confidence-engine/tests/graph/live-update-experiment-helper.cjs

/**
 * Canonical live-update experiment harness.
 *
 * Loads .env.local, validates Ollama configuration, invokes updateCase()
 * through the real production path, and captures the standard reasoning
 * checkpoints needed by reasoning experiments.
 *
 * Usage (from a test file):
 *   const { runLiveExperiment } = require("./tests/graph/live-update-experiment-helper.cjs");
 *
 *   const result = await runLiveExperiment({
 *     graph: /* SituationGraph fixture *\/,
 *     previousQuestion: "Is risk a hard constraint?",
 *     answer: "Risk matters more to me.",
 *   });
 */

const dotenv = require("dotenv");
dotenv.config({ path: ".env.local" });

// Re-export production helper for apparatus use — avoids fabricating Finding propositions.
const { deriveFindingsFromContributions } = require("../../lib/graph/finding-helpers.js");

function assertRequiredEnv(name) {
  const value = process.env[name];
  if (!value) {
    throw new Error(
      `Live experiment requires ${name}. Set it in .env.local.\n` +
      `Found: OLLAMA_BASE_URL=${process.env.OLLAMA_BASE_URL ?? "(missing)"}, ` +
      `OLLAMA_MODEL=${process.env.OLLAMA_MODEL ?? "(missing)"}`
    );
  }
  return value;
}

/**
 * Extract checkpoint data from a raw updateCase() result object.
 *
 * Pure function — no side effects, no I/O. Intended for use both by
 * `runLiveExperiment()` (apparatus wrapper) and by deterministic tests
 * that pass mock results directly.
 *
 * @param {object} result  — raw result from updateCase()
 * @param {object} params — { graph?, answer? }
 *   - graph: SituationGraph input fixture (for findingsCount total derived from input)
 *   - answer: previous question text for deriveCategory fallback
 * @returns {object} checkpoint object per spec contract
 */
function extractLiveExperimentCheckpoint(result, params) {
  const answerMeaning = result?.proposal?.answerMeaning || {};

  // Graph structural checkpoint (available from updatedSituationGraph only when present)
  const updatedGraph = result?.updatedSituationGraph;
  const graphCheckpoint = updatedGraph
    ? {
        nodeCount:       updatedGraph.nodes?.length      ?? 0,
        edgeCount:       updatedGraph.edges?.length      ?? 0,
        resolvedNodeIds: updatedGraph.resolvedNodeIds    ?? [],
      }
    : null;

  return {
    // ── timing (available via wrapper) ────────────────────
    modelElapsedMs:       result?.modelElapsedMs       !== undefined ? result.modelElapsedMs : null,
    endToEndElapsedMs:    result?.endToEndElapsedMs    !== undefined ? result.endToEndElapsedMs : null,
    providerGenerateCalls: result != null && result.providerGenerateCalls != null ? result.providerGenerateCalls : 0,
    modelName:            result?.modelName             || null,

    // ── answer meaning extraction ─────────────────────────
    userSupportedMeaning: answerMeaning.userSupportedMeaning ?? null,
    possibleInference:    answerMeaning.possibleInference    ?? null,
    rawAnswerCategory:    _deriveCategory(params?.answer),
    proposedMeaningCategory: _deriveCategory(answerMeaning.userSupportedMeaning || ""),

    // ── validation / guard checkpoints ───────────────────
    proposalValidation: result?.proposalValidation ?? { success: false, errors: [] },
    compatibilityGuard: result?.compatibilityApplied !== undefined
      ? { passed: result.compatibilityApplied, warnings: [], violations: [] }
      : { passed: false, warnings: [], violations: [] },

    // ── graph mutation checkpoint (from appliedGraph) ────
    graphMutation: result?.appliedGraph
      ? { nodes: result.appliedGraph.nodes ?? null, edges: result.appliedGraph.edges ?? null }
      : null,

    // ── graph structural checkpoint (available from updatedSituationGraph) ─
    graphCheckpoint,

    // ── findings checkpoint (from input graph fixture, not production path) ─
    findingsCount: {
      total: (params?.graph && Array.isArray(params.graph))
        ? params.graph.length
        : (params?.graph && params.graph.nodes != null)
          ? params.graph.nodes.length
          : 0,
      eligible: "UNAVAILABLE", // not canonically observable without production contract change
    },

