# SilverSight Modules — Pure Mathematical Description

This document describes each SilverSight module in pure mathematical
terms. No code, no implementation details — only the mathematical
structures, theorems, and insights each module embodies.

---

## SidonSets

### What it does

Defines the **Sidon property** for finite sets of integers and proves
extremal bounds on Sidon subsets of $\{1, \ldots, N\}$.

A finite set $A \subset \mathbb{Z}$ is **Sidon** (or a $B_2$-set) if
every pairwise sum is unique up to reordering:

$$\forall\, a, b, c, d \in A:\quad a + b = c + d \implies \{a,b\} = \{c,d\}$$

The **modular variant** $A$ is Sidon modulo $M$ when:

$$M \mid (a+b) - (c+d) \implies \{a,b\} = \{c,d\}$$

The **extremal function** $h(N) = \max\{|A| : A \subseteq \{1,\ldots,N\}
\text{ is Sidon}\}$ is shown to exist and be unique for every $N$.

Two upper bounds are proved:

1. **Difference-counting bound:** $h(N) \leq \sqrt{2N} + 1$, obtained by
   counting the $\binom{|A|}{2}$ distinct positive differences $a - b$
   and noting they all lie in $\{1, \ldots, N-1\}$.

2. **Lindström's bound** (via the Johnson/Cauchy-Schwarz method):
   $h(N) \leq \sqrt{N} + \sqrt[4]{N} + 2$ for $N \geq 16$.
   This is proved by considering $m$ shifted copies $A, A+1, \ldots,
   A+(m-1)$ of a Sidon set, applying the incidence inequality
   $(\sum_i |S_i|)^2 \leq |U| \cdot \sum_{i,j} |S_i \cap S_j|$,
   and using the Sidon property to bound off-diagonal intersections
   by 1.

The **Singer construction** produces, for every prime $p$, a Sidon set
modulo $p^2 + p + 1$ of cardinality $p + 1$. The construction uses:

- The degree-3 Galois extension $\mathbb{F}_{p^3} / \mathbb{F}_p$.
- The trace kernel $V = \ker(\text{Tr}_{\mathbb{F}_{p^3}/\mathbb{F}_p})$,
  a 2-dimensional $\mathbb{F}_p$-subspace.
- The quotient group $\mathbb{F}_{p^3}^\times / \mathbb{F}_p^\times$,
  which is cyclic of order $p^2 + p + 1$.
- A geometric lemma: for $\alpha \notin \mathbb{F}_p$, the intersection
  $V \cap \alpha^{-1}V$ has dimension exactly 1. This is the core
  geometric fact that forces the Sidon property in the quotient.

### Why it does it

Sidon sets are the address-space backbone of the compressor. The 8
strands of the BraidStorm use Sidon labels $\{1, 2, 4, 8, 16, 32, 64,
128\}$ (powers of 2), which form a Sidon set because all pairwise sums
are distinct. The Singer construction provides optimal Sidon sets for
larger address budgets, and the extremal bounds quantify how much
address space a given problem size requires.

The Erdős Problem 30 conjecture — that $h(N) = \sqrt{N} + O(N^\varepsilon)$
— remains open. The Lindström bound $\sqrt{N} + \sqrt[4]{N} + 2$ is the
best unconditional upper bound proved here.

### Pure math version

A graph calculator would need:

- **Finite set arithmetic:** membership, pairwise sums, cardinality.
- **Modular arithmetic:** divisibility, residue classes $\mathbb{Z}/M\mathbb{Z}$.
- **Extremal combinatorics:** maximizing set cardinality under combinatorial constraints.
- **Cauchy-Schwarz inequality** for the incidence bound.
- **Finite field arithmetic:** $\mathbb{F}_p$, $\mathbb{F}_{p^3}$, field traces.
- **Linear algebra over finite fields:** subspaces, dimension, rank-nullity.
- **Group theory:** cyclic groups, quotient groups, cosets.
- **Projective geometry:** Singer's theorem on points and lines in $PG(2, p)$.

