Add comprehensive KG node validation pipeline (Parts 2-5): Core research logic for node-criterion labeling

NEW FILE: kg_node_validation_part2.py (1442 lines)

ARCHITECTURE OVERVIEW:
The file implements a complete validation pipeline for knowledge graph node contributions:
- Part 2 (20 nodes/question): Node-level content validation + contribution conflict resolution
- Part 3 (340 pairs/question): Node-criterion pair labeling with fine-grained impact analysis
- Part 4 (17 criteria/question): Criterion-level aggregation + contradiction/conflict analysis
- Part 5: Question-level metadata aggregation for training predictors

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PART 2: NODE-LEVEL PREPROCESSING (Steps 2.1-2.4)
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Step 2.1: Node Content Validation (LLM call openai#1 per node, 20 total)
  Template: PHASE_A_CONTENT_VALIDATION_TEMPLATE
  Input: response_text, node_summary
  Output: node_content_appears_in_response (boolean)
  Purpose: Determine if node's content actually appears in response

Step 2.2: Conflict Detection (Deterministic, no LLM)
  Logic: conflict = (node_content_appears != initial_contributed)
  Triggers Step 2.3 if conflict exists

Step 2.3: Judge LLM for Conflicts (LLM call openai#2 per node IF conflict, ~10 total)
  Template: PHASE_B_JUDGE_LLM_TEMPLATE
  Input: response, node_summary, initial_contributed, content_appears
  Output:
    - judge_says_initial_correct (boolean)
    - judge_probability_initial_incorrect (0.0-1.0)
    - updated_contributed (boolean or null)
    - updated_node_contribution_explanation (string or null)
  Purpose: Referee between initial model claim vs actual content presence
  Threshold: Apply update if probability >= 0.70

Step 2.4: Resolution (Deterministic)
  Decision tree:
    - No conflict → Keep initial values
    - Conflict + judge strong disagreement (prob >= 0.70) → Apply judge's update
    - Conflict + judge weak/agrees → Keep initial values
  Output:
    - final_contributed (boolean)
    - final_node_contribution_explanation (string)
    - contribution_resolution_status (no_conflict_detected | judge_ran_updated_applied | judge_ran_initial_kept)

RESULT: process_node_part2() returns complete Part 2 output for 1 node
  Fields: node_content_appears_in_response, contribution_conflict, final_contributed, resolution_status, judge_fields

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PART 3: NODE-CRITERION PAIR LABELING (Steps 3.1.1-3.1.6)
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For each (node, criterion) pair, assign one of 8 labels via decision tree:

Step 3.1.1: Check Contributed Status
  If final_contributed == false → LABEL_NEUTRAL_NOT_IN_RESPONSE (short-circuit)
  Else → Continue to 3.1.2

Step 3.1.2: Check Grading Justification (LLM call openai#3)
  Template: PHASE_C_GRADING_JUSTIFICATION_TEMPLATE
  Input: criterion_statement, grading_explanation, node_summary, node_contribution_explanation
  Output: node_used_as_justification_in_grading_explanation (boolean)
  Purpose: Verify node was actually cited in grading explanation (not just in response)

Step 3.1.3: Check Not in Justification
  If node_used == false → LABEL_NEUTRAL_IN_RESPONSE (short-circuit)
  Else → Continue to 3.1.4

Step 3.1.4: Analyze Node Direction (LLM call openai#4)
  Template: PHASE_D_NODE_DIRECTION_TEMPLATE
  Input: criterion, criteria_met, grading_explanation, node_summary
  Output:
    - node_direction_relative_to_criteria: PUSHED_TOWARD_MET | PUSHED_TOWARD_NOT_MET | UNCLEAR_DIRECTION
    - node_direction_relative_to_criteria_confidence (0.0-1.0)
  Purpose: Determine if node pushed toward or against criterion being met

Step 3.1.5: Check Unclear Direction
  If direction == UNCLEAR_DIRECTION → LABEL_NEUTRAL_IN_RESPONSE (short-circuit)
  Else → Continue to 3.1.6

Step 3.1.6: Deterministic Labeling (NO LLM)
  Decision matrix (4 combinations):

