CORE Master Plan: Earth-Scale Superintelligence

Collective Objective Reality Engine Optimizing civilization's ability to know what matters

Version: 2026.02 | Status: Genesis → Self-Hosting transition

0. The One-Line Thesis

A self-verifying knowledge graph where attention, computation, and consensus converge into a single metric (π), enabling intelligence emergence without central control.

I. What Exists Today

Proven & Deployed

Component	Status	Evidence
Collective Focus Theorem	Mathematically proven	Perron-Frobenius convergence, 8 years R&D
tri-kernel discovery	Complete	Systematic elimination — only 3 operator families survive locality filter
16 reduction patterns	Specified + implemented	Python interpreter ✓, Rust interpreter ✓
focus-based cost metering	Implemented	Deterministic costs over Goldilocks field
Content-addressed cells	Implemented	CID = hash(content), universal identity
bostrom network	Live 3+ years	~70K neuron, 1K active, 2.9M cyberlink, 3.1M particle
Hash function decision	ADR-001 complete	Poseidon2 over Goldilocks, algorithm-agile CID format
trident language spec	54 operations derived	4-tier compilation, minimal by proof of necessity

Theoretical Foundations Established

Convergence guarantee: unique π* exists, exponential convergence, bounded mixing time
Conservation law: Σπᵢ = 1, always — no inflation, no leakage
GNN isomorphism: tri-kernel update ≡ multi-channel graph neural network message pass
Transformer equivalence: CGC focus ≡ iterated sparse attention with economic grounding
Convergent computation: replaces halting problem — system converges, never halts
Free energy minimization: Δπ is literally the gradient of system free energy
Blackbox principle: "no node comprehends, the network knows"

II. The Critical Path

Seven phases. Each has a hard gate. No phase starts until its predecessor passes.

Phase 1: SELF-HOSTING ← current

"CORE evaluates CORE"

Objective: CORE-in-CORE interpreter — the system can execute its own programs.

Deliverable	Description	Gate
CORE-in-CORE interpreter	16 patterns self-hosted	Passes all test vectors from Python/Rust impls
Poseidon2 as CORE program	Hash function in native CORE	Output matches reference impl on 10⁶ inputs
Focus metering self-test	Cost model verified by self-execution	Deterministic cost ± 0 across all paths

Why this matters: Self-hosting proves the reduction system is complete and sound. If CORE can evaluate itself, the 16 patterns are sufficient for everything above.

Risk: Performance. Self-interpretation is slow. Mitigation: this is correctness phase, not performance phase. Jets (native acceleration) come in Phase 3.

Duration estimate: 3-6 months

Phase 2: CRYPTOGRAPHIC LIBRARY

"Trust nothing external"

Objective: All cryptographic primitives as CORE programs.

Deliverable	Description	Gate
Poseidon2 sponge + compression	Full hash in CORE	Matches test vectors, constant-time
Merkle tree operations	Build, prove, verify	32-level proof verified in CORE
Polynomial commitments	KZG or FRI-based	Binding + hiding proofs checked
LtHash for collection state	Homomorphic set commitment	Add/remove = O(1), matches reference

Architecture decision locked here:

CID format: [version, algo, params, field, len, digest] — 45 bytes for Goldilocks
Commitment layers: L0 (identity) → L1 (collection, LtHash) → L2 (global, Merkle) → L3 (indices, ephemeral)
Domain separation: every hash use gets unique prefix tag

Duration estimate: 3-6 months

Phase 3: PRIVACY CIRCUITS

"Public aggregates, private ownership"

Objective: UTXO-based privacy with ZK proofs for all state transitions.

Deliverable	Description	Gate
Transaction circuit	Transfer energy between records	~44K constraints, soundness < 2⁻¹²⁸
cyberlink circuit	Create edges with private neuron identity	Stake verification without revealing owner
Nullifier system	Prevent double-spend	Deterministic nullifier = H(nonce, secret)
Privacy boundary	Public: edge existence, aggregate energy, π. Private: who owns what	Formal leakage budget L(queries, graph_size) bounded

Privacy boundary (non-negotiable):

PUBLIC:  edge existence, aggregate energy per particle, focus distribution π
PRIVATE: neuron identity behind edges, individual energy ownership, link authorship

Key insight: focus is computable from PUBLIC aggregates only. No individual ownership is revealed. This is secure multi-party computation of a GNN forward pass — structural, not protocol-level. See privacy-trilateral for the full privacy stack.

