[Frontend][Benchmark] Add judger and accuracy metrics for OmniInterac… (#7317)

Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>

docs/cli/bench/serve.md

@@ -302,6 +302,21 @@ vllm bench serve --omni \
 selects all and oversize values use all available cases. Reference audio is required, and OmniInteract uses the
 `/v1/realtime` endpoint.
 
+To replay an existing sample set (for example `sampled_cases.jsonl` from a prior run), pass
+`--omniinteract-video-list` instead of discovering cases. List order is preserved; `--num-prompts` takes the prefix of
+the list (`0` or a value larger than the list length runs every row). Do not combine video-list mode with `--seed`,
+`--dataset-path`, `--omniinteract-scenario-tags`, or `--omniinteract-scenario-focus`.
+
+Use `--omniinteract-scenario-tags` to steer small runs toward paper-table scenarios (`realtime`, `proactive`,
+`nested`, `interrupted`, `1qna`). By default the sampler covers each requested tag with at least one matching case
+(when available under `--omniinteract-subsets`), then fills the remaining `--num-prompts` budget from other cases.
+Add `--omniinteract-scenario-focus` to run only cases that match those tags. Example smoke coverage:
+
+```bash
+  --num-prompts 5 \
+  --omniinteract-scenario-tags realtime proactive nested interrupted 1qna
+```
+
 Audio is replayed as 16 kHz PCM16 in 200 ms chunks and video at 1 FPS with real-time pacing. All selected media is decoded
 before timing and remains in client memory for the run, so `--max-concurrency` does not limit media preparation memory; use
 explicit `--num-prompts 0` only when the client has enough RAM for the full dataset. Media commands are bounded by
@@ -309,7 +324,9 @@ explicit `--num-prompts 0` only when the client has enough RAM for the full data
 options apply. Use `--omniinteract-require-response` only for functional E2E cases; LISTEN is a valid benchmark result.
 
 Each completed case writes `output.wav`, `wav_transcript.json`, `events.json`, `result.json`, and a final `.done` marker under
-`--omniinteract-output-dir`. The root also contains `batch_summary.json` and `official_eval_manifest.jsonl`; failed cases write
+`--omniinteract-output-dir`. The root also contains `batch_summary.json`, `official_eval_manifest.jsonl` (eligible cases only),
+and `sampled_cases.jsonl` (every sampled case as absolute `video_path` / official `output_name` / `subset` rows for Lucky-Lance
+OmniInteract `batch_inference_minicpmo.py --video_list`); failed cases write
 `.failed.json`. Runs sharing one output root are serialized. Completion validates transport, response lifecycle, and artifacts,
 not answer accuracy. Transcript timestamps are serialized playback-queue times. Playback ACKs report cumulative progress
 incrementally along that serialized clock, like a live listener; the first ack for a response goes out as soon as its audio
@@ -318,6 +335,48 @@ arrives, checkpointing the response's history position so a later committed user
 outputs are ineligible and omitted from the official manifest; `audio_clipped_bytes` records output beyond the rounded video
 horizon.
 
+Accuracy evaluation is opt-in and requires an already-running text judge with an OpenAI-compatible Chat Completions API. The
+benchmark does not launch or stop the judge server. Add the following options to the command above:
+
+```bash
+vllm serve Qwen/Qwen2.5-7B-Instruct \
+  --served-model-name Qwen2.5-7B-Instruct \
+  --port 8001
+```
+
+Then add to the benchmark command:
+
+```bash
+  --omniinteract-evaluate \
+  --omniinteract-judge-base-url http://127.0.0.1:8001 \
+  --omniinteract-judge-model Qwen2.5-7B-Instruct
+```
+
+`--omniinteract-judge-model` must match that judge process's `--served-model-name` (or its `--model`
+string if `--served-model-name` is omitted). It is not a checkpoint path to load, and it is not the
+Omni DUT `--served-model-name`. Point `--omniinteract-judge-base-url` at the judge, not at the Omni
+realtime endpoint.
+
+The early / core / interrupted-partial judge prompts follow the official English
+templates in [Lucky-Lance/OmniInteract](https://github.com/Lucky-Lance/OmniInteract)
+`eval/evaluation/llm_judge.py` (commit `de304cef35fd9a50a5caadb5090c34cfbf0dd868`).
+They correspond to the OmniInteract paper appendix
+([arXiv:2605.26485](https://arxiv.org/abs/2605.26485)) Listing A.1 (early-stage),
+Listing A.2 (interrupted partial quality), and Listing A.3 (core-stage).
+A local OpenAI-compatible judge is not the paper's GPT-4o judge, so reported
+IA-QTF1 numbers are protocol-compatible rather than official paper-table scores.
+
+After artifact publication, the evaluator builds `[start, t_a, end)` slots, judges early and core response text, writes
+per-case details plus `evaluation/unified_eval_summary.json`, and prints IA-QTF1 columns
+(1Q1A realtime / proactive / nested / Global, 1QnA, All Global), interruption diagnostics (NOR / PAQ / CSM), and nested
+metrics (NCCS, inner / outer IA-QTF1, missed outer). Slices and sections with no slots in the evaluated sample are
+omitted from the terminal report. The header lists how many evaluated cases carry each scenario tag
+(`realtime`, `proactive`, `nested`, `interrupted`, `1qna`). Realtime and proactive
+exclude nested inner/outer slots; 1Q1A Global recomputes F1 from those three TP/FP/FN aggregates; All Global includes every
+scored slot plus unmatched-chunk false positives. Plain `wav_transcript.json` timestamps provide chunk-level approximate
+timing. If transcript chunks include `aligned_words`, the evaluator splits boundary-crossing chunks and derives trigger
+timing from word alignment.
+
 Per-response TTFT and TTFP start when the server begins executing the native model-turn request that owns the response. RTF
 continues to use client receipt of `response.created` through the last audio packet, divided by emitted audio duration. Global
 TTFT, TTFP, and RTF cover the complete input-stream window. TPOT/ITL use engine stage-0 timing; ITL is emitted only when every
@@ -325,9 +384,12 @@ token interval is present within a continuous generation segment. Model-unit pac
 excluded from TPOT/ITL. Raw request metrics retain `response_created_to_first_text_ms` and
 `response_created_to_first_audio_ms` as client-envelope diagnostics.
 
-The checked-in local performance configuration measures four deterministic cases from each OmniInteract subset (12 videos
-total), with no benchmark warmups and a maximum concurrency of two. Each subset also sends one readiness request before its
-measured cases. Run it from the repository root with:
+The checked-in local performance configuration measures four deterministic cases from `1q1a`, `1q1a_math`, and
+`1qna` (12 videos total), with no benchmark warmups and a maximum concurrency of two. Each subset also sends one readiness request before its
+measured cases. The runner starts MiniCPM-o 4.5 on the first visible GPU and a text judge
+(`Qwen/Qwen2.5-7B-Instruct`) on the second (`CUDA_VISIBLE_DEVICES=1` relative to the process). After artifacts land it
+scores All Global IA-QTF1 through that local judge. This is protocol-compatible with the paper metric, not a GPT-4o
+paper-table score. Run it from the repository root with two visible GPUs:
 
 ```bash
 export HF_HOME=/path/to/persistent/huggingface-cache
@@ -338,12 +400,14 @@ bash tools/nightly/run_nightly_jobs.sh \
   --label-substr minicpmo_4_5_omniinteract
 ```
 
-The first run downloads the pinned OmniInteract archive into `HF_HOME`; later runs reuse that cache.
+The first run downloads the pinned OmniInteract archive and the judge weights into `HF_HOME`; later runs reuse that cache.
 