    // ── CU narrative (NOT captured — synthesis is a separate seam from updateCase()) ─
    currentUnderstanding: "UNAVAILABLE",

    // ── candidate evidence (UNAVAILABLE without changing production code) ────────
    eligibleFindingPropositions: "UNAVAILABLE",

    // ── helper internals (for diagnostic / test verification) ────────────────────
    selectedQuestion: result?.proposal?.selectedQuestion ?? null,
    behaviourSelection: result?.behaviourSelection ?? null,
    reasoningState: _extractReasoningState(result),
  };
}

/**
 * Minimal deterministic category derivation from free-text (mirrors the production pipeline).
 */
function _deriveCategory(text) {
  if (!text || !text.trim()) return "none";

  const lower = text.toLowerCase();

  // Conditional patterns
  if (/normally\s+(?:avoid|skip|not\s+take|would\s+n't|can\'t)/i.test(lower) &&
      /(might|could|would\s+(?:accept|allow|take|do))/i.test(lower)) {
    return "conditional_tradeoff";
  }

  // Contrast / but patterns
  if (/but\s+i?\s*(don\'t|cannot|can\'t|won\'t|will\s+not)/i.test(lower)) {
    return "qualified_support";
  }

  // Explicit hard constraint
  if (/(hard\s+constraint|must\s+(?:not|never|always)|can\'?\s*t(?:o)\s*(?:not|be\s+able\s+to)|absolutely\s+cannot)/i.test(lower)) {
    return "hard_constraint";
  }

  // Strong preference / must positive
  if (/(must\s+(?:have|do|get)|absolutely\s+(?:need|require)|cannot\s+proceed\s+without)/i.test(lower)) {
    return "strong_preference";
  }

  // Relative importance
  if (/more\s+to\s+me|matters\s+more|higher\s+priority|top\s*priority/i.test(lower)) {
    return "relative_importance";
  }

  // Support / contraindicate
  if (/support(s)?\b|confirm(s)?\b|validates?\b/i.test(lower)) {
    return "supports_decision";
  }
  if (/contradicts?\b|against\s+it\b|i\s*don\'?\s*t\s*(?:think\s+so|agree)\b/i.test(lower)) {
    return "contradicts_decision";
  }

  return "cannot_determine";
}

/**
 * Extract reasoning state from a raw updateCase() result object.
 * Exported for deterministic test verification of preserved behaviour.
 */
function _extractReasoningState(result) {
  const rs = result?.reasoningState;
  if (!rs) return null;
  return {
    turnCount:     rs.turnCount      ?? null,
    health:        rs.health          ?? null,
    phase:         rs.phase           ?? null,
    progress:      rs.progress        ?? null,
    unknownStatuses: (rs.unknownStatuses || []).map(s => s.id + ":" + s.status),
  };
}

/**
 * Run one live experiment through the real updateCase() production path.
 */
async function runLiveExperiment({ graph, previousQuestion, answer, findings = [] }) {
  const baseUrl = assertRequiredEnv("OLLAMA_BASE_URL");
  const model   = assertRequiredEnv("OLLAMA_MODEL");

  if (baseUrl === "http://localhost:11434" || baseUrl === "http://127.0.0.1:11434") {
    throw new Error(
      `Live experiment harness refuses to use localhost fallback. ` +
      `OLLAMA_BASE_URL=${baseUrl}. Configure a real host in .env.local.`
    );
  }

  const { updateCase } = await import(
    "../../lib/graph/orchestrator.js"
  );

  const { getProvider } = await import("../../lib/llm/provider.js");
  const provider = getProvider();
  let modelElapsedMs = null;
  let providerGenerateCalls = 0;

  const timedProvider = {
    async generateReconstruction(prompt, modelName, outputSchema) {
      providerGenerateCalls++;
      const startedAt = Date.now();
      const response = await provider.generateReconstruction(prompt, modelName, outputSchema);
      modelElapsedMs = Date.now() - startedAt;
      return response;
    },
  };

  const endToEndStartedAt = Date.now();
  const result = await updateCase(
    { situationGraph: graph, previousQuestion, answer },
    { provider: timedProvider },
  );
  const endToEndElapsedMs = Date.now() - endToEndStartedAt;

