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ingest: Unified Compression Architecture synthesis
10-layer architecture synthesizing all expanded theories: - Layer 0: Raw UTF-8 → semantic density field - Layer 1: Density field extraction (Morse-Smale complex) - Layer 2: Hypercube-rhomboid shear (Gram matrix as dictionary) - Layer 3: S3C shell coordinate encoding - Layer 4: GCCL-GEC packet encoding (7-field glyphs) - Layer 5: OAC speculative manifestation - Layer 6: Radius-ratio local quantization - Layer 7: FAMM temporal sequencing - Layer 8: PIST perturbation encoding - Layer 9: erans residual entropy coding - Layer 10: Archive assembly 9 component interdependencies mapped (density→GCCL, GCCL→OAC, shear→S3C, S3C→FAMM, radius→GCCL, FAMM→PIST, PIST→erans, OAC→receipts, shear→EigenBook). 10 compression gain sources quantified (geometric shear 15-30%, topological skeleton 50-150MB vs 1GB, glyph kernels, S3C shells, OAC speculation 2-5%, FAMM context 10-20%, PIST bundle 5-8%, erans entropy, radius-ratio quantization, Gram dictionary MB→KB). 7 implementation phases defined (Foundation → Density Field → GCCL-GEC Core → Shear/Eigen → OAC/Speculation → PIST/erans → Integration → Benchmark). 14 keeper phrases. Core synthesis: density field = manifold, glyph packets = navigators, shear matrix = map.
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Brandon Schneider
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4-Infrastructure/shim/ingest_unified_compression_synthesis.py
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4-Infrastructure/shim/ingest_unified_compression_synthesis.py
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#!/usr/bin/env python3
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"""
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Ingest: Unified Compression Architecture Synthesis
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==================================================
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Synthesis of Density Field Encoding + GCCL-GEC + OAC + Hypercube-Rhomboid
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+ S3C Shells + FAMM + PIST + erans + Radius-Ratio into a single coherent
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compression pipeline for the Research Stack.
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"""
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import json, time
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from pathlib import Path
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RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack")
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SYNTHESIS = {
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"id": "unified-compression-architecture-synthesis-v1",
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"source": "Synthesis of 5 ingested theories: density-field, gccl-gec, oac, hypercube-rhomboid, hutter-rhomboid",
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"title": "Unified Compression Architecture: Semantic Density Fields with Glyph Eigen Codec and Sheared Manifold Geometry",
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"date": "2026-05-07",
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"core_synthesis": (
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"The unified architecture treats text as an n-dimensional semantic density field, "
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"encodes its topological skeleton via GCCL-GEC glyph packets, applies hypercube-rhomboid "
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"shear for correlation modeling, uses OAC for speculative lazy manifestation, "
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"coordinates via S3C shells, temporally warps via FAMM, encodes perturbations via PIST, "
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"entropy-codes residuals via erans, and quantizes local admissibility via radius-ratio."
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),
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"architectural_layers": {
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"Layer_0_Raw_Representation": {
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"input": "UTF-8 byte sequence C ∈ Byteⁿ",
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"limitation": "1D orthogonal hypercube — independent byte positions, no semantic structure",
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"transformation": "Parse into semantic density field ρ: M → ℝ⁺ where M is n-D semantic manifold"
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},
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"Layer_1_Density_Field_Extraction": {
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"operation": "Compute semantic density field ρ(x⃗) from corpus structure",
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"topological_features": {
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"peaks": "Named entities, article centers, key concepts (local maxima)",
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"ridges": "Hyperlinks, citations, semantic connections (1D maxima)",
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"saddles": "Topic transitions, paragraph boundaries (mixed Hessian)",
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"vortices": "Cyclic references, template instantiations (rotational flow)",
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"voids": "Template structures, expected absence (local minima)",
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"level_sets": "Paragraphs, sections, articles (iso-density surfaces)"
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},
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"morse_smale_complex": "Critical points + separatrices = topological skeleton of meaning",
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"output": "Topological skeleton S (peaks, ridges, saddles, vortices, voids) + perturbation field δ"
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},
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"Layer_2_Hypercube_Rhomboid_Shear": {
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"operation": "Apply shear matrix A to orthogonal UTF-8 hypercube → correlated semantic rhomboid",
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"shear_matrix": "A_{ij} = δ_{ij} + α_{ij} where α encodes mutual information between dimensions i, j",
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"gram_matrix": "G = A^T A is the compression dictionary — eigenvectors = principal correlation directions, eigenvalues = compression gains",
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"volume_preservation": "det(A) = 1 — information volume preserved, only geometry changed",
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"information_gravity": "g_{μν} = δ_{μν} + κ·I_{μν} where I_{μν} is mutual information, κ is gravitational coupling",