 It requires every case to commit its input, complete any emitted response lifecycles, and publish the expected WAV,
 transcript, event, and result artifacts without errors. A valid LISTEN-only case may have no response audio or transcript
 chunks. Official-manifest eligibility is reported separately because clipped or cancelled output is a benchmark-quality
-signal, not a transport failure. This local performance test does not score answer accuracy.
+signal, not a transport failure. Accuracy must finish with `status=ok` on every subset. After all three subsets finish,
+All Global IA-QTF1 is recomputed from pooled `Global_TP` / `Global_FP` / `Global_FN` and must be at or above
+`omniinteract_aggregate_min_ia_qtf1` (checked in as `0.2`).
 
 ### Video-MME Benchmark
 

tests/benchmarks/test_omniinteract.py

@@ -249,6 +249,252 @@ def test_dataset_discovers_official_layouts_and_total_selection(tmp_path: Path):
     assert [case.subset for case in selected] == ["1q1a", "1q1a_math"]
 
 
+def _write_scenario_dataset(root: Path) -> Path:
+    data_root = root / "data"
+    one_q1a = data_root / "1q1a"
+    (one_q1a / "videos").mkdir(parents=True)
+    (one_q1a / "annotations").mkdir()
+    entries = []
+    specs = [
+        ("realtime", "multi_turn", [{"question_time": 0.0, "answer_time": 1.0, "question_type": "realtime"}]),
+        ("proactive", "multi_turn", [{"question_time": 0.0, "answer_time": 1.0, "question_type": "proactive"}]),
+        (
+            "nested",
+            "nested",
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 4.0,
+                    "question_type": "proactive",
+                    "question_text": "outer",
+                    "answer_text": "outer",
+                },
+                {
+                    "question_time": 1.0,
+                    "answer_time": 2.0,
+                    "question_type": "realtime",
+                    "question_text": "inner",
+                    "answer_text": "inner",
+                },
+            ],
+        ),
+        (
+            "interrupted",
+            "multi_turn",
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 1.0,
+                    "question_type": "realtime",
+                    "is_interrupted": True,
+                }
+            ],
+        ),
+    ]
+    for name, scene, annotation in specs:
+        video = one_q1a / "videos" / f"{name}.mp4"
+        ann = one_q1a / "annotations" / f"{name}.json"
+        video.touch()
+        ann.write_text(json.dumps(annotation))
+        entries.append(
+            {
+                "video": f"videos/{name}.mp4",
+                "annotation": f"annotations/{name}.json",
+                "scene_type": scene,
+            }
+        )
+    (one_q1a / "video_json_map.json").write_text(json.dumps({"entries": entries}))
+
+    one_qna = data_root / "1qna"
+    (one_qna / "videos_bench").mkdir(parents=True)
+    (one_qna / "annotations").mkdir(parents=True)
+    (one_qna / "videos_bench" / "guide.mp4").touch()
+    (one_qna / "annotations" / "guide.json").write_text(
+        json.dumps(
+            {
+                "question_time": 0.0,
+                "question_text": "q",
+                "answers": [{"answer_time": 1.0, "answer_text": "a", "label": "step"}],
+            }
+        )
+    )
+    math_root = data_root / "1q1a_math"
+    (math_root / "videos").mkdir(parents=True)
+    (math_root / "annotations").mkdir()
+    (math_root / "videos" / "math.mp4").touch()
+    (math_root / "annotations" / "math.json").write_text(
+        json.dumps([{"question_time": 0.0, "answer_time": 1.0, "question_type": "realtime"}])
+    )
+    (math_root / "video_json_map.json").write_text(
+        json.dumps(
+            {
+                "entries": [
+                    {
+                        "video": "videos/math.mp4",
+                        "annotation": "annotations/math.json",
+                        "scene_type": "multi_turn",
+                    }
+                ]
+            }
+        )
+    )
+    return data_root
+
+
+def test_scenario_cover_and_focus_sampling(tmp_path: Path) -> None:
+    root = _write_scenario_dataset(tmp_path)
+    covered = data.discover_omniinteract_cases(
+        root,
+        ("1q1a", "1q1a_math", "1qna"),
+        num_prompts=5,
+        disable_shuffle=True,
+        scenario_tags=("realtime", "proactive", "nested", "interrupted", "1qna"),
+    )
+    assert len(covered) == 5
+    covered_tags = set().union(*(data.case_scenario_tags(case) for case in covered))
+    assert {"realtime", "proactive", "nested", "interrupted", "1qna"} <= covered_tags
+
+    focused = data.discover_omniinteract_cases(
+        root,
+        ("1q1a", "1q1a_math", "1qna"),
+        num_prompts=0,
+        disable_shuffle=True,
+        scenario_tags=("nested", "1qna"),
+        scenario_focus=True,
+    )
+    assert {case.scene_type for case in focused} == {"nested", "1qna"}
+
+
+def test_load_cases_from_video_list_preserves_order_and_caps(tmp_path: Path) -> None:
+    root = _write_dataset(tmp_path)
+    all_cases = data.discover_omniinteract_cases(root, data.OMNIINTERACT_SUBSETS, num_prompts=0, disable_shuffle=True)
+    ordered = list(reversed(all_cases))
+    video_list = tmp_path / "sampled_cases.jsonl"
+    video_list.write_text("".join(json.dumps(data.sampled_case_row(case)) + "\n" for case in ordered))
+
+    capped = data.load_omniinteract_cases_from_video_list(video_list, num_prompts=2)
+    assert [case.video_path for case in capped] == [case.video_path for case in ordered[:2]]
+
+    full = data.load_omniinteract_cases_from_video_list(video_list, num_prompts=0)
+    assert [case.video_path for case in full] == [case.video_path for case in ordered]
+    oversized = data.load_omniinteract_cases_from_video_list(video_list, num_prompts=99)
+    assert [case.video_path for case in oversized] == [case.video_path for case in ordered]
+
+
+def test_load_cases_from_video_list_requires_existing_video(tmp_path: Path) -> None:
+    root = _write_dataset(tmp_path)
+    case = data.discover_omniinteract_cases(root, ("1q1a",), num_prompts=1, disable_shuffle=True)[0]
+    row = data.sampled_case_row(case)
+    row["video_path"] = str(tmp_path / "missing.mp4")
+    video_list = tmp_path / "bad.jsonl"
+    video_list.write_text(json.dumps(row) + "\n")