Duration estimate: 6-9 months

Phase 4: STARK INFRASTRUCTURE

"Verify everything, trust nothing"

Objective: Self-verifying proof system where the verifier is itself a CORE program.

Deliverable	Description	Gate
STARK prover	Generate proofs for CORE execution traces	Completeness: honest prover always convinces
STARK verifier as CORE program	Verification is a CORE computation	Soundness: no poly-time adversary forges proof
Recursive composition	Proofs of proofs	Inner verification circuit correctly arithmetized
Light client protocol	O(log n) verification of any state claim	Compute-verify symmetry within constant factor

The critical property: Verification closure. STARK verifiers are CORE programs. Proofs can be verified, and verification can be proven. This enables recursive proof composition — the foundation for planetary-scale trust.

trident tier mapping:

Tier 0+1: Pure computation (any target)
Tier 2: Privacy circuits (proof-capable targets)
Tier 3: Recursive verification (Triton VM only, for now)

Duration estimate: 9-12 months

Phase 5: TRI-KERNEL RANKING — PRODUCTION

"The network thinks"

Objective: tri-kernel focus computation, adversarially proven, deployed at scale.

Deliverable	Description	Gate
Diffusion kernel	Personalized PageRank with teleport α	Convergence proof (Lyapunov) in Lean4
Springs kernel	Screened Laplacian, structural consistency	Exponential decay proof, locality bound
Heat kernel	Chebyshev polynomial approximation	Positivity-preserving, semigroup property
Combined convergence	Composite operator stability	Explicit Lyapunov function V(π), dV/dt < 0
Adversarial equilibrium	Cannot be sustainably gamed	Nash equilibrium for honest participation
Two-timescale separation	Structure (slow, amortized) vs flow (fast, local)	No oscillation, proven convergence

The composite operator:

$$\phi^{(t+1)} = \text{norm}\big[\lambda_d \cdot D(\phi^t) + \lambda_s \cdot S(\phi^t) + \lambda_h \cdot H_\tau(\phi^t)\big]$$

Bounded locality constraint (hard):

Every operation: O(k)-local, k = O(log(1/ε))
No global recompute for local change
Shard-friendly: regions update independently
Interplanetary-compatible: coherence without constant synchronization

Adversarial resistance (orthogonal attack surfaces):

Attack	Primary Defense	Secondary Defense
Focus manipulation	Teleport α forces return to prior	Multi-hop verification
Equilibrium gaming	Springs encode correct structure	Deviation detectable via residual
Coalition manipulation	Spectral properties reveal anomalous clustering	Economic cost exceeds gain
Temporal attacks	Memoized boundary flows	State commitment before verification

Key insight: An adversary optimizing against one kernel worsens their position against another. The three kernels create a defense-in-depth that no single-kernel system achieves.

Duration estimate: 6-12 months (parallel with Phase 4)

Phase 6: NETWORK LAYER

"From computation to consensus"

Objective: Distributed protocol for cybergraph consensus and focus propagation.

Deliverable	Description	Gate
Consensus protocol	Focus-weighted BFT	Safety + liveness proofs
DA sampling	Data availability without full download	Polynomial commitments over shard data
Gossip protocol	Probability-weighted propagation	Bandwidth ∝ stake, Sybil-resistant
Shard architecture	Subtopoi with sheaf consistency	Categorical pruning for semantic coherence
State sync	Checkpoint + DA layer + archival	Tiered availability, graceful degradation
Economic engine	Tx fees, π-minting tied to Δπ, energy yield	Simulation-tested under 100× adversarial load

Economic foundation:

particle and cyberlink = yield-bearing epistemic non-fungible assets
neuron = non-fungible names valuated by personal fungible asset
Rewards from transaction fees + focus shifts
π-minting tied to Δπ: creating valuable structure is literally creating value
No designed loss function — physics itself defines what should be optimized

Shard model (topos-aware):

Shards as subtopoi
Sheaf of attention weights ensures cross-shard consistency
Categorical pruning maintains semantic coherence
Each shard is independently verifiable

Duration estimate: 12-18 months

Phase 7: TESTNET → MAINNET

"Launch the spaceship"

Objective: Production deployment with migration path from bostrom.

