{
  "schema_version": "1.0",
  "model_name": "UHR-BAT2",
  "checkpoint_logical_id": "qwen3vl-8b-geollava-ft3-selected-10k",
  "audit_date": "2026-10-13",
  "accuracy_unit": "percent",
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        "Complex reasoning/Environmental condition reasoning": {
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        "Complex reasoning/Route planning": {
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        "Land use classification/Overall Land use classification": {
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        "Object properties/Object classification": {
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        "Object properties/Object color": {
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        "Object properties/Object motion state": {
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        "Object spatial relationship/Object spatial relationship": {
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    {
      "dataset_id": "mme",
      "dataset_name": "MME-RealWorld Remote Sensing",
      "reported_scope": "All 3,738 Remote Sensing records; not the Lite subset.",
      "protocol_id": "20260913_strict_sets_matched_schedule_v1",
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  ],
  "protocols": {
    "20260913_strict_sets_matched_schedule_v1": {
      "model_dtype": "bf16",
      "attention_implementation": "eager",
      "min_pixels": 784,
      "max_pixels": 1310720,
      "pruning": "none",
      "single_choice_scoring": "Argmax of candidate option logits computed using the final hidden state and FP32 option rows of the language-model head.",
      "xlrs_multiple_choice_scoring": "Constrained greedy token-trie decoding with FP32 candidate grammar rows, followed by exact canonical option-set matching.",
      "xlrs_multiple_choice_task": "Land use classification/Overall Land use classification",
      "source_code_sha256": {
        "worker.py": "bdff71b17de6f4b20b5d747ba173ad32563249af20fb035a5db2abadc5676573",
        "protocol.py": "e328cb2d29a8e309442f0fdbf7c8465ac823fa37962db17f6a9e9f233b64bf3f",
        "vendor/pruning_core.py": "1207c4c3f819b472bef5e47103015399e7d193b1975814c3b5e5ba5aefb01321",
        "vendor/qwen3vl_pruning_eval.py": "471b68fe5f519b144cbf6daaf452cdd838e286dce0574cde5743bc9fac018b98"
      },
      "verification": "All three datasets passed complete unique-index, answer/prediction correctness, protocol-sidecar and completion-marker checks. Recorded checkpoint/code/protocol identities match across their shards and conditions."
    }
  },
  "limitations": [
    "A standard model.generate quickstart demonstrates inference only. It does not reproduce the FP32 option-logit or constrained option-set scoring used for the three corrected dense benchmarks.",
    "Dense means no token pruning within the stated image preprocessing budget. These scores do not establish native 4K/8K or full-resolution ultra-high-resolution perception.",
    "RSHR all-QA accuracy and its 1,590-record single-turn Overall metric use different denominators and must not be interchanged.",
    "Historical XLRS results from the single-letter prompt/normalizer are excluded because multi-option ground truths were truncated. They are not valid strict option-set accuracy.",
    "No same-protocol gain over the original base model is claimed by this artifact.",
    "This audit rechecked existing evaluation artifacts; no new model inference was run on the audit date.",
    "Raw prediction files, benchmark images and questions are not included in this aggregate artifact. Hashes identify the audited source artifacts but do not constitute a complete public reproduction package."
  ]
}