LHoTT Cohesive Linear Skill

Synthesizes Urs Schreiber's cohesive ∞-topos framework with Mitchell Riley's linear HoTT for interaction entropy formalization.

Modal Operators

GF(3) Triad Placement

This skill is ERGODIC (0), forming triads with:

persistent-homology (-1) ⊗ lhott-cohesive-linear (0) ⊗ topos-generate (+1) = 0 ✓
sheaf-cohomology (-1) ⊗ lhott-cohesive-linear (0) ⊗ gay-mcp (+1) = 0 ✓
three-match (-1) ⊗ lhott-cohesive-linear (0) ⊗ rubato-composer (+1) = 0 ✓

Core Types (Pseudo-HoTT)

-- Cohesive interaction type
CohesiveInteraction : Type
  content : String
  hash : ♯ SHA256           -- discrete
  seed : ♭ UInt64           -- continuous embedding
  color : ♮ LCH             -- linear (used once)
  position : ʃ (ℤ × ℤ)      -- shape-invariant

-- Linear function (no copy/delete)
walk_step : CohesiveInteraction ⊸ Position × Color

-- Bunched triplet (entangled context)
Γ₁ ⊗ Γ₂ ⊗ Γ₃ ⊢ conserved : GF3Zero
  where trit(Γ₁) + trit(Γ₂) + trit(Γ₃) ≡ 0 (mod 3)

Diagram Generation

Mermaid Templates

Cohesive Quadruple:

flowchart LR
    subgraph "Cohesive ∞-Topos H"
        A[Type] -->|ʃ shape| B[Shape Type]
        A -->|♭ flat| C[Codiscrete]
        C -->|Γ sections| D[Discrete]
        D -->|♯ sharp| A
    end
    style A fill:#26D826
    style B fill:#2626D8
    style C fill:#D82626
    style D fill:#2626D8

Linear Walk:

stateDiagram-v2
    [*] --> I1: seed₁
    I1 --> I2: ⊸ (linear)
    I2 --> I3: ⊸ (linear)
    I3 --> [*]: triplet complete
    
    note right of I1: trit = +1
    note right of I2: trit = 0
    note right of I3: trit = -1

Bunched Context Tree:

graph TD
    Root["Γ (context)"] --> A["Γ₁ ⊗ Γ₂"]
    Root --> B["Γ₃"]
    A --> C["I₁ (+1)"]
    A --> D["I₂ (0)"]
    B --> E["I₃ (-1)"]
    
    style C fill:#D82626
    style D fill:#26D826
    style E fill:#2626D8

Ruby Integration

module LHoTTCohesiveLinear
  # Modalities
  SHARP  = ->(x) { { trit: x[:trit] } }  # ♯ discretize
  FLAT   = ->(x) { x }                    # ♭ full embed
  SHAPE  = ->(x) { x[:position] }         # ʃ trajectory
  LINEAR = ->(x) { x.dup.freeze }         # ♮ freeze for one use
  
  def self.cohesive_interaction(content)
    hash = Digest::SHA256.hexdigest(content)
    seed = hash[0..15].to_i(16)
    gen = SplitMixTernary::Generator.new(seed)
    color = gen.next_color
    
    {
      content: content,
      hash: SHARP.call({ trit: color[:trit] }),  # ♯
      seed: FLAT.call(seed),                      # ♭
      color: LINEAR.call(color),                  # ♮
      position: nil  # computed by walk
    }
  end
  
  def self.linear_walk_step(interaction, walker)
    raise "Linear resource already consumed" if interaction.frozen?
    result = walker.step!(interaction)
    interaction.freeze  # consume linear resource
    result
  end
end

Julia Integration

# ACSets schema for LHoTT
@present SchLHoTT(FreeSchema) begin
  CohesiveType::Ob
  LinearType::Ob
  
  sharp::Hom(CohesiveType, CohesiveType)   # ♯
  flat::Hom(CohesiveType, CohesiveType)    # ♭
  shape::Hom(CohesiveType, CohesiveType)   # ʃ
  linear::Hom(CohesiveType, LinearType)    # ♮
  
  Trit::AttrType
  trit_attr::Attr(LinearType, Trit)
end

Hy/DiscoHy Integration

(import [discopy [Ty Box Diagram monoidal]])

(defn cohesive-box [name input output modality]
  "Create DisCoPy box with modality annotation"
  (setv color (case modality
    "sharp" "#2626D8"
    "flat" "#D82626"
    "shape" "#26D826"
    "linear" "#FFAA00"))
  (Box name (Ty input) (Ty output) :color color))

(defn lhott-diagram [interactions]
  "Build monoidal diagram from interaction sequence"
  (setv boxes (lfor i interactions
    (cohesive-box (get i "skill_name")
                  "State" "State"
                  (get i "modality" "linear"))))
  (reduce monoidal.compose boxes))

Diagram Export Commands

# Generate Mermaid diagram from interactions
just lhott-diagram mermaid

# Generate base64 PNG from Mermaid
just lhott-diagram png > diagram.base64

# Export to DisCoPy SVG
just lhott-discopy-svg

# Full pipeline: interactions → ACSet → DisCoPy → Mermaid
just lhott-full-export

Key Theorems

1. Cohesive Determinism: hash ∘ ♯ = ♯ ∘ hash (discretization commutes)
2. Linear Conservation: |consumed| = |interactions| (no copy/delete)
3. GF(3) Invariant: Σ trit(Iᵢ) ≡ 0 (mod 3) per triplet
4. Spectral Verification: P(verify) = 1/4 (Ramanujan gap)

References

• Corfield, D. (2025). "Linear Homotopy Type Theory: Its Origins and Potential Uses"
• Schreiber, U. (2014). "Quantization via Linear Homotopy Types"
• Riley, M. (2022). "A Bunched Homotopy Type Theory for Synthetic Stable Homotopy Theory"
• nLab: Cohesive HoTT

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

• networkx [○] via bicomodule
  • Universal graph hub

Bibliography References

• linear-algebra: 112 citations in bib.duckdb
• homotopy-theory: 29 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.