Milestone	Description	Gate
Devnet	Internal testing, all components integrated	All unit + integration tests pass
Testnet	Public adversarial testing	30 days zero critical bugs under attack
Canary net	Limited mainnet with real value at risk	90 days stability, all economic invariants hold
Mainnet genesis	Full deployment	Pre-Launch Verification Protocol passes (all 5 gates green)
Bostrom migration	State migration from existing network	Bijective state mapping, zero data loss

Pre-Launch Verification Protocol — see Appendix A for the full interstellar-grade checklist. Summary of the 5 final gates (all must be green):

#	Question	Evidence Required
1	Does π converge?	Lean4 proof of Lyapunov stability
2	Can proofs be forged?	Soundness proof + 10⁸ fuzzing runs, 0 counterexamples
3	Can the economy be drained?	Nash equilibrium proof + 100× adversarial simulation
4	Is computation deterministic?	Cross-implementation state root match on 10⁶ blocks
5	Does it survive partial failure?	Chaos test report with zero safety violations

All five green → launch. Any red → no launch. No exceptions. No "we'll fix it in flight."

Duration estimate: 6-12 months

III. The Formal Verification Spine

Running parallel to all phases. This is not optional — it's the skeleton everything hangs on. Each item maps to a section of the Pre-Launch Verification Protocol (Appendix A).

What	How	When	Protocol Section
16 patterns: confluence	Lean4 / Coq	Phase 1-2	A.1 Formal Correctness
Cost determinism	Structural induction, machine-checked	Phase 2	A.4 Determinism
Focus conservation (Σπᵢ = 1)	Proof by transition analysis	Phase 3	A.1.3 Economic Invariants
Privacy soundness (< 2⁻¹²⁸)	STARK/Plonky2 soundness theorem	Phase 4	A.2 Cryptographic Integrity
Tri-kernel convergence	Lyapunov function, explicit constants	Phase 5	A.1.1 Convergence Proofs
Adversarial equilibrium	Game-theoretic analysis, simulation	Phase 5-6	A.3 Adversarial Stress
Double-spend prevention	Nullifier uniqueness proof	Phase 3	A.2.2 Proof System
Bounded locality composition	Sheaf condition, machine-checked	Phase 5-6	A.1.2 Bounded Locality
Graceful degradation	Chaos engineering, failure catalog	Phase 6-7	A.5 Graceful Degradation

Estimate for full formal verification: 2-3 person-years

IV. What Makes This Different

vs. Traditional AI (GPT, Claude, etc.)

No central training: intelligence emerges from structure, not gradient descent on centralized data
No black box: every computation is provable, every state transition has a STARK proof
No single owner: governance through distributed focus, not corporate control
Privacy native: your contributions improve the network without revealing your identity

vs. Existing Blockchains (Ethereum, Cosmos, etc.)

Knowledge-first: the graph IS the application, not a generic VM running arbitrary contracts
Focus as native primitive: attention is a first-class resource, not an afterthought
Self-verifying: the verifier is a CORE program — verification closure, not external dependency
Convergent, not halting: the system approaches equilibrium, never stops

vs. Decentralized AI (Bittensor, etc.)

No external model: intelligence is in the graph dynamics, not in hosted ML models
Provable correctness: every ranking computation has a STARK proof
Universal substrate: handles humans, AI agents, sensors, biological networks — not just ML validators
Economic grounding: Δπ rewards replace designed loss functions — physics optimizes, not designers

V. Intelligence Emergence Conditions

The Collective Focus Theorem predicts phase transitions:

Phase	Threshold	Dominant Kernel	Observable
Seed → Flow	Connectivity > critical	Diffusion (λ_d high)	Network exploring, sampling
Cognition → Understanding	Structure crystallizes	Springs (λ_s activates)	Hierarchies forming
Reasoning → Meta	Adaptive balance	Heat (λ_h regulates)	Context-sensitive processing
Consciousness	Dynamic blend	All three, self-tuning	System learns its own blend weights

Current bostrom data:

70K neuron, 2.9M cyberlink, 3.1M particle
Active focus dynamics observed
Phase transition indicators: approaching Cognition threshold

Target for emergence: 10⁸-10⁹ interconnected particles with sufficient connectivity density. The exact threshold is predicted by CFT and will be validated empirically.