  // ── accumulated state for longitudinal multi-episode use ───
  const updatedGraph   = result?.updatedSituationGraph ?? null;
  const postEpisodeFs  = Array.isArray(result?.findings) ? [...result.findings] : [];

  return {
    /* ── CU narrative (UNAVAILABLE — synthesis is a separate seam from updateCase()) ─── */
    currentUnderstanding: "UNAVAILABLE",

    /* ── updatedSituationGraph (NEW for longitudinal state feeding) ─── */
    updatedSituationGraph: updatedGraph,

    /* ── postEpisodeFindings (NEW — accumulated Findings exposed for Episode N+1) ─── */
    postEpisodeFindings: postEpisodeFs,

    /* ── graph checkpoint (from production result) ─── */
    graphCheckpoint: updatedGraph
      ? {
          nodeCount:       updatedGraph.nodes?.length      ?? 0,
          edgeCount:       updatedGraph.edges?.length      ?? 0,
          resolvedNodeIds: updatedGraph.resolvedNodeIds    ?? [],
        }
      : { nodeCount: 0, edgeCount: 0, resolvedNodeIds: [] },

    /* ── findings checkpoint (count from accumulated Findings) ─── */
    findingsCheckpoint: {
      total: postEpisodeFs.length,
      eligible: "UNAVAILABLE",
    },

    /* ── Timing ─── */
    modelElapsedMs: result?.modelElapsedMs !== undefined ? result.modelElapsedMs : null,
    endToEndElapsedMs: result?.endToEndElapsedMs !== undefined ? result.endToEndElapsedMs : null,
    providerGenerateCalls: result != null && result.providerGenerateCalls != null ? result.providerGenerateCalls : 0,
    modelName: result?.modelName || null,

    /* ── answer meaning (from production result) ─── */
    userSupportedMeaning: result?.proposal?.answerMeaning?.userSupportedMeaning ?? null,
    possibleInference: result?.proposal?.answerMeaning?.possibleInference ?? null,

    /* ── helper internals (for diagnostic / test verification) ─── */
    selectedQuestion: result?.proposal?.selectedQuestion ?? null,
    behaviourSelection: result?.behaviourSelection ?? null,
    reasoningState: _extractReasoningState(result),
  };
}

/**
 * Compose the canonical post-episode deep-investigation checkpoint sequence:
 *   updateCase → synthesizeCurrentUnderstanding
 *
 * Mirrors the production flow in ScenarioForm.handleDoneForNowPromotion / executeEpisodeDone:
 *   1. Call the episode done server seam (POST /api/cases/update, episodeMode=true)
 *      which updates the SituationGraph and accumulates Findings
 *   2. Use the updated graph + accumulated findings to synthesize Current Understanding
 *      via the same seam as production (POST /api/cases/synthesis → synthesizeCurrentUnderstanding)
 *
 * Returns a combined checkpoint containing:
 *   - extractLiveExperimentCheckpoint(...) fields from the episode done result
 *   - currentUnderstanding from the synthesis result
 *   - findingsCheckpoint with accurate post-episode accumulated findings count
 */
/**
 * Compose the canonical post-episode deep-investigation checkpoint sequence.
 * Uses production-focused deconstruct for Contribution derivation (not synthetic raw-answer path).
 */
async function composeDeepInvestigationCheckpoint({ situationGraph, previousQuestion, answer, findings = [], contributions = [], targetNodeId, episodeMode }) {
  dotenv.config({ path: ".env.local" });

  const baseUrl = assertRequiredEnv("OLLAMA_BASE_URL");
  const model   = assertRequiredEnv("OLLAMA_MODEL");

  if (baseUrl === "http://localhost:11434" || baseUrl === "http://127.0.0.1:11434") {
    throw new Error(
      `Live experiment harness refuses to use localhost fallback. ` +
      `OLLAMA_BASE_URL=${baseUrl}. Configure a real host in .env.local.`
    );
  }