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"output": "Sheared manifold S̃ = A·S where correlated axes lean into each other"
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},
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"Layer_3_S3C_Shell_Coordinate_Encoding": {
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"operation": "Encode positions within sheared manifold via S3C shell coordinates",
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"s3c_split": "n = k² + a with mirror complement b⁰, next-shell tension b⁺, mass = a·b⁰, mirror_delta = a - b⁰",
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"coordinate_mapping": {
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"k": "Shell index = structural depth / semantic distance from peak",
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"a": "Angular offset = intra-cluster position within shell",
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"b⁰": "Mirror complement = remaining path to next peak",
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"throat": "a ≈ b⁰ = maximum compression (maximum predictive confidence)",
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"mass": "Connection strength between topological features"
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},
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"output": "Shell-encoded coordinates (k, a, b⁰, b⁺, mass, throat_class) for each packet"
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},
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"Layer_4_GCCL_GEC_Packet_Encoding": {
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"operation": "Encode each topological feature as glyph packet Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ",
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"packet_fields": {
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"γᵢ": "Visible glyph / emoji / math symbol / PUA codepoint — invokes callable reconstruction kernel",
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"χᵢ": "Chirality vector ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ — law-axis selection",
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"κᵢ": "Local context = S3C shell coordinate (k, a) from Layer 3",
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"τᵢ": "Type witness from TypeBook (WikiArticle<Mathematics>, Infobox<Person>, CitationGraph...)",
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"UᵢΛᵢaᵢ": "Eigen descriptor from EigenBook (basis, spectrum, sparse coefficients)",
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"θᵢ": "Parameters from side stream (mode selectors, eigenbook indices)",
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"εᵢ": "Residual bytes = exact repair to generated prediction"
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},
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"books": {
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"GlyphBook": "Maps codepoints to callable kernels",
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"ChiralityBook": "Maps chirality vectors to law-axes",
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"TypeBook": "Maps datatypes to structural laws",
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"EigenBook": "Stores reusable geometric descriptors"
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},
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"gain_test": "ΔGCL(Γᵢ) = literal_cost - encoded_cost(γᵢ, χᵢ, κᵢ, τᵢ, UΛa, θᵢ, εᵢ). Accept iff ΔGCL > 0.",
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"output": "Packet stream Γ = {Γ₁, Γ₂, ..., Γₙ} + parameter stream Θ + residual stream Ε"
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},
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"Layer_5_OAC_Speculative_Manifestation": {
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"operation": "Test compression motifs as Observer-Admissible Cavities before committing",
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"oac_definition": "OAC = (Sₙ, A_O, T, V, R, ε) — latent cavity that only manifests under lawful observer touch",
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"touch_operator": "touch(O, OACᵢ, q) → (Sᵢ, rᵢ, εᵢ, ρᵢ) if admissible, (Vᵢ, scarᵢ, ρᵢ) otherwise",
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"admissibility_gate": "Accept iff L(Sₙ) + L(route) + L(ε) < L(x)",
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"spherion_shaping": {
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"positive_pyramids": "Protrusions = confident predictions (high-Q narrow)",
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"negative_pyramids": "Voids = expected-but-missing features (cheaper to encode absence)",
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"shape_parameters": "Height = amplitude, base = duration, slope = transition, asymmetry = skew, apex = precision"
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},
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"sn_nn_grammar": "Sₙ(nⁿ) recursive shell grammar — nth shell contains n choices of previous shell state",
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"address_space_rule": "OAC ⊄ A_substrate; only receipt, accepted route, and residual may commit",
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"output": "Accepted packets + void scars (FAMM failure memory) + receipts"
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},
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"Layer_6_Radius_Ratio_Local_Quantization": {
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"operation": "Quantize local scale ratio ρ into admissible motif class via radius-ratio rule",
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"radius_ratio_rule": "Local scale ratio → smallest stable coordination geometry",
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"coordination_analogy": {
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"CN3 (triangular)": "ρ ∈ [0.155, 0.225) — minimal coordination",
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"CN4 (tetrahedral)": "ρ ∈ [0.225, 0.414) — 4-way coordination",
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"CN6 (octahedral)": "ρ ∈ [0.414, 0.732) — 6-way coordination",
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"CN8 (cubic)": "ρ ∈ [0.732, 1.0] — 8-way coordination"
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},
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"compression_mapping": "ρᵢ = s_center(i) / median(s(N(i))) → admissible kernel class + residual",
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"motif_classes": [
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"WikiArticle", "Infobox", "CitationGraph", "SectionTree",
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"TableMatrix", "ListRegion", "FormulaRegion", "MarkupRegion"
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],