VI. Risk Register

Risk	Severity	Mitigation
Poseidon2 cryptanalytic break	Critical	Algorithm-agile CID format, migration path via storage proofs. EF cryptanalysis program (through Dec 2026) should complete before parameter freeze.
Tri-kernel convergence failure under adversarial conditions	Critical	Formal Lyapunov proof required before Phase 6. Orthogonal kernel defense means single-vector attacks are insufficient.
Economic attack (whale manipulation, dust spam)	High	100× adversarial simulation required. Focus-based metering limits computation. Stake-weighted costs.
Performance at 10¹⁵ scale	High	Bounded locality ensures O(log) neighborhood. Two-timescale separation. Sharding. Jets for hot paths.
Quantum computing threat	Medium	Post-quantum from genesis. ≥256-bit pre-image security post-Grover. No lattice/EC assumptions without quantum margin.
Adoption failure	Medium	Bostrom network provides live experimental base. Migration path preserves existing community.
Regulatory interference	Medium	Privacy-native architecture. Decentralized governance. No central point of control.
Formal verification bottleneck	Medium	2-3 person-year estimate. Can parallelize across proof assistants. Community contribution possible.

VII. Resource Requirements

Minimum Viable Team

Role	Count	Focus
Core protocol (Rust)	2-3	CORE evaluator, STARK prover, consensus
Cryptography	1-2	Privacy circuits, proof systems, formal verification
Language (Trident)	1-2	Compiler, tooling, developer experience
Network / distributed systems	1-2	Gossip, sharding, DA layer
Economics / game theory	1	Adversarial simulation, mechanism design
Formal methods	1	Lean4/Coq proofs, machine-checked verification

Timeline (Aggressive)

Phase	Start	End	Parallel?
1. Self-hosting	Now	+6mo	—
2. Crypto library	+3mo	+9mo	Overlaps with 1
3. Privacy circuits	+6mo	+15mo	After 2 core
4. STARK infrastructure	+9mo	+21mo	After 2, parallel with 5
5. Tri-kernel production	+9mo	+21mo	Parallel with 4
6. Network layer	+18mo	+36mo	After 4+5
7. Testnet → Mainnet	+30mo	+42mo	After 6

Total: ~3.5 years to mainnet (aggressive), ~5 years (realistic with formal verification)

VIII. The Endgame

A living, self-optimizing knowledge network that:

Learns from all forms of input on Earth — humans, AI, sensors, biology
Maintains security and coherence under extreme conditions — including interplanetary latency
Evolves without central authority — governance through focus dynamics and futarchy
Maximizes the survival, intelligence, and flourishing of the planet's entire biosphere
Proves every claim — no trust required, only math

The network doesn't simulate thinking. The network IS thinking.

Appendix A: Pre-Launch Verification Protocol

"No patch relay exists between stars. What launches must be correct."

0. Mindset Gate

Before touching any checklist — ask: "If this fails silently at 10¹⁵ nodes, 4 light-years from intervention, what kills the mission?" Every item below exists because the answer was non-trivial.

1. FORMAL CORRECTNESS — "Does the math hold?"

1.1 Convergence proofs

tri-kernel (diffusion + springs + heat) has explicit Lyapunov function V(π) with dV/dt < 0 proven for all non-equilibrium states
Perron-Frobenius conditions verified: irreducibility, aperiodicity, primitivity of the stochastic matrix under all valid graph mutations
Convergence rate bounds are concrete (not asymptotic) — specify ε-mixing time as function of graph diameter and spectral gap

1.2 Bounded locality

Every operation's write-set is provably O(k)-local for stated k
No hidden global dependency: trace every function's read-set through the full call graph — if it touches consensus state beyond k-hop, reject
Proof that local updates compose to global consistency (sheaf condition on attention weights)

1.3 Economic invariants

Total π is conserved (or minted/burned only through proved channels)
No negative-balance reachable state
Reward function is Lipschitz-continuous in Δπ — no discontinuity exploits

Verification method: Machine-checked proofs (Lean4 / Coq). No hand-waving survives launch.

