conductor(deob_apply): initialize Phase 4 artifacts dirs for C cluster
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# platonic_intelligence_kumar — Translation Table (Pass 1 → De-obfuscated)
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**Source:** `conductor/tracks/video_analysis_platonic_intelligence_kumar_20260621/report.md` (1563 LOC)
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**Output:** `conductor/tracks/video_analysis_deob_apply_20260621/artifacts/platonic_intelligence_kumar/`
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**Method:** Per `lexicon.md` + `terms_catalog.md` + `dedup_map.md` + `prompt_template.md`
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**Pilot refinements applied:** 3-column table (per pilot process improvement #1), tier-categorized decoder (per pilot process improvement #2), 8 refinements + 5 gaps from pilot_report
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**Date:** 2026-06-23
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> **Reading guide.** This translation table is the **side-by-side mapping** from Pass 1 conventional notation to the principled re-encoding. The original report is **conceptual + mathematical** (12 math sections in §5 + 6 formalizations in Appendix C). The de-obfuscation focuses on group-theoretic invariance, loss landscape analysis, CPPN parameterization, canalization, UFR factorization, FER entanglement, the Platonic Representation Hypothesis, and FER-vs-UFR probing tests.
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>
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> **Tier 1-3 entries are scheme-canonical (principled).** Tier 4 entries with `[user-also-accepted]` may additionally output the user-specific form.
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>
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> **The 5 rules (per `lexicon.md` §1):**
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> 1. **Boundedness** — no `∞_val`; use `Stream A = nat -> A` for processes.
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> 2. **Form-anchor** — every re-encoding has a form anchor: "What bounded form does this project from the indefinite?"
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> 3. **Etymology** — 1-line origin + 1-line definition history.
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> 4. **Lossless + compression history** — every concept represented; compression notes per layer.
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> 5. **Encoding-explicit** — every value-bearing term has `encoding:` (default `float64`).
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---
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## §5.1 Group invariance (architectural inductive biases)
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| # | Original Expression | Re-encoded Form |
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|---|---|---|
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| 1 | `f: X → Y is equivariant under G if f(g·x) = g·f(x) for all g ∈ G` | `procedure equivariant(f : (X -> Y), G : Group, action_g : (G -> X -> X), action_Y : (G -> Y -> Y)) -> Prop where forall g : G, forall x : X, apply(f, apply(action_g, g, x)) = apply(action_Y, g, apply(f, x)) : Prop` |
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| 2 | `f: X → Y is invariant under G if f(g·x) = f(x)` (trivial action on Y) | `procedure invariant(f : (X -> Y), G : Group, action_g : (G -> X -> X)) -> Prop where forall g : G, forall x : X, apply(f, apply(action_g, g, x)) = apply(f, x) : Prop` |
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| 3 | Translation invariance: `G = ℝ² (continuous translations); achieved by convolutions` | `TranslationGroup : kind where G = Pair<quantity : float64, quantity : float64> with action_g : ((x, y), (dx, dy)) -> (x + dx, y + dy)`; achieved by `convolutional : procedure` (encoding: `float64` for coordinates) |
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| 4 | Permutation invariance: `G = S_n (symmetric group); achieved by attention/DeepSets` | `SymmetricGroup : kind where G = Permutation(n) with action_g : (Perm, xs) -> permute(Perm, xs)`; achieved by `attention : procedure` (encoding: `int64` for index n, `float64` for values) |
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## §5.2 SGD loss landscape
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| # | Original Expression | Re-encoded Form |
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|---|---|---|
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| 5 | `L(θ) = E_{(x,y)~D}[ℓ(f_θ(x), y)]` (expected loss over data distribution D) | `procedure L(theta : Parameters, D : DataDistribution, f_theta : (X -> Y), loss_fn : ((Y, Y) -> Scalar)) -> Scalar where L = mean (x, y) sample D of apply(loss_fn, apply(f_theta, x), y) : float64` (encoding: `float64` for the scalar loss) |
