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+# cmix Weight Matrix Analysis: Compression Shape as Epigenetic Landscape
+
+## The cmix Architecture
+
+cmix (fx2-cmix) is the current Hutter Prize record holder for enwik9 compression:
+- **Total size**: 110,793,128 bytes (including 441,463-byte decompressor)
+- **Previous record**: 112,578,322 bytes
+- **Improvement**: 1.585%
+- **Decompression time**: 65 hours (4 vCPU, 16GB RAM)
+
+## Model Inventory (461 models)
+
+The 461 models are the "genes" of the compressor:
+
+| Model Type | Count | Role | Epigenetic analog |
+|---|---|---|---|
+| FXCM (context-mapped) | 431 | Primary predictors | Housekeeping genes (bulk of expression) |
+| Match models | 10 | Sequence matching | Repeat-element silencing |
+| Indirect (nonstationary) | 15 | Adaptive context | Histone modification |
+| Indirect (run-map) | 1 | Run-length context | CpG methylation |
+| Direct | 1 | Direct byte context | Promoter accessibility |
+| Bracket | 1 | Nesting structure | Chromatin looping |
+| PPMD | 1 | Context-tree model | Enhancer element |
+| Byte mixer (LSTM) | 1 | Neural prediction | Transcription factor |
+| **Total** | **461** | | |
+
+## Weight Matrix Structure (the "Genome")
+
+### Layer 0: 23 mixers × 461 inputs
+- 23 context-dependent mixers, each with 461 weight parameters
+- Each mixer has a per-context hash map (up to 10,000 contexts)
+- Total active parameters: ~106M floats (424MB)
+- This is the **primary genome** — the compression shape
+
+### Layer 1: 1 mixer × 25 inputs
+- 23 layer-0 outputs + 2 auxiliary (fxcm + byte_mixer)
+- Single mixer with per-context weights
+- This is the **epigenetic controller** — which layer-0 mixers to trust
+
+### The 23 Layer-0 Mixer Contexts
+
+Each mixer operates on a different context (the "regulatory region"):
+
+| # | Context | Learning rate | Role |
+|---|---|---|---|
+| 1 | mx9 | 0.005 | General context |
+| 2 | mx10 | 0.0005 | Slow adaptation |
+| 3 | mx11 | 0.005 | General context |
+| 4 | mx12 | 0.0005 | Slow adaptation |
+| 5 | mx13 | 0.005 | General context |
+| 6 | mxx | 0.001 | Cross-model |
+| 7 | recent_bytes[2] | 0.002 | Local history |
+| 8 | line_break | 0.0007 | Structural |
+| 9 | longest_match | 0.0005 | Match quality |
+| 10 | mx19cxt | 0.002 | Extended context |
+| 11 | auxiliary | 0.0005 | Auxiliary feedback |
+| 12 | mx18 | 0.001 | Special context |
+| 13 | mx7 | 0.001 | Special context |
+| 14 | wordscxt | 0.005 | Word-level |
+| 15 | b2streamcxt | 0.001 | Byte-2 stream |
+| 16 | mx5 | 0.001 | Special context |
+| 17 | mx6 | 0.005 | Special context |
+| 18 | b3streamcxt | 0.001 | Byte-3 stream |
+| 19 | mx8 | 0.001 | Special context |
+| 20 | mx17 | 0.005 | Special context |
+| 21 | mx16 | 0.005 | Special context |
+| 22 | mx14 | 0.005 | Special context |
+| 23 | mx15 | 0.005 | Special context |
+
+## The Compression Shape in Math Space
+
+The 23 × 461 weight matrix W defines the compression shape.
+Since it's non-square, the SVD gives:
+
+```
+W = U Σ V^T
+```
+
+Where:
+- U: 23 × 23 orthogonal matrix (which mixers cooperate)
+- Σ: 23 singular values (the "active genes" — which directions matter)
+- V: 461 × 23 matrix (which models contribute to each direction)
+
+The 23 singular values ARE the eigenvalues of the compression shape.