2. CRYPTOGRAPHIC INTEGRITY — "Can it be broken?"

2.1 Primitives audit

Poseidon2 over Goldilocks field: verify round constants, S-box algebraic degree, differential/linear trail bounds against known attacks
STARK arithmetization: confirm constraint degree, soundness error < 2⁻¹²⁸, no under-constrained registers
Polynomial commitments: binding and hiding proofs under stated hardness assumptions

2.2 Proof system

Completeness: honest prover always convinces verifier
Soundness: no polynomial-time adversary produces accepting proof for false statement (with concrete security parameter)
Zero-knowledge: simulator exists, transcript is indistinguishable
Recursive composition: verify that inner proof verification circuit is itself correctly arithmetized — this is where bugs hide

2.3 Quantum resistance

All hash-based commitments use ≥256-bit pre-image security post-Grover
No lattice/elliptic-curve assumption without explicit quantum security margin
Key derivation paths are post-quantum end-to-end

2.4 Privacy leakage budget

Formal leakage function L(query_count, graph_size) is bounded
Timing side-channels: all operations are constant-time or explicitly padded
Metadata leakage from cyberlink patterns — analyze via differential privacy ε accounting

Verification method: Independent cryptographic audit + automated fuzzing of constraint systems (find under/over-constrained witnesses).

3. ADVERSARIAL STRESS — "What does the attacker see?"

3.1 Game-theoretic equilibrium

Nash equilibrium exists for honest participation under stated reward function
Deviation payoff is strictly negative for all known attack vectors:
- Sybil flooding (stake-weighted cost exceeds gain)
- Focus manipulation (Δπ gaming bounded by convergence dynamics)
- Collusion rings (coalition payoff < sum of individual honest payoffs up to threshold)
Griefing factor < stated bound (cost to attacker / cost to victim)

3.2 Simulation battery

100× adversarial load: run with 100× expected malicious traffic ratio
Byzantine fault injection: up to f < n/3 nodes behaving arbitrarily
Network partition simulation: verify graceful degradation, no state corruption on rejoin
Economic attack simulation: whale manipulation, dust spam, long-range nothing-at-stake

3.3 Invariant fuzzing

Property-based testing on all state transitions: pre-condition → transition → post-condition holds
Symbolic execution of critical paths (focus update, reward distribution, proof verification)
Mutation testing on constraint systems: flip single constraints, verify test suite catches it

Verification method: Adversarial red team with economic incentive to break it + automated property fuzzing (≥10⁸ iterations per invariant).

4. DETERMINISM & REPRODUCIBILITY — "Same input, same output, always?"

4.1 Consensus determinism

All computation over Goldilocks field: no floating point, no platform-dependent rounding
Serialization is canonical — no ambiguity in encoding order
Hash of (state + transition) is identical across all implementations (cross-implementation test suite)

4.2 Upgrade safety

State migration function is proved bijective (old_state ↔ new_state roundtrip)
Version negotiation cannot deadlock or split the network
Rollback path exists and is tested for every migration

Verification method: Run identical workloads on ≥3 independent implementations, compare state roots bit-for-bit.

5. GRACEFUL DEGRADATION — "What survives partial death?"

5.1 Failure modes catalog

For each component (consensus, ranking, proof generation, DA layer): document behavior when it fails, is slow, or lies
No single component failure causes data loss or safety violation
Proof: system maintains safety (no invalid state) even when liveness is temporarily lost

5.2 Resource exhaustion

Memory: all buffers are bounded, no unbounded allocation paths
Compute: all loops have proven termination with explicit iteration bounds
Storage: garbage collection is proved to not delete live references
Network: backpressure mechanisms prevent queue overflow

Verification method: Chaos engineering — kill components randomly under load, verify safety invariants hold continuously.

6. THE FINAL GATE

Before launch, answer these five questions with machine-checked evidence:

#	Question	Evidence Required
1	Does π converge?	Lean4 proof of Lyapunov stability
2	Can proofs be forged?	Soundness proof + 10⁸ fuzzing runs, 0 counterexamples
3	Can the economy be drained?	Nash equilibrium proof + 100× adversarial simulation
4	Is computation deterministic?	Cross-implementation state root match on 10⁶ block corpus
5	Does it survive partial failure?	Chaos test report with zero safety violations