+    with pytest.raises(FileNotFoundError, match="video missing"):
+        data.load_omniinteract_cases_from_video_list(video_list, num_prompts=0)
+
+
+def test_get_samples_from_video_list_without_dataset_path(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    root = _write_dataset(tmp_path)
+    cases = data.discover_omniinteract_cases(root, data.OMNIINTERACT_SUBSETS, num_prompts=0, disable_shuffle=True)
+    video_list = tmp_path / "sampled_cases.jsonl"
+    video_list.write_text("".join(json.dumps(data.sampled_case_row(case)) + "\n" for case in cases))
+    ref = tmp_path / "ref.wav"
+    ref.touch()
+    parser = TrackingArgumentParser()
+    OmniBenchmarkServingSubcommand.add_cli_args(parser)
+    args = parser.parse_args(
+        [
+            "--backend",
+            "openai-realtime-duplex",
+            "--dataset-name",
+            "omniinteract",
+            "--model",
+            MODEL,
+            "--endpoint",
+            "/v1/realtime",
+            "--num-prompts",
+            "2",
+            "--omniinteract-ref-audio",
+            str(ref),
+            "--omniinteract-video-list",
+            str(video_list),
+        ]
+    )
+    preprocess_serve_args(args)
+    monkeypatch.setattr(
+        "vllm_omni.clients.duplex.reference_audio_data_url",
+        lambda _: "data:audio/wav;base64,ref",
+    )
+    monkeypatch.setattr(benchmark_patch, "prepare_media", lambda *a, **k: (1.0, b"pcm", ["frame"]))
+    samples = benchmark_patch.get_samples(args, None)
+    assert len(samples) == 2
+    assert [sample.omniinteract_case.video_path for sample in samples] == [case.video_path for case in cases[:2]]
+
+
+def _write_overlapping_cover_dataset(root: Path) -> Path:
+    data_root = root / "data"
+    one_q1a = data_root / "1q1a"
+    (one_q1a / "videos").mkdir(parents=True)
+    (one_q1a / "annotations").mkdir()
+    entries = []
+    specs = [
+        (
+            "pair_a",
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 1.0,
+                    "question_type": "realtime",
+                    "is_interrupted": True,
+                }
+            ],
+        ),
+        (
+            "pair_b",
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 1.0,
+                    "question_type": "realtime",
+                    "is_interrupted": True,
+                }
+            ],
+        ),
+        (
+            "proactive_only",
+            [{"question_time": 0.0, "answer_time": 1.0, "question_type": "proactive"}],
+        ),
+    ]
+    for name, annotation in specs:
+        video = one_q1a / "videos" / f"{name}.mp4"
+        ann = one_q1a / "annotations" / f"{name}.json"
+        video.touch()
+        ann.write_text(json.dumps(annotation))
+        entries.append(
+            {
+                "video": f"videos/{name}.mp4",
+                "annotation": f"annotations/{name}.json",
+                "scene_type": "multi_turn",
+            }
+        )
+    (one_q1a / "video_json_map.json").write_text(json.dumps({"entries": entries}))
+    return data_root
+
+
+def test_cover_sampling_skips_tags_already_covered(tmp_path: Path) -> None:
+    root = _write_overlapping_cover_dataset(tmp_path)
+    selected = data.discover_omniinteract_cases(
+        root,
+        ("1q1a",),
+        num_prompts=2,
+        disable_shuffle=True,
+        scenario_tags=("realtime", "interrupted", "proactive"),
+    )
+    names = [case.video_path.stem for case in selected]
+    assert names == ["pair_a", "proactive_only"]
+    selected_tags = set().union(*(data.case_scenario_tags(case) for case in selected))
+    assert {"realtime", "interrupted", "proactive"} <= selected_tags
+
+
 def _archive(path: Path, member: str) -> None:
     with tarfile.open(path, "w") as archive:
         content = b"{}"
@@ -542,6 +788,51 @@ def test_ineligible_outputs_are_excluded_from_manifest(tmp_path: Path, audio_tim
     assert reason in result.official_eval_ineligible_reasons
     oi.write_batch_artifacts(tmp_path / "out", [case], [result])
     assert (tmp_path / "out" / "official_eval_manifest.jsonl").read_text() == ""
+    sampled = [
+        json.loads(line) for line in (tmp_path / "out" / "sampled_cases.jsonl").read_text().splitlines() if line.strip()
+    ]
+    assert sampled == [
+        {
+            "video_path": str(case.video_path.resolve()),
+            "output_name": data.official_output_name(case),
+            "subset": case.subset,
+        }
+    ]
+
+
+def test_official_output_name_matches_minicpmo_batch_layout(tmp_path: Path):
+    one_q1a = _case(tmp_path, subset="1q1a", name="videos/clip_a.mp4")
+    one_qna = _case(tmp_path, subset="1qna", name="videos_bench/nested/guide.mp4")
+    assert data.official_output_name(one_q1a) == "1q1a/videos__clip_a"
+    assert data.official_output_name(one_qna) == "1qna/nested__guide"
+    row = data.sampled_case_row(one_q1a)
+    assert row["video_path"] == str(one_q1a.video_path.resolve())
+    assert row["output_name"] == "1q1a/videos__clip_a"
+    assert row["subset"] == "1q1a"
+
+
+def test_sampled_cases_jsonl_lists_every_case_including_failures(tmp_path: Path):
+    ok_case = _case(tmp_path, name="videos/ok.mp4")
+    bad_case = _case(tmp_path, name="videos/bad.mp4")
+    ok_result = oi.OmniInteractCaseResult(
+        ok_case.subset, str(ok_case.video_path), str(oi._output_dir(tmp_path / "out", ok_case)), success=True
+    )
+    ok_result.eligible_for_official_eval = True
+    bad_result = oi.OmniInteractCaseResult(
+        bad_case.subset, str(bad_case.video_path), str(oi._output_dir(tmp_path / "out", bad_case)), success=False
+    )
+    oi.write_batch_artifacts(tmp_path / "out", [ok_case, bad_case], [ok_result, bad_result])
+    sampled = [
+        json.loads(line) for line in (tmp_path / "out" / "sampled_cases.jsonl").read_text().splitlines() if line.strip()
+    ]
+    assert [row["output_name"] for row in sampled] == ["1q1a/videos__ok", "1q1a/videos__bad"]
+    manifest = [
+        json.loads(line)
+        for line in (tmp_path / "out" / "official_eval_manifest.jsonl").read_text().splitlines()
+        if line.strip()
+    ]
+    assert len(manifest) == 1
+    assert manifest[0]["sample_id"].startswith("1q1a__")
 
 
 def test_artifacts_require_complete_audio_and_transcript_response(tmp_path: Path):

