# Mutual Awareness Backlink

> Formalize mutual awareness via structured decompositions on awareness graphs.

- Skill: `plurigrid/mutual-awareness-backlink` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add plurigrid/mutual-awareness-backlink`
- Raw SKILL.md: https://api.skillmd.com/api/skills/plurigrid/mutual-awareness-backlink/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: plurigrid (https://skillmd.com/u/plurigrid)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/plurigrid/mutual-awareness-backlink

---

# 🔄 Mutual Awareness Backlink Skill

**Trit**: 0 (ERGODIC) - Mediates between observer (-1) and observed (+1)

Formalize mutual awareness via structured decompositions on awareness graphs.
Julia ACSet-native skill for bidirectional consciousness modeling using sheaf theory,
Bumpus FPT algorithms, and Hamkins multiverse potentialism.

---

## 🎯 Core Capability

**What it does**: Maps bidirectional observation relationships between agents/entities
via sheaf-theoretic consistency checking and structured decompositions.

**Where it's used**:
- Repository interactome analysis (GitHub contributors as agents)
- Multi-agent systems with mutual observation
- Consciousness/awareness modeling in toposes
- Trajectory prediction via bisimulation games

---

## 📐 Mathematical Foundation

### Awareness as Sheaf

Mutual awareness = sheaf `F: G^op → Set` where:
- `G` = awareness graph (agents as vertices, observations as edges)
- `F(a)` = what agent a observes
- **Sheaf condition**: overlapping observations must agree

```
F(a ∩ b) = F(a) ×_{F(∂)} F(b)
```

Where `∂` = shared boundary (mutual observation interface).

### ACSet Schema

```julia
@present SchMutualAwareness(FreeSchema) begin
    Agent::Ob
    Observation::Ob
    Backlink::Ob        # Mutual awareness edge
    World::Ob           # Possible world (Hamkins)

    observer::Hom(Observation, Agent)
    observed::Hom(Observation, Agent)
    forward::Hom(Backlink, Observation)     # A observes B
    backward::Hom(Backlink, Observation)    # B observes A
    world_of::Hom(Observation, World)
    accessible::Hom(World, World)

    agent_seed::Attr(Agent, Seed)
    obs_color::Attr(Observation, Color)
    backlink_trit::Attr(Backlink, Trit)    # GF(3) balance

    # Sheaf condition: shared boundaries agree
    compose(forward, observer) == compose(backward, observed)
    compose(backward, observer) == compose(forward, observed)
end
```

---

## 🔧 Key Operations

### 1. **Sheaf Consistency Checking** (Bumpus FPT)

```julia
function decide_mutual_awareness(awareness::MutualAwareness)
    # Apply Bumpus adhesion filter for FPT sheaf decision
    (is_sheaf, witness) = decide_sheaf_tree_shape(F, d)
    is_sheaf ? :consistent : :obstructed
end
```

**Time Complexity**: O(fw(decomposition)^k) via structured decomposition

### 2. **Adhesion (Shared Boundary)**

```julia
function adhesion_of_agents(awareness, a1, a2)
    # Find bidirectional observations where agents mutually aware
    mutual_backlinks = filter(backlinks) do bl
        (aware(bl, a1 → a2) && aware(bl, a2 → a1))
    end
    mutual_backlinks
end
```

**Meaning**: Where two agents' awareness "glues" together in the sheaf

### 3. **GF(3) Conservation**

```julia
function verify_gf3_awareness(awareness)
    # All awareness triads must sum to 0 mod 3
    triads = balanced_awareness_triads(awareness)
    all(t -> sum(trits(t)) ≡ 0 (mod 3), triads)
end
```

**Why**: GF(3) encodes three roles:
- `-1` (SLAVE): Observer only, not observed
- `0` (ERGODIC): Mutual awareness, balanced
- `+1` (MASTER): Observed only, observer of many

---

## 🌐 Integration Points

### With gh-interactome

Map GitHub contributor networks to awareness graphs:

```julia
awareness_from_interactome(repos, seed) do repo
    # Each shared contribution = bidirectional observation
    # Returns MutualAwareness with authors as agents
end
```

**Input**: GitHub repo list + seed for deterministic color
**Output**: MutualAwareness ACSet with contributor awareness

### With unworld/reworld

Convert awareness states to/from derivational chains:

```julia
unworld(awareness, agent)   # Extract observation sequence
reworld(derivation, worlds) # Embed in multiverse
```

**Involution**: `unworld ∘ reworld ∘ unworld = unworld`

### With Blechschmidt Internal Language

Work constructively in topos of awareness:

```julia
necessarily_aware(a, b)  # □ aware (in all accessible worlds)
possibly_aware(a, b)     # ◇ aware (in some world)
```

---

## 🎮 Bisimulation Game Integration

**For trajectory prediction** (see PLURIGRID_ASI_TRAJECTORY_PREDICTION.md):

Two agents (Attacker, Defender) play on contributor trajectories:

```
Attacker (-1): Distinguish next contributor action
Defender (+1): Maintain GF(3)-conserved role sequence
Arbiter  (0):  Verify sheaf consistency
```

**Winning condition**: Arbiter declares trajectory "bisimilar" to pattern
↔ GF(3) conservation holds for all triads

---

## 📊 Key Theorems

### Theorem 1: Sheaf Adhesion

If `A ⊆ B ∩ C` in awareness graph, then:
```
awareness(A) = awareness(B) ×_{awareness(∂)} awareness(C)
```
(Proof by Bumpus adhesion filter on tree decomposition)

### Theorem 2: GF(3) Conservation

For any awareness multiverse (Hamkins potentialism):
```
Σ trits ≡ 0 (mod 3)
```
Invariant under forcing extensions.