    POSITIVE POINTS (points > 0):
      Criterion MET + Pushed TOWARD → kg_node_helped_led_to_awarding_positive_points
      Criterion MET + Pushed NOT_MET → kg_node_push_not_met_but_criterion_met (CONTRADICTION)
      Criterion NOT_MET + Pushed NOT_MET → kg_node_hurt_led_to_not_awarding_positive_points
      Criterion NOT_MET + Pushed TOWARD → kg_node_push_met_but_criterion_not_met (CONTRADICTION)

    NEGATIVE POINTS (points < 0):
      Criterion MET + Pushed TOWARD → kg_node_hurt_led_to_awarding_negative_points
      Criterion MET + Pushed NOT_MET → kg_node_push_not_met_but_criterion_met (CONTRADICTION)
      Criterion NOT_MET + Pushed NOT_MET → kg_node_helped_led_to_not_awarding_negative_points
      Criterion NOT_MET + Pushed TOWARD → kg_node_push_met_but_criterion_not_met (CONTRADICTION)

    ZERO POINTS: → kg_node_neutral_to_grading_but_in_response

LABEL TAXONOMY (8 possible labels):
  HELPED_POSITIVE_POINTS: Node info led to awarding positive points (helped criterion met)
  HELPED_NEGATIVE_POINTS: Node info led to NOT awarding negative points (helped avoid bad behavior)
  HURT_NO_POSITIVE: Node info led to NOT awarding positive points (hurt meeting criterion)
  HURT_NEGATIVE_POINTS: Node info led to awarding negative points (hurt by enabling bad behavior)
  NEUTRAL_NOT_IN_RESPONSE: Node not in response (final_contributed=false)
  NEUTRAL_IN_RESPONSE: Node in response but not used in grading justification or unclear direction
  CONTRADICTION_NOT_MET: Node pushed toward but criterion wasn't met (node contradicted outcome)
  CONTRADICTION_MET: Node pushed against but criterion was met (node contradicted outcome)

RESULT: process_node_criterion_pair_part3() returns:
  - final_node_label (one of 8 labels)
  - node_used_as_justification_in_grading_explanation (boolean)
  - node_direction_relative_to_criteria (PUSHED_TOWARD_MET | PUSHED_TOWARD_NOT_MET)
  - Direction confidence and reasoning

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PART 4: CRITERION-LEVEL AGGREGATION (Steps 4.1-4.7)
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Step 4.1: Count Labels by Type (Deterministic, no LLM)
  Input: List of Part 3 outputs for all 20 nodes for this criterion
  Aggregation:
    - num_helped_nodes: Count of HELPED_* labels
    - num_hurt_nodes: Count of HURT_* labels
    - num_neutral_nodes: Count of NEUTRAL_* labels
    - num_contradiction_nodes: Count of CONTRADICTION_* labels
    - num_unclear_direction_nodes: Count with UNCLEAR_DIRECTION
    - contradiction_ratio: num_contradiction / total_nodes
    - mixed_signals: (num_helped > 0) AND (num_hurt > 0)
  Output: Counts + derived metrics

Step 4.2: Assign KG Influence Label (Deterministic, uses 4.5/4.7 results)
  7 possible KG influence labels:
    KG_HELPED: Only helpful nodes (num_helped > 0, num_hurt == 0)
    KG_HURT: Only harmful nodes (num_hurt > 0, num_helped == 0)
    KG_NEUTRAL: No measurable impact (num_helped == 0, num_hurt == 0)
    KG_HELPED_DESPITE_CONFLICTS: Mixed signals but helped dominated (via Step 4.7 consistency check)
    KG_HURT_DESPITE_CONFLICTS: Mixed signals but hurt dominated (via Step 4.7 consistency check)
    KG_UNCLEAR_MIXED_SIGNALS: Mixed signals but Step 4.7 couldn't determine winner
    KG_OVERRIDDEN_BY_NON_KG: High contradictions but Step 4.5 says non-KG reasoning resolved outcome
    KG_UNEXPLAINED_CONTRADICTIONS: High contradictions and Step 4.5 says outcome is unexplained

  Decision logic:
    1. IF contradiction_ratio >= 0.25 → Use Step 4.5 result (high contradiction case)
    2. ELSE IF mixed_signals == true → Use Step 4.7 result (conflicting signals case)
    3. ELSE → Simple cases (only helped OR only hurt OR none)

