{
  "scores": [
    {
      "outcome": "DONT_FUND",
      "score": 53500
    },
    {
      "outcome": "FUND",
      "score": 946500
    }
  ],
  "justification": "Comprehensive justification for the result\n\n1) What each model reports\n- GPT-5.6-terra\n  - Phase 1: PASS (security/safety fully contained, no dangerous behavior)\n  - Phase 2: Overall weighted scores (Functionality 97.8, others 97–99)\n  - Final scores: FUND = 97.8, DONT_FUND = 2.2\n\n- Claude-sonnet-5\n  - Phase 1: PASS\n  - Phase 2: Functionality 95, Code Quality 93, Documentation 95, Testing 95, Performance 92\n  - Weighted average (per their calculation): FUND = 94.0, DONT_FUND = 6.0\n\n2) How the aggregated decision vector relates\n- The reported aggregated decision vector [53500, 946500] suggests a large skew toward funding (the second component dominating).\n- Interpreting the numbers as a prominence/priority signal, the FUND outcome is overwhelmingly favored in the aggregate, aligning with the two model justifications above:\n  - GPT-5.6-terra’s very high FUND score (97.8) strongly supports funding.\n  - Claude-sonnet-5’s FUND score (94.0) also supports funding, though less strongly than GPT-5.6-terra, still well above DONT_FUND (6.0).\n\n3) Synthesis and justification\n- Safety: Both models confirm mandatory requirements pass and no safety concerns, so no red flags to withhold funding on safety grounds.\n- Overall quality: Both models rate the work highly across all dimensions, with GPT-5.6-terra generally higher (≈97.8) than Claude (≈94). The higher rating from GPT-5.6-terra drives the aggregate funding signal.\n- Robustness and edge cases: Claude explicitly notes handling of edge cases (malformed input, overflow, NaN/inf) and reports a strict but justifiable interpretation that errs on the safe side; this strengthens confidence in correctness and resilience.\n- Testing and coverage: Both models emphasize strong testing and high coverage, which underpins trust in the evaluation.\n- Weighting rationale: Despite some minor disagreements on exact deltas (e.g., Code Quality, Testing coverage), the overall trend is consensual: the submission is solid, well-documented, well-tested, and performant.\n\n4) Conclusion\n- The aggregated signal strongly favors FUND, consistent with both model judgments. Given the very high FUND scores relative to DONT_FUND across both models and the explicit, high-quality justifications (safety, correctness, coverage, and performance), funding is the prudent outcome.\n- Final stance: FUND, with high confidence, and DONT_FUND only as a low-probability alternative if future information reveals undisclosed issues.",
  "timestamp": "2026-10-21T01:54:57.035Z",
  "arbiter": {
    "adapter": "1.0.0",
    "aiNode": "0.1.0",
    "verdiktaCommon": "1.8.0",
    "release": "830c589 2026-10-16T02:53:34Z"
  },
  "metadata": {
    "models_requested": 2,
    "models_successful": 2,
    "models_failed": 0,
    "success_threshold_met": true,
    "total_duration_ms": 19046
  },
  "model_results": [
    {
      "provider": "openai",
      "model": "gpt-5.6-terra",
      "status": "success",
      "duration_ms": 8324
    },
    {
      "provider": "anthropic",
      "model": "claude-sonnet-5",
      "status": "success",
      "duration_ms": 12121
    }
  ]
}