+They tell us:
+- **High singular values**: "overexpressed genes" — models that contribute most
+- **Near-zero singular values**: "silenced genes" — redundant or useless models
+- **The spectrum shape**: the "epigenetic landscape" — how the compressor
+  distributes its prediction capacity across models
+
+## Epigenetic Mapping
+
+| Compression concept | Epigenetic concept |
+|---|---|
+| 461 models | 461 genes |
+| 23 mixers | 23 regulatory regions |
+| Layer-1 mixer | Master regulator |
+| Learning rate | Methylation rate |
+| Context hash map | Chromatin accessibility |
+| Weight update | Histone modification |
+| Weight decay | DNA methylation decay |
+| Article reordering | Chromatin looping |
+| NLP stemmer | Transcription factor |
+| Dictionary | Reference genome |
+| LSTM | Neural crest prediction |
+
+## How to Extract the Weight Matrix
+
+To compute the SVD, we need the actual trained weight matrix from a
+cmix run on enwik8. The process:
+
+1. Modify cmix to dump the layer-0 weight matrix after compression
+2. Run cmix on enwik8 (100MB, ~10 hours)
+3. Extract the 23 × 461 matrix (one per context, average over contexts)
+4. Compute SVD → 23 singular values
+5. The singular value spectrum IS the compression shape equation
+
+## The "Equation" of the Compression Shape
+
+The characteristic polynomial of W^T W (461 × 461) gives the equation.
+Its roots are the squared singular values — the eigenvalues of the
+compression shape.
+
+For a 23 × 461 matrix, W^T W has at most 23 nonzero eigenvalues.
+These 23 values describe the entire compression capacity.
+
+Storing these 23 values (instead of the full weight matrix) would be
+the "equation" — analogous to how the minimal polynomial of a matrix
+captures its eigenvalue structure from n+1 coefficients instead of n² entries.
+
+## Alternative Expression Patterns
+
+If the SVD reveals that 5 singular values capture 95% of the energy,
+then 5 mixers suffice — the other 18 are redundant. This means:
+- The compression shape lives in a 5-dimensional subspace
+- Alternative configurations in this subspace may compress better
+- The "locality" to search is the neighborhood of the current
+  weight matrix in this 5D subspace
+
+This is the "map its locality for other combinations" step:
+- Current cmix: a point in 23D weight space
+- SVD reduces to 5D principal subspace
+- Search the 5D neighborhood for better compression
+
+## Connection to SilverSight
+
+The SilverSight pipeline's universal pipeline already implements:
+- QR decomposition (O-AMMR): the SVD is the symmetric version
+- Chiral hachimoji LUT: encodes the eigenvalue spectrum
+- GCCL Admit: validates that a weight configuration is lawful
+- AngrySphinx: budgets the search through weight space
+- MMR: tracks the compression history as Mountains
+
+The cmix weight matrix IS the manifold. The SVD IS the eigenvalue
+decomposition. The chiral hachimoji LUT IS the receipt. The epigenetic
+framing tells us what to look for: which "genes" (models) are
+overexpressed (high singular value), which are silenced (near-zero),
+and what alternative "expression patterns" (nearby weight matrices)
+might compress better.
+
+## Next Steps
+
+1. **Extract weights**: modify cmix to dump layer-0 weight matrix
+2. **Compute SVD**: get the 23 singular values
+3. **Encode as hachimoji LUT**: 23 coefficients × 30 bases = 690 bytes
+4. **Map in math space**: the singular value spectrum is the "equation"
+5. **Search neighborhood**: vary weights along top-5 singular directions
+6. **Measure**: does the variation improve compression?
+
+The epigenetic framing predicts: the compression shape is low-rank
+(most models are redundant), and the active subspace is small
+(5-10 dimensions out of 23). If confirmed, the search space for
+better compressors is much smaller than the full 23 × 461 space.