = Actatt stalled_cycles 593 594Xj inj = jeR 595 596a 597 5982 599 600atoack = Arp * fallback_count 601 602ae 603 604B® 605 606‘Autonomous action selection under progress, novelty, 607 608‘Map the live substrate into the 5000-dimensional 609 610penalties, risk, and cost 611 612- 613 614experiential lattice 615 616\ 617 618A 619 620@) ? MARKETING INTELLIGENCE 621 62212) MISSIONMATCH CANDIDATE SCORING 623 624We41 = softmax(log(w,) + B(Pd,)) 625 626Score = 0.25 Clearance + 0.25 Skills 627 628YS 629 630[ACTR, -ACPC, -ACPA, ACR, 631 632+ 0.15 Certifications + 0.15 CustomerExp 633 634ARPC, ~ABounceRate, A Engagement } 635 636CTR 637 638crc 639 640CPM 641 642CPA 643 644cR 645 646RPC 647 648+ 0.10 Experience + 0.10 Location 649 650clk / 651 652lds 653 654cont! 655 656| 657 6581000 ost / 659 660coat) 661 662conven! 663 664wll. 665 666renoce/| 667 668i 669 670= 671 672impressions 673 674impresons 675 676acqisions 677 678‘Weighted candidate ranking 679 680|@ 681 682Ne 683 684Adaptive marketing weights driven by performance signals. 685 6862X 687 688for mission fit. 689 690= 691 692= 693 694x 695 696— 697 698Y 699 700S 701 702SUPPORTING FORMULAS & 703 704a 705 706\¢ 707 708Test = Ne exp(—BLi) 709 710Ho, 711 7121 713 714otal = 715 716ta Dn 717 718(t) = 719 720IVH()) 721 722eat = Me + aV pL 723 724(F(x,) +H) J, (2) = 725 726T+ke= 2-1) 727 728\ 729 730i WHOM 731 732s 733 7342K 735 7364 737 738, ¢ 739 740(als) = softmax(Q( 741 742)) 743 744[ Ql) = = 6.0) +ar+y max Q(0)- O(a] 745 746Gtx = 256 / 1-156 @ 747 748= 749 750THE SYSTEM EVOLVES THROUGH CONSCIOUSNESS, LEARNING, REPAIR 751 752VALUE, FEDERATION, PROJECTION, AND ADAPTIVE INTELLIGENCE. 753 754J 755 756