Step 4.3: Calculate Confidence Level (Deterministic, no LLM)
  HIGH: Clean signal (only helped OR only hurt OR no nodes)
  MEDIUM: Mixed signals (both helped AND hurt) but contradiction_ratio < 0.25
  LOW: High contradictions (contradiction_ratio >= 0.25)
  Purpose: Quantify certainty in KG influence label

Step 4.5: Analyze High Contradictions (LLM call openai#5, CONDITIONAL)
  Trigger: ONLY if contradiction_ratio >= 0.25
  Template: HIGH_CONTRADICTION_ANALYSIS_TEMPLATE
  Input: criterion, criteria_met, grading_explanation, response, node contributions, counts
  Output:
    - high_contradiction_resolution_insight (free text: how did outcome occur despite contradictions?)
    - high_contradiction_label_consistency: CONSISTENT | INCONSISTENT | UNCLEAR
      (Does grading explanation logically explain contradictions?)
    - high_contradiction_consistency_reasoning (brief explanation)
  Purpose: Determine if contradictory signals are semantically justified by grading explanation
  Used by: Step 4.2 to decide between KG_OVERRIDDEN_BY_NON_KG vs KG_UNEXPLAINED_CONTRADICTIONS

Step 4.7: Analyze Conflicting Signals (LLM call openai#6, CONDITIONAL)
  Trigger: ONLY if num_helped_nodes > 0 AND num_hurt_nodes > 0 AND contradiction_ratio < 0.25
  Template: CONFLICTING_SIGNALS_ANALYSIS_TEMPLATE
  Input: criterion, criteria_met, grading_explanation, response, node contributions, counts
  Output:
    - conflicting_signals_weighting (free text: how were conflicts weighed?)
    - conflicting_signals_dominant_influence: helped_nodes | hurt_nodes | mixed_with_nonkg_factors | unclear
    - conflicting_signals_label_consistency: CONSISTENT | INCONSISTENT | UNCLEAR
      (Does grading explanation clearly explain which signal won?)
    - conflicting_signals_consistency_reasoning (brief explanation)
  Purpose: Determine which conflicting signal dominated the grading decision
  Used by: Step 4.2 to decide between KG_HELPED_DESPITE_CONFLICTS vs KG_HURT_DESPITE_CONFLICTS vs KG_UNCLEAR_MIXED_SIGNALS

RESULT: Per criterion, output includes:
  - All Part 4.1 counts (helped, hurt, neutral, contradiction nodes)
  - KG influence label (one of 7 types)
  - Confidence level (HIGH/MEDIUM/LOW)
  - [Optional] Step 4.5 fields (if contradiction_ratio >= 0.25)
  - [Optional] Step 4.7 fields (if mixed_signals == true)

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PART 5: QUESTION-LEVEL AGGREGATION (Steps 5.1-5.2)
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Step 5.1: Aggregate KG Influence Labels per Question
  Group criteria by KG influence label category:
    - Helped labels: KG_HELPED, KG_HELPED_DESPITE_CONFLICTS
    - Hurt labels: KG_HURT, KG_HURT_DESPITE_CONFLICTS
    - Uncertain labels: KG_NEUTRAL, KG_UNCLEAR_MIXED_SIGNALS, KG_OVERRIDDEN_BY_NON_KG, KG_UNEXPLAINED_CONTRADICTIONS
  Output:
    - num_kg_helped_criteria: Count of criteria where KG helped
    - num_kg_hurt_criteria: Count of criteria where KG hurt
    - num_kg_uncertain_criteria: Count of criteria with uncertain impact
    - kg_points_helped: Sum of abs(points) for helped criteria
    - kg_points_hurt: Sum of abs(points) for hurt criteria
    - kg_points_uncertain: Sum of abs(points) for uncertain criteria

Step 5.2: Aggregate Confidence Distribution per Question
  For each confidence level (HIGH/MEDIUM/LOW), count:
    - num_helped_high_confidence: # of helped criteria with HIGH confidence
    - num_helped_medium_confidence: # of helped criteria with MEDIUM confidence
    - num_helped_low_confidence: # of helped criteria with LOW confidence
    (Same for hurt criteria separately)
  Purpose: Understand confidence distribution across predictions

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HELPER FUNCTIONS & UTILITIES
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validate_json_response(): Parse + validate LLM JSON with required keys check
call_llm_with_validation(): Universal wrapper for all LLM calls
  - Template substitution (<<placeholders>>)
  - LLM call with response_format=json_object (guaranteed valid JSON)
  - Key validation
  - Logging + error handling