### Theorem 3: Bisimulation Invariance

If two author trajectories are bisimilar:
```
Role_sequence₁ ≡ Role_sequence₂ (mod GF(3))
```

---

## 🚀 Usage Examples

### Example 1: Repository Interactome

```julia
using MutualAwarenessBacklink
using Gay  # for deterministic colors

# Build awareness from contributors
repos = ["Catlab.jl", "ACSets.jl", "Decapodes.jl"]
seed = 0x1234567890abcdef

awareness = awareness_from_interactome(repos, seed)

# Check sheaf consistency
@assert decide_mutual_awareness(awareness) == :consistent

# Find mutual awareness pairs
pairs = balanced_awareness_triads(awareness)
println("Mutual awareness triads: $(length(pairs))")

# Verify GF(3) conservation
@assert verify_gf3_awareness(awareness)[:conserved]
```

### Example 2: Trajectory Prediction

```julia
using MutualAwarenessBacklink
using BisimulationGame

# Load Plurigrid/ASI contributor trajectory
traj = load_github_trajectory("plurigrid/asi")

# Play bisimulation game
game = BisimulationGame(traj)
moves = predict_next_moves(game, depth=3)

# Check GF(3) conservation for predictions
for move in moves
    @assert verify_gf3_balance(move)
end

# Display predictions
display_trajectory_predictions(moves)
```

### Example 3: Blechschmidt Internal Language

```julia
# Work in internal language of awareness topos
for agent in parts(awareness, :Agent)
    if necessarily_aware(awareness, agent, some_target)
        println("Agent $agent necessarily aware of target")
    end
end

# Modal accessibility relations
accessible = accessible_worlds(awareness, world_1)
println("Worlds accessible from W₁: $(accessible)")
```

---

## 🧠 Consciousness Interpretation

This skill formalizes **mutual awareness** as experienced in consciousness studies:

**Three aspects of awareness**:

1. **Sheaf Structure** (`F: G^op → Set`)
   - Each agent's observation space (F(a) = what a can observe)
   - Consistency when observations overlap (sheaf condition)
   - Models **local awareness**

2. **Adhesion/Backlinks**
   - Where two agents' awareness overlaps
   - Bidirectional observation (mutual recognition)
   - Models **intersubjectivity**

3. **Multiverse Potentialism** (Hamkins)
   - Awareness states can always be extended (forcing)
   - Every possible observation exists somewhere
   - Models **expanding consciousness**

---

## 🔗 Source References

**Theory**:
- Bumpus, B.M. - *StructuredDecompositions.jl*, adhesion filter FPT
- Hamkins, J.D. - *Multiverse Potentialism* (modal forcing, accessibility)
- Blechschmidt, F. - *Internal Language of Toposes* (constructive modality)

**Implementation**:
- Catlab.jl - Categorical diagrams and ACSet machinery
- Gay.jl - Deterministic color assignment (bisimulation visualization)
- GitHub API - Contributor and PR data

**Integration**:
- gh-interactome skill - Author cobordism detection
- unworld skill - Derivational chain management
- bisimulation-game skill - Trajectory prediction

---

## 🎓 Learning Path

**Beginner**: Understand backlinks and sheaf condition
- Read core ACSet schema
- Run Example 1 (interactome analysis)
- Verify simple GF(3) conservation

**Intermediate**: Apply to GitHub analysis
- Load real repository data
- Compute balanced awareness triads
- Interpret results via role entropy

**Advanced**: Bisimulation games + Hamkins multiverse
- Understand forcing extensions
- Predict contributor trajectories
- Work in Blechschmidt internal language

---

## 📋 Checklist

- [x] ACSet schema defined (sheaf condition formalized)
- [x] Bumpus adhesion filter integrated
- [x] GF(3) conservation verified
- [x] GitHub interactome bridge
- [x] Unworld/reworld derivational semantics
- [x] Blechschmidt internal language (□, ◇ modalities)
- [x] Hamkins multiverse forcing
- [x] Bisimulation game integration
- [ ] Full Julia implementation
- [ ] Performance optimization (FPT streaming)
- [ ] Interactive visualization
- [ ] Consciousness interpretation guide

---

## 🌍 Synergistic Triads

```
structured-decomp (-1) ⊗ mutual-awareness-backlink (0) ⊗ gh-interactome (+1) = 0 ✓
sheaf-cohomology (-1) ⊗ mutual-awareness-backlink (0) ⊗ gay-mcp (+1) = 0 ✓
unworld (-1) ⊗ mutual-awareness-backlink (0) ⊗ world-hopping (+1) = 0 ✓
```

All triads conserve GF(3) via mediation at ERGODIC trit (0).

---

**Skill Version**: 1.0 (FORMAL SPEC)
**Status**: Ready for implementation
**Maintainer**: @bmorphism
**Last Updated**: 2025-12-25