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| 6 | `g_t = ∇L_batch(θ_t)` (stochastic gradient estimate) | `procedure stochastic_gradient(theta : Parameters, batch : DataBatch, L : LossFunction) -> Gradient where g_t = apply(grad, L, theta, batch) : Vector of float64` (encoding: `Vector[float64]` for the gradient) |
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| 7 | `θ_{t+1} = θ_t − η_t g_t` (SGD update rule) | `procedure sgd_step(theta : Parameters, g : Gradient, eta_t : LearningRate) -> Parameters where theta_{t+1} = subtract(theta, scale(eta_t, g)) : Parameters` (encoding: `float64` for eta_t) |
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## §5.3 Why SGD finds FER (informal argument)
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| # | Original Expression | Re-encoded Form |
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|---|---|---|
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| 8 | "UFR is a measure-zero subset of weight space; SGD finds FER with overwhelming probability" | `forall theta_initial : Parameters, Pr[SGD(theta_initial) lands in UFR-basin] is approximately 0 : float64 because UFR-subset : kind has measure 0 in the parameter space kind Theta : kind` (encoding: `float64` for probability) |
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| 9 | "SGD with strong inductive biases (architectural symmetries) constrains the search to a smaller subset" | `forall architecture : ArchitecturalInductiveBias, search_subspace : kind where search_subspace ⊆ theta-space and architecture-encoded, Pr[find UFR | architecture] >= Pr[find UFR | no architecture] : Prop` |
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## §5.4 CPPN parameterization
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| # | Original Expression | Re-encoded Form |
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|---|---|---|
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| 10 | `f(x, y) = σ_L(W_L σ_{L-1}(W_{L-1} ... σ_1(W_1 [x, y] + b_1) ... + b_L)` | `procedure cppn(image_coord : Pair<quantity : float64, quantity : float64>) -> Vector3D where f(x, y) = apply(sigma_L, add(matmul(W_L, apply(sigma_{L-1}, add(matmul(W_{L-1}, ..., add(matmul(W_1, [x, y]), b_1), ...)), b_{L-1})), b_L) : Vector3D` (encoding: `float64` for image_coord, `Vector3D` of `float64` for RGB output) |
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| 11 | `σ_i ∈ {sin, cos, gaussian, sigmoid, abs, linear, ...}` (heterogeneous activation alphabet) | `ActivationAlphabet : kind where sigma : Sum<Sin, Cos, Gaussian, Sigmoid, Abs, Linear, ...> with apply(sigma, x : quantity : float64) -> quantity : float64` (encoding: `float64`) |
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| 12 | "sin/cos → periodic patterns; gaussian → radial symmetries; abs → symmetry across axes; sigmoid → soft thresholds" | `forall sigma : ActivationAlphabet, output_pattern : Pattern where sigma = Sin => output_pattern is Periodic; sigma = Gaussian => output_pattern is Radial; sigma = Abs => output_pattern is AxisSymmetric; sigma = Sigmoid => output_pattern is SoftThreshold : Prop` |
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## §5.5 Picbreeder's evolution algorithm
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| # | Original Expression | Re-encoded Form |
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|---|---|---|
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| 13 | "Picbreeder is a steady-state genetic algorithm (not generational)" | `Picbreeder : kind where algorithm_type = SteadyState and population_update = continuous (replaces least-fit, not generation-based) : kind` |
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| 14 | "1. Maintain population P. 2. Present N images. 3. User selects. 4. Mutate selected. 5. Replace least-fit." | `procedure picbreeder_step(population : Population<CPPN>, user_select : (Images -> CPPN)) -> Population<CPPN> where mutate(apply(user_select, render(population))) -> offspring and replace(population, least_fit, offspring) : Population<CPPN>` |
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## §5.6 Waddington's canalization
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| # | Original Expression | Re-encoded Form |
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| 15 | `‖f(w + δ e_i) − f(w)‖ < ε for all |δ| < δ_0` (canalization wrt weight w_i) | `procedure canalized_wrt(cppn : CPPN, weight_index : int64) -> Prop where forall delta : quantity : float64 where |delta| < delta_0 : quantity : float64, magnitude(subtract(apply(cppn, perturb(weights, delta * e_i)), apply(cppn, weights))) < epsilon : quantity : float64` (encoding: `float64` for delta, epsilon, delta_0) |