---

## BraidEigensolid

### What it does

Defines an **8-strand braid state** and a **crossing step** operator,
then proves two compressor-correctness theorems.

A **BraidState** consists of 8 strands, each carrying:
- A 2D phase accumulator $\mathbf{z}_i = (x_i, y_i) \in \mathbb{Z}^2$
  (represented in Q16.16 fixed-point).
- A slot label $s_i \in \{1, 2, 4, 8, 16, 32, 64, 128\}$ (Sidon labels).
- A bracket (crossing weight) with a **kappa** value $\kappa_i$.
- A residue $\varepsilon_i$.

The **crossing step** $\sigma$ pairs adjacent strands $(0,1), (2,3),
(4,5), (6,7)$ and applies the braid crossing operator to each pair.
The crossing operator merges phase vectors by component-wise addition:
$\mathbf{z}_i' = \mathbf{z}_i + \mathbf{z}_j$, and computes a new
bracket via an octagonal norm approximation.

An **eigensolid** is a fixed point: $\sigma(s) = s$, meaning every
strand's data is unchanged by the crossing step. This is the DC baseline
— the converged state of the braid loop.

**Theorem 1 (Eigensolid Convergence):** If $\sigma(s)$ is already an
eigensolid, then applying $\sigma$ again changes nothing:
$\sigma(\sigma(s)) = \sigma(s)$.

**Theorem 2 (Receipt Invertibility):** The receipt tuple
$(C, \sigma, k, \varepsilon_{\text{seq}}, t, \emptyset_{\text{scars}})$
bijectively encodes the eigensolid state. Given two eigensolid states
with identical receipts, all per-strand residues, the crossing matrix,
the slot of strand 7, and the step count are equal.

A **topological triviality** result is proved under non-saturation:
if no phase component is at the fixed-point boundary, then eigensolid
states have $\kappa_i = 0$ for all strands (genus-0 layer).

The module also defines a **torus carrier** enrichment: the 8-strand
braid lives on a genus-1 torus $T^2$ with two winding numbers
$(a, b) \in H_1(T^2; \mathbb{Z}) \cong \mathbb{Z} \oplus \mathbb{Z}$.

### Why it does it

The eigensolid is the compressor's convergence target. Every compressor
must prove two things: (1) the braid loop converges (eigensolid
convergence), and (2) the receipt encodes the state losslessly (receipt
invertibility). Together, these guarantee that the compressed
representation — the receipt — can be inverted to recover the original
state.

The torus carrier enriches the planar braid with topological information:
winding numbers track how many times the braid wraps around the two
fundamental cycles of the torus, which matters for phase-sensitive
applications.

### Pure math version

A graph calculator would need:

- **Fixed-point iteration:** detecting when $f(x) = x$ for a discrete
  dynamical system on a finite state space.
- **Vector addition in $\mathbb{Z}^2$** with saturating arithmetic.
- **Octagonal norm:** $\|z\| = \max(|x|, |y|) + \frac{3}{8}\min(|x|, |y|)$.
- **XOR operations** on bit vectors (for slot arithmetic).
- **Tuple encoding/decoding:** injective maps between state tuples and
  receipt tuples.
- **Algebraic topology (genus-0 layer):** when a braid has no persistent
  2-cycles in its crossing graph.
- **Torus homology:** $H_1(T^2; \mathbb{Z})$, winding numbers.

---

## BraidSpherionBridge

### What it does

Proves a **structural correspondence** between two different
formalisms of the same physical system:

1. **SpherionState:** a multi-scale merging and renormalization (MMR)
   model with mountains, spikes, and an RG flow via $\beta$-steps.
2. **BraidState:** 8 strands with crossing steps.

The correspondence operates at two levels:

**Type bridge:** The integer-node coordinates of a Mountain's apex map
to the phase vector of a BraidStrand via a coordinate-wise encoding:
the first two coordinates become the $(x, y)$ components of a 2D phase
vector, with nonnegative integer coordinates mapped by exact scaling
(factor of $2^{16}$).

