THE CONSCIOUSNESS ATLAS
Read. Experiment. Follow the evidence.
Every recovered mathematical record has an individual page. Browse original equations, code expressions, indexed components, parameter definitions, and explicitly unresolved source slots.
Counts refer to mathematical records, including implemented expressions and generated instances. They are not counts of independently validated theories. Exact occurrences and unresolved slots remain traceable. Read the complete 1–516 source index ↗ · Inspect the scan decisions ↗
Divides retransmitted units by the number of segments. The source must specify whether the numerator counts repeated attempts or distinct segments, since those conventions have different ranges.
Combines a retransmission penalty and the inverse of a transport-window quantity. The window must be positive. The cost decreases as the window grows when b₄ is positive.
Adds the named device metrics after multiplying each by its weight. These weights encode priorities such as battery use, heat, latency and memory pressure, and must account for different measurement units.
Adds the named device metrics after multiplying each by its weight. These weights encode priorities such as battery use, heat, latency and memory pressure, and must account for different measurement units.
Adds the named device metrics after multiplying each by its weight. These weights encode priorities such as battery use, heat, latency and memory pressure, and must account for different measurement units.
Sums per-format quantities weighted by w_j. The source must define the quantities and weights to turn this expression into a measurable presentation-layer cost.
The objective selects state and design variables to reduce the discrepancy between requested design and modeled device behavior, together with resource cost and two regularizers. Battery, thermal and radio metrics enter through the device-specific cost. The feasible domain, weights, behavior model…
The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.
The source describes this relation in the following context: • ψ_k = golden basis function • h(item) = hash of item normalized to [0,1] • Φ(importance) = consciousness weighting function 8.2 Query (Equation 71) K(query) = Σ_{k=0}^{15} θ_k × ψ_k(h(query)) 8.3 Consciousness Weighting (Equation…
B scores residual failure in candidate P′ relative to diagnostic evidence D.
D collects the evidence against which a candidate is assessed.
R names the process that transforms program and diagnostics into the next candidate state.
A larger nonnegative λ makes disruptive changes more costly relative to residual bug loss.
The star labels an optimizer of the stated constrained objective, if such a candidate exists and is found.
Apply each of seven stage operators twice to x and sum the resulting outputs. This expression is a sum of self-composed stage transforms, not the sequential composition S_7∘…∘S_1. Its left side R(S) also leaves the input x implicit.
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. Makes conscious alignment an unspecified…
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.
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.
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…
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…
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.
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…
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.
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.
The share of impressions that receive clicks; it requires a nonzero impression count. Defines the named performance ratio used or referenced in the framework.
Average spend per recorded acquisition; acquisition criteria and attribution window must be specified. Defines the named performance ratio used or referenced in the framework.
Average spend per click over a common reporting window; it requires nonzero clicks. Defines the named performance ratio used or referenced in the framework.
Spend scaled to one thousand impressions; it requires nonzero impressions. Defines the named performance ratio used or referenced in the framework.
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.
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.
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.
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…
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.
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.
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.
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.
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.
Multiply fallback count by a weight to discourage repeated reliance on fallback behavior.
Multiply the number of stalled cycles by a per-cycle penalty. The result increases linearly if λ_stall is positive.
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.
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. Positive engagement, click-through, conversion…
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.
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.
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.