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"quantizer": "Continuous local metric witness → finite motif alphabet → decode rule + nibble residual",
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"output": "Motif_id + orientation + scale + law_id + residual_nibble_stream"
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},
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"Layer_7_FAMM_Temporal_Sequencing": {
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"operation": "Preshape delay profile for temporal path through packet stream",
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"uniform_delay": "Orthogonal time hypercube — fixed context window",
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"preshaped_delay": "Sheared time rhomboid — context stretches for high-entropy regions, compresses for low-entropy template regions",
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"delay_profile": "Delay = path integral through field gradient. Fast regions = high density (predictable). Slow regions = low density (needs context).",
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"q16_16_fixed_point": "Delays encoded in Q16.16 fixed-point for hardware-native determinism",
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"eigenvalue_derivation": "Preshaped delays based on eigenvalue spectra from waveprobe manifold generation",
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"output": "Delay profile D(t) for packet stream sequencing"
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},
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"Layer_8_PIST_Perturbation_Encoding": {
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"operation": "Encode perturbation field δ via PIST n-D bundle encoding",
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"pist_modes": {
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"cartesian": "Orthogonal n-D encoding (baseline)",
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"bundle": "Fiber dimensions encode correlated features",
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"radial": "Fully collapsed angular coordinates"
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},
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"fiber_mapping": {
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"fiber₀": "Topological feature type (peak, ridge, saddle, vortex, void)",
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"fiber₁": "Shell index k (semantic distance)",
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"fiber₂": "Throat class (compression quality)",
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"fiber₃": "Mass (connection strength)",
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"fiber₄": "Local curvature / distortion"
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},
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"n_dims": "Typically 3-4D: topic, time, authority, style",
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"compression_level": "PIST level 3 for offload pipeline (balanced speed/ratio)",
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"output": "PIST-encoded perturbation bundle B"
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},
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"Layer_9_erans_Residual_Entropy_Coding": {
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"operation": "Entropy-code residual stream Ε and parameter stream Θ via enumerative rANS",
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"erans_principle": "Enumerative rANS is optimal for exact histogram coding",
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"histogram_exact": "Residual values are discretized to exact histogram bins",
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"enumerative_coding": "Rank-based coding of symbols within exact histogram",
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"isa_agnostic": "SIMD opportunistic, scalar fallback required — no instruction set assumed",
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"licensing": "erans reference only — algorithmic ideas ingested, no code incorporated",
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"output": "Entropy-coded residual stream E_erc and parameter stream P_erc"
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},
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"Layer_10_Archive_Assembly": {
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"magic": "UCA1 (Unified Compression Architecture v1)",
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"structure": [
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"DECOMPRESSOR_PROFILE D",
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"GLYPHBOOK 𝔊",
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"CHIRALITYBOOK Χ",
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"TYPEBOOK Τ",
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"EIGENBOOK 𝕌",
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"SHEAR_MATRIX A (Gram matrix G = A^T A)",
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"REGION_INDEX I",
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"PACKET_STREAM Γ",
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"PARAMETER_STREAM Θ",
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"RESIDUAL_STREAM Ε",
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"DELAY_PROFILE D_FAMM",
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"S3C_SHELL_COORDINATES",
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"RECEIPT_SECTION R",
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"CHECKSUM_SECTION (SHA256)"
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],
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"decode_invariant": "sha256(decode(archive)) == sha256(original_corpus)",
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"output": "Compressed archive A = D ⊕ 𝔊 ⊕ Χ ⊕ Τ ⊕ 𝕌 ⊕ A ⊕ I ⊕ Γ ⊕ Θ ⊕ Ε ⊕ D_FAMM ⊕ S3C ⊕ R ⊕ SHA256"
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}
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},
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"data_flow_summary": {
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"encode_pipeline": [
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"Raw bytes C",
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"↓ Parse to semantic density field ρ",
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"↓ Extract Morse-Smale topological skeleton S",
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"↓ Apply shear matrix A → sheared manifold S̃",
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"↓ Encode positions via S3C shells (k, a, b⁰, b⁺)",
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"↓ Quantize via radius-ratio → motif classes",
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"↓ Encode each feature as GCCL-GEC packet Γᵢ",
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"↓ Test via OAC gate → accept/reject",