**Operation bridge:** The braid crossing operator on strands $(i, j)$
corresponds to the Mountain merge operation on the corresponding pair.
Both are linear accumulation in their respective spaces:
- Braid crossing: $\mathbf{z}' = \mathbf{z}_i + \mathbf{z}_j$ (phase vector addition).
- Mountain merge: $\text{apex}' = \text{apex}_1 + \text{apex}_2$ (coordinate-wise integer addition).

The encoding preserves addition on nonnegative coordinates, so the
merged apex maps to the merged phase vector.

**Flow correspondence:** After $k$ spikes (mountain merge operations),
the step count equals $k$. The receipt dimensions correspond:
- Crossing matrix $C$ ↔ basin geometry.
- Sidon slack $\sigma$ ↔ merge debt.
- Step count $k$ ↔ scale decrement count.
- Residual series $\varepsilon_{\text{seq}}$ ↔ void topology (Betti cycles).
- Scar absence $\emptyset_{\text{scars}}$ ↔ IR fixed point (no pending merges).

### Why it does it

The bridge shows that the braid formalism and the MMR formalism are two
views of the same coarse-graining process. The braid view is better for
convergence proofs (eigensolid convergence), while the MMR view is
better for multi-scale analysis (RG flow). The bridge ensures that
receipts produced by one formalism can be interpreted by the other.

### Pure math version

A graph calculator would need:

- **Coordinate-wise maps** between $\mathbb{Z}^n$ and fixed-point vectors.
- **Additive homomorphisms** on nonnegative integers.
- **Graph morphisms** between the crossing graph and the merge graph.
- **Monotone counter tracking** across formalisms.
- **Structural induction** on spike lists.

---

## HachimojiLUT

### What it does

Constructs a **virtual lookup-table hierarchy** for classifying
equations by their position on a manifold.

The **phase circle** $\mathbb{Z}/360\mathbb{Z}$ has 360 discrete angular
positions. The 8 canonical Hachimoji states occupy the octagon vertices
at $\{0°, 45°, 90°, \ldots, 315°\}$.

Each phase $\theta$ embeds into the 15-sphere $S^{15} \subset \mathbb{R}^{16}$
via:

$$q_1(\theta) = \cos(\theta \cdot \pi/180), \quad q_3(\theta) = \sin(\theta \cdot \pi/180)$$

with all other coordinates zero. This is a full-period embedding (corrected
from an earlier half-period version), and $\cos^2 + \sin^2 = 1$ guarantees
unit norm.

The 8 canonical phases embed to **8 distinct points** on $S^{15}$,
forming a regular octagon in the $(q_1, q_3)$-plane. The chord length
between adjacent vertices is $2\sin(\pi/8)$.

The **virtual LUT hierarchy** defines three levels of equation grouping:
- **Binary LUT** ($k=2$): how two equations compose (8×8 = 64 entries).
- **Codon LUT** ($k=6$): one atomic mathematical operation (Genome18 primitive).
- **Genome LUT** ($k=50$): universal function (50-token address space).

**Stability points** under conjugation $\theta \mapsto -\theta$ are
exactly $\{0°, 180°\}$ — the self-complementary (ambidextrous) bases
$\Phi$ and $\Omega$.

### Why it does it

The Hachimoji LUT answers "where does this equation live?" on the
manifold. Each equation's shape (number of variables, operators, depth,
quantifiers, relations) classifies to one of 8 regime states, which
embeds as a point on $S^{15}$. The LUT hierarchy provides compositional
structure: binary composition, atomic operations, and universal functions
all reduce to geometry on the sphere.

### Pure math version

A graph calculator would need:

- **Modular arithmetic:** $\mathbb{Z}/360\mathbb{Z}$, phase addition.
- **Trigonometric functions:** $\cos$, $\sin$, exact values at multiples of $\pi/4$.
- **Unit sphere in $\mathbb{R}^{16}$:** norm verification, chord distances.
- **Injectivity proofs** for finite maps (8 canonical phases → 8 distinct points).
- **Classification functions:** mapping combinatorial parameters to discrete labels.
- **Composition tables:** binary operations on finite sets.
- **Fixed-point detection** under involutions (conjugation).