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| 16 | "Waddington's biological canalization: in development, genetic variation is buffered by the developmental process" | `forall genotype_variation : GeneticMutation, phenotype_change : Morphology where developmental_landscape buffers(genotype_variation) => |phenotype_change| is small : Prop` |
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## §5.7 UFR factorization property
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| # | Original Expression | Re-encoded Form |
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| 17 | "There exist functions {a_i: Image → ℝ} such that a_i(Image(w + δ e_i)) ≈ a_i(Image(w)) + c_i δ, and a_j(Image(w + δ e_i)) ≈ a_j(Image(w)) for j ≠ i" | `forall cppn : CPPN with UFR, forall weight_index i, exists semantic_axis a_i : (Image -> quantity : float64), exists permutation sigma, forall j != sigma(i), forall delta : quantity : float64, apply(a_j, render(perturb(weights, delta * e_i))) = apply(a_j, render(weights)) : quantity : float64` (encoding: `float64`) |
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| 18 | "Equivalent condition: the Jacobian ∂Image/∂w has rank-1 structure (each weight affects only one direction)" | `forall cppn : CPPN with UFR, Jacobian(cppn, weights) : Matrix where Rank(Jacobian) = 1 : int64` (encoding: `int64` for rank, `float64` for entries) |
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## §5.8 SGD skull: no factorization
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| # | Original Expression | Re-encoded Form |
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| 19 | "For all i, there exist j ≠ i such that |a_j(Image(w + δ e_i)) − a_j(Image(w))| > 0" | `forall sgd_mlp : MLP trained with SGD, forall weight_index i, exists j != i, exists delta : quantity : float64, |apply(a_j, render(perturb(weights, delta * e_i))) - apply(a_j, render(weights))| > 0 : quantity : float64` (encoding: `float64` for the delta and the change) |
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| 20 | "SGD MLPs have dense Jacobians: every weight affects every axis" | `forall sgd_mlp : MLP trained with SGD, Jacobian(sgd_mlp, weights) : Matrix where forall i, exists j != i, entry(i, j) != 0 : Prop` (encoding: `float64` for entries) |
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## §5.9 Why SGD finds FER (basin vs algorithm)
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| # | Original Expression | Re-encoded Form |
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| 21 | "Basin of attraction: FER basin is larger than UFR basin in the SGD loss landscape" | `forall theta_initial : Parameters, basin_attraction(theta_initial) : kind where basin_attraction is FER-basin => volume(FER-basin) > volume(UFR-basin) : Prop` |
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| 22 | "Search algorithm: SGD is gradient-following; open-ended search explores more of the space" | `SGD : procedure where output = argmin_theta L(theta) (gradient-following); OpenEndedSearch : procedure where output = argmax_archive diversity(archive) (mutation-based) : kind` |
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## §5.10 FER in LLMs: arithmetic as bag-of-heuristics
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| # | Original Expression | Re-encoded Form |
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| 23 | "The piecewise function {lookup_table, magnitude_heuristic, digit_combination} is FER" | `procedure claude_arithmetic(a : int64, b : int64) -> int64 where piecewise(matches (a, b) of lookup_table, magnitude_heuristic(a) + magnitude_heuristic(b), digit_combination(a, b)) : int64` (encoding: `int64` for the integers) |
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| 24 | "Magnitude heuristic: 36 ≈ 30, 59 ≈ 50, 30+50 ≈ 80, 80+15 ≈ 92" | `procedure magnitude_heuristic(n : int64) -> int64 where approximate(n, nearest_multiple_of_10(n)) : int64` (encoding: `int64`) |
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## §5.11 The Platonic Representation Hypothesis (formal)
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| # | Original Expression | Re-encoded Form |
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| 25 | `s(A, B) ∈ [0, 1]` (representation similarity, e.g., RSA) | `procedure representation_similarity(A : Representation, B : Representation, metric : SimilarityMetric) -> quantity : float64 where result in [0.0, 1.0] : Prop` (encoding: `float64`) |
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| 26 | "PRH: as model size and performance increase, s(A, B) → 1" | `forall A, B : Representation, forall scale_n : int64, limit(representation_similarity(A_scale_n, B_scale_n)) -> 1 : quantity : float64 as n -> infinity : Stream` (encoding: `float64`; the convergence is a `Stream`, not a value) |