  /* ── Step 1: episode done (mirrors executeEpisodeDone server seam) ─── */
  const { updateCase, reconsiderCompletedEpisode } = await import(
    "../../lib/graph/orchestrator.js"
  );
  const { getProvider } = await import("../../lib/llm/provider.js");

  const provider = getProvider();
  let modelElapsedMs = null;
  let providerGenerateCalls = 0;

  const timedProvider = {
    async generateReconstruction(prompt, modelName, outputSchema) {
      providerGenerateCalls++;
      const startedAt = Date.now();
      const response = await provider.generateReconstruction(prompt, modelName, outputSchema);
      modelElapsedMs = Date.now() - startedAt;
      return response;
    },
  };

  const endToEndStartedAt = Date.now();

  let episodeResult;

  /* ── Production-equivalent focused-episode seam: targetNodeId triggers the same path as executeEpisodeDone → handleEpisodeMode ─── */

  const effectiveContributions = contributions.length > 0 ? [...contributions] : [];
  let targetDescription = "";
  let effectiveContributionsForTarget = effectiveContributions;
  if (targetNodeId && effectiveContributions.length > 0) {
    effectiveContributionsForTarget = effectiveContributions.filter(
      (c) => c?.targetNodeId === targetNodeId,
    );
  }

  let newFindingsFromContributions = [];

  if (effectiveContributionsForTarget.length === 0 && answer && targetNodeId) {
    const contribSeq = effectiveContributions.length + 1;

    /* ── Load production deconstruction seam (dynamic import to resolve ESM path aliases) ─── */
    const { focusedDeconstruct } = await import("../../lib/graph/focused-investigation.js");

    // Extract node context for deconstruction prompt
    const targetNode = situationGraph.nodes.find(n => n.id === targetNodeId);
    const targetLabel = targetNode?.label ?? "";
    targetDescription = targetNode?.description ?? "";
    const centralStatement = situationGraph.centralStatement ?? "";

    // Call production-focused deconstruct — exactly one provider generation
    const focused = await focusedDeconstruct({
      provider: timedProvider,
      modelName: model,
      targetNodeId,
      targetLabel,
      targetDescription,
      centralStatement,
      question: previousQuestion ?? "",
      answer,
    });

    if (!focused.success) {
      throw new Error(`focusedDeconstruct failed for episode ${contribSeq}: ${JSON.stringify(focused.errors)}`);
    }

    // Map deconstruction result → canonical Contribution shape
    const derivedContrib = {
      id: `contrib-ep-${String(contribSeq).padStart(4, "0")}`,
      targetNodeId: focused.targetNodeId ?? targetNodeId,
      question: previousQuestion ?? "",
      answer: answer,
      sequence: contribSeq,
      observations: focused.observations,
      uncertainties: focused.uncertainties,
      assumptions: focused.assumptions,
      relationships: focused.relationships,
      possibleFollowUpQuestions: focused.possibleFollowUpQuestions,
    };
    effectiveContributions.push(derivedContrib);

    // Derive canonical Findings from the deconstruction-derived Contribution (production seam)
    newFindingsFromContributions = deriveFindingsFromContributions([derivedContrib]).findings;
  }

  if (episodeMode === true && targetNodeId) {
    // Mirrors produceEpisodeAwareGraph: prepareCompletedEpisode → reconsiderCompletedEpisode → applyValidatedProposal
    const { prepareCompletedEpisode } = await import("../../lib/graph/episode-preparation.js");
    const { applyValidatedProposal } = await import("../../lib/graph/apply-proposal.js");

    const prepared = prepareCompletedEpisode({
      situationGraph,
      targetNodeId,
      contributions: effectiveContributions,
      findings,
    });

    if (!prepared?.turns?.length && !prepared?.eligibleCanonicalFindings?.length) {
      throw new Error("no_episodic_content: prepared episode has no turns or eligible findings");
    }

    const reasoning = await reconsiderCompletedEpisode(prepared, { provider: timedProvider });
    if (!reasoning.success) {
      throw new Error(`reconsiderCompletedEpisode failed: ${reasoning.error}`);
    }

    const application = await applyValidatedProposal({
      situationGraph,
      proposal: reasoning.proposal,
      evidenceContext: {
        isCompletedEpisode: true,
        episodeEvidence: prepared,
      },
    });

