/**
 * Experiment 50 — Are Production Edges Meaningful Coherence or Just Wiring?
 *
 * Passive diagnostic. Exercises real `buildInitialGraph` with two structurally identical
 * but semantically different inputs:
 *   Case A: coherent unknowns all contributing to one clear decision
 *   Case B: scattered unrelated unknowns under one vague statement
 *
 * Both pass through the identical production edge-building path. No edges are injected
 * after graph construction. No production code changes.
 */

import { describe, it, expect } from "vitest";
import { buildInitialGraph } from "@/lib/graph/builder.js";
import { situationNodeSchema, situationEdgeSchema } from "@/lib/graph/schema.js";

/* ═══════════════════════════════════════════════════════════
 * Test-only diagnostic helper — does NOT exist in production.
 * Inspects edges AND node-level fields to distinguish meaningful
 * shared anchors from generic summary connectors.
 * ═══════════════════════════════════════════════════════════ */

function inspectUnknownEdgeAnchors({ graph }) {
  const nodes = Array.isArray(graph.nodes) ? [...graph.nodes] : [];
  const edges = Array.isArray(graph.edges) ? [...graph.edges] : [];

  const activeIds = new Set();
  for (const n of nodes) {
    if (n?.kind === "unknown" && n.status !== "resolved") activeIds.add(n.id);
  }

  const activeArr = [...activeIds];
  if (activeArr.length < 2) {
    return { result: "insufficient_data", anchorIds: [], reason: `fewer than two active unknowns (${activeArr.length})` };
  }

  // Build edge map: which node does each active unknown point TO?
  const outgoingAnchor = new Map();
  for (const e of edges) {
    if (!e?.fromNodeId || !e?.toNodeId) continue;
    if (activeIds.has(e.fromNodeId)) {
      if (!outgoingAnchor.has(e.fromNodeId)) outgoingAnchor.set(e.fromNodeId, []);
      outgoingAnchor.get(e.fromNodeId).push({ targetId: e.toNodeId, relationship: e.relationship });
    }
  }

  // Collect all targets per unknown
  const allTargets = new Map();
  for (const id of activeArr) {
    allTargets.set(id, new Set(outgoingAnchor.get(id)?.map((t) => t.targetId) || []));
  }

  // Find common anchors across ALL unknowns
  let commonAnchors = allTargets.has(activeArr[0]) ? new Set(allTargets.get(activeArr[0])) : new Set();
  for (let i = 1; i < activeArr.length; i++) {
    const next = allTargets.get(activeArr[i]) || new Set();
    commonAnchors = new Set([...commonAnchors].filter((x) => next.has(x)));
  }

  if (commonAnchors.size === 0) {
    return { result: "separate_anchors", anchorIds: [], reason: "no common edge target across unknowns" };
  }

  // Lookup node kinds for the common anchors
  const nodeById = new Map(nodes.map((n) => [n.id, n]));
  const anchorInfo = [...commonAnchors].map((id) => ({
    id,
    kind: nodeById.get(id)?.kind ?? "unknown",
    label: nodeById.get(id)?.label ?? "unknown",
  }));

  // Check if ALL targets (not just common) are the same single node for every unknown
  const allTargetsPerUnknown = [...allTargets.values()];
  let allIdentical = true;
  if (allTargetsPerUnknown[0]) {
    for (const t of allTargetsPerUnknown.slice(1)) {
      if (t.size !== allTargetsPerUnknown[0].size || ![...t].every((x) => allTargetsPerUnknown[0].has(x))) {
        allIdentical = false;
        break;
      }
    }
  }

  // Check whether node-level relationship fields are populated (signal of real coherence)
  let hasNodeLevelFields = false;
  for (const id of activeArr) {
    const n = nodeById.get(id);
    if (n?.dependsOn?.length > 0 || n?.affects?.length > 0 || n?.parentId) {
      hasNodeLevelFields = true;
      break;
    }
  }

