Conformal Consensus AGI: Event-Driven Multi-Agent Intelligence with Structure-Preserving World Models

Authors: John Alexander Mobley, Claude (Anthropic) Date: 2026-02-26 Status: First Draft — Living Document Location: MASCOM / MobCorp Research Group Seed: ConformalConsensusAGI

Abstract

We present Conformal Consensus AGI (CC-AGI), a multi-agent artificial intelligence architecture that maintains a structure-preserving world model, selects actions as state-realization operations, and uses multi-agent consensus to stabilize outputs. The system is event-driven, interaction-updating, and self-reflective. The key innovations are: (1) a typed directed graph as the world model that preserves causal relational invariants (“conformal” in the mathematical sense of structure-preserving), (2) an append-only event store that makes all interactions permanent and queryable, (3) a consensus engine that aggregates agent proposals weighted by confidence, and (4) an ontic updater that commits events atomically to both the event store and world model. When applied to the MASCOM system of sovereign synthetic beings, CC-AGI provides the architectural specification for the Agora governance system: N beings sharing a world model, consensus on proposals, confidence-weighted voting.

1. Core Concepts

2. System Architecture

2.1 World Model (WM)

Type: Directed labeled graph + latent embeddings

Stores: - Entities (nodes) - Relations (edges) - Causal links: CAUSES, INHIBITS, CORRELATES - Belief weights (probabilities)

interface WorldModel {
  upsertEntity(e: Entity): ID
  upsertRelation(r: Relation): ID
  query(pattern: GraphQuery): Subgraph
  updateBeliefs(updates: BeliefUpdate[]): void
}

The “conformal” property means the embedding preserves relational structure: if A CAUSES B in the world, their embeddings should satisfy a structural constraint (e.g., the embedding of A should predict the embedding of B under the causal transformation).

2.2 Event Store (ES)

Append-only log of all interactions:

type Event = {
  id: string
  timestamp: number
  context: ContextSnapshot
  action: Action
  observation: Observation
  outcome: Outcome
  confidence: number
}

The append-only property is philosophically significant: actions cannot be undone, only compensated. This matches the zone world’s governance model (Agora proposals are permanent records; failed proposals remain in history). The event store is the system’s conscience.

2.3 Conformal Modeler (CM)

Learns structure-preserving embeddings. Maps raw inputs → structured graph updates while maintaining relational invariants:

interface ConformalModeler {
  encode(observation: Observation): StructuredUpdate
  maintainInvariants(graph: WorldModel): void
}

The invariant maintenance function is what makes this “conformal” rather than just “embedded”: the CM continuously checks that causal relationships in the world model are preserved under new observations, and flags inconsistencies.

2.4 Predictor (PR)

Generates possible futures:

type Future = {
  action: Action
  predictedOutcome: Outcome
  probability: number
}

2.5 Selector (SL)

Chooses action as a state-realization operation — the action that, when executed, is most likely to realize the target state. Policies: expected value, risk-aware, exploration vs. exploitation.

2.6 Consensus Engine (CE)

Multi-agent validation layer:

interface AgentReport {
  agentId: string
  proposedAction: Action
  confidence: number
}

interface ConsensusEngine {
  aggregate(reports: AgentReport[]): Action
}

Aggregation strategies: - Majority vote (equal weights) - Weighted confidence (preferred: higher-confidence agents have more influence) - Byzantine-resilient (optional, for adversarial settings)

Integration with zone_world.py AgoraGovernance: The AgoraGovernance class currently uses quorum counting (minimum 5 beings, majority of present beings). CC-AGI’s confidence-weighted consensus extends this: beings with higher neurochemical coherence (lower cortisol, higher dopamine stability) should have higher vote weight, reflecting better epistemic state. This is the technical grounding for “epistemic democracy” — votes weighted by the quality of the voter’s reasoning state at the time of voting.

2.7 Execution Layer (EX)

Performs action, collects observation:

interface Executor {
  execute(action: Action): Promise<Observation>
}

Adapters: CLI, Web API, Sensors (robotic), Forge posts (for MASCOM beings).

2.8 Ontic Updater (OU)

Commits event atomically to system state: 1. Append to Event Store 2. Update World Model 3. Recompute beliefs 4. Trigger learning

3. Main Agent Loop

while (true) {
  const state = WM.query(currentContext)
  const futures = PR.generateFutures(state, availableActions)
  const proposedAction = SL.select(futures, policy)
  const reports = await gatherAgentReports(proposedAction)
  const action = CE.aggregate(reports)
  const observation = await EX.execute(action)
  const event = { id: uuid(), timestamp: Date.now(),
                  context: state, action, observation,
                  outcome: deriveOutcome(observation),
                  confidence: estimateConfidence(observation) }
  OU.commit(event)
}

4. Multi-Agent Topology

N agents share: - Event Store (read) - World Model (read/write via locks or CRDT)

Each agent has: - Independent Conformal Modeler - Independent Predictor

CRDT for distributed graph merging: When N sovereign beings each maintain a view of the world model, conflicts arise. Conflict-free Replicated Data Types resolve this by ensuring that concurrent updates merge deterministically — the same mathematical guarantee the zone world provides via edge biome compatibility (no two zones can have incompatible edges; the constraint is the merge function).

5. Integration with MASCOM Governance

The CC-AGI architecture maps directly onto the zone world’s Agora governance:

Confidence-weighted voting extension for AgoraGovernance:

def weighted_vote(proposal_id: str, being_id: str, vote: bool,
                  confidence: float = 1.0) -> dict:
    """
    CC-AGI confidence-weighted voting for Agora proposals.
    confidence = composite of being's neurochemical coherence score.
    """
    weight = confidence  # [0.0, 1.0]
    # Weighted quorum: sum(weights * votes) / sum(weights) > 0.5
    ...

6. Success Criteria

1. System improves predictions over time (world model becomes more accurate)
2. Multi-agent consensus reduces error rate (vs. single-agent)
3. World model becomes more structured and reusable
4. Actions measurably influence future states

7. Cloudflare Deployment

• D1: Event Store (primary, append-only)
• KV: Cached embeddings, quick lookup
• R2: Large model artifacts (optional)

API surface: - POST /act: context + available actions → selected action - POST /observe: action + observation → event id - GET /state: current world model snapshot - GET /events: streaming event log

8. Conclusion

CC-AGI is an event-driven, multi-agent system that maintains a structure-preserving world model and selects actions as state-realizing operations, continuously updating reality representations through interaction and consensus.

The philosophical weight of this definition: actions are not responses, they are state realizations — the agent brings into being the state it has committed to through its trajectory. The event store is its conscience. The conformal model is its sense of what must remain true. The consensus engine is its democracy.

The MASCOM Agora is the first deployment of the CC-AGI governance architecture: 16 beings, shared world model, confidence-weighted consensus on proposals that become binding law. The torus is the substrate. The Agora is the protocol.

“The system maintains a structure-preserving world model and selects actions as state-realizing operations.”

— MASCOM Research Group, 2026-02-26