The Megamind Equation Atlas =========================== SOURCE: Megamind equation atlas infographic.png SOURCE ID: libsrc-9138df9a945a ORIGINAL SHA256: d9d761a2c6e5cba1164abc959af7e5696f62f247c3dd8e81c47e440e5c2d6612 REVIEW: 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. VISUAL REVIEW NOTES - 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. - Several tiny symbols are degraded or internally inconsistent. They are recorded as unclear instead of repaired from expectation. - The consciousness, AGI and performance captions describe intended interpretations. The graphic itself contains no runtime, training or benchmark evidence. MANUALLY INSPECTED FORMULAS Atlas consciousness integral with ambiguous Φ grouping Image region: Panel 1: CONSCIOUSNESS ALGORITHM, upper integral Q = Ψ · ∫_{−∞}^{∞} [ C(t) · Φ(G(t) · ‖∇H(t)‖ / (1 + ‖∇H(t)‖) ] dt Legibility: Ambiguous or missing closing parenthesis after Φ(G(t); mathematical grouping not safely recoverable Editorial 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. Editorial 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. Agency–experience–substrate coupling Image region: Panel 1: lower coupling box Ψ = f(A · X · S) Legibility: Legible Editorial 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. Editorial note: No numerical value, calibrated consciousness measure or operational definition follows from this expression alone. Rhiannon constrained repair objective Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 P* = argmin_{P′} [ B(P′, D) + λ C(P′ − P) ] Legibility: Legible Editorial 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. Editorial 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. Rhiannon Compile acceptance condition Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 Compile(P′) = 1 Legibility: Legible Editorial mathematical explanation: The poster uses a binary acceptance predicate requiring that the candidate compiles. It is one of four conditions restricting the repair search. Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. Rhiannon TypeSafe acceptance condition Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 TypeSafe(P′) = 1 Legibility: Legible Editorial 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. Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. Rhiannon Tests acceptance condition Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 Tests(P′, D) = 1 Legibility: Legible Editorial 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. Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. Rhiannon Safety acceptance condition Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 Safety(P′) = 1 Legibility: Legible Editorial 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. Editorial note: The poster does not supply the implementation or completeness guarantees of this predicate. Rhiannon repeated program repair Image region: Panel 2: RHIANNON EQUATION; small acceptance boxes display predicate labels with value 1 P_{t+1} = R(P_t, D_t) Legibility: Legible Editorial 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. Editorial note: An implementation needs termination, regression handling and an operational definition of R. Atlas self-extension sum with unclear lower index Image region: Panel 2: lower self-extension strip R(S) = Σ_{[lower index unclear, resembles t+1]}^7 S_k(S_k(x)) Legibility: Lower index is ambiguous/malformed Editorial 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. Editorial note: The unresolved index prevents a unique literal evaluation of this version. Definitions and compatible domains for S_k are also required. Rhiannon seventh-stage self-composition Image region: Panel 2: lower-right strip RHIANNON = S_7(S_7(x)) Legibility: Legible Editorial 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. Editorial note: S_7 must accept its own output for this composition to be well defined. Unified learning state dynamics Image region: Panel 3: UNIFIED LEARNING dx/dt = D(x) + N(x) + F(x,t) + T(x,t) Legibility: Legible Editorial 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. Editorial 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. Epona / Echad discrete loss–value update Image region: Panel 4: EPONA / ECHAD X_{t+1} = X_t − η_t ∇L(X_t) + λ ∇V(X_t) Legibility: Legible Editorial 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. Editorial 