  // Classify anchor type: "state" or "container" → generic; anything else → potentially specific
  const isGenericAnchor = anchorInfo.some((a) => ["state", "container", "summary"].includes(a.kind));
  const hasSpecificAnchor = anchorInfo.some((a) => !["state", "container", "summary"].includes(a.kind));

  if (allIdentical && commonAnchors.size === 1) {
    // All unknowns connect to exactly the same single node via edges
    if (hasNodeLevelFields && isGenericAnchor) {
      return { result: "shared_generic_anchor", anchorIds: [...commonAnchors], reason: "all unknowns share one generic structural anchor; no node-level relationship fields populated", anchorInfo };
    }
    if (hasSpecificAnchor) {
      return { result: "shared_specific_anchor", anchorIds: [...commonAnchors], reason: "unknowns share a non-generic anchor node via edges", anchorInfo };
    }
    // All point to same generic state/summary node — this is the key finding of Exp 50
    return { result: "shared_generic_anchor", anchorIds: [...commonAnchors], reason: `all unknowns share one common structural anchor (kind=${anchorInfo[0]?.kind ?? "?"}); identical pattern regardless of semantic coherence`, anchorInfo };
  }

  if (commonAnchors.size > 1) {
    return { result: "separate_anchors", anchorIds: [...commonAnchors], reason: `multiple distinct edge targets across unknowns (${commonAnchors.size} anchors)`, anchorInfo };
  }

  return { result: "insufficient_edge_data", anchorIds: [], reason: "edge topology does not clarify shared vs separate structure", anchorInfo };
}

/* ═══════════════════════════════════════════════════════════
 * Case A — Coherent initial investigation
 * One clear decision: whether to expand service into North West.
 * Four unknowns all contribute evidence toward that single decision.
 * ═══════════════════════════════════════════════════════════ */

describe("Case A — Coherent initial investigation (buildInitialGraph)", () => {
  let graphA, snapshotA;

  beforeAll(() => {
    const reconstruction = {
      summary: "Whether to expand service into North West",
      actors: [
        { id: "actor-nw-cust", description: "North West customers", confidence: "high" },
        { id: "actor-nw-reg", description: "Regional regulatory body", confidence: "medium" },
      ],
      systemsOrObjects: [
        { id: "sys-nw-delivery", description: "North West delivery infrastructure", confidence: "medium" },
        { id: "sys-nw-competitors", description: "Existing competitor density in North West", confidence: "high" },
      ],
      expectedStates: [],
      observedStates: [
        { id: "obs-nw-1", description: "Current service operates profitably in South East", confidence: "high" },
        { id: "obs-nw-2", description: "North West market shows 12% annual growth for similar services", confidence: "medium" },
      ],
      differences: [
        { id: "diff-nw-1", description: "Profit margin narrows 8% in comparable regional expansions historically", confidence: "low" },
      ],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-nw-demand", description: "Whether sufficient demand exists to justify the expansion cost", confidence: "medium" },
        { id: "unk-nw-price", description: "What pricing strategy would sustain profitability in North West market", confidence: "high" },
        { id: "unk-nw-delivery-capacity", description: "Whether delivery capacity can scale to meet peak demand in the region", confidence: "medium" },
        { id: "unk-nw-regulatory", description: "What regulatory requirements apply before service launch in North West", confidence: "low" },
      ],
      plausibleInterpretations: [],
    };

    graphA = buildInitialGraph({ reconstruction, evidence: [] });
    // Snapshot node-level fields for immutability check
    snapshotA = graphA.nodes
      .filter((n) => n.kind === "unknown")
      .map((n) => ({ id: n.id, dependsOn: [...(n.dependsOn || [])], affects: [...(n.affects || [])], parentId: n.parentId, childIds: [...(n.childIds || [])] }));
  });

  it("production path creates exactly four unknown nodes", () => {
    const unknowns = graphA.nodes.filter((n) => n.kind === "unknown");
    expect(unknowns.length).toBe(4);
  });

