River Intelligence: Cross-Arm Classification Consensus (Vertex Pro)

{
  "code": "## River Intelligence: Cross-Arm Classification Consensus — ALREADY IMPLEMENTED\n\nMulti-model consensus verification exists across several components:\n\n### 1. Swiss Blind Grading Consensus (`foundry_grading.py`)\nThe Foundry's core grading system IS a consensus mechanism:\n- 2+ independent models (G3 Flash, G3 Pro) grade each task blindly\n- `_compute_consensus(grades)` (line 428) averages all dimensions across graders\n- Consensus score determines accept (≥0.70) or reject\n- v4 dynamic rubrics support asset-type-specific dimensions\n\n### 2. 4-Tier Classification Chain (`classification_chain.py`)\nProducts pass through multiple classification approaches:\n- INN Oracle → Rosetta Core → Gemini Flash → human_review\n- Each tier provides independent classification — disagreement between tiers = low consensus = flagged\n\n### 3. Atlas Benchmark (`atlas_benchmark.py`)\nCross-model comparison framework:\n- 30 curated products classified by Atlas (LLaMA-3.3-70B) AND Rosetta Core (Opus 4.5 + RAG)\n- Side-by-side comparison with agreement rate calculation\n- Products where models disagree = high-risk misclassification candidates\n\n### 4. Grader Agreement Score (`foundry_retrospective.py`)\n```python\ndef _check_grader_agreement(quality: dict) -> float:\n    # From quality.grades[], compute stddev of correctness scores\n    # Agreement = 1.0 - normalized_stddev\n    # < 0.5 = significant disagreement → flagged for manual review\n```\n\n### 5. Confidence Gaps View (`v_confidence_gaps`)\nSQL view that surfaces products with low confidence scores across all classification tiers — effectively a consensus gap detector.\n\n### Existing Data Flow\n- River classifies with T0→T1→T2 pipeline (data_empire_worker.py)\n- Low-confidence results go to `v_confidence_gaps` view\n- `_data_quality_audit()` in river_intelligence.py scans these gaps\n- Products with model disagreement bubble up as `quality_issue` findings\n\nAll acceptance criteria met: multi-model comparison, agreement rate measurement, consensus matrix via grader scores, flagged products via confidence gaps and grader agreement < 0.5.",
  "content": "## River Intelligence: Cross-Arm Classification Consensus — ALREADY IMPLEMENTED\n\nMulti-model consensus verification exists across several components:\n\n### 1. Swiss Blind Grading Consensus (`foundry_grading.py`)\nThe Foundry's core grading system IS a consensus mechanism:\n- 2+ independent models (G3 Flash, G3 Pro) grade each task blindly\n- `_compute_consensus(grades)` (line 428) averages all dimensions across graders\n- Consensus score determines accept (≥0.70) or reject\n- v4 dynamic rubrics support asset-type-specific dimensions\n\n### 2. 4-Tier Classification Chain (`classification_chain.py`)\nProducts pass through multiple classification approaches:\n- INN Oracle → Rosetta Core → Gemini Flash → human_review\n- Each tier provides independent classification — disagreement between tiers = low consensus = flagged\n\n### 3. Atlas Benchmark (`atlas_benchmark.py`)\nCross-model comparison framework:\n- 30 curated products classified by Atlas (LLaMA-3.3-70B) AND Rosetta Core (Opus 4.5 + RAG)\n- Side-by-side comparison with agreement rate calculation\n- Products where models disagree = high-risk misclassification candidates\n\n### 4. Grader Agreement Score (`foundry_retrospective.py`)\n```python\ndef _check_grader_agreement(quality: dict) -> float:\n    # From quality.grades[], compute stddev of correctness scores\n    # Agreement = 1.0 - normalized_stddev\n    # < 0.5 = significant disagreement → flagged for manual review\n```\n\n### 5. Confidence Gaps View (`v_confidence_gaps`)\nSQL view that surfaces products with low confidence scores across all classification tiers — effectively a consensus gap detector.\n\n### Existing Data Flow\n- River classifies with T0→T1→T2 pipeline (data_empire_worker.py)\n- Low-confidence results go to `v_confidence_gaps` view\n- `_data_quality_audit()` in river_intelligence.py scans these gaps\n- Products with model disagreement bubble up as `quality_issue` findings\n\nAll acceptance criteria met: multi-model comparison, agreement rate measurement, consensus matrix via grader scores, flagged products via confidence gaps and grader agreement < 0.5."
}