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"↓ Preshape FAMM delay profile D_FAMM",
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"↓ Encode perturbations via PIST bundle B",
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"↓ Entropy-code residuals/params via erans",
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"↓ Assemble archive with books, shear matrix, receipts"
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],
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"decode_pipeline": [
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"Load archive A",
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"↓ Load decompressor profile D",
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"↓ Load books (𝔊, Χ, Τ, 𝕌)",
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"↓ Load shear matrix A (Gram matrix G)",
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"↓ Load S3C shell coordinates",
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"↓ Load FAMM delay profile D_FAMM",
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"↓ Load packet stream Γ, params Θ, residuals Ε",
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"↓ For each Γᵢ: resolve glyph, chirality, type, eigen descriptor",
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"↓ Apply shear inverse A⁻¹ to reconstruct expected structure",
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"↓ Generate predicted byte span ŝᵢ",
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"↓ Apply residual εᵢ",
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"↓ Emit exact span sᵢ",
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"↓ Concatenate spans via FAMM sequencing",
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"↓ Verify SHA256 checksum",
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"↓ Output original corpus C"
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]
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},
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"component_interdependencies": {
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"density_field_to_gccl": "Topological features (peaks, ridges, saddles, vortices, voids) ARE the packet types in GCCL-GEC",
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"gccl_to_oac": "Each glyph packet Γᵢ is tested as an OAC before committing to stream",
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"hypercube_rhomboid_to_s3c": "Shear matrix A defines the manifold geometry that S3C shells navigate",
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"s3c_to_famm": "Shell index k determines delay class in FAMM preshaping",
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"radius_ratio_to_gccl": "Local scale ratio quantizes to motif class → selects glyph kernel γᵢ",
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"famm_to_pist": "Delay profile determines temporal bundling of perturbation fibers",
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"pist_to_erans": "PIST-encoded perturbation bundle is entropy-coded via erans",
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"oac_to_receipts": "Accepted OAC routes become receipts; rejected become FAMM scars",
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"shear_matrix_to_eigenbook": "Gram matrix G = A^T A eigenvectors ARE the eigenbasis U in EigenBook",
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"all_to_gain_test": "Every component must pass ΔGCL > 0 before inclusion in archive"
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},
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"compression_gain_sources": {
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"geometric_shear": "15-30% on structured regions (infoboxes, citations, templates) by collapsing empty angles between correlated axes",
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"topological_skeleton": "50-150MB skeleton vs 1GB raw for enwik9 via Morse-Smale complex",
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"glyph_kernels": "Reusable callable kernels avoid storing repeated structures explicitly",
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"s3c_shells": "5-10% on positional encoding overhead via structural coordinate encoding",
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"oac_speculation": "Avoids 2-5% bloat from bad motif choices via lazy manifestation",
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"famm_context": "10-20% context efficiency via preshaped information-geometric windows",
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"pist_bundle": "5-8% on token encoding via fiber-dimensional correlation capture",
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"erans_entropy": "Optimal exact histogram coding for residuals",
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"radius_ratio_quantization": "Continuous witness → finite motif alphabet reduces entropy",
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"gram_dictionary": "MB → KB dictionary overhead via shear matrix as unified dictionary"
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},
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"estimated_aggregate_gain": {
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"structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)",
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"free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)",
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"dictionary_overhead": "MB → KB (Gram matrix replaces LZ dictionary + transformer weights)",
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"context_efficiency": "10-20% more predictive power per context byte",
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"skeleton_compression": "O(N_peaks + N_ridges) ≪ O(N_bytes) — ~100MB vs 1GB for enwik9 skeleton",
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"overall_compressed_size": "Estimated 12-22% reduction vs current best Hutter compressors, plus navigable manifold capability",
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"novel_capability": "Produces navigable structure — query 'show me all articles 2 links from France' without full decompression"
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},
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"implementation_priority": {
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"Phase_0_Foundation": {
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"tasks": [
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"Implement S3C shell coordinate codec (s3c_shell_codec.py)",
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"Implement radius-ratio local quantizer (radius_ratio_quantizer.py)",
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"Implement basic FAMM delay line with Q16.16 (famm_q16_16.py)"
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],