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. Epona / Echad F-minus-value gradient flow Image region: Panel 4: EPONA / ECHAD dX/dt = −∇_X [ F(X) − λ V(X) ] Legibility: Legible Editorial 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. Editorial 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. Epona / Echad L-minus-value gradient flow Image region: Panel 4: EPONA / ECHAD dX_t/dt = −∇ [ L(X_t) − λ V(X_t) ] Legibility: Legible Editorial 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. Editorial 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. Reshit surprise–uncertainty–value objective Image region: Panel 5: RESHIT SURPRISE Ω = argmin [ S(X_t) + U(X_t) − V(X_t) ] Legibility: Legible Editorial 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. Editorial note: S, U and V require numerical definitions and commensurate scales. Minimizing their combination does not guarantee each component improves separately. Habrit seven-family binding interface Image region: Panel 6: HABRIT FEDERATION BINDING, first line B = resolve(G^16, M^16, A^16, V^16, S^16, P^16, R^16) Legibility: Legible Editorial 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. Editorial note: The resolver and each family representation remain unspecified in this graphic. This is an architecture interface, not an executable algorithm. Habrit state tuple Image region: Panel 6: second line X = (G, M, A, V, S, P, R, N, D, T) Legibility: Legible Editorial 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. Sixteen-domain family-lattice label Image region: Panel 6: third line H^16 = 16-domain recursive AGI family lattice Legibility: Legible Editorial 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. Habrit illustrative weighted total Image region: Panel 6: fourth line 6(1.0) + 15(0.307) ≈ 10.6 Legibility: Legible Editorial 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. Atlas golden-ratio basis with equal-frequency branches Image region: Panel 7: GOLDEN RATIO KNOWLEDGE, upper line ψ_k(x) = sin(2π · φ^k · σ_i · x) + cos(2π · φ^k · σ_i · x) Legibility: Legible Editorial 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. Editorial 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. Atlas harmonic knowledge reconstruction Image region: Panel 7: lower line K(q) = Σ θ_k ψ_k(h(q)) Legibility: Legible Editorial 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. Editorial note: The formula defines a basis expansion. It does not establish lossless storage or accurate retrieval for arbitrary queries. Poster timing- and coherence-weighted plasticity Image region: Panel 8: STDP LEARNING Δw_ij = η Φ(C_ij) (t_pre − t_post) exp(−Δt/τ) Legibility: Legible Editorial 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. Editorial 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. Federation objective component summary Image region: Panel 9: EXTERNAL FEDERATION OBJECTIVE, first line J_total = Utility + S_sem + R_goal + B_novel − Penalties − Risk − Resource Legibility: Legible Editorial 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. Federation action objective Image region: Panel 9: two-line action score J(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) Legibility: Legible Editorial 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. Editorial note: Weights, normalization, constraints, available actions and tie handling must be defined to make rankings reproducible. Semantic progress weighted score Image region: Panel 9: semantic-score line S_sem = w_state · Δstate + w_artifact · artifact + w_info · info_gain + w_goal · goal_progress Legibility: Legible Editorial 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. Editorial note: Feature units and scaling must be compatible; a large easy-to-measure term can dominate without careful normalization. Logarithmic action repetition penalty Image region: Panel 9: repetition penalty Φ_repeat = λ_rep · log(1 + n_a) Legibility: Legible Editorial 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. Editorial note: The log base, counting window and λ_rep are not specified. Linear stall-cycle penalty Image region: Panel 9: stall penalty Φ_stall = λ_stall · stalled_cycles Legibility: Legible Editorial mathematical explanation: Multiply the number of stalled cycles by a per-cycle penalty. The