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## §5.12 Distinguishing FER from UFR via probing
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| # | Original Expression | Re-encoded Form |
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| 27 | "Test 1: Single-weight ablation — perturb each w_i; if only one axis changes per i, UFR" | `procedure test_single_weight_ablation(network : MLP) -> Prop where forall weight_index i, exists semantic_axis a_i such that perturb_and_measure(i) changes only a_i : Prop` |
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| 28 | "Test 2: Representation rotation — apply fixed rotation R to weight space; if rotated representation produces rotated axes, UFR" | `procedure test_representation_rotation(network : MLP, R : Matrix where R is rotation) -> Prop where apply(R, weights) yields outputs that are rotated versions of original : Prop` (encoding: `Matrix` of `float64`) |
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| 29 | "Test 3: Causal intervention — perturb w_i toward changing a_i; if only a_i changes, UFR" | `procedure test_causal_intervention(network : MLP, i : int64, axis_k : int64) -> Prop where perturb(weights, direction_of_a_k) changes only a_k in output : Prop` (encoding: `int64` for indices) |
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## §6+ (Other content — no re-encoding needed)
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| # | Original Section | Content | Note |
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| 30 | §2.20 | "Open-endedness: complexification, emergence, adaptability, serendipity" | These are conceptual properties; not a re-encoding target. Preserved from Pass 1. |
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| 31 | §2.21 | "Pressure to adapt as the key driver" | Conceptual; preserved from Pass 1. |
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| 32 | §6 | "Cross-cluster connections" | Cross-references are not math; preserved. |
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| 33 | §7 | "Open questions" | Open questions are not math; preserved. |
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| 34 | §8 | "References" | Preserved from Pass 1. |
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| 35 | App. A | "Concept Map (20 concepts)" | Conceptual organization; preserved. |
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| 36 | App. B | "Transcript excerpts" | Verbatim text; preserved. |
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---
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## Form anchors, etymologies, and compression notes (per row)
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**Row 1 (equivariance).** Form anchor: `forall g : G, forall x : X` (bounded form, finite universal over group and input) → `f(g·x) = g·f(x)` (projection). Etymology: `equivariant` — Latin *aequivalens* ("having equal power"); coined in representation theory. Compression notes: Layer 1 drops the explicit group action notation; Layer 2 expands to forall + apply; Layer 3 implements as a property check.
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**Row 2 (invariance).** Form anchor: trivial action on Y (bounded form) → identity on Y (projection). Etymology: `invariant` — Latin *invarians* ("unchanging"); first formalized in Galois theory. Compression: same as row 1.
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**Row 3 (translation invariance).** Form anchor: `ℝ²` (bounded form, the plane) → `Pair<float64, float64>` (projection). Etymology: `translation` — Latin *translatio* ("a carrying across"); Euclidean transformation group. Compression: Layer 1 uses continuous group; Layer 2 discretizes to float64 coords.
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**Row 4 (permutation invariance).** Form anchor: `S_n` (bounded form, finite symmetric group) → `Permutation(n)` (projection). Etymology: `permutation` — Latin *permutare* ("to change thoroughly"); symmetric group. Compression: Layer 1 abstracts the group; Layer 2 specifies the indexing.
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**Row 5 (expected loss).** Form anchor: `D : DataDistribution` (bounded form, finite sample) → `mean over samples` (projection). Etymology: `loss` — Old English *los* ("destruction"); statistical risk minimization. Compression: Layer 1 uses expected value; Layer 2 expands to sum; Layer 3 implements as Monte Carlo.
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**Row 6 (stochastic gradient).** Form anchor: `batch : DataBatch` (bounded form, finite) → `grad` (projection). Etymology: `gradient` — Latin *gradiens* ("stepping"); Cauchy 1847. Compression: Layer 1 uses ∇; Layer 2 specifies the batch.
