Wald-4B / evaluation /v1.2 /summary.json
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main = Wald-Q4B v1.2 (02600-f19, robustness release): weights from v1.2-release, v1.2 serving.json (effort none) and runbook; card: v1.2 on main, v1.1 at tag v1.1
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{
"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
}
}
}