---

## ChentsovFinite

### What it does

Proves the **finite Chentsov theorem** for $n = 8$ outcomes: on the
probability simplex $\Delta^7 = \{p \in \mathbb{R}^8 : p_i > 0, \sum p_i = 1\}$,
the Fisher information metric is the **unique** Riemannian metric (up to
positive constant) that is invariant under all Markov splitting embeddings.

The **Fisher metric** is:

$$g_p(X, Y) = \sum_{i=1}^{n} \frac{X_i \cdot Y_i}{p_i}$$

where $X, Y$ are tangent vectors ($\sum X_i = \sum Y_i = 0$).

A **Markov splitting embedding** refines one outcome into two
sub-outcomes with conditional probabilities $q$ and $1-q$. A metric $g$
is **Chentsov-invariant** if:

$$g_p(X, Y) = g_{f(p)}(f_*X, f_*Y)$$

for all splitting embeddings $f$, where $f_*$ is the pushforward of
tangent vectors.

The proof proceeds by:

1. Deriving the **functional equation** for the diagonal factor
   $H(t) = g_p(e_i - e_0, e_i - e_0)$ when $p_i = t$:
   $$H(t) = q^2 H(qt) + (1-q)^2 H((1-q)t)$$
2. Substituting $K(t) = t \cdot H(t)$ to linearize:
   $$K(t) = q \cdot K(qt) + (1-q) \cdot K((1-q)t)$$
3. Proving $K(t) = K(t/2^n)$ for all $n$, hence $K(rt) = K(t)$ for all
   positive rationals $r$.
4. By continuity and density of $\mathbb{Q}$ in $\mathbb{R}$: $K$ is
   constant, so $H(t) = c/t$.
5. Therefore $g = c \cdot g_{\text{Fisher}}$.

**Corollary for Hachimoji:** The 8-state manifold has a **canonical
metric** — the Fisher metric is forced by the invariance requirement,
not an arbitrary choice.

### Why it does it

Chentsov's theorem is the mathematical foundation for the Hachimoji
geometry. It says that if you demand your metric be invariant under
coarse-graining (splitting outcomes), then there is only one possible
metric (up to scale). This is why the Fisher metric appears: it is the
unique geometric structure compatible with the Markov refinement
semantics of the 8-state system.

### Pure math version

A graph calculator would need:

- **Probability simplex:** $\Delta^{n-1}$, tangent spaces, basis vectors
  $e_i - e_j$.
- **Riemannian metrics on manifolds:** bilinear forms, positive
  definiteness, symmetry.
- **Markov embeddings:** stochastic matrices, pushforward of tangent vectors.
- **Functional equations:** $H(t) = q^2 H(qt) + (1-q)^2 H((1-q)t)$,
  uniqueness of solutions.
- **Real analysis:** continuity, density of $\mathbb{Q}$, limits.
- **Permutation invariance:** symmetric group actions on the simplex.

---

## DynamicCanal

### What it does

Defines a **fluid-dynamics-inspired transport model** on a directed
graph, with three execution regimes and a pressure-adaptive canal law.

The **DIAT encoding** (Dual-Interval Algebraic Transform) represents
an integer $n$ as a tuple $(k, a, b, ab, a-b)$ where $k = \lfloor\sqrt{n}\rfloor$,
$a = n - k^2$, $b = (k+1)^2 - n$. This captures the "shell" position
and asymmetry of $n$ relative to adjacent perfect squares.

The **Dynamic Canal Law** governs effective resistance:

$$\lambda_{\text{eff}}(P) = \lambda_0 \left[\sigma + (1 - \sigma) e^{-\xi P}\right]$$

where $P$ is pressure, $\lambda_0$ is base resistance, $\xi$ is
elasticity (pressure sensitivity), and $\sigma$ is saturation (minimum
fraction). As pressure increases, resistance decreases exponentially
toward $\sigma \lambda_0$.