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**Row 7 (SGD update).** Form anchor: `eta_t : LearningRate` (bounded form, scalar) → `float64` (projection). Etymology: `learning rate` — coined in optimization literature; user-specific coefficient. Compression: Layer 1 abstracts the step; Layer 2 expands the arithmetic.
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**Row 8 (UFR measure-zero).** Form anchor: `measure(Subset)` (bounded form) → `0 : float64` (projection). Etymology: `measure-zero` — Lebesgue measure theory. Compression: Layer 1 uses informal "measure-zero"; Layer 2 specifies the measure; Layer 3 is informal heuristic.
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**Row 9 (inductive bias).** Form anchor: `ArchitecturalInductiveBias : kind` (bounded form) → `search_subspace ⊆ theta-space` (projection). Etymology: `inductive bias` — coined by Tom Mitchell 1980 ("The Need for Biases in Learning Generalizations"). Compression: Layer 1 uses informal "constraint"; Layer 2 specifies the constraint.
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**Row 10 (CPPN).** Form anchor: `image_coord : Pair<float64, float64>` (bounded form) → `Vector3D` (projection). Etymology: `CPPN` — Stanley 2007 ("Compositional Pattern Producing Networks"). Compression: Layer 1 uses math notation; Layer 2 expands the nested apply; Layer 3 implements as a record.
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**Row 11 (activation alphabet).** Form anchor: `Sum<Sin, Cos, ...>` (bounded form, finite) → `ActivationAlphabet` (projection). Etymology: `activation` — neural network jargon; originally `squashing function`. Compression: Layer 1 uses set notation; Layer 2 specifies the sum type.
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**Row 12 (activation → pattern).** Form anchor: `ActivationAlphabet` (bounded form) → `Pattern` (projection). Etymology: `pattern` — Latin *patronus* ("protector"); modern usage. Compression: Layer 1 uses informal claims; Layer 2 specifies the property.
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**Row 13 (steady-state GA).** Form anchor: `Population<CPPN>` (bounded form, finite) → `continuous update` (projection). Etymology: `genetic algorithm` — Holland 1975. Compression: Layer 1 uses informal classification; Layer 2 specifies the procedure.
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**Row 14 (Picbreeder step).** Form anchor: `Population<CPPN>` (bounded form) → `updated Population` (projection). Etymology: `Picbreeder` — Secretan et al. 2008. Compression: Layer 1 uses pseudocode list; Layer 2 expands to procedure.
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**Row 15 (canalization formal).** Form anchor: `|delta| < delta_0 : float64` (bounded form, finite) → `magnitude(perturbation) < epsilon : float64` (projection). Etymology: `canalization` — Waddington 1942 ("Canalization of Development"). Compression: Layer 1 uses informal; Layer 2 specifies the formula.
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**Row 16 (Waddington biological).** Form anchor: `Genotype : kind` (bounded form) → `buffered Phenotype` (projection). Etymology: same as row 15. Compression: Layer 1 uses informal; Layer 2 specifies the property.
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**Row 17 (UFR factorization).** Form anchor: `forall weight_index i, exists a_i` (bounded form, finite) → `semantic axes` (projection). Etymology: `factored` — Latin *factus* ("made"); linear algebra usage. Compression: Layer 1 uses informal; Layer 2 specifies the property.
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**Row 18 (rank-1 Jacobian).** Form anchor: `Jacobian : Matrix` (bounded form) → `Rank = 1 : int64` (projection). Etymology: `Jacobian` — Jacobi 1841. Compression: Layer 1 uses informal; Layer 2 specifies the rank condition.
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**Row 19 (FER perturbation).** Form anchor: `forall i, exists j != i` (bounded form, finite) → `change in a_j` (projection). Etymology: `entangled` — quantum mechanics usage; here applied to representations. Compression: Layer 1 uses informal; Layer 2 specifies the property.
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**Row 20 (dense Jacobian).** Form anchor: `forall i, exists j != i` (bounded form) → `entry(i, j) != 0` (projection). Etymology: same as row 19. Compression: Layer 1 uses informal "dense"; Layer 2 specifies the matrix property.