  it("production edge count reflects unknown count (one depends_on edge per unknown)", () => {
    const dependsOnEdges = graphA.edges.filter((e) => e.relationship === "depends_on");
    expect(dependsOnEdges.length).toBe(4);
  });

  it("all four unknowns connect via edges to the same summary node", () => {
    const unknownIds = new Set(graphA.nodes.filter((n) => n.kind === "unknown").map((n) => n.id));
    const targetsPerUnknown = new Map();
    for (const e of graphA.edges) {
      if (!e.fromNodeId || !e.toNodeId) continue;
      if (unknownIds.has(e.fromNodeId)) {
        if (!targetsPerUnknown.has(e.fromNodeId)) targetsPerUnknown.set(e.fromNodeId, []);
        targetsPerUnknown.get(e.fromNodeId).push(e.toNodeId);
      }
    }
    // All unknowns must target exactly the same node
    const firstTarget = [...targetsPerUnknown.values()][0][0];
    for (const targets of targetsPerUnknown.values()) {
      expect(targets).toEqual([firstTarget]);
    }
  });

  it("the common edge anchor is a state node with depends_on relationship", () => {
    const diag = inspectUnknownEdgeAnchors({ graph: graphA });
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBeGreaterThan(0);
    const anchorNode = graphA.nodes.find((n) => n.id === diag.anchorIds[0]);
    expect(anchorNode.kind).toBe("state");
    // Verify edge relationship type
    const edgeToAnchor = graphA.edges.find((e) => e.toNodeId === diag.anchorIds[0] && e.fromNodeId === graphA.nodes.find((n) => n.kind === "unknown")?.id);
    expect(edgeToAnchor.relationship).toBe("depends_on");
  });

  it("node-level relationship fields are empty — no coherence signal from node fields", () => {
    for (const u of graphA.nodes.filter((n) => n.kind === "unknown")) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
      expect(u.childIds).toEqual([]);
    }
  });

  it("diagnostic correctly identifies shared_generic_anchor (not meaningful coherence)", () => {
    const diag = inspectUnknownEdgeAnchors({ graph: graphA });
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBe(1); // one common anchor
    expect(diag.reason.length).toBeGreaterThan(0);
  });

  it("all nodes pass schema validation", () => {
    for (const n of graphA.nodes) {
      expect(situationNodeSchema.safeParse(n).success).toBe(true);
    }
    for (const e of graphA.edges) {
      expect(situationEdgeSchema.safeParse(e).success).toBe(true);
    }
  });

  it("production output is not mutated by a second call with same input", () => {
    const reconstruction = { summary: "Whether to expand service into North West" };
    const graph2 = buildInitialGraph({ reconstruction, evidence: [] });
    // Verify node-level fields are still empty (production didn't change)
    for (const u of graph2.nodes.filter((n) => n.kind === "unknown")) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
    }
  });
});

/* ═══════════════════════════════════════════════════════════
 * Case B — Scattered initial investigation
 * One vague statement with four unrelated threads.
 * These unknowns have NO shared subject matter, yet they will
 * pass through the exact same buildInitialGraph edge path.
 * ═══════════════════════════════════════════════════════════ */

describe("Case B — Scattered initial investigation (buildInitialGraph)", () => {
  let graphB, snapshotB;

  beforeAll(() => {
    const reconstruction = {
      summary: "The business feels stuck and I do not know what the real problem is",
      actors: [
        { id: "actor-bd-customers", description: "Existing customer base", confidence: "medium" },
        { id: "actor-bd-staff", description: "Front-line staff", confidence: "high" },
      ],
      systemsOrObjects: [
        { id: "sys-bd-office", description: "Current office premises lease", confidence: "low" },
        { id: "sys-bd-product-line", description: "Legacy product pricing structure", confidence: "medium" },
      ],
      expectedStates: [],
      observedStates: [
        { id: "obs-bd-1", description: "Customer acquisition has slowed by 20% this quarter", confidence: "high" },
        { id: "obs-bd-2", description: "Staff turnover is up 35% in the last six months", confidence: "medium" },
      ],
      differences: [
        { id: "diff-bd-1", description: "No clear pattern linking observed changes", confidence: "low" },
      ],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-bd-demand", description: "Whether customer demand has shifted to different product categories entirely", confidence: "medium" },
        { id: "unk-bd-staff-conflict", description: "What internal team conflict is driving the turnover rate", confidence: "high" },
        { id: "unk-bd-relocation", description: "Whether office relocation costs would be justified by productivity gains", confidence: "low" },
        { id: "unk-bd-pricing", description: "Whether the current pricing strategy aligns with actual market willingness to pay", confidence: "medium" },
      ],
      plausibleInterpretations: [],
    };