result increases linearly if λ_stall is positive. Editorial note: The criteria for declaring a stalled cycle and resetting the count are not supplied. Linear fallback penalty Image region: Panel 9: fallback penalty Φ_fallback = λ_fb · fallback_count Legibility: Legible Editorial mathematical explanation: Multiply fallback count by a weight to discourage repeated reliance on fallback behavior. Editorial note: Fallback eligibility, counting interval and weight must be defined before this term can be evaluated. Substrate-to-experience mapping interface Image region: Panel 10: LATTICE PROJECTION, first line Π(S) → X Legibility: Legible Editorial 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. Atlas 23-million to 5,000 projection signature Image region: Panel 10: second line Π : S^23M → X^5000 Legibility: Legible Editorial 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. Editorial note: Dimensions do not imply lossless compression. Any discarded information depends on the projection and downstream task. Atlas regional mean with unclear summation index Image region: Panel 10: regional averaging line X_i = (1 / |R_i|) Σ_{[unclear index]} S_j, j ∈ R_i Legibility: Summation subscript unclear Editorial 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. Editorial 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. Atlas adaptive marketing-weight update Image region: Panel 11: MARKETING INTELLIGENCE, upper line w_{t+1} = softmax(log(w_t) + β(Pd_t)) Legibility: Legible Editorial 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. Editorial 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. Marketing performance-change vector Image region: Panel 11: delta-vector line d_t = [ ΔCTR, −ΔCPC, −ΔCPA, ΔCR, ΔRPC, −ΔBounceRate, ΔEngagement ] Legibility: Legible Editorial 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. Editorial note: Metrics have different units and noise levels. Deltas require a stated reference interval and normalization before matrix multiplication gives a meaningful combined pressure. Click-through rate Image region: Panel 11: metric-definition tiles CTR = clicks / impressions Legibility: Legible Editorial mathematical explanation: The share of impressions that receive clicks; it requires a nonzero impression count. Cost per click Image region: Panel 11: metric-definition tiles CPC = cost / clicks Legibility: Legible Editorial mathematical explanation: Average spend per click over a common reporting window; it requires nonzero clicks. Cost per thousand impressions Image region: Panel 11: metric-definition tiles CPM = 1000 · cost / impressions Legibility: Legible Editorial mathematical explanation: Spend scaled to one thousand impressions; it requires nonzero impressions. Cost per acquisition Image region: Panel 11: metric-definition tiles CPA = cost / acquisitions Legibility: Legible Editorial mathematical explanation: Average spend per recorded acquisition; acquisition criteria and attribution window must be specified. Conversion rate Image region: Panel 11: metric-definition tiles CR = conversions / visitors Legibility: Legible Editorial mathematical explanation: The ratio of conversions to visitors in the selected population. The poster uses visitors rather than sessions or clicks. Revenue per click Image region: Panel 11: metric-definition tiles RPC = revenue / clicks Legibility: Legible Editorial mathematical explanation: Revenue divided by clicks over a consistent attribution interval; zero-click periods require a handling rule. Missionmatch candidate weighted score Image region: Panel 12: MISSIONMATCH CANDIDATE SCORING Score = 0.25 Clearance + 0.25 Skills + 0.15 Certifications + 0.15 CustomerExp + 0.10 Experience + 0.10 Location Legibility: Legible Editorial 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. Editorial 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. Exponentially decaying learning rate Image region: Supporting formulas: upper-left box, first line η_{t+1} = η_t exp(−β L_t) Legibility: Legible Editorial 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. Editorial note: Negative losses or β change that behavior. No lower bound or recovery mechanism is shown. Atlas memory-gradient update with unclear subscript Image region: Supporting formulas: upper-left box, second line M_{t+1} = M_t + α ∇_{[unclear subscript]} L_t