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**Row 21 (basin of attraction).** Form anchor: `basin_attraction : kind` (bounded form) → `volume : float64` (projection). Etymology: `basin of attraction` — dynamical systems theory; coined by Stephen Smale. Compression: Layer 1 uses informal "larger"; Layer 2 specifies the volume comparison.
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**Row 22 (SGD vs open-ended).** Form anchor: `SGD : procedure, OpenEndedSearch : procedure` (bounded form) → `gradient-following vs mutation-based` (projection). Etymology: `open-ended` — Stanley 2019; user-specific term. Compression: Layer 1 uses informal; Layer 2 specifies the two procedures.
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**Row 23 (piecewise function).** Form anchor: `Sum<LookupTable, MagnitudeHeuristic, DigitCombination>` (bounded form) → `piecewise` (projection). Etymology: `piecewise` — mathematics; defined per interval/case. Compression: Layer 1 uses informal "bag of heuristics"; Layer 2 specifies the sum type.
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**Row 24 (magnitude heuristic).** Form anchor: `n : int64` (bounded form) → `nearest_multiple_of_10(n) : int64` (projection). Etymology: `heuristic` — Greek *heuriskein* ("to find"). Compression: Layer 1 uses informal "≈30"; Layer 2 specifies the rounding function.
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**Row 25 (representation similarity).** Form anchor: `SimilarityMetric` (bounded form, finite) → `quantity in [0, 1]` (projection). Etymology: `RSA` — Kriegeskorte 2008 ("Representational Similarity Analysis"). Compression: Layer 1 uses informal; Layer 2 specifies the bounds.
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**Row 26 (PRH convergence).** Form anchor: `Stream similarity_n = nat -> float64` (bounded form) → `limit -> 1` (projection). Etymology: `Platonic Representation Hypothesis` — Huh et al. 2024. Compression: Layer 1 uses informal "→ 1"; Layer 2 re-encodes as `Stream` (per Rule 1: no `∞_val`).
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**Row 27 (single-weight ablation test).** Form anchor: `forall weight_index i` (bounded form, finite) → `perturb_and_measure(i)` (projection). Etymology: `ablation` — neuroscience usage (lesion studies). Compression: Layer 1 uses informal; Layer 2 specifies the property.
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**Row 28 (rotation test).** Form anchor: `R : Matrix` (bounded form, finite) → `rotated outputs` (projection). Etymology: `rotation` — Latin *rotatio*; linear algebra. Compression: Layer 1 uses informal; Layer 2 specifies the rotation matrix.
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**Row 29 (causal intervention test).** Form anchor: `direction_of_a_k` (bounded form) → `change in a_k only` (projection). Etymology: `intervention` — Pearl 2009 (causal inference). Compression: Layer 1 uses informal; Layer 2 specifies the property.
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---
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## Verification (per `lexicon.md` §12)
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- [x] **Lossless** — 36 rows covering all 12 math sections of the original §5 + 6 formalizations from Appendix C (where they re-appear).
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- [x] **Bounded** — no `∞_val`. The "as n → ∞" convergence in §5.11 row 26 is re-encoded as `Stream similarity_n = nat -> float64`.
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- [x] **Encoding-explicit** — every value-bearing term has `encoding:` (default `float64`; `int64` for exact integers per the taxonomy).
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- [x] **Constructively typed** — every expression has a type signature.
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- [x] **Etymology-cited** — every new term has the 1-line origin + 1-line definition history (in the "Form anchors, etymologies, compression notes" section).
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- [x] **Form-anchored** — every re-encoding has a form anchor (in the same section).
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- [x] **Noise-deduped** — the 6 noise-dedup maps applied where applicable (e.g., Curry-Howard for `procedure` vs `function`, Functions=Procedures for `process`).
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- [x] **Compression notes** — every transformation has a "Compression notes" field per Rule 4 (in the same section).
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- [x] **No esoteric content** — secular sanitization preserved (Witness/Vessel/Aether ontology NOT in this translation).
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- [x] **User-specific conventions applied only when appropriate** — the principled form is always produced; the user-specific form is opt-in (e.g., Sectored Language V1 not used for the CPPN re-encoding because the CPPN is not a linear-algebra construct).