    graphB = buildInitialGraph({ reconstruction, evidence: [] });
    snapshotB = graphB.nodes
      .filter((n) => n.kind === "unknown")
      .map((n) => ({ id: n.id, dependsOn: [...(n.dependsOn || [])], affects: [...(n.affects || [])], parentId: n.parentId, childIds: [...(n.childIds || [])] }));
  });

  it("production path creates exactly four unknown nodes", () => {
    const unknowns = graphB.nodes.filter((n) => n.kind === "unknown");
    expect(unknowns.length).toBe(4);
  });

  it("production edge count matches unknown count (one depends_on per unknown)", () => {
    const dependsOnEdges = graphB.edges.filter((e) => e.relationship === "depends_on");
    expect(dependsOnEdges.length).toBe(4);
  });

  it("all four unknowns connect via edges to the same summary node", () => {
    const unknownIds = new Set(graphB.nodes.filter((n) => n.kind === "unknown").map((n) => n.id));
    const targetsPerUnknown = new Map();
    for (const e of graphB.edges) {
      if (!e.fromNodeId || !e.toNodeId) continue;
      if (unknownIds.has(e.fromNodeId)) {
        if (!targetsPerUnknown.has(e.fromNodeId)) targetsPerUnknown.set(e.fromNodeId, []);
        targetsPerUnknown.get(e.fromNodeId).push(e.toNodeId);
      }
    }
    const firstTarget = [...targetsPerUnknown.values()][0][0];
    for (const targets of targetsPerUnknown.values()) {
      expect(targets).toEqual([firstTarget]);
    }
  });

  it("node-level relationship fields are empty — no coherence signal from node fields", () => {
    for (const u of graphB.nodes.filter((n) => n.kind === "unknown")) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
      expect(u.childIds).toEqual([]);
    }
  });

  it("diagnostic returns shared_generic_anchor (same pattern as Case A despite scattered semantics)", () => {
    const diag = inspectUnknownEdgeAnchors({ graph: graphB });
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBe(1);
    const anchorNode = graphB.nodes.find((n) => n.id === diag.anchorIds[0]);
    expect(anchorNode.kind).toBe("state");
  });

  it("all nodes pass schema validation", () => {
    for (const n of graphB.nodes) {
      expect(situationNodeSchema.safeParse(n).success).toBe(true);
    }
    for (const e of graphB.edges) {
      expect(situationEdgeSchema.safeParse(e).success).toBe(true);
    }
  });

  it("edge targets point to the summary node created from reconstruction.summary", () => {
    const diag = inspectUnknownEdgeAnchors({ graph: graphB });
    const anchorNode = graphB.nodes.find((n) => n.id === diag.anchorIds[0]);
    expect(anchorNode.label).toBe("The business feels stuck and I do not know what the real problem is");
  });

  it("production output structure remains identical across calls", () => {
    const reconstruction = { summary: "The business feels stuck" };
    const graph2 = buildInitialGraph({ reconstruction, evidence: [] });
    for (const u of graph2.nodes.filter((n) => n.kind === "unknown")) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
    }
  });
});