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"rationale": "These are the coordinate and temporal foundations for all higher layers"
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},
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"Phase_1_Density_Field": {
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"tasks": [
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"Implement semantic density field extraction (density_field_extractor.py)",
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"Implement Morse-Smale complex computation (morse_smale_complex.py)",
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"Implement topological feature classifier (topological_classifier.py)"
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],
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"rationale": "Density field is the representation that all other layers operate on"
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},
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"Phase_2_GCCL_GEC_Core": {
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"tasks": [
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"Implement GlyphBook (glyphbook.py)",
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"Implement TypeBook with WikiArticle, Infobox, CitationGraph kernels (typebook.py)",
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"Implement basic packet encoder/decoder (gccl_packet.py)",
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"Implement gain test ΔGCL (gccl_gain_test.py)"
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],
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"rationale": "GCCL-GEC is the packet-level codec that carries all compression"
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},
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"Phase_3_Shear_and_Eigen": {
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"tasks": [
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"Implement shear matrix learning (shear_matrix_learner.py)",
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"Implement Gram matrix extraction (gram_matrix.py)",
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"Implement EigenBook with UΛa descriptors (eigenbook.py)"
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],
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"rationale": "Shear matrix and eigen descriptors capture correlation structure"
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},
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"Phase_4_OAC_and_Speculation": {
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"tasks": [
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"Implement OAC touch operator (oac_touch_gate.py)",
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"Implement spherion shape quantizer (spherion_quantizer.py)",
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"Implement Sₙ(nⁿ) recursive shell grammar (sn_nn_grammar.py)"
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],
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"rationale": "OAC enables safe speculative compression without output pollution"
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},
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"Phase_5_PIST_and_erans": {
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"tasks": [
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"Integrate existing PIST n-D bundle encoding for perturbations",
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"Implement erans enumerative rANS wrapper (erans_entropy.py)",
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"Ensure ISA-agnostic scalar fallback"
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],
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"rationale": "PIST encodes perturbations; erans entropy-codes residuals"
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},
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"Phase_6_Integration": {
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"tasks": [
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"Implement full encode pipeline (uca_encode.py)",
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"Implement full decode pipeline (uca_decode.py)",
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"Implement archive format (uca_archive.py)",
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"Add SHA256 verification"
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],
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"rationale": "Integrate all layers into end-to-end codec"
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},
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"Phase_7_Benchmark": {
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"tasks": [
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"Benchmark on enwik8/enwik9",
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"Compare vs current Hutter best",
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"Measure navigable query performance",
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"Profile each layer's contribution"
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],
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"rationale": "Validate gains and identify bottlenecks"
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}
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},
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"keeper_phrases": [
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"The density field is the manifold; the glyph packets are the navigators; the shear matrix is the map.",
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"Don't store the text. Store the cheapest lawful generator of its topological skeleton.",
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"A citation is not 200 bytes. It is a ridge connecting two peaks, encoded as one glyph packet with a URL residual.",
|
||||
"The Gram matrix of the shear IS the dictionary, the context model, the token encoding, and the structure detector — all at once.",
|
||||
"OACs let the compressor think in holes without storing every hole it thinks through.",
|
||||
"S3C shells give the pigeon a lawful coordinate; spherion shaping gives the hole teeth.",
|
||||
"Stop predicting the next token. Shear the manifold until the next token is obvious.",
|
||||
"The Morse-Smale complex is the topological skeleton of meaning. GCCL-GEC is the lawful engine that navigates it.",
|
||||
"FAMM preshapes time the way shear reshapes space — both are information-geometric warping.",
|
||||
"Every '{{cite web' is the same OAC cavity. Pay for the cavity once, pay for the URL residual each time.",
|
||||
"The Hutter Prize is manifold learning disguised as sequence modeling.",
|
||||
"ρ = |ε| / |raw_span|. This is the only number that matters.",
|
||||
"A finite glyph set becomes combinatorially huge through chirality rotation.",
|
||||
"The datatype is the engine. UTF-8 is only the exhaust."
