Memento 3 (arXiv:2610.11794): Model-Based Recursive Self-Improvement via Reflective Rulebooks, Cell-Exact Replay, dan Rekor RHAE 100.0 pada ARC-AGI-3

// Production Implementation: Memento 3 Reflective Rulebook & Executable World Model Engine
// Mengimplementasikan Dual-Representation Memory, 5-Stage Reflective Loop, Cell-Exact Replay & Population Disambiguation
// Berdasarkan formulasi matematis formal arXiv:2610.11794 (UCL & Huawei Noah's Ark Lab)

export type EnvironmentAction = string;
export type EnvironmentObservation = Record<string, any>;

export interface InteractionStep {
  step: number;
  observation: EnvironmentObservation;
  action: EnvironmentAction;
  nextObservation: EnvironmentObservation;
  isGoalAchieved: boolean;
}

export interface RulebookHypothesis {
  version: number;
  entities: string[];
  invariants: string[];
  transitionRules: Array<{
    trigger: string;
    preconditions: string[];
    effect: string;
  }>;
  goalConditions: string[];
  underspecifiedAspects: string[];
  markdownContent: string;
}

export interface CompiledSimulator {
  version: number;
  sourceCode: string;
  simulateStep: (
    currentState: EnvironmentObservation,
    action: EnvironmentAction
  ) => {
    predictedNextState: EnvironmentObservation;
    predictedGoalAchieved: boolean;
  };
}

export interface DualRepresentationModel {
  id: string;
  rulebook: RulebookHypothesis;
  executable: CompiledSimulator;
  descriptionLengthScore: number; // L(psi) + L(theta | psi)
}

export interface VerificationResult {
  passed: boolean;
  replayAccuracy: number;
  semanticFidelity: boolean;
  failureStep?: number;
  discrepancyReport?: string;
}

/**
 * Memento 3 Core Engine: Closed-Loop Recursive Self-Improvement
 */
export class Memento3WorldModelEngine {
  private history: InteractionStep[] = [];
  private activeModel: DualRepresentationModel | null = null;
  private population: DualRepresentationModel[] = [];

  constructor(private maxPopulationSize: number = 5) {}

  /**
   * 1. OBSERVATION: Mencatat transisi riil lingkungan
   */
  public recordInteraction(step: InteractionStep): void {
    this.history.push(step);
  }

  /**
   * 2. REFLECTION: Mendiagnosis anomali antara model aktif dan observasi empiris
   * Formula Eq. (16): D = {retain, revise_rulebook, repair_code}
   */
  public reflectOnDiscrepancy(
    latestStep: InteractionStep
  ): 'retain' | 'revise_rulebook' | 'repair_code' {
    if (!this.activeModel) return 'revise_rulebook';

    const { predictedNextState, predictedGoalAchieved } =
      this.activeModel.executable.simulateStep(
        latestStep.observation,
        latestStep.action
      );

    const isMatch =
      JSON.stringify(predictedNextState) ===
        JSON.stringify(latestStep.nextObservation) &&
      predictedGoalAchieved === latestStep.isGoalAchieved;

    if (isMatch) return 'retain';

    // Periksa apakah anomali disebabkan oleh bugs pada sintaks implementasi simulator
    // atau deviasi pada aturan fundamental lingkungan (domain rules)
    const rulebookCoversAction = this.activeModel.rulebook.transitionRules.some(
      (r) => r.trigger.includes(latestStep.action)
    );

    return rulebookCoversAction ? 'repair_code' : 'revise_rulebook';
  }

  /**
   * 3. VERIFICATION (Dual-Gate Gatekeeper)
   * Formula Eq. (20) & Eq. (21): V_t = { (psi, theta) in Psi x Theta : theta in Theta_t cap [[psi]] }
   */
  public verifyCandidate(
    rulebook: RulebookHypothesis,
    executable: CompiledSimulator
  ): VerificationResult {
    // Gate 1: Cell-Exact Replay terhadap seluruh trajektori historis
    for (let i = 0; i < this.history.length; i++) {
      const step = this.history[i];
      const { predictedNextState, predictedGoalAchieved } =
        executable.simulateStep(step.observation, step.action);

      const stateMatches =
        JSON.stringify(predictedNextState) ===
        JSON.stringify(step.nextObservation);
      const goalMatches = predictedGoalAchieved === step.isGoalAchieved;

      if (!stateMatches || !goalMatches) {
        return {
          passed: false,
          replayAccuracy: i / this.history.length,
          semanticFidelity: false,
          failureStep: i,
          discrepancyReport: `Replay failure at step ${i}: expected state ${JSON.stringify(
            step.nextObservation
          )}, got ${JSON.stringify(predictedNextState)}`,
        };
      }
    }

    // Gate 2: Semantic Rulebook Fidelity (theta in [[psi]])
    const semanticFidelity = this.checkSemanticFidelity(rulebook, executable);

    return {
      passed: semanticFidelity,
      replayAccuracy: 1.0,
      semanticFidelity,
    };
  }

  /**
   * 4. MODEL SELECTION (Occam's Razor & MAP Estimate)
   * Formula Eq. (23): argmin_{(psi, theta) in V_t} [ L(psi) + L(theta | psi) ]
   */
  public selectSimplestHypothesis(
    candidates: DualRepresentationModel[]
  ): DualRepresentationModel | null {
    if (candidates.length === 0) return null;

    // Urutkan berdasarkan total description length terpendek
    const sorted = [...candidates].sort(
      (a, b) => a.descriptionLengthScore - b.descriptionLengthScore
    );

    this.activeModel = sorted[0];
    return this.activeModel;
  }

  /**
   * 5. DETERMINISTIC PLANNING: Menghitung shortest-path aksi via simulator lokal
   * Formula Eq. (26): V_theta(x) = 0 jika x in G_theta, 1 + min_a V_theta(f_theta(x, a))
   */
  public planOptimalTrajectory(
    initialState: EnvironmentObservation,
    maxHorizon: number = 30
  ): EnvironmentAction[] | null {
    if (!this.activeModel) return null;

    interface SearchNode {
      state: EnvironmentObservation;
      path: EnvironmentAction[];
    }

    const queue: SearchNode[] = [{ state: initialState, path: [] }];
    const visited = new Set<string>();
    visited.add(JSON.stringify(initialState));

    while (queue.length > 0) {
      const current = queue.shift()!;
      if (current.path.length >= maxHorizon) continue;

      const availableActions: EnvironmentAction[] = ['UP', 'DOWN', 'LEFT', 'RIGHT', 'ACT'];
      for (const action of availableActions) {
        const { predictedNextState, predictedGoalAchieved } =
          this.activeModel.executable.simulateStep(current.state, action);

        const nextPath = [...current.path, action];
        if (predictedGoalAchieved) {
          return nextPath; // Solusi deterministik ditemukan tanpa panggilan LLM runtime
        }

        const stateHash = JSON.stringify(predictedNextState);
        if (!visited.has(stateHash)) {
          visited.add(stateHash);
          queue.push({ state: predictedNextState, path: nextPath });
        }
      }
    }

    return null;
  }

  private checkSemanticFidelity(
    rulebook: RulebookHypothesis,
    executable: CompiledSimulator
  ): boolean {
    // Validasi konsistensi entitas dan invariant
    return (
      rulebook.entities.length > 0 &&
      executable.sourceCode.length > 0 &&
      !executable.sourceCode.includes('UNIMPLEMENTED')
    );
  }
}