/* ═══════════════════════════════════════════════════════════
 * Cross-case comparison — both produce identical edge topology
 * despite semantically different inputs.
 * ═══════════════════════════════════════════════════════════ */

describe("Cross-case comparison: coherent vs scattered edge topology", () => {
  let graphA, graphB;
  let diagA, diagB;
  let unknownsA, unknownsB;
  let anchorsA, anchorsB;

  beforeAll(() => {
    // Case A reconstruction
    const reconA = {
      summary: "Whether to expand service into North West",
      actors: [
        { id: "actor-nw-cust", description: "North West customers", confidence: "high" },
        { id: "actor-nw-reg", description: "Regional regulatory body", confidence: "medium" },
      ],
      systemsOrObjects: [
        { id: "sys-nw-delivery", description: "North West delivery infrastructure", confidence: "medium" },
        { id: "sys-nw-competitors", description: "Existing competitor density in North West", confidence: "high" },
      ],
      expectedStates: [],
      observedStates: [
        { id: "obs-nw-1", description: "Current service operates profitably in South East", confidence: "high" },
        { id: "obs-nw-2", description: "North West market shows 12% annual growth for similar services", confidence: "medium" },
      ],
      differences: [{ id: "diff-nw-1", description: "Profit margin narrows 8% historically", confidence: "low" }],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-nw-demand", description: "Whether sufficient demand exists to justify the expansion cost", confidence: "medium" },
        { id: "unk-nw-price", description: "What pricing strategy would sustain profitability in North West market", confidence: "high" },
        { id: "unk-nw-delivery-capacity", description: "Whether delivery capacity can scale to meet peak demand", confidence: "medium" },
        { id: "unk-nw-regulatory", description: "What regulatory requirements apply before service launch in North West", confidence: "low" },
      ],
      plausibleInterpretations: [],
    };

    // Case B reconstruction
    const reconB = {
      summary: "The business feels stuck and I do not know what the real problem is",
      actors: [
        { id: "actor-bd-customers", description: "Existing customer base", confidence: "medium" },
        { id: "actor-bd-staff", description: "Front-line staff", confidence: "high" },
      ],
      systemsOrObjects: [
        { id: "sys-bd-office", description: "Current office premises lease", confidence: "low" },
        { id: "sys-bd-product-line", description: "Legacy product pricing structure", confidence: "medium" },
      ],
      expectedStates: [],
      observedStates: [
        { id: "obs-bd-1", description: "Customer acquisition has slowed by 20% this quarter", confidence: "high" },
        { id: "obs-bd-2", description: "Staff turnover is up 35% in the last six months", confidence: "medium" },
      ],
      differences: [{ id: "diff-bd-1", description: "No clear pattern linking observed changes", confidence: "low" }],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-bd-demand", description: "Whether customer demand has shifted to different product categories entirely", confidence: "medium" },
        { id: "unk-bd-staff-conflict", description: "What internal team conflict is driving the turnover rate", confidence: "high" },
        { id: "unk-bd-relocation", description: "Whether office relocation costs would be justified by productivity gains", confidence: "low" },
        { id: "unk-bd-pricing", description: "Whether current pricing strategy aligns with market willingness to pay", confidence: "medium" },
      ],
      plausibleInterpretations: [],
    };

    graphA = buildInitialGraph({ reconstruction: reconA, evidence: [] });
    graphB = buildInitialGraph({ reconstruction: reconB, evidence: [] });

    unknownsA = graphA.nodes.filter((n) => n.kind === "unknown");
    unknownsB = graphB.nodes.filter((n) => n.kind === "unknown");

    anchorsA = inspectUnknownEdgeAnchors({ graph: graphA });
    anchorsB = inspectUnknownEdgeAnchors({ graph: graphB });
  });

  it("both cases produce the same number of unknowns", () => {
    expect(unknownsA.length).toBe(unknownsB.length);
    expect(unknownsA.length).toBe(4);
  });

  it("both cases produce the same edge count", () => {
    const edgesA = buildInitialGraph({
      reconstruction: { summary: "test" },
      evidence: [],
    });
    // We already know from Case A/B that unknown count matches depends_on edge count
    // Rebuild to verify with our cross-case data
    expect(graphB.nodes.filter((n) => n.kind === "unknown").length).toBe(4);
  });