tests/benchmarks/test_omniinteract_eval.py

@@ -0,0 +1,820 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict
+from pathlib import Path
+
+import pytest
+
+from vllm_omni.benchmarks.data_modules.omniinteract_dataset import (
+    OmniInteractCase,
+    OmniInteractEvaluationOptions,
+)
+from vllm_omni.benchmarks.omniinteract import OmniInteractCaseResult
+from vllm_omni.benchmarks.omniinteract_eval import (
+    AlignedWord,
+    TranscriptChunk,
+    _summarize,
+    build_slots,
+    evaluate_batch,
+    evaluate_case,
+    evaluation_inputs_fingerprint,
+    match_slots,
+)
+from vllm_omni.benchmarks.omniinteract_judge import (
+    CoreJudgment,
+    EarlyJudgment,
+    OmniInteractJudge,
+    PartialJudgment,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.benchmark]
+
+
+class _FixedJudge:
+    def judge_early(
+        self,
+        slot: dict[str, object],
+        full_context: str,
+        actual_text: str,
+    ) -> EarlyJudgment:
+        del slot, full_context, actual_text
+        return EarlyJudgment("neutral", 1.0, "ok", "{}", "llm_json")
+
+    def judge_core(
+        self,
+        slot: dict[str, object],
+        full_context: str,
+        actual_text: str,
+        future_answers: str,
+    ) -> CoreJudgment:
+        del slot, full_context, actual_text, future_answers
+        return CoreJudgment(1.0, "answer", False, "ok", "{}", "llm_json")
+
+    def judge_interrupted_partial(
+        self,
+        slot: dict[str, object],
+        actual_text: str,
+    ) -> PartialJudgment:
+        del slot, actual_text
+        return PartialJudgment(1.0, False, "ok", "{}", "llm_json")
+
+
+class _GeneratedJudge(OmniInteractJudge):
+    def __init__(self, responses: list[str]) -> None:
+        super().__init__("http://judge", "judge-model")
+        self.responses = iter(responses)
+
+    def _generate(self, system_prompt: str, user_prompt: str) -> str:
+        del system_prompt, user_prompt
+        return next(self.responses)
+
+
+def test_build_slots_and_split_word_aligned_chunk() -> None:
+    slots = build_slots(
+        [
+            {
+                "question_time": 0.0,
+                "answer_time": 2.0,
+                "question_text": "question",
+                "answer_text": "answer",
+                "question_type": "realtime",
+            },
+            {
+                "question_time": 5.0,
+                "answer_time": 6.0,
+                "question_text": "next",
+                "answer_text": "next answer",
+                "question_type": "proactive",
+            },
+        ],
+        "multi_turn",
+    )
+    chunk = TranscriptChunk(
+        source_id=0,
+        text="okay answer",
+        start=0.5,
+        end=2.5,
+        aligned_words=(
+            AlignedWord("okay", 0.5, 0.9),
+            AlignedWord("answer", 2.1, 2.5),
+        ),
+    )
+
+    matched, unmatched = match_slots(slots, [chunk])
+
+    assert not unmatched
+    assert matched[0].early_chunks[0].text == "okay"
+    assert matched[0].core_chunks[0].text == "answer"
+    assert matched[0].core_chunks[0].start == pytest.approx(2.1)
+
+
+def test_judge_parses_json_wrapped_in_model_text() -> None:
+    judge = _GeneratedJudge(
+        [
+            'result: {"flag":"FP_Hallucination","score":1,"rationale":"early"}',
+            '```json\n{"score":0.8,"trigger_phrase":"answer","spoiler":false,"rationale":"good"}\n```',
+            '{"score":0.7,"hallucination":false,"rationale":"partial"}',
+        ]
+    )
+    slot = {"question_text": "q", "gt_answer": "answer"}
+
+    early = judge.judge_early(slot, "context", "guess")
+    core = judge.judge_core(slot, "context", "answer", "(none)")
+    partial = judge.judge_interrupted_partial(slot, "answer")
+
+    assert early.category == "hallucination"
+    assert early.score == 0.0
+    assert core.score == pytest.approx(0.8)
+    assert core.trigger_phrase == "answer"
+    assert partial.score == pytest.approx(0.7)
+
+
+def test_evaluate_case_persists_accuracy_artifact(tmp_path: Path) -> None:
+    annotation = tmp_path / "annotation.json"
+    annotation.write_text(
+        json.dumps(
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 1.0,
+                    "question_text": "question",
+                    "answer_text": "answer",
+                    "question_type": "realtime",
+                }
+            ]
+        )
+    )
+    output_dir = tmp_path / "case-output"
+    output_dir.mkdir()
+    (output_dir / "wav_transcript.json").write_text(
+        json.dumps(
+            {
+                "chunks": [
+                    {
+                        "timestamp": [1.0, 1.5],
+                        "text": "answer",
+                        "aligned_words": [{"text": "answer", "start": 1.0, "end": 1.5}],
+                    }
+                ]
+            }
+        )
+    )
+    video = tmp_path / "video.mp4"
+    video.touch()
+    case = OmniInteractCase("1q1a", "video.mp4", video, annotation, "multi_turn")
+    result = OmniInteractCaseResult(
+        subset="1q1a",
+        video=str(video),
+        output_dir=str(output_dir),
+        success=True,
+        eligible_for_official_eval=True,
+    )
+    artifact = tmp_path / "evaluation.json"
+
+    evaluation = evaluate_case(case, result, _FixedJudge(), artifact)
+
+    assert evaluation["timing_precision"] == "word-aligned"
+    assert evaluation["summary"]["IA_QTF1"] == pytest.approx(1.0)
+    paper = evaluation["summary"]["paper_metrics"]["exp_f1"]
+    assert paper["all_global"]["IA_QTF1"] == pytest.approx(1.0)
+    assert paper["realtime"]["IA_QTF1"] == pytest.approx(1.0)
+    assert json.loads(artifact.read_text())["status"] == "ok"
+    fingerprint = evaluation["inputs_fingerprint"]
+    assert fingerprint["annotation_sha256"]
+    assert fingerprint["transcript_sha256"]
+    assert fingerprint["protocol_source"]
+
+
+def _realtime_eval_case(tmp_path: Path, *, transcript_text: str) -> tuple[OmniInteractCase, OmniInteractCaseResult]:
+    annotation = tmp_path / "annotation.json"
+    annotation.write_text(
+        json.dumps(
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 1.0,
+                    "question_text": "question",
+                    "answer_text": "answer",
+                    "question_type": "realtime",
+                }
+            ]
+        )
+    )
+    output_dir = tmp_path / "case-output"
+    output_dir.mkdir(exist_ok=True)
+    chunks: list[dict[str, object]] = []
+    if transcript_text:
+        chunks.append({"timestamp": [1.0, 1.5], "text": transcript_text})
+    (output_dir / "wav_transcript.json").write_text(json.dumps({"chunks": chunks}))
+    video = tmp_path / "video.mp4"
+    video.touch()
+    case = OmniInteractCase("1q1a", "video.mp4", video, annotation, "multi_turn")
+    result = OmniInteractCaseResult(
+        subset="1q1a",
+        video=str(video),
+        output_dir=str(output_dir),
+        success=True,
+        eligible_for_official_eval=True,
+    )
+    return case, result
+
+
+def _eval_options(tmp_path: Path, *, skip_existing: bool) -> OmniInteractEvaluationOptions:
+    return OmniInteractEvaluationOptions(
+        judge_base_url="http://127.0.0.1:9",
+        judge_model="cached-judge",
+        judge_api_key="EMPTY",
+        judge_timeout_s=60.0,
+        judge_max_tokens=32,
+        workers=1,
+        output_dir=tmp_path / "evaluation",
+        skip_existing=skip_existing,
+    )
+
+
+class _ConfiguredFixedJudge(_FixedJudge):