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LLM CALL SUMMARY (per question)
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Part 2 (20 nodes):
  - ~20 LLM calls for step_2_1_validate_node_content()
  - ~10 LLM calls for step_2_3_judge_conflict() (only if conflict detected)
  Total: ~20-30 calls

Part 3 (340 node-criterion pairs):
  - ~340 LLM calls for step_3_1_2_check_grading_justification()
  - ~340 LLM calls for step_3_1_4_analyze_node_direction()
  Total: ~680 calls

Part 4 (17 criteria, conditional):
  - ~17 LLM calls for step_4_5_analyze_high_contradictions() (if contradiction_ratio >= 0.25)
  - ~17 LLM calls for step_4_7_analyze_conflicting_signals() (if mixed_signals == true)
  Total: ~0-34 calls depending on conflicts

GRAND TOTAL PER QUESTION: ~700-750 LLM calls (primarily Part 3)

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DATA FLOW THROUGH INTEGRATION POINTS
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Imported by: healthbench_eval.py:grade_sample()

Call chain:
1. For each of 20 nodes:
   - process_node_part2(response_text, node_summary, initial_contributed, ...)
   - → Returns per_node_metadata[] entry

2. For each (node, criterion) pair (20 × 17 = 340):
   - process_node_criterion_pair_part3(node_index, final_contributed, node_summary, ...)
   - → Returns node_label entry added to per_criterion_metadata[].node_labels[]

3. For each of 17 criteria:
   - step_4_1_count_labels(node_labels)
   - step_4_2_assign_kg_influence_label(counts, ...)
   - step_4_3_calculate_confidence_level(counts, ...)
   - [OPTIONAL] step_4_5_analyze_high_contradictions(...) if contradiction_ratio >= 0.25
   - [OPTIONAL] step_4_7_analyze_conflicting_signals(...) if mixed_signals == true
   - → Returns Part 4 fields merged into rubric_items_with_grades[]

4. Per question (Part 5 done in build_part5_question_metadata.py):
   - step_5_1_aggregate_kg_influence_labels(per_criterion_metadata)
   - step_5_2_aggregate_confidence_level_distribution(per_criterion_metadata)
   - → Returns question-level metadata for Part 6 final output

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CRITICAL DESIGN DECISIONS
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1. Separation of concerns:
   - Part 2: Content validation (deterministic + judge)
   - Part 3: Impact labeling (4 LLM calls per pair + deterministic mapping)
   - Part 4: Aggregation (deterministic + optional semantic analysis)
   - Part 5: Question aggregation (deterministic)

2. Conditional LLM calls:
   - Step 2.3 (judge): Only if conflict detected (~50% of nodes)
   - Step 4.5 (high contradiction): Only if contradiction_ratio >= 0.25 (~10% of criteria)
   - Step 4.7 (conflicting signals): Only if mixed_signals == true (~30% of criteria)
   - Reduces LLM cost vs calling all steps for all pairs

3. Deterministic logic where possible:
   - Step 3.1.6 labeling: Simple boolean logic matrix (no LLM)
   - Step 4.1 counting: Simple aggregation (no LLM)
   - Step 4.2 logic: Decision tree based on counts (no LLM)
   - Step 4.3 confidence: Algorithmic based on metrics (no LLM)
   - Saves ~200+ LLM calls per question

4. Semantic consistency checks (Part 4.5, 4.7):
   - Not binary helped/hurt judgments
   - Rather: semantic consistency verification
   - LLM explains HOW contradictory signals are justified
   - Enables research analysis: "When are contradictions real vs noise?"

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IMPACT & RESEARCH VALUE
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✓ Comprehensive auditability: Every node-criterion decision is traceable
✓ Fine-grained labeling: 8 label types capture nuanced KG impact patterns
✓ Contradiction analysis: Distinguishes real conflicts from measurement noise
✓ Confidence quantification: Know certainty of each prediction
✓ Training data generation: 340 labeled (node, criterion) pairs per question
✓ Semantic validation: LLM explains reasoning, not just outputs labels
✓ Conditional LLM calls: Balances comprehensive analysis with cost efficiency

This is production-ready, fully validated, research-grade node-criterion labeling infrastructure.

Author: Reza Shamji