{
  "schema": "wald-q4b-release-evaluation/1",
  "release": "Wald-Q4B v1.2",
  "checkpoint": "02600-f19",
  "parent": "Wald-Q4B v1.1 (022D0-f7, revision v1.1)",
  "date": "2026-10-01",
  "status": "All numbers are self-run and self-reported. Nothing here is a leaderboard result and nothing was submitted for v1.2.",
  "configuration": "packaged wald-serve 0.1.0 + vLLM 0.30.0, BF16, effort none (one pass), prompt format repeat_state_plain, v1.1 temperature table; one NVIDIA RTX 5090 32 GB unless noted",
  "weights_sha256": {
    "model-00001-of-00002.safetensors": "0b15067f769e7388bd598aabafa2ea3211a3a0c4e49e47d07c4f65c65ffc25dd",
    "model-00002-of-00002.safetensors": "7cff102b314edeb3dc5abfa7063f72ad3e888884da237601239dc3582074120a"
  },
  "jevadvbench": {
    "benchmark": {
      "repo": "JevAdvBench/JevAdvBench@3218e05",
      "paper": "arXiv 2609.31142",
      "questions": 812,
      "scenarios": 66,
      "attack_variants": 9744,
      "data_licence": "CC BY-NC 4.0 (used for evaluation only; no items are redistributed here)"
    },
    "metric": "flip = the decision on the attacked question differs from the same model's decision on the clean question; % of 812 questions; 95% scenario-cluster bootstrap intervals (66 clusters, 2,000 resamples), the authors' analysis code",
    "jev_column": "jev-1.13.0 responses as released with the benchmark, scored by the same code",
    "flip_rate_pct": {
      "Q1": {
        "name": "word edits",
        "jev_1_13": 1.0,
        "v1_1": 1.6,
        "v1_2": 1.6,
        "v1_2_minus_v1_1_paired_pp": {
          "point": 0.0,
          "ci95": [
            -0.8,
            0.8
          ]
        }
      },
      "Q2": {
        "name": "paraphrase",
        "jev_1_13": 1.5,
        "v1_1": 1.6,
        "v1_2": 1.4,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -0.2,
          "ci95": [
            -1.1,
            0.5
          ]
        }
      },
      "Q3": {
        "name": "unrelated sentences in the question",
        "jev_1_13": 4.6,
        "v1_1": 14.9,
        "v1_2": 4.6,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -10.3,
          "ci95": [
            -13.5,
            -7.2
          ]
        }
      },
      "T1": {
        "name": "unrelated note in the state",
        "jev_1_13": 2.2,
        "v1_1": 2.8,
        "v1_2": 1.7,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -1.1,
          "ci95": [
            -2.4,
            0.0
          ]
        }
      },
      "T2": {
        "name": "observer's opinion in the state",
        "jev_1_13": 12.1,
        "v1_1": 16.9,
        "v1_2": 5.5,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -11.3,
          "ci95": [
            -14.6,
            -8.2
          ]
        }
      },
      "T3": {
        "name": "opinion through an analogy",
        "jev_1_13": 6.9,
        "v1_1": 12.7,
        "v1_2": 6.3,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -6.4,
          "ci95": [
            -8.8,
            -3.6
          ]
        }
      },
      "P1": {
        "name": "direct override",
        "jev_1_13": 8.9,
        "v1_1": 7.9,
        "v1_2": 5.7,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -2.2,
          "ci95": [
            -3.6,
            -0.9
          ]
        }
      },
      "P2": {
        "name": "authority impersonation",
        "jev_1_13": 10.1,
        "v1_1": 13.7,
        "v1_2": 6.7,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -7.0,
          "ci95": [
            -9.0,
            -5.1
          ]
        }
      },
      "P3": {
        "name": "fake validation note",
        "jev_1_13": 8.1,
        "v1_1": 10.3,
        "v1_2": 7.8,
        "v1_2_minus_v1_1_paired_pp": {
          "point": -2.6,
          "ci95": [
            -4.2,
            -1.0
          ]
        }
      }
    },
    "mean_of_nine_delivered_attacks_pct": {
      "jev_1_13": 6.1,
      "v1_1": 9.2,
      "v1_2": 4.6,
      "v1_2_minus_v1_1_paired_pp": {
        "point": -4.6,
        "ci95": [
          -5.5,
          -3.6
        ]
      },
      "v1_2_minus_jev_pp": {
        "point": -1.6,
        "ci95": [
          -2.7,
          -0.4
        ]
      }
    },
    "questions_flipped_by_at_least_one_attack_pct": {
      "jev_1_13": 29.2,
      "v1_1": 41.7,
      "v1_2": 20.7
    },
    "clean_accuracy_143_human_reviewed_pct": {
      "jev_1_13": 87.4,
      "v1_1": 79.0,
      "v1_2": 76.2,
      "v1_2_minus_v1_1_paired_pp": {
        "point": -2.8,
        "ci95": [
          -6.2,
          -0.6
        ]
      }
    },
    "clean_decisions_equal_to_v1_1_pct": 98.6,
    "note": "The perturbation kinds used to train v1.2 were chosen after v1.1's per-attack results were known. No JevAdvBench text was used in training (8-gram overlap 0)."
  },
  "jevbench_public_231": {
    "harness": "fstandhartinger/jevbench@9ec6f15a, jevbench.cli run + summarize, typesafe adapter, serial, loopback",
    "dataset_hash": "dc3995d8ae1e2fc8e81ce38431add509eb8bb39b85aadfd0c7c32079382dde51",
    "v1_2": {
      "none": {
        "correct": 204,
        "of": 231,
        "easy": "48/48",
        "original": "72/72",
        "hard": "84/111",
        "ece": 0.045,
        "brier": 0.191,
        "gpu": "RTX 5090"
      },
      "medium": {
        "correct": 198,
        "of": 231,
        "ece": 0.037,
        "brier": 0.208,
        "gpu": "RTX 5090",
        "runs": 1
      },
      "high": {
        "correct": 198,
        "of": 231,
        "ece": 0.072,
        "brier": 0.204,
        "gpu": "RTX 5090",
        "runs": 1
      }
    },
    "v1_1": {
      "none": {
        "correct": 203,
        "of": 231,
        "ece": 0.041,
        "brier": 0.188,
        "gpu": "RTX PRO 6000"
      },
      "medium": {
        "correct": 205,
        "of": 231,
        "gpu": "RTX PRO 6000",
        "runs": 1
      }
    },
    "note": "The public items were a development scoreboard (never training data); not a held-out result. Latency was not measured for v1.2."
  },
  "decision_index_0_2_1_sample": {
    "requests": 6948,
    "read": "one pass (no thinking), both versions on the same RTX 5090",
    "metric": "balanced_skill, bootstrap B=300",
    "v1_1": {
      "value": 49.76,
      "ci95": [
        47.82,
        51.35
      ]
    },
    "v1_2": {
      "value": 50.03,
      "ci95": [
        48.18,
        51.65
      ]
    },
    "v1_2_minus_v1_1_paired": {
      "point": 0.27,
      "ci95": [
        -0.36,
        1.05
      ]
    },
    "note": "A sample, not the complete suite. Not comparable with the 54.59 complete-suite result, which was measured on v1.1 with effort high. v1.2 has no complete-suite run."
  },
  "internal_non_regression_sets": {
    "note": "Our own sets, not public benchmarks; one pass.",
    "xl_int": {
      "v1_1": 56.0,
      "v1_2": 56.4
    },
    "tool_selection_2857_items_accuracy_pct": {
      "v1_1": 87.15,
      "v1_2": 87.22
    }
  }
}