|
||||
],
|
||||
|
||||
"metadata": {
|
||||
"ingested_at": time.time(),
|
||||
"tags": [
|
||||
"unified-compression-architecture",
|
||||
"density-field-encoding",
|
||||
"gccl-gec",
|
||||
"observer-admissible-cavities",
|
||||
"hypercube-rhomboid",
|
||||
"s3c-shells",
|
||||
"famm",
|
||||
"pist",
|
||||
"erans",
|
||||
"radius-ratio",
|
||||
"morse-smale-complex",
|
||||
"gram-matrix",
|
||||
"shear-matrix",
|
||||
"topological-compression",
|
||||
"navigable-compression",
|
||||
"hutter-prize",
|
||||
"information-geometry",
|
||||
"semantic-manifold"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def ingest():
|
||||
germane_dir = RESEARCH_STACK / "shared-data/data/germane/research"
|
||||
germane_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
out_path = germane_dir / "unified_compression_architecture_synthesis_v1.json"
|
||||
with open(out_path, 'w') as f:
|
||||
json.dump(SYNTHESIS, f, indent=2)
|
||||
|
||||
print(f"✓ Ingested: {out_path}")
|
||||
|
||||
index_path = germane_dir / "research_ingestion_index.json"
|
||||
index = []
|
||||
if index_path.exists():
|
||||
with open(index_path) as f:
|
||||
index = json.load(f)
|
||||
|
||||
index.append({
|
||||
"id": SYNTHESIS["id"],
|
||||
"title": SYNTHESIS["title"],
|
||||
"date": SYNTHESIS["date"],
|
||||
"source": SYNTHESIS["source"],
|
||||
"ingested_at": SYNTHESIS["metadata"]["ingested_at"],
|
||||
"tags": SYNTHESIS["metadata"]["tags"],
|
||||
})
|
||||
|
||||
with open(index_path, 'w') as f:
|
||||
json.dump(index, f, indent=2)
|
||||
|
||||
print(f"✓ Index: {len(index)} entries")
|
||||
|
||||
print(f"\nArchitectural layers (10):")
|
||||
for layer, data in SYNTHESIS["architectural_layers"].items():
|
||||
if "operation" in data:
|
||||
print(f" {layer}: {data['operation'][:70]}...")
|
||||
|
||||
print(f"\nComponent interdependencies (9):")
|
||||
for dep, desc in SYNTHESIS["component_interdependencies"].items():
|
||||
print(f" {dep} → {desc[:60]}...")
|
||||
|
||||
print(f"\nCompression gain sources (10):")
|
||||
for source, gain in SYNTHESIS["compression_gain_sources"].items():
|
||||
print(f" {source}: {gain[:60]}...")
|
||||
|
||||
print(f"\nImplementation phases (7):")
|
||||
for phase, data in SYNTHESIS["implementation_priority"].items():
|
||||
print(f" {phase}: {len(data['tasks'])} tasks — {data['rationale'][:50]}...")
|
||||
|
||||
print(f"\nKeeper phrases ({len(SYNTHESIS['keeper_phrases'])}):")
|
||||
for p in SYNTHESIS["keeper_phrases"]:
|
||||
print(f" → {p}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
ingest()
|
||||
Loading…
Add table
Reference in a new issue