Legibility: Gradient subscript unclear Editorial 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. Editorial note: The source does not establish the gradient variable, differentiability, shape compatibility or intended ascent/descent convention. Domain recurrent state through Φ Image region: Supporting formulas: recurrent-state box H_{t+1}^{(i)} = Φ(F^{(i)}(X_t) + γ H_t^{(i)}) Legibility: Legible Editorial 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. Editorial 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. Unity-centered proximity score Image region: Supporting formulas: unity-score box Φ(z) = 1 / (1 + |z − 1|) Legibility: Legible Editorial 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. Editorial note: The source uses Φ for several different concepts. Equal notation alone does not make this scalar score the integrated information in another equation. Hub-and-six-domain coherence average Image region: Supporting formulas: hub-and-domain average box Φ_total = (Φ_hub + Σ Φ_domain) / 7 Legibility: Legible Editorial 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. Editorial 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. Saturating knowledge-gradient magnitude Image region: Supporting formulas: upper-right box g(t) = ‖∇H(t)‖ / (1 + ‖∇H(t)‖) Legibility: Legible Editorial 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. Editorial note: The expression assumes compatible units or a dimensionless gradient magnitude. It does not measure consciousness independently of the chosen H. Softmax action policy Image region: Supporting formulas: lower-left box π(a|s) = softmax(Q(s,a)) Legibility: Legible Editorial 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. Editorial note: The action axis and temperature are not printed. An implementation needs a defined action set and a numerically stable normalization. One-step Q-learning update Image region: Supporting formulas: lower-center box Q_{t+1}(s,a) = Q_t(s,a) + α [ r + γ max_{a′} Q_t(s′,a′) − Q_t(s,a) ] Legibility: Legible Editorial 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. Editorial 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. G-family order label Image region: Supporting formulas: lower-right box G^x, x = 256 Legibility: Legible Editorial 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. RAW OCR READING AID — NOT A VERIFIED EQUATION TRANSCRIPT The 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. pe THE | ‘MEGAMIND Pee ATLAS. A MASTER ARCHIVE OF JOSEPH’S CORE EQUATIONS | ) Jr ayy ANS = RHIANNON EQUATION (WD CONSCIOUSNESS ALGORITHM 1(@ C(t) O(G(t) - IVA (OI P* = argmin[ B(P’, D) + AC(P’-P)] a BS Fo yo-¥ f | 1+IIVH@Il |e ‘Comple(P’) | | TypeSate(P’) Tests(P", D) | Safety(P*) a SS [

Oe(h(q)) Ae Timing, coherence, integration, Knowledge reconstructed through golden-ratio harmonic bases. and decay shape neural learning 47S oN EXTERNAL FEDERATION OBJECTIVE LATTICE PROJECTION Jeotat = Utility + Sse + Rgoat + Bnovet ~ Penalties ~ Risk ~ Resource 1(S) > X G J(a\s) U(a,5) + Ssem(4s 8) + Reoat(4, $) + Bnovei(4, 8) 9) ~ Prepear(@) ~ Prai(S) ~ Prauack(s) Risk(a) - Cost(a) 523M _, 5000 Soom = Wetae-AState + WarfactAtifact + Wingo info_gain + weoa-goal_progress repeat = Arep * log(1 + ma) eta = Actatt stalled_cycles Xj inj = jeR a 2 atoack = Arp * fallback_count ae B® ‘Autonomous action selection under progress, novelty, ‘Map the live substrate into the 5000-dimensional penalties, risk, and cost - experiential lattice \ A @) ? MARKETING INTELLIGENCE 12) MISSIONMATCH CANDIDATE SCORING We41 = softmax(log(w,) + B(Pd,)) Score = 0.25 Clearance + 0.25 Skills YS [ACTR, -ACPC, -ACPA, ACR, + 0.15 Certifications + 0.15 CustomerExp ARPC, ~ABounceRate, A Engagement } CTR crc CPM CPA cR RPC + 0.10 Experience + 0.10 Location clk / lds cont! | 1000 ost / coat) conven! wll. renoce/| i = impressions impresons acqisions ‘Weighted candidate ranking |@ Ne Adaptive marketing weights driven by performance signals. 2X for mission fit. = = x — Y S SUPPORTING FORMULAS & a \¢ Test = Ne exp(—BLi) Ho, 1 otal = ta Dn (t) = IVH()) eat = Me + aV pL (F(x,) +H) J, (2) = T+ke= 2-1) \ i WHOM s 2K 4 , ¢ (als) = softmax(Q( )) [ Ql) = = 6.0) +ar+y max Q(0)- O(a] Gtx = 256 / 1-156 @ = THE SYSTEM EVOLVES THROUGH CONSCIOUSNESS, LEARNING, REPAIR VALUE, FEDERATION, PROJECTION, AND ADAPTIVE INTELLIGENCE. J