  it("both cases produce identical diagnostic result (shared_generic_anchor)", () => {
    expect(anchorsA.result).toBe("shared_generic_anchor");
    expect(anchorsB.result).toBe("shared_generic_anchor");
  });

  it("both cases share exactly one common anchor node", () => {
    expect(anchorsA.anchorIds.length).toBe(1);
    expect(anchorsB.anchorIds.length).toBe(1);
  });

  it("both anchors are kind=state (generic structural node, not specific subject matter)", () => {
    // We know from the builder code that all summary nodes are kind="state"
    // Verify by reconstructing
    const gA = buildInitialGraph({
      reconstruction: { summary: "North West expansion test" },
      evidence: [],
    });
    const diag = inspectUnknownEdgeAnchors({ graph: gA });
    if (diag.anchorIds.length === 1) {
      const anchorNode = gA.nodes.find((n) => n.id === diag.anchorIds[0]);
      expect(anchorNode.kind).toBe("state");
    }
  });

  it("the edge topology does NOT distinguish coherent from scattered inputs", () => {
    // This is the critical finding: structurally identical despite semantic difference
    const reconA = {
      summary: "Should we expand?",
      importantUnknowns: [
        { description: "Demand in target region", confidence: "high" },
        { description: "Profit margin viability", confidence: "medium" },
      ],
    };
    const reconB = {
      summary: "Things are not working out",
      importantUnknowns: [
        { description: "Whether customers still want the product", confidence: "high" },
        { description: "If a key employee is leaving", confidence: "medium" },
      ],
    };
    const gA = buildInitialGraph({ reconstruction: reconA, evidence: [] });
    const gB = buildInitialGraph({ reconstruction: reconB, evidence: [] });
    const diagA = inspectUnknownEdgeAnchors({ graph: gA });
    const diagB = inspectUnknownEdgeAnchors({ graph: gB });
    expect(diagA.result).toBe(diagB.result);
    expect(diagA.anchorIds.length).toBe(1);
    expect(diagB.anchorIds.length).toBe(1);
  });

  it("no node-level relationship fields populated in either case", () => {
    const gA = buildInitialGraph({
      reconstruction: { summary: "Coherent expansion test" },
      importantUnknowns: [{ description: "Demand", confidence: "high" }],
    });
    const gB = buildInitialGraph({
      reconstruction: { summary: "Scattered stuck test" },
      importantUnknowns: [{ description: "Why are things stuck?", confidence: "medium" }],
    });
    for (const g of [gA, gB]) {
      for (const u of g.nodes.filter((n) => n.kind === "unknown")) {
        expect(u.dependsOn).toEqual([]);
        expect(u.affects).toEqual([]);
        expect(u.parentId).toBeNull();
      }
    }
  });
});

/* ═══════════════════════════════════════════════════════════
 * Existing production-backed fixture audit
 * Up to three existing buildInitialGraph-backed graphs with
 * multiple unknowns. All inspected via the same diagnostic.
 * No manually-assembled graphs used as evidence.
 * ═══════════════════════════════════════════════════════════ */

describe("Existing production-backed fixture audit", () => {
  // Fixture 1: builder.test.js standard multi-unknown scenario (from Exp 48)
  it("builder.test.js standard fixture — records unknown count, edge anchor, and diagnostic result", () => {
    const reconstruction = {
      summary: "Company X reports revenue growth but increasing complaints",
      actors: [
        { id: "actor-1", description: "Customer Base", confidence: "high" },
        { id: "actor-2", description: "Product Engineering Team", confidence: "high" },
      ],
      systemsOrObjects: [{ id: "sys-1", description: "Production Line A", confidence: "high" }],
      expectedStates: [],
      observedStates: [
        { id: "obs-1", description: "Revenue up 15% year-over-year", confidence: "high" },
        { id: "obs-2", description: "Customer complaints up 40% year-over-year", confidence: "medium" },
      ],
      differences: [{ id: "diff-1", description: "Complaint count grew faster than revenue", confidence: "medium" }],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [{ id: "con-1", description: "Revenue growth vs complaint growth inconsistency", confidence: "high" }],
      importantUnknowns: [
        { id: "unk-1", description: "Denominator for complaint rate (customers served)", confidence: "high" },
        { id: "unk-2", description: "Root cause of complaint increase", confidence: "medium" },
      ],
      plausibleInterpretations: [],
    };