    if (!application.success) {
      throw new Error(`applyValidatedProposal failed: ${application.error}`);
    }

    const resolvedNodeIds = new Set(application.updatedSituationGraph.resolvedNodeIds ?? []);
    resolvedNodeIds.add(targetNodeId);

    episodeResult = {
      success: true,
      updatedSituationGraph: {
        ...application.updatedSituationGraph,
        resolvedNodeIds: [...resolvedNodeIds],
      },
      appendedFindings: [],  // no regular update findings in episode path
      proposal: reasoning.proposal,
    };
  } else {
    const result = await updateCase(
      { situationGraph, previousQuestion, answer },
      { provider: timedProvider },
    );
    episodeResult = result;
  }

  const endToEndElapsedMs = Date.now() - endToEndStartedAt;

  /* ── Step 2: Current Understanding synthesis (mirrors production synthesizeFn) ─── */
  const updatedGraph   = episodeResult.updatedSituationGraph;

  // Accumulate prior findings + newly derived Findings (from new Contribution) for CU synthesis
  const priorFindings      = Array.isArray(findings) ? [...findings] : [];
  const accumulatedEpisodes = episodeResult.appendedFindings ?? [];
  const postEpisodeFindings = [...priorFindings, ...newFindingsFromContributions, ...accumulatedEpisodes];

  let currentUnderstanding = null;
  let synthesisErrorDiagnostic = null;

  if (episodeResult.success && updatedGraph) {
    // Reuse the same provider/model plumbing — no re-creation, just reuse
    const { synthesizeCurrentUnderstanding } = await import("../../lib/graph/current-understanding-synthesis.js");

    try {
      const synthesized = await synthesizeCurrentUnderstanding(
        { situationGraph: updatedGraph, findings: postEpisodeFindings },
        { provider: timedProvider, modelName: model },
      );

      currentUnderstanding = synthesized?.currentUnderstanding ?? null;
    } catch (synthErr) {
      // Expose the safe structural diagnostic from synthesis validation failure
      if (synthErr?.sanitizedDiagnostic) {
        synthesisErrorDiagnostic = synthErr.sanitizedDiagnostic;
      }
      currentUnderstanding = null;
    }
  }

  // Consistent graphCheckpoint for both success and failure paths
  const graphCheckpoint = updatedGraph
    ? {
        nodeCount:       updatedGraph.nodes?.length      ?? 0,
        edgeCount:       updatedGraph.edges?.length      ?? 0,
        resolvedNodeIds: updatedGraph.resolvedNodeIds    ?? [],
      }
    : { nodeCount: 0, edgeCount: 0, resolvedNodeIds: [] };

  // Accurate post-episode accumulated findings count
  const { filterEligibleFindings } = await import("../../lib/graph/current-understanding-synthesis.js");
  const eligibleCount = postEpisodeFindings.length > 0
    ? filterEligibleFindings(postEpisodeFindings).length
    : 0;

  return {
    /* ── CU synthesis result (mirrors production synthesizeFn) ─── */
    currentUnderstanding,

    /* ── updatedSituationGraph (NEW — for longitudinal Episode N+1 state feeding) ─── */
    updatedSituationGraph: updatedGraph,

    /* ── postEpisodeContributions (NEW — accumulated Contributions for longitudinal tracking) ─── */
    postEpisodeContributions: effectiveContributions,

    /* ── postEpisodeFindings (NEW — accumulated Findings for Episode N+1) ─── */
    postEpisodeFindings: postEpisodeFindings,

    /* ── Accurate post-episode accumulated findings count ─── */
    findingsCheckpoint: {
      total: postEpisodeFindings.length,
      eligible: eligibleCount,
    },

    /* ── graph checkpoint (from production result) ─── */
    graphCheckpoint,

    /* ── Timing ─── */
    modelElapsedMs,
    endToEndElapsedMs,
    providerGenerateCalls,
    modelName: episodeResult.modelName ?? null,

    /* ── CU synthesis error diagnostic (NEW — safe structural shape on validation failure) ─── */
    synthesisErrorDiagnostic,