Three **execution regimes** govern lane updates:

1. **Coherent:** stable transport with relaxation and healing.
2. **Stressed:** distorted transport with torsion and mismatch accumulation.
3. **Throat:** wormhole-like lossy transfer with maximum energy extraction.

Regime classification is by mismatch and stress thresholds:
- Coherent: mismatch $\leq \theta_c$ and stress $\leq \theta_s$.
- Throat: mismatch $\geq \theta_t$ and edge is a throat.
- Stressed: everything else.

The **canal section** (fluid mode) tracks density, capacity, flux,
pressure, compliance, and roughness, with coarse-graining that reduces
precision as loop iterations increase.

All operations are proved **total** (every Q16.16 operation produces a
result; no partial functions).

### Why it does it

The Dynamic Canal provides a physics-motivated model for adaptive
transport on graphs. The canal law captures the intuition that
"pressure opens the channel" — higher pressure reduces resistance,
allowing more flow. The three regimes model different operating
conditions: normal operation (coherent), degraded operation (stressed),
and catastrophic transfer (throat). The coarse-graining mechanism
trades precision for throughput as iterations accumulate.

### Pure math version

A graph calculator would need:

- **Directed graphs:** nodes, edges, edge attributes.
- **Saturating arithmetic** on a bounded interval $[-2^{15}, 2^{15}-1]$.
- **Exponential decay:** $e^{-\xi P}$ in fixed-point.
- **Piecewise-linear regime classification** by threshold comparison.
- **Conservation laws:** density = inflow - outflux - siphon.
- **Coarse-graining maps:** precision reduction functions parameterized
  by iteration count.
- **Square root and integer decomposition** (DIAT encoding).

---

## Schema

### What it does

Defines a **wire-level schema** as a type class with two fields:
- `byteSize`: the number of bytes in the wire representation.
- `wellFormed`: a predicate that must hold for valid values.

Instances are provided for `UInt8` (1 byte), `Bool` (1 byte),
`UInt32` (4 bytes), `UInt64` (8 bytes), `Q16_16` (4 bytes), and
`Q0_16` (2 bytes).

### Why it does it

The schema is the bridge between mathematical types and their byte-level
representations. Every type that can be serialized has a schema that
declares its wire size and a well-formedness check. This is the
foundation for the wire format and receipt encoding.

### Pure math version

A graph calculator would need:

- **Type-theoretic maps** from abstract types to natural numbers
  (byte sizes).
- **Predicates** on types (well-formedness).
- **Finite type enumeration** (the set of supported types).

---

## WireFormat

### What it does

Defines a **wire format** as a certified encode/decode cycle for a
type under a layout (row-major or columnar).

A WireFormat for type $\alpha$ under layout $L$ consists of:
- An **encode** function: $\alpha \to \text{ByteArray}$.
- A **decode** function: $\text{ByteArray} \to \text{Option}(\alpha)$.
- A **size proof:** $|\text{encode}(a)| = \text{byteSize}(\alpha)$ for all $a$.
- A **roundtrip proof:** $\text{decode}(\text{encode}(a)) = a$ for all $a$.

The roundtrip property is the mathematical content: encoding is injective,
and decoding is its left inverse.

### Why it does it

The wire format ensures that mathematical values can be losslessly
serialized to bytes and back. The roundtrip proof guarantees that no
information is lost in the encoding — the receipt's byte representation
is a faithful encoding of the mathematical state.

### Pure math version

A graph calculator would need:

- **Injective maps** from abstract types to byte sequences.
- **Left inverses:** proving $f^{-1} \circ f = \text{id}$.
- **Length functions** on byte sequences.
- **Layout permutations:** row-major vs. columnar ordering of fields.

---

## Receipt

### What it does

Defines a **receipt** as a 5-tuple:
$$R = (\text{gateType},\; \text{cost},\; \text{invariant},\; \text{timestamp},\; \text{wellFormed})$$

where:
- `gateType` $\in \{\text{encode}, \text{decode}, \text{compose}, \text{validate}, \text{transform}\}$.
- `cost` $\in \mathbb{Q}_{16.16}$ (fixed-point cost).
- `invariant` $\in \Sigma^*$ (string describing what was preserved).
- `timestamp` $\in \mathbb{N}$ (monotone nonce for ordering).
- `wellFormed` $\in \{0, 1\}$.