+    def __init__(
+        self,
+        base_url: str,
+        model: str,
+        *,
+        api_key: str = "EMPTY",
+        timeout_s: float = 60.0,
+        max_tokens: int = 512,
+    ) -> None:
+        del api_key, timeout_s
+        self.base_url = base_url
+        self.model = model
+        self.max_tokens = max_tokens
+
+
+def test_skip_existing_reuses_matching_fingerprint(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    import vllm_omni.benchmarks.omniinteract_eval as eval_mod
+
+    case, result = _realtime_eval_case(tmp_path, transcript_text="answer")
+    options = _eval_options(tmp_path, skip_existing=False)
+    monkeypatch.setattr(eval_mod, "OmniInteractJudge", _ConfiguredFixedJudge)
+    first = evaluate_batch([case], [result], options)
+    assert first["summary"]["IA_QTF1"] == pytest.approx(1.0)
+
+    calls: list[str] = []
+
+    def _forbidden(
+        case: OmniInteractCase,
+        result: OmniInteractCaseResult,
+        judge: object,
+        output_path: Path,
+        config: object = None,
+        inputs_fingerprint: object = None,
+    ) -> dict[str, object]:
+        del case, result, judge, output_path, config, inputs_fingerprint
+        calls.append("evaluate_case")
+        raise AssertionError("matching fingerprint must reuse the cached evaluation")
+
+    monkeypatch.setattr(eval_mod, "evaluate_case", _forbidden)
+    reused = evaluate_batch([case], [result], _eval_options(tmp_path, skip_existing=True))
+    assert calls == []
+    assert reused["summary"]["IA_QTF1"] == pytest.approx(1.0)
+
+
+def test_skip_existing_rejects_stale_transcript(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    import vllm_omni.benchmarks.omniinteract_eval as eval_mod
+
+    case, result = _realtime_eval_case(tmp_path, transcript_text="answer")
+    options = _eval_options(tmp_path, skip_existing=True)
+    fingerprint = evaluation_inputs_fingerprint(
+        annotation_path=case.annotation_path,
+        transcript_path=Path(result.output_dir) / "wav_transcript.json",
+        judge_model=options.judge_model,
+        judge_base_url=options.judge_base_url,
+        judge_max_tokens=options.judge_max_tokens,
+    )
+    sample_id = f"{case.subset}__{Path(result.output_dir).name}"
+    cache_path = options.output_dir / f"{sample_id}.unified_eval.json"
+    cache_path.parent.mkdir(parents=True)
+    cache_path.write_text(
+        json.dumps(
+            {
+                "status": "ok",
+                "sample_id": sample_id,
+                "inputs_fingerprint": asdict(fingerprint),
+                "summary": {
+                    "IA_QTF1": 1.0,
+                    "num_slots": 1,
+                    "num_unmatched_chunks": 0,
+                    "Global_TP": 1,
+                    "Global_FP": 0,
+                    "Global_FN": 0,
+                },
+                "slots": [],
+            }
+        )
+    )
+    (Path(result.output_dir) / "wav_transcript.json").write_text(json.dumps({"chunks": []}))
+    monkeypatch.setattr(eval_mod, "OmniInteractJudge", _ConfiguredFixedJudge)
+    fresh = evaluate_batch([case], [result], options)
+    assert fresh["summary"]["IA_QTF1"] == pytest.approx(0.0)
+
+
+class _MissingCoreScoreJudge(_ConfiguredFixedJudge):
+    def judge_core(
+        self,
+        slot: dict[str, object],
+        full_context: str,
+        actual_text: str,
+        future_answers: str,
+    ) -> CoreJudgment:
+        del slot, full_context, actual_text, future_answers
+        return CoreJudgment(0.0, "", False, "Could not evaluate", "{}", "llm_parse_failed")
+
+
+def test_evaluate_batch_reports_missing_score_as_parse_failure(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    import vllm_omni.benchmarks.omniinteract_eval as eval_mod
+
+    case, result = _realtime_eval_case(tmp_path, transcript_text="answer")
+    options = _eval_options(tmp_path, skip_existing=False)
+    monkeypatch.setattr(eval_mod, "OmniInteractJudge", _MissingCoreScoreJudge)
+    evaluation = evaluate_batch([case], [result], options)
+    assert evaluation["status"] == "failed"
+    assert evaluation["evaluated"] == 0
+    assert evaluation["failed"] == 1
+    sample_id = f"{case.subset}__{Path(result.output_dir).name}"
+    cached = json.loads((options.output_dir / f"{sample_id}.unified_eval.json").read_text())
+    assert cached["status"] == "parse_failed"
+
+
+class _CoreRationaleNumberJudge(OmniInteractJudge):
+    def __init__(
+        self,
+        base_url: str,
+        model: str,
+        *,
+        api_key: str = "EMPTY",
+        timeout_s: float = 60.0,
+        max_tokens: int = 512,
+    ) -> None:
+        super().__init__(base_url, model, api_key=api_key, timeout_s=timeout_s, max_tokens=max_tokens)
+
+    def _generate(self, system_prompt: str, user_prompt: str) -> str:
+        del system_prompt, user_prompt
+        return '{"rationale":"Cannot score: 1 required reference is missing."}'
+
+
+def test_evaluate_batch_rejects_core_rationale_number_as_parse_failure(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    import vllm_omni.benchmarks.omniinteract_eval as eval_mod
+
+    case, result = _realtime_eval_case(tmp_path, transcript_text="answer")
+    options = _eval_options(tmp_path, skip_existing=False)
+    monkeypatch.setattr(eval_mod, "OmniInteractJudge", _CoreRationaleNumberJudge)
+    evaluation = evaluate_batch([case], [result], options)
+    assert evaluation["status"] == "failed"
+    assert evaluation["evaluated"] == 0
+    assert evaluation["failed"] == 1
+    sample_id = f"{case.subset}__{Path(result.output_dir).name}"
+    cached = json.loads((options.output_dir / f"{sample_id}.unified_eval.json").read_text())
+    assert cached["status"] == "parse_failed"
+    assert cached["slots"][0]["stage_core"]["parse_source"] == "llm_parse_failed"
+    assert cached["slots"][0]["stage_core"]["S_core"] == 0.0
+
+
+def test_skip_existing_rejects_parse_failed_cache(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    import vllm_omni.benchmarks.omniinteract_eval as eval_mod
+
+    case, result = _realtime_eval_case(tmp_path, transcript_text="answer")
+    options = _eval_options(tmp_path, skip_existing=True)
+    fingerprint = evaluation_inputs_fingerprint(
+        annotation_path=case.annotation_path,
+        transcript_path=Path(result.output_dir) / "wav_transcript.json",
+        judge_model=options.judge_model,
+        judge_base_url=options.judge_base_url,
+        judge_max_tokens=options.judge_max_tokens,
+    )
+    sample_id = f"{case.subset}__{Path(result.output_dir).name}"
+    cache_path = options.output_dir / f"{sample_id}.unified_eval.json"
+    cache_path.parent.mkdir(parents=True)
+    cache_path.write_text(
+        json.dumps(
+            {
+                "status": "parse_failed",
+                "sample_id": sample_id,
+                "inputs_fingerprint": asdict(fingerprint),
+                "summary": {
+                    "IA_QTF1": 0.0,
+                    "num_slots": 1,
+                    "num_unmatched_chunks": 0,
+                    "Global_TP": 0,
+                    "Global_FP": 0,
+                    "Global_FN": 1,
+                },
+                "slots": [
+                    {
+                        "stage_core": {"parse_source": "llm_parse_failed", "S_core": 0.0},
+                        "TP_n": 0.0,
+                        "FP_delta": 0,
+                        "FN_delta": 1,
+                    }
+                ],