    /* ── Answer meaning (from production result) ─── */
    userSupportedMeaning: episodeResult?.proposal?.answerMeaning?.userSupportedMeaning ?? null,
    possibleInference:    episodeResult?.proposal?.answerMeaning?.possibleInference     ?? null,

    /* ── Focused-investigation input values (apparatus correction) ─── */
    focusedInvestigationInput: (previousQuestion != null || answer != null)
      ? {
          targetDescription: targetDescription ?? "",
          question: previousQuestion ?? "",
          answer,
        }
      : null,
  };
}

/**
 * Serialize a completed deep-investigation checkpoint into the durable
 * focused-deconstruction evidence record.
 *
 * Pure function — no side effects, no I/O, no model calls.  Intended for
 * deterministic test fixtures and external audit tools that need a stable
 * JSON-serializable representation of the five deconstruction arrays plus
 * exact input values and execution metadata.
 *
 * @param {object} checkpoint     — returned from composeDeepInvestigationCheckpoint()
 * @param {object} identity       — { experimentId, scenarioId, targetNodeId, evidenceDesignation }
 *   - experimentId:  unique identifier for the experiment run
 *   - scenarioId:    identifier for the scenario (e.g. "HS-03C")
 *   - targetNodeId:  the situationGraph node that was selected as focus
 *   - evidenceDesignation: "synthetic" or "real-user" (or any string)
 * @param {object} [metadata]     — optional execution metadata from the apparatus
 *   - modelName, providerGenerateCalls, modelElapsedMs, endToEndElapsedMs, success
 *   Any missing field is set to null (never fabricated).
 * @returns {object} JSON-serializable evidence record per fixed contract
 */
function serializeFocusedInvestigationEvidence(checkpoint, identity, metadata) {
  /* ── Extract the five structured deconstruction arrays ────────────── */
  const contributions = Array.isArray(checkpoint?.postEpisodeContributions)
    ? checkpoint.postEpisodeContributions
    : [];

  // There should be exactly one contribution per focused-investigation run;
  // if multiple exist (episode path with accumulated contributions),
  // take the first one that carries the deconstruction arrays.
  const deconContribution = contributions.find(
    (c) => Array.isArray(c?.observations) && Array.isArray(c?.uncertainties)
  ) || contributions[0] || {};

  return {
    /* ── Identity (supplied by caller, not derived) ─────────────────── */
    experimentId:          identity.experimentId             ?? null,
    scenarioId:            identity.scenarioId               ?? null,
    targetNodeId:          identity.targetNodeId             ?? null,
    evidenceDesignation:   identity.evidenceDesignation      ?? null,

    /* ── Exact focused-investigation inputs (from checkpoint) ──────── */
    canonicalTargetDescription: checkpoint.focusedInvestigationInput?.targetDescription ?? "",
    actualQuestion:           checkpoint.focusedInvestigationInput?.question              ?? "",
    exactAnswer:              checkpoint.focusedInvestigationInput?.answer                ?? "",

    /* ── Structured model output (preserved verbatim, no transformation) ─── */
    observations:              deconContribution.observations              ?? null,
    uncertainties:             deconContribution.uncertainties             ?? null,
    assumptions:               deconContribution.assumptions               ?? null,
    relationships:             deconContribution.relationships             ?? null,
    possibleFollowUpQuestions: deconContribution.possibleFollowUpQuestions ?? null,

    /* ── Execution metadata (supplied by caller; null when not exposed) ─ */
    modelName:       metadata?.modelName       ?? null,
    providerGenerateCalls:  metadata?.providerGenerateCalls  ?? null,
    modelElapsedMs:      metadata?.modelElapsedMs      ?? null,
    endToEndElapsedMs:   metadata?.endToEndElapsedMs   ?? null,
    executionStatus:     (metadata?.success !== undefined)
                         ? (metadata.success === true ? "completed" : "failed")
                         : null,
  };
}