Predicates include:
- **Validity:** well-formedness flag is true.
- **Positive cost:** cost $> 0$.
- **Shared invariant:** two receipts preserve the same property.

### Why it does it

The receipt is the unit of attestation in SilverSight. Every gate
(encode, decode, compose, validate, transform) produces a receipt
proving it completed successfully. The cost field enables resource
accounting. The invariant field records what property was preserved.
The timestamp enables causal ordering.

### Pure math version

A graph calculator would need:

- **5-tuples** over mixed types (enum, fixed-point, string, natural, boolean).
- **Comparison operators** on each component.
- **String equality** for invariant matching.

---

## Bind

### What it does

Defines **receipt composition** as a binary operation:

$$\text{bind}(R_1, R_2) = (\text{compose},\; c_1 + c_2,\; I_1 \wedge I_2,\; \max(t_1, t_2),\; w_1 \wedge w_2)$$

where costs add, invariants conjoin (with "$\wedge$" separator),
timestamps take the maximum, and well-formedness is the logical AND.

Proved properties:
- **Well-formedness preservation:** $\text{bind}(R_1, R_2)$ is
  well-formed iff both $R_1$ and $R_2$ are well-formed.
- **Cost additivity:** $\text{cost}(\text{bind}(R_1, R_2)) =
  \text{cost}(R_1) + \text{cost}(R_2)$.
- **Timestamp commutativity:** $\max(t_1, t_2) = \max(t_2, t_1)$.
- **Associativity of well-formedness:**
  $\text{wf}(\text{bind}(\text{bind}(a,b),c)) =
  \text{wf}(\text{bind}(a,\text{bind}(b,c)))$.
- **Gate type:** bind always produces a compose gate.

### Why it does it

Bind is the fundamental composition primitive for the receipt ledger.
It allows chaining verification steps: if gate $A$ produces receipt
$R_1$ and gate $B$ produces receipt $R_2$, then $\text{bind}(R_1, R_2)$
is a single receipt attesting that both gates completed. The additive
cost model enables resource accounting across chains. The conjunction
of invariants tracks which properties are preserved by the composite
operation.

### Pure math version

A graph calculator would need:

- **Binary operations** on 5-tuples.
- **Addition** on fixed-point numbers.
- **String concatenation** with a separator.
- **Maximum** on natural numbers.
- **Logical AND** on booleans.
- **Associativity verification** for each component.

---

## Summary Table

| Module | Core Mathematical Object | Key Theorem |
|--------|------------------------|-------------|
| SidonSets | Sidon ($B_2$) sets in $\mathbb{Z}$ | Singer construction: $\exists$ Sidon mod $p^2+p+1$ of size $p+1$ |
| BraidEigensolid | 8-strand braid with crossing step | Eigensolid convergence + receipt invertibility |
| BraidSpherionBridge | Morphism between SpherionState and BraidState | Phase vector ↔ apex coordinate correspondence |
| HachimojiLUT | Phase circle $\mathbb{Z}/360\mathbb{Z}$ embedded in $S^{15}$ | 8 canonical phases are distinct; stability points = $\{0°, 180°\}$ |
| ChentsovFinite | Fisher metric on $\Delta^7$ | Unique Chentsov-invariant metric = $c \cdot g_{\text{Fisher}}$ |
| DynamicCanal | Canal law $\lambda_{\text{eff}}(P) = \lambda_0[\sigma + (1-\sigma)e^{-\xi P}]$ | Totality of all operations |
| Schema | Type → byte-size map | Byte sizes are nonnegative |
| WireFormat | Certified encode/decode cycle | Roundtrip: $\text{decode} \circ \text{encode} = \text{id}$ |
| Receipt | 5-tuple attestation record | Validity = well-formedness |
| Bind | Binary receipt composition | Associativity on well-formedness; cost additivity |