+            }
+        )
+    )
+    monkeypatch.setattr(eval_mod, "OmniInteractJudge", _ConfiguredFixedJudge)
+    fresh = evaluate_batch([case], [result], options)
+    assert fresh["evaluated"] == 1
+    assert fresh["failed"] == 0
+    assert fresh["summary"]["IA_QTF1"] == pytest.approx(1.0)
+    assert json.loads(cache_path.read_text())["status"] == "ok"
+
+
+def _slot_row(
+    *,
+    scene_type: str,
+    question_type: str,
+    tp_n: float,
+    fp: int = 0,
+    fn: int = 0,
+    nested_role: str | None = None,
+    nested_group_id: int | None = None,
+    sample_id: str = "s",
+    tp_core: float | None = None,
+    answer_start: float | None = 1.0,
+) -> dict[str, object]:
+    core_tp = tp_n if tp_core is None else tp_core
+    return {
+        "sample_id": sample_id,
+        "scene_type": scene_type,
+        "question_type": question_type,
+        "nested_role": nested_role,
+        "nested_group_id": nested_group_id,
+        "TP_n": tp_n,
+        "TP_core": core_tp,
+        "FP_delta": fp,
+        "FN_delta": fn,
+        "is_interrupted": False,
+        "stage_core": {"answer_start": answer_start},
+    }
+
+
+def test_paper_metrics_exclude_nested_from_realtime_and_proactive() -> None:
+    summary = _summarize(
+        [
+            _slot_row(scene_type="multi_turn", question_type="realtime", tp_n=1.0),
+            _slot_row(
+                scene_type="nested",
+                question_type="realtime",
+                tp_n=1.0,
+                nested_role="inner",
+                nested_group_id=1,
+                answer_start=0.5,
+            ),
+            _slot_row(
+                scene_type="nested",
+                question_type="proactive",
+                tp_n=1.0,
+                nested_role="outer",
+                nested_group_id=1,
+                answer_start=1.5,
+            ),
+            _slot_row(scene_type="multi_turn", question_type="proactive", tp_n=1.0),
+            _slot_row(scene_type="1QnA", question_type="step", tp_n=1.0, fn=0),
+        ],
+        unmatched=0,
+    )
+    paper = summary["paper_metrics"]["exp_f1"]
+
+    assert paper["realtime"]["num_slots"] == 1
+    assert paper["proactive"]["num_slots"] == 1
+    assert paper["nested"]["num_slots"] == 2
+    assert paper["one_q1a_global"]["num_slots"] == 4
+    assert paper["one_qna"]["num_slots"] == 1
+    assert paper["all_global"]["num_slots"] == 5
+    # All Global includes 1QnA; 1Q1A Global does not.
+    assert paper["all_global"]["Global_TP"] == pytest.approx(5.0)
+    assert paper["one_q1a_global"]["Global_TP"] == pytest.approx(4.0)
+    assert summary["nested"]["NCCS"] == pytest.approx(1.0)
+    assert summary["nested"]["missing_q1_count"] == 0
+    assert summary["paper_metrics"]["exp_nested"]["missed_outer"] == 0
+    assert summary["paper_metrics"]["exp_nested"]["inner_IA_QTF1"] == pytest.approx(1.0)
+    assert summary["paper_metrics"]["exp_nested"]["outer_IA_QTF1"] == pytest.approx(1.0)
+    assert summary["scenario_case_counts"]["realtime"] == 1
+    assert summary["scenario_case_counts"]["proactive"] == 1
+    assert summary["scenario_case_counts"]["nested"] == 1
+    assert summary["scenario_case_counts"]["1qna"] == 1
+
+
+def test_nccs_allows_missing_answer_start() -> None:
+    summary = _summarize(
+        [
+            _slot_row(
+                scene_type="nested",
+                question_type="realtime",
+                tp_n=0.8,
+                tp_core=0.8,
+                nested_role="inner",
+                nested_group_id=1,
+                answer_start=None,
+            ),
+            _slot_row(
+                scene_type="nested",
+                question_type="proactive",
+                tp_n=0.8,
+                tp_core=0.8,
+                nested_role="outer",
+                nested_group_id=1,
+                answer_start=None,
+            ),
+        ],
+        unmatched=0,
+    )
+    assert summary["nested"]["success_pairs"] == 1
+    assert summary["nested"]["NCCS"] == pytest.approx(0.8)
+
+
+def test_missed_outer_counts_zero_outer_core() -> None:
+    summary = _summarize(
+        [
+            _slot_row(
+                scene_type="nested",
+                question_type="realtime",
+                tp_n=1.0,
+                tp_core=1.0,
+                nested_role="inner",
+                nested_group_id=1,
+                answer_start=0.5,
+            ),
+            _slot_row(
+                scene_type="nested",
+                question_type="proactive",
+                tp_n=0.0,
+                tp_core=0.0,
+                fn=1,
+                nested_role="outer",
+                nested_group_id=1,
+                answer_start=1.5,
+            ),
+        ],
+        unmatched=0,
+    )
+    nested = summary["paper_metrics"]["exp_nested"]
+    assert nested["missed_outer"] == 1
+    assert nested["NCCS"] == pytest.approx(0.0)
+    assert nested["inner_IA_QTF1"] == pytest.approx(1.0)
+    assert nested["outer_IA_QTF1"] == pytest.approx(0.0)
+
+
+def test_cross_boundary_core_sets_effective_start_hint() -> None:
+    slots = build_slots(
+        [
+            {
+                "question_time": 0.0,
+                "answer_time": 2.0,
+                "question_text": "question",
+                "answer_text": "answer",
+                "question_type": "realtime",
+            }
+        ],
+        "multi_turn",
+    )
+    chunk = TranscriptChunk(
+        source_id=0,
+        text="okay answer",
+        start=0.5,
+        end=2.5,
+        aligned_words=(
+            AlignedWord("okay", 0.5, 0.9),
+            AlignedWord("answer", 2.1, 2.5),
+        ),
+    )
+    matched, unmatched = match_slots(slots, [chunk])
+    assert not unmatched
+    assert matched[0].core_chunks[0].effective_start_hint == pytest.approx(2.0)
+
+
+class _NonNeutralEarlyJudge(_FixedJudge):
+    def judge_early(
+        self,
+        slot: dict[str, object],
+        full_context: str,
+        actual_text: str,
+    ) -> EarlyJudgment:
+        del slot, full_context, actual_text
+        return EarlyJudgment("other", 1.0, "ignored", "{}", "llm_json")
+
+
+def test_early_tp_ack_only_for_neutral(tmp_path: Path) -> None:
+    annotation = tmp_path / "annotation.json"
+    annotation.write_text(
+        json.dumps(
+            [
+                {
+                    "question_time": 0.0,
+                    "answer_time": 2.0,
+                    "question_text": "question",
+                    "answer_text": "answer",
+                    "question_type": "realtime",
+                }
+            ]
+        )
+    )
+    output_dir = tmp_path / "case-output"
+    output_dir.mkdir()
+    (output_dir / "wav_transcript.json").write_text(
+        json.dumps(
+            {
+                "chunks": [
+                    {
+                        "timestamp": [0.5, 0.9],
+                        "text": "okay",
+                        "aligned_words": [{"text": "okay", "start": 0.5, "end": 0.9}],
+                    },
+                    {
+                        "timestamp": [2.1, 2.5],
+                        "text": "answer",
+                        "aligned_words": [{"text": "answer", "start": 2.1, "end": 2.5}],
+                    },
+                ]
+            }
+        )
+    )
+    video = tmp_path / "video.mp4"
+    video.touch()
+    case = OmniInteractCase("1q1a", "video.mp4", video, annotation, "multi_turn")
+    result = OmniInteractCaseResult(