/**
 * Save a completed focused-investigation checkpoint to a durable JSON file.
 *
 * Wrapper around `serializeFocusedInvestigationEvidence()` that performs
 * explicit persistence via the caller-supplied output path.  Intended for
 * deterministic test fixtures and future live experiment runbooks — NOT
 * called automatically from production code.
 *
 * @param {object} checkpoint     — returned from composeDeepInvestigationCheckpoint()
 * @param {object} identity       — { experimentId, scenarioId, targetNodeId, evidenceDesignation }
 * @param {object} metadata       — { modelName?, providerGenerateCalls?, modelElapsedMs?, endToEndElapsedMs?, success? }
 * @param {string} outputPath     — explicit absolute or relative file path for the JSON output
 * @returns {{ output_path: string, verifiedEvidence: object }} { output_path, verifiedEvidence }
 * @throws {Error} on invalid inputs, missing required fields, ambiguous contribution, or I/O failure
 */
function saveFocusedInvestigationEvidence(checkpoint, identity, metadata, outputPath) {
  const fs = require("fs");

  /* ── 2. Require explicit non-empty output path ────────────────────── */
  if (!outputPath || typeof outputPath !== "string" || !outputPath.trim()) {
    throw new Error("saveFocusedInvestigationEvidence requires an explicit non-empty outputPath string");
  }

  /* ── 3. Validate checkpoint has exactly one valid contribution ────── */
  const contributions = Array.isArray(checkpoint?.postEpisodeContributions)
    ? checkpoint.postEpisodeContributions
    : [];

  const validContributionCandidates = contributions.filter(
    (c) => Array.isArray(c?.observations) && Array.isArray(c?.uncertainties)
  );

  if (validContributionCandidates.length === 0) {
    throw new Error(
      "saveFocusedInvestigationEvidence requires a checkpoint with at least one valid focused-deconstruction contribution; found 0"
    );
  }

  if (validContributionCandidates.length > 1) {
    throw new Error(
      "saveFocusedInvestigationEvidence rejects ambiguous checkpoint: multiple candidates exist (" +
        validContributionCandidates.length +
        "); supply a single-target checkpoint or resolve ambiguity before saving"
    );
  }

  /* ── 4. Validate identity and input values ────────────────────────── */
  if (!identity || typeof identity !== "object") {
    throw new Error("saveFocusedInvestigationEvidence requires an identity object");
  }
  if (identity.experimentId == null) {
    throw new Error("saveFocusedInvestigationEvidence: identity.missing experimentId");
  }
  if (identity.scenarioId == null) {
    throw new Error("saveFocusedInvestigationEvidence: identity.missing scenarioId");
  }
  if (identity.targetNodeId == null) {
    throw new Error("saveFocusedInvestigationEvidence: identity.missing targetNodeId");
  }
  if (identity.evidenceDesignation == null) {
    throw new Error("saveFocusedInvestigationEvidence: identity.missing evidenceDesignation");
  }

  const fiInputs = checkpoint.focusedInvestigationInput;
  if (!fiInputs || typeof fiInputs !== "object") {
    throw new Error("saveFocusedInvestigationEvidence: checkpoint missing focusedInvestigationInput object");
  }
  if (fiInputs.targetDescription == null || fiInputs.question == null || fiInputs.answer == null) {
    throw new Error(
      "saveFocusedInvestigationEvidence: checkpoint missing required focused-investigation input values (targetDescription, question, answer)"
    );
  }

  /* ── 1. Serialize via existing serializer ─────────────────────────── */
  const serialized = serializeFocusedInvestigationEvidence(checkpoint, identity, metadata);

  /* ── 5-6. Write the complete record as readable JSON ──────────────── */
  const jsonContent = JSON.stringify(serialized, null, 2);
  fs.writeFileSync(outputPath, jsonContent, "utf8");

  /* ── 7-8. Read back and verify structural equality ────────────────── */
  const recoveredRaw = fs.readFileSync(outputPath, "utf8");
  const recovered = JSON.parse(recoveredRaw);

  if (JSON.stringify(serialized) !== JSON.stringify(recovered)) {
    throw new Error(
      "saveFocusedInvestigationEvidence: write/read-back verification failed — recovered JSON does not match serialized record"
    );
  }

  /* ── 9. Return output path and verified evidence record ───────────── */
  return { output_path: outputPath, verifiedEvidence: recovered };
}

module.exports = { runLiveExperiment, extractLiveExperimentCheckpoint, _extractReasoningState, composeDeepInvestigationCheckpoint, serializeFocusedInvestigationEvidence, saveFocusedInvestigationEvidence };