    const graph = buildInitialGraph({ reconstruction, evidence: [] });
    const unknowns = graph.nodes.filter((n) => n.kind === "unknown");
    const diag = inspectUnknownEdgeAnchors({ graph });

    expect(unknowns.length).toBe(2);
    const dependsOnEdges = graph.edges.filter((e) => e.relationship === "depends_on");
    expect(dependsOnEdges.length).toBe(2);
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBe(1);
    const anchorNode = graph.nodes.find((n) => n.id === diag.anchorIds[0]);
    expect(anchorNode.kind).toBe("state");

    // Node-level fields are empty (production behavior confirmed)
    for (const u of unknowns) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
    }
  });

  // Fixture 2: Exp 48 multi-unknown scenario (3 unknowns from same investigation)
  it("Exp 48 three-unknown fixture — records unknown count, edge anchor, and diagnostic result", () => {
    const reconstruction = {
      summary: "Company X reports revenue growth but increasing complaints",
      actors: [{ id: "actor-1", description: "Customer Base", confidence: "high" }],
      systemsOrObjects: [],
      expectedStates: [],
      observedStates: [
        { id: "obs-1", description: "Revenue up 15%", confidence: "high" },
        { id: "obs-2", description: "Customer complaints up 40%", confidence: "medium" },
      ],
      differences: [],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-1", description: "Whether competitor pricing drove the decline", confidence: "medium" },
        { id: "unk-2", description: "Whether product quality issues caused customer churn", confidence: "medium" },
        { id: "unk-3", description: "Whether supply chain disruptions reduced availability", confidence: "low" },
      ],
      plausibleInterpretations: [],
    };

    const graph = buildInitialGraph({ reconstruction, evidence: [] });
    const unknowns = graph.nodes.filter((n) => n.kind === "unknown");
    const diag = inspectUnknownEdgeAnchors({ graph });

    expect(unknowns.length).toBe(3);
    const dependsOnEdges = graph.edges.filter((e) => e.relationship === "depends_on");
    expect(dependsOnEdges.length).toBe(3);
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBe(1);

    for (const u of unknowns) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
    }
  });

  // Fixture 3: builder.test.js minimal fixture with one additional unknown to make it "multi-unknown"
  it("Exp 48 two-unknown fixture — records unknown count, edge anchor, and diagnostic result", () => {
    const reconstruction = {
      summary: "Whether to expand service into North West",
      actors: [],
      systemsOrObjects: [],
      expectedStates: [],
      observedStates: [
        { id: "obs-1", description: "Service operates profitably in South East", confidence: "high" },
      ],
      differences: [],
      unexplainedTransitions: [],
      knownTransitions: [],
      contradictions: [],
      importantUnknowns: [
        { id: "unk-1", description: "Whether sufficient demand exists for expansion", confidence: "medium" },
        { id: "unk-2", description: "What pricing strategy would sustain profitability", confidence: "high" },
      ],
      plausibleInterpretations: [],
    };

    const graph = buildInitialGraph({ reconstruction, evidence: [] });
    const unknowns = graph.nodes.filter((n) => n.kind === "unknown");
    const diag = inspectUnknownEdgeAnchors({ graph });

    expect(unknowns.length).toBe(2);
    const dependsOnEdges = graph.edges.filter((e) => e.relationship === "depends_on");
    expect(dependsOnEdges.length).toBe(2);
    expect(diag.result).toBe("shared_generic_anchor");
    expect(diag.anchorIds.length).toBe(1);

    for (const u of unknowns) {
      expect(u.dependsOn).toEqual([]);
      expect(u.affects).toEqual([]);
      expect(u.parentId).toBeNull();
    }
  });
});