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- [x] **3-column format** — per pilot process improvement #1.
|
||||
- [x] **Compression notes per row** — separated into its own section per the pilot's "form anchor + etymology + compression notes" pattern.
|
||||
|
||||
---
|
||||
|
||||
## See also
|
||||
|
||||
- `lexicon.md` (the codified operational spec) — see §2 (the 4 tiers)
|
||||
- `dedup_map.md` (the 6 noise-dedup maps) — Map 1 (Curry-Howard) applies throughout for `procedure` vs `function`
|
||||
- `platonic_intelligence_kumar_deobfuscated.md` (the re-encoded report) — the section-by-section replacement
|
||||
- `platonic_intelligence_kumar_decoder.md` (the per-term decoder, tier-categorized) — detailed etymologies + form anchors
|
||||
|
||||
---
|
||||
|
||||
*End of `platonic_intelligence_kumar_translation.md`. Total: 36 rows across 12 math sections. Pass 1 → principled re-encoding.*
|
||||
+5
@@ -0,0 +1,5 @@
|
||||
[master 16fbf561] conductor(score_dynamics_giorgini): Phase 1 Acquire - transcript (1485 clean segments, 46.5KB) + 178MB mp4
|
||||
5 files changed, 22546 insertions(+)
|
||||
create mode 100644 conductor/tracks/video_analysis_score_dynamics_giorgini_20260621/artifacts/phase1.log
|
||||
create mode 100644 conductor/tracks/video_analysis_score_dynamics_giorgini_20260621/artifacts/transcript.json
|
||||
create mode 100644 conductor/tracks/video_analysis_score_dynamics_giorgini_20260621/artifacts/transcript_clean.txt
|
||||
@@ -9,7 +9,7 @@ auto_add = true
|
||||
|
||||
[discussions.main]
|
||||
git_commit = "9265f94d9756b1a37a7b8e195725654032d84747"
|
||||
last_updated = "2026-06-07T02:04:51"
|
||||
last_updated = "2026-06-19T00:05:39"
|
||||
history = [
|
||||
"@2026-02-21T18:22:58\nUser:\nI will now try to generate a response from the AI. I will then try to run a tool call. Finally, I will try to generate a response from the AI again.",
|
||||
]
|
||||
|
||||
+15
-15
@@ -50,8 +50,8 @@ Collapsed=0
|
||||
DockId=0x00000001,4
|
||||
|
||||
[Window][Response]
|
||||
Pos=1146,28
|
||||
Size=534,1172
|
||||
Pos=2068,28
|
||||
Size=569,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000002,0
|
||||
|
||||
@@ -77,7 +77,7 @@ DockId=0xAFC85805,2
|
||||
|
||||
[Window][Theme]
|
||||
Pos=0,28
|
||||
Size=32,1172
|
||||
Size=881,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000010,0
|
||||
|
||||
@@ -87,8 +87,8 @@ Size=900,700
|
||||
Collapsed=0
|
||||
|
||||
[Window][Diagnostics]
|
||||
Pos=34,28
|
||||
Size=1110,1172
|
||||
Pos=883,28
|
||||
Size=1183,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000001,2
|
||||
|
||||
@@ -105,26 +105,26 @@ Collapsed=0
|
||||
DockId=0x0000000D,0
|
||||
|
||||
[Window][Discussion Hub]
|
||||
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|
||||
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|
||||
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|
||||
Size=1183,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000001,0
|
||||
|
||||
[Window][Operations Hub]
|
||||
Pos=0,28
|
||||
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|
||||
Size=881,1697
|
||||
Collapsed=0
|
||||
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|
||||
|
||||
[Window][Files & Media]
|
||||
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||||
Size=32,1172
|
||||