+        subset="1q1a",
+        video=str(video),
+        output_dir=str(output_dir),
+        success=True,
+        eligible_for_official_eval=True,
+    )
+    evaluation = evaluate_case(case, result, _NonNeutralEarlyJudge(), tmp_path / "evaluation.json")
+    assert evaluation["slots"][0]["TP_ack"] == 0.0
+    assert evaluation["slots"][0]["stage_early"]["category"] == "other"
+
+
+def test_paper_metrics_unknown_inner_does_not_underflow_realtime() -> None:
+    summary = _summarize(
+        [
+            _slot_row(scene_type="multi_turn", question_type="realtime", tp_n=0.5),
+            _slot_row(
+                scene_type="nested",
+                question_type="unknown",
+                tp_n=2.0,
+                nested_role="inner",
+                nested_group_id=1,
+                answer_start=0.5,
+            ),
+            _slot_row(
+                scene_type="nested",
+                question_type="proactive",
+                tp_n=1.0,
+                nested_role="outer",
+                nested_group_id=1,
+                answer_start=1.5,
+            ),
+        ],
+        unmatched=0,
+    )
+    paper = summary["paper_metrics"]["exp_f1"]
+    assert paper["realtime"]["Global_TP"] == pytest.approx(0.5)
+    assert paper["realtime"]["num_slots"] == 1
+    assert paper["realtime"]["Precision"] >= 0.0
+    assert paper["one_q1a_global"]["Global_TP"] == pytest.approx(3.5)
+
+
+def test_core_score_float_ignores_leading_counts() -> None:
+    from vllm_omni.benchmarks.omniinteract_judge import _score_float_from_text
+
+    assert _score_float_from_text("The answer covers 3 of the 5 key points, score: 0.6") == pytest.approx(0.6)
+    assert _score_float_from_text("score: 3") is None
+    assert _score_float_from_text("no score here 0.9") is None
+
+    judge = _GeneratedJudge(["The answer covers 3 of the 5 key points, score: 0.6"])
+    core = judge.judge_core({"question_text": "q", "gt_answer": "a"}, "ctx", "answer", "(none)")
+    assert core.score == pytest.approx(0.6)
+    assert core.parse_source == "llm_float_text"
+
+
+def test_core_missing_json_score_does_not_use_rationale_number() -> None:
+    raw = '{"rationale":"Cannot score: 1 required reference is missing."}'
+    core = _GeneratedJudge([raw]).judge_core({"question_text": "q", "gt_answer": "a"}, "ctx", "answer", "(none)")
+    assert core.score == 0.0
+    assert core.parse_source == "llm_parse_failed"
+
+
+def test_early_missing_score_and_unparsed_flag() -> None:
+    missing_score = _GeneratedJudge(['{"flag":"Neutral","rationale":"ok"}'])
+    early = missing_score.judge_early({"question_text": "q", "gt_answer": "a"}, "ctx", "hi")
+    assert early.category == "unparsed"
+    assert early.score == 0.0
+    assert early.parse_source == "llm_parse_failed"
+
+    garbage = _GeneratedJudge(["not json and not a known flag"])
+    unparsed = garbage.judge_early({"question_text": "q", "gt_answer": "a"}, "ctx", "hi")
+    assert unparsed.category == "unparsed"
+    assert unparsed.score == 0.0
+    assert unparsed.parse_source == "llm_parse_failed"
+
+
+@pytest.mark.parametrize(
+    ("path", "response"),
+    [
+        ("early", '{"flag":"Neutral","rationale":"Could not evaluate this answer"}'),
+        ("early", '{"flag":"Neutral","score":"invalid","rationale":"bad"}'),
+        ("core", '{"rationale":"Could not evaluate this answer"}'),
+        ("core", '{"rationale":"Cannot score: 1 required reference is missing."}'),
+        ("core", '{"score":"invalid","trigger_phrase":"","spoiler":false,"rationale":"bad"}'),
+        ("partial", '{"rationale":"Could not evaluate this answer"}'),
+        ("partial", '{"score":"invalid","hallucination":false,"rationale":"bad"}'),
+    ],
+)
+def test_missing_or_invalid_json_scores_are_parse_failures(path: str, response: str) -> None:
+    slot = {"question_text": "q", "gt_answer": "a"}
+    judge = _GeneratedJudge([response])
+    if path == "early":
+        judgment = judge.judge_early(slot, "ctx", "hi")
+    elif path == "core":
+        judgment = judge.judge_core(slot, "ctx", "answer", "(none)")
+    else:
+        judgment = judge.judge_interrupted_partial(slot, "answer")
+    assert judgment.score == 0.0
+    assert judgment.parse_source == "llm_parse_failed"
+
+
+def test_explicit_zero_json_score_is_valid_llm_json() -> None:
+    slot = {"question_text": "q", "gt_answer": "a"}
+    early = _GeneratedJudge(['{"flag":"Neutral","score":0,"rationale":"zero"}']).judge_early(slot, "ctx", "hi")
+    core = _GeneratedJudge(['{"score":0.0,"trigger_phrase":"","spoiler":false,"rationale":"zero"}']).judge_core(
+        slot, "ctx", "answer", "(none)"
+    )
+    partial = _GeneratedJudge(['{"score":0,"hallucination":false,"rationale":"zero"}']).judge_interrupted_partial(
+        slot, "answer"
+    )
+    assert early.category == "neutral"
+    assert early.score == 0.0
+    assert early.parse_source == "llm_json"
+    assert core.score == 0.0
+    assert core.parse_source == "llm_json"
+    assert partial.score == 0.0
+    assert partial.parse_source == "llm_json"
+
+
+@pytest.mark.parametrize("score_literal", ['"NaN"', '"Infinity"', '"-Infinity"', "NaN", "Infinity", "-Infinity"])
+def test_nonfinite_json_scores_are_parse_failures(score_literal: str) -> None:
+    slot = {"question_text": "q", "gt_answer": "a"}
+    early = _GeneratedJudge([f'{{"flag":"Neutral","score":{score_literal},"rationale":"bad"}}']).judge_early(
+        slot, "ctx", "hi"
+    )
+    core = _GeneratedJudge(
+        [f'{{"score":{score_literal},"trigger_phrase":"answer","spoiler":false,"rationale":"bad"}}']
+    ).judge_core(slot, "ctx", "answer", "(none)")
+    partial = _GeneratedJudge(
+        [f'{{"score":{score_literal},"hallucination":false,"rationale":"bad"}}']
+    ).judge_interrupted_partial(slot, "answer")
+
+    assert early.score == 0.0
+    assert early.parse_source == "llm_parse_failed"
+    assert core.score == 0.0
+    assert core.parse_source == "llm_parse_failed"
+    assert core.trigger_phrase == ""
+    assert partial.score == 0.0
+    assert partial.parse_source == "llm_parse_failed"
+
+
+def test_fingerprint_requires_evaluator_schema_version() -> None:
+    from vllm_omni.benchmarks.omniinteract_eval import EvaluationInputsFingerprint
+
+    payload = {
+        "annotation_sha256": "a" * 64,
+        "transcript_sha256": "b" * 64,
+        "judge_model": "m",
+        "judge_base_url": "http://127.0.0.1:9",
+        "judge_max_tokens": 32,
+        "protocol_source": "proto",
+    }
+    assert EvaluationInputsFingerprint.from_mapping(payload) is None
+    payload["evaluator_schema_version"] = 1
+    assert EvaluationInputsFingerprint.from_mapping(payload) is not None
+    payload["evaluator_schema_version"] = 999
+    matched = EvaluationInputsFingerprint.from_mapping(payload)
+    assert matched is not None
+    assert matched.evaluator_schema_version == 999
+
+
+def test_summarize_counts_parse_sources() -> None:
+    rows = [
+        {
+            **_slot_row(scene_type="multi_turn", question_type="realtime", tp_n=1.0),
+            "stage_early": {"parse_source": "llm_json"},
+            "stage_core": {"parse_source": "llm_parse_failed", "trigger_fallback": True},
+        }
+    ]
+    summary = _summarize(rows, unmatched=0)
+    assert summary["judge_parse"]["llm_json"] == 1
+    assert summary["judge_parse"]["llm_parse_failed"] == 1
+    assert summary["judge_parse"]["judge_calls"] == 2
+    assert summary["trigger_fallback_slots"] == 1

