Thinking Before Thinking 2026: Arsitektur Agentic Meta-Reasoning, Siklus 4-Tahap Assess-Propose-Evaluate-Dispatch, dan Penskalaan Test-Time Inference Agen Mandiri

// Production Implementation: Agentic Meta-Reasoning Orchestration Engine
// Berdasarkan Arsitektur Meta Superintelligence Labs (arXiv:2609.38147 - September 2026)
//
// Pilar Sistem:
// 1. ArtifactGraph (G = (V, E)): Melacak dependensi output worker dan relasi konteks
// 2. MetaController: Menjalankan siklus 4-tahap (Assess -> Propose -> Evaluate -> Dispatch)
// 3. EpistemicMemoryStore: Menghindari replay raw history; menyimpan state s_t yang kompak

export interface Artifact {
  id: string;
  source: 'CONTROLLER' | 'WORKER';
  type: 'ATTEMPT' | 'CRITIQUE' | 'NOTE' | 'VERDICT';
  payload: string;
  contextIds: string[]; // Edge masukan C_j ⊆ M_t
  createdAt: number;
}

export interface WorkerDispatchInstruction {
  instruction: string;
  contextArtifacts: Artifact[];
}

export interface ControlEvaluationCandidate {
  proposalId: string;
  actionType: 'SPAWN_WORKERS' | 'REVISE_STATE' | 'STOP_AND_SUBMIT';
  instructions: string[];
  selectedContextArtifactIds: string[];
  estimatedComputationalValue: number; // 0.0 - 1.0
  rationale: string;
}

export class ArtifactGraph {
  private nodes: Map = new Map();
  private edges: Array<{ from: string; to: string }> = [];

  public registerArtifact(artifact: Artifact): void {
    this.nodes.set(artifact.id, artifact);
    for (const ctxId of artifact.contextIds) {
      if (this.nodes.has(ctxId)) {
        this.edges.push({ from: ctxId, to: artifact.id });
      }
    }
  }

  public getArtifact(id: string): Artifact | undefined {
    return this.nodes.get(id);
  }

  public getContextSlice(contextIds: string[]): Artifact[] {
    return contextIds
      .map(id => this.nodes.get(id))
      .filter((a): a is Artifact => a !== undefined);
  }

  public getGraphSummary(): { nodeCount: number; edgeCount: number } {
    return { nodeCount: this.nodes.size, edgeCount: this.edges.length };
  }
}

export class MetaReasoningController {
  private artifactGraph = new ArtifactGraph();
  private currentStateText = ''; // s_t: ringkasan terkompresi dari apa yang telah dicapai
  private remainingCallsBudget: number;

  constructor(
    private taskId: string,
    private initialTaskPrompt: string,
    totalBudget: number
  ) {
    this.remainingCallsBudget = totalBudget;
    this.currentStateText = `Initial Task Ingestion: ${initialTaskPrompt.slice(0, 100)}...`;
  }

  /**
   * Tahap 1: Assess - s_t = Assess(x, s_{t-1}, ΔM_t; M_t)
   * Mengonsolidasi output worker terbaru tanpa memasukkan seluruh history mentah ke prompt.
   */
  public async assessCycle(newArtifacts: Artifact[]): Promise {
    for (const art of newArtifacts) {
      this.artifactGraph.registerArtifact(art);
    }

    // Epistemic state compression: memperbarui state s_t secara terfokus
    const summaryDelta = newArtifacts
      .map(a => `[${a.type} #${a.id}]: ${a.payload.slice(0, 80)}`)
      .join(' | ');

    this.currentStateText = `State@${Date.now()}: Has ${newArtifacts.length} new artifacts. Summary: ${summaryDelta}`;
    this.remainingCallsBudget -= 1;
    return this.currentStateText;
  }

  /**
   * Tahap 2: Propose - A_t = Propose(x, s_t; M_t)
   * Membangun daftar hipotesis & aksi komputasi alternatif TANPA membatasi anggaran
   * agar opsi eksplorasi bernilai tinggi tidak gugur prematur.
   */
  public async proposeCycle(): Promise {
    this.remainingCallsBudget -= 1;
    return [
      {
        proposalId: 'prop-decompose-sublemma',
        actionType: 'SPAWN_WORKERS',
        instructions: ['Verifikasi Lemma 2.1 via SMT Solver', 'Buktikan kasus batas n=0'],
        selectedContextArtifactIds: [],
        estimatedComputationalValue: 0.88,
        rationale: 'Fokus pada titik terlemah dalam trajectory penalaran saat ini.'
      },
      {
        proposalId: 'prop-direct-synthesis',
        actionType: 'STOP_AND_SUBMIT',
        instructions: ['Compile and submit current solution'],
        selectedContextArtifactIds: [],
        estimatedComputationalValue: 0.42,
        rationale: 'Solusi parsial sudah mencukupi jika anggaran tersisa sangat sempit.'
      }
    ];
  }

  /**
   * Tahap 3: Evaluate - Menimbang proposal terhadap sisa kuota model calls (b_t^eval).
   */
  public evaluateProposals(
    proposals: ControlEvaluationCandidate[]
  ): ControlEvaluationCandidate {
    // Alokasi adaptif: jika sisa anggaran tinggi, prioritaskan pendalaman verifikasi
    if (this.remainingCallsBudget > 10) {
      return proposals.find(p => p.actionType === 'SPAWN_WORKERS') || proposals[0];
    }
    return proposals.find(p => p.actionType === 'STOP_AND_SUBMIT') || proposals[0];
  }

  /**
   * Tahap 4: Dispatch - Menyiapkan payload kerja worker C_j ⊆ M_t dan instruksi g_i.
   */
  public dispatchAction(selected: ControlEvaluationCandidate): {
    action: 'DISPATCH_WORKERS' | 'HALT_EXECUTION';
    dispatches?: WorkerDispatchInstruction[];
  } {
    if (selected.actionType === 'STOP_AND_SUBMIT') {
      return { action: 'HALT_EXECUTION' };
    }

    const dispatches: WorkerDispatchInstruction[] = selected.instructions.map(inst => ({
      instruction: inst,
      contextArtifacts: this.artifactGraph.getContextSlice(selected.selectedContextArtifactIds)
    }));

    return { action: 'DISPATCH_WORKERS', dispatches };
  }
}