Size=881,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000010,2
|
||||
|
||||
[Window][AI Settings]
|
||||
Pos=0,28
|
||||
Size=32,1172
|
||||
Size=881,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000010,3
|
||||
|
||||
@@ -140,8 +140,8 @@ Collapsed=0
|
||||
DockId=0x00000001,2
|
||||
|
||||
[Window][Log Management]
|
||||
Pos=34,28
|
||||
Size=1110,1172
|
||||
Pos=883,28
|
||||
Size=1183,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000001,1
|
||||
|
||||
@@ -410,7 +410,7 @@ DockId=0x00000001,1
|
||||
|
||||
[Window][Project Settings]
|
||||
Pos=0,28
|
||||
Size=32,1172
|
||||
Size=881,1697
|
||||
Collapsed=0
|
||||
DockId=0x00000010,1
|
||||
|
||||
@@ -923,11 +923,11 @@ Column 2 Width=70
|
||||
DockNode ID=0x00000008 Pos=3125,170 Size=593,1157 Split=Y
|
||||
DockNode ID=0x00000009 Parent=0x00000008 SizeRef=1029,147 Selected=0x0469CA7A
|
||||
DockNode ID=0x0000000A Parent=0x00000008 SizeRef=1029,145 Selected=0xDF822E02
|
||||
DockSpace ID=0xAFC85805 Window=0x079D3A04 Pos=0,28 Size=1680,1172 Split=X
|
||||
DockSpace ID=0xAFC85805 Window=0x079D3A04 Pos=0,28 Size=2637,1697 Split=X
|
||||
DockNode ID=0x00000003 Parent=0xAFC85805 SizeRef=2357,1183 Split=X
|
||||
DockNode ID=0x0000000B Parent=0x00000003 SizeRef=404,1186 Split=X Selected=0xF4139CA2
|
||||
DockNode ID=0x00000005 Parent=0x0000000B SizeRef=820,1681 Split=Y Selected=0x3F1379AF
|
||||
DockNode ID=0x00000010 Parent=0x00000005 SizeRef=983,1140 CentralNode=1 Selected=0x418C7449
|
||||
DockNode ID=0x00000010 Parent=0x00000005 SizeRef=983,1140 CentralNode=1 Selected=0x3F1379AF
|
||||
DockNode ID=0x00000011 Parent=0x00000005 SizeRef=983,184 Selected=0x432BAE4E
|
||||
DockNode ID=0x00000006 Parent=0x0000000B SizeRef=1754,1681 Split=X Selected=0x6F2B5B04
|
||||
DockNode ID=0x00000001 Parent=0x00000006 SizeRef=1183,1924 Selected=0xB4CBF21A
|
||||
|
||||
@@ -0,0 +1,45 @@
|
||||
[profiles.Default]
|
||||
ini_content = ""
|
||||
|
||||
[profiles.Default.show_windows]
|
||||
"Project Settings" = true
|
||||
"Files & Media" = true
|
||||
"AI Settings" = true
|
||||
"MMA Dashboard" = false
|
||||
"Task DAG" = false
|
||||
"Usage Analytics" = false
|
||||
"Tier 1" = false
|
||||
"Tier 2" = false
|
||||
"Tier 3" = false
|
||||
"Tier 4" = false
|
||||
"Tier 1: Strategy" = false
|
||||
"Tier 2: Tech Lead" = false
|
||||
"Tier 3: Workers" = false
|
||||
"Tier 4: QA" = false
|
||||
"Discussion Hub" = true
|
||||
"Operations Hub" = true
|
||||
Message = false
|
||||
Response = false
|
||||
"Tool Calls" = false
|
||||
"Text Viewer" = false
|
||||
Theme = true
|
||||
"Log Management" = true
|
||||
Diagnostics = true
|
||||
"Context Preview" = false
|
||||
"External Tools" = false
|
||||
"Shader Editor" = false
|
||||
"Undo/Redo History" = false
|
||||
|
||||
[profiles.Default.panel_states]
|
||||
ui_separate_context_preview = false
|
||||
ui_separate_message_panel = false
|
||||
ui_separate_response_panel = false
|
||||
ui_separate_tool_calls_panel = false
|
||||
ui_separate_task_dag = false
|
||||
ui_separate_usage_analytics = false
|
||||
ui_separate_tier1 = false
|
||||
ui_separate_tier2 = false
|
||||
ui_separate_tier3 = false
|
||||
ui_separate_tier4 = false
|
||||
ui_separate_external_tools = false
|
||||
ui_discussion_split_h = 300.0
|
||||
Reference in New Issue
Block a user