On-chain GF(3) entropy storage via Aptos Move - bulk-boundary correspondence where entropy lives in the interior and observables project to agents
Scanned 9/2/2026
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---
name: hyperbolic-bulk
description: On-chain GF(3) entropy storage via Aptos Move - bulk-boundary correspondence where entropy lives in the interior and observables project to agents
version: 1.0.0
---
# Hyperbolic Bulk Skill
**Status**: ✅ Production Ready
**Trit**: 0 (ERGODIC - mediates bulk ↔ boundary)
**Principle**: AdS/CFT correspondence for entropy
**Chain**: Aptos (Move language)
---
## Overview
The **Hyperbolic Bulk** implements on-chain entropy storage with GF(3) conservation. Named after the AdS/CFT bulk-boundary correspondence:
- **BULK** (interior): Entropy records, triads, reafference proofs
- **BOUNDARY** (observable): Agents, skills, colors
```
BOUNDARY (Observable)
┌─────────────────────────────┐
│ Agents │ Skills │ Colors │
└─────────────┬───────────────┘
│ project
▼
┌─────────────────────────────┐
│ HYPERBOLIC BULK │
│ ┌─────────────────────┐ │
│ │ EntropyRecord │ │
│ │ drand ⊕ eeg ⊕ vrf │ │
│ └──────────┬──────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ EntropyTriad │ │
│ │ GF(3) = 0 conserved│ │
│ └──────────┬──────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ ReafferenceProof │ │
│ │ predict = observe │ │
│ └─────────────────────┘ │
└─────────────────────────────┘
```
---
## Entropy Sources
| Source | Type | Property |
|--------|------|----------|
| **DRAND** | League of Entropy | Public, verifiable, unpredictable |
| **EEG** | Brainwave bands | Private, embodied, cognitive state |
| **Aptos VRF** | On-chain randomness | Consensus-secured, tamper-proof |
**Combination**: `combined = drand_seed ⊕ eeg_seed ⊕ onchain_rand`
---
## GF(3) Conservation
Triads must sum to 0 mod 3:
```
MINUS (-1) ≡ 2 (mod 3) — Verification/Constraint
ERGODIC (0) — Coordination/Balance
PLUS (+1) — Generation/Exploration
Conservation: trit_1 + trit_2 + trit_3 ≡ 0 (mod 3)
```
**Strict Mode**: `form_conserved_triad()` reverts if not conserved.
---
## Move Contract
```move
module hyperbolic_bulk::entropy_triads {
struct EntropyRecord has store, drop, copy {
drand_round: u64,
drand_seed: u256,
eeg_seed: u256,
combined_seed: u256,
timestamp: u64,
trit: u8,
color_hex: vector<u8>,
}
struct EntropyTriad has store, drop, copy {
record_id_1: u64,
record_id_2: u64,
record_id_3: u64,
gf3_sum: u8,
gf3_conserved: bool,
skill_1: vector<u8>,
skill_2: vector<u8>,
skill_3: vector<u8>,
}
struct ReafferenceProof has store, drop, copy {
seed: u256,
predicted_color: vector<u8>,
observed_color: vector<u8>,
matched: bool,
loop_type: vector<u8>, // "loopy_strange" or "exafference"
}
#[randomness]
entry fun store_entropy(...) { /* combines drand ⊕ eeg ⊕ vrf */ }
entry fun form_conserved_triad(...) { /* enforces GF(3) = 0 */ }
entry fun record_reafference(...) { /* proves prediction = observation */ }
}
```
---
## Integration with World-Memory-Worlding
| Autopoietic Phase | Bulk Operation | Trit |
|-------------------|----------------|------|
| **MEMORY** | `store_entropy()` | -1 |
| **REMEMBERING** | `get_triad()` | 0 |
| **WORLDING** | `form_conserved_triad()` | +1 |
The loop closes when worlded triads become new memory records.
---
## Reafference Proofs
On-chain proof that prediction matched observation:
```move
struct ReafferenceProof {
seed: u256,
predicted_color: vector<u8>,
observed_color: vector<u8>,
matched: bool, // prediction == observation
loop_type: vector<u8>, // "loopy_strange" iff matched
}
```
**Loopy Strange**: Generator ≡ Observer when same seed produces same color.
---
## GF(3) Triads
```
bisimulation-game (-1) ⊗ hyperbolic-bulk (0) ⊗ gay-mcp (+1) = 0 ✓
duckdb-timetravel (-1) ⊗ hyperbolic-bulk (0) ⊗ world-hopping (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ hyperbolic-bulk (0) ⊗ operad-compose (+1) = 0 ✓
```
---
## Python Integration
```python
from drand_skill_sampler import DrandSkillSampler, EEGEntropySource
# Create entropy sources
eeg = EEGEntropySource(
delta=0.15, theta=0.25, alpha=0.35,
beta=0.20, gamma=0.05
)
# Sample skills with DRAND entropy
sampler = DrandSkillSampler(drand_seed=10770320150143512701, eeg_source=eeg)
# Generate Aptos transaction
tx = sampler.to_aptos_transaction()
# {
# "function": "hyperbolic_bulk::entropy_triads::store_entropy",
# "arguments": [drand_round, drand_seed, eeg_seed, color_hex]
# }
```
---
## Ruler Configuration
```toml
[entropy]
drand_round = 24634579
eeg_dominant = "alpha"
aptos_module = "hyperbolic_bulk::entropy_triads"
[mcp]
enabled = true
servers = ["gay", "drand", "localsend"]
[agents.codex]
trit = 0
bulk_address = "0x..."
```
---
## Commands
```bash
# Deploy contract
aptos move publish --package-dir hyperbolic_bulk
# Store entropy
aptos move run --function-id 'hyperbolic_bulk::entropy_triads::store_entropy' \
--args u64:24634579 u256:0x9577dd1cea89307d u256:0x8219ed722cbf7d6a
# Form conserved triad
aptos move run --function-id 'hyperbolic_bulk::entropy_triads::form_conserved_triad' \
--args u64:0 u64:1 u64:2 'vector<u8>:skill1' 'vector<u8>:skill2' 'vector<u8>:skill3'
# Query stats
aptos move view --function-id 'hyperbolic_bulk::entropy_triads::get_stats'
```
---
## The Bulk-Boundary Insight
**Why "hyperbolic"?**
In AdS/CFT, the hyperbolic (anti-de Sitter) bulk contains more information than the flat boundary. Similarly:
- **Bulk**: Full entropy (drand × eeg × vrf), all triads, all proofs
- **Boundary**: Projected observables (colors, skill names, agent states)
The boundary is a *lossy projection* of the bulk. But GF(3) conservation is preserved across the projection—it's a **geometric invariant**.
**Reafference as Holography**:
- When prediction = observation, the boundary faithfully represents the bulk
- "Loopy strange" = holographic consistency (no information loss)
- "Exafference" = external perturbation (bulk ≠ boundary)
---
## See Also
- [`world-memory-worlding`](../world-memory-worlding/SKILL.md) — Autopoietic loop
- [`gay-mcp`](../gay-mcp/SKILL.md) — Deterministic color generation
- [`drand_skill_sampler.py`](../../ies/drand_skill_sampler.py) — Entropy sampling
---
**Skill Name**: hyperbolic-bulk
**Type**: On-Chain Entropy / GF(3) Conservation
**Trit**: 0 (ERGODIC - bulk-boundary mediation)
**Chain**: Aptos Move
**Contract**: `hyperbolic_bulk::entropy_triads`
## 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
- `general`: 734 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.Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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