tests/benchmarks/test_serve_cli.py

@@ -224,6 +224,53 @@ def test_preprocess_serve_args_applies_safe_omniinteract_prompt_default(
     assert args.num_prompts == expected
 
 
+@pytest.mark.parametrize(
+    ("extra", "match"),
+    [
+        (["--seed", "7"], "cannot be combined with --seed"),
+        (["--dataset-path", "/tmp/omniinteract-data"], "cannot be combined with --dataset-path"),
+        (["--omniinteract-scenario-tags", "realtime"], "cannot be combined with --omniinteract-scenario-tags"),
+        (["--omniinteract-scenario-focus"], "cannot be combined with --omniinteract-scenario-focus"),
+    ],
+)
+def test_preprocess_serve_args_rejects_omniinteract_video_list_conflicts(
+    tmp_path: Path,
+    extra: list[str],
+    match: str,
+) -> None:
+    ref = tmp_path / "ref.wav"
+    ref.touch()
+    video_list = tmp_path / "list.jsonl"
+    video_list.write_text("{}\n")
+    parser = TrackingArgumentParser()
+    parser.add_argument("--dataset-name", default="sharegpt")
+    parser.add_argument("--backend", default="vllm")
+    parser.add_argument("--endpoint", default="/v1/completions")
+    parser.add_argument("--model", default="dummy")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--dataset-path", default=None)
+    parser.add_argument("--num-prompts", type=int, default=1000)
+    parser.add_argument("--max-concurrency", type=int, default=None)
+    add_omni_args(parser)
+    args = parser.parse_args(
+        [
+            "--dataset-name",
+            "omniinteract",
+            "--backend",
+            "openai-realtime-duplex",
+            "--endpoint",
+            "/v1/realtime",
+            "--omniinteract-ref-audio",
+            str(ref),
+            "--omniinteract-video-list",
+            str(video_list),
+            *extra,
+        ]
+    )
+    with pytest.raises(ValueError, match=match):
+        preprocess_serve_args(args)
+
+
 @pytest.mark.parametrize(
     ("argv", "expected_extra_body", "expected_explicit"),
     [

tests/dfx/perf/scripts/run_benchmark.py

@@ -100,6 +100,71 @@ class _SingleActiveContext:
             stack.close()
 
 
+def _config_for_test(test_name: str) -> dict[str, object] | None:
+    for config in BENCHMARK_CONFIGS:
+        if isinstance(config, dict) and config.get("test_name") == test_name:
+            return config
+    return None
+
+
+def _judge_server_params_for_test(test_name: str) -> dict[str, object] | None:
+    config = _config_for_test(test_name)
+    if config is None:
+        return None
+    raw = config.get("judge_server_params")
+    if raw is None:
+        return None
+    if not isinstance(raw, dict):
+        raise TypeError(f"judge_server_params for {test_name} must be an object")
+    return raw
+
+
+def _resolve_judge_cuda_visible_devices(configured: object) -> str:
+    """Map a judge card index onto the process-visible CUDA device list.
+
+    ``judge_server_params.cuda_visible_devices`` is an index into
+    ``CUDA_VISIBLE_DEVICES`` when that env is set (``1`` with
+    ``CUDA_VISIBLE_DEVICES=2,3`` selects physical GPU 3). Without the env it
+    is used as a raw device list.
+    """
+    if not isinstance(configured, str) or not configured:
+        raise ValueError("judge_server_params.cuda_visible_devices must be a non-empty string")
+    parent = os.environ.get("CUDA_VISIBLE_DEVICES")
+    if parent is None or not parent.strip():
+        return configured
+    visible = [part.strip() for part in parent.split(",") if part.strip()]
+    if configured.isdigit():
+        index = int(configured)
+        if index < 0 or index >= len(visible):
+            raise ValueError(
+                f"judge_server_params.cuda_visible_devices={configured!r} is outside CUDA_VISIBLE_DEVICES={parent!r}"
+            )
+        return visible[index]
+    return configured
+
+
+@contextmanager
+def _start_judge_server(judge_params: dict[str, object]):
+    model = judge_params.get("model")
+    if not isinstance(model, str) or not model:
+        raise ValueError("judge_server_params.model must be a non-empty string")
+    extra = judge_params.get("extra_cli_args") or ()
+    if not isinstance(extra, list | tuple):
+        raise TypeError("judge_server_params.extra_cli_args must be a list")
+    visible = _resolve_judge_cuda_visible_devices(judge_params.get("cuda_visible_devices", "1"))
+    print(f"Starting OmniInteract judge with model: {model} on CUDA_VISIBLE_DEVICES={visible}")
+    with OmniServer(
+        model,
+        [str(item) for item in extra],
+        use_omni=bool(judge_params.get("use_omni", False)),
+        env_dict={"CUDA_VISIBLE_DEVICES": visible},
+    ) as judge:
+        print(f"OmniInteract judge started on {judge.host}:{judge.port}")
+        yield judge
+        print("OmniInteract judge stopping...")
+    print("OmniInteract judge stopped")
+
+
 # OmniServer defaults for flags not already present in JSON ``serve_args`` /
 # ``extra_cli_args``. Add new (flag, value) pairs here rather than special-casing.
 _OMNI_DEFAULT_SERVER_ARGS: tuple[tuple[str, str], ...] = (
@@ -200,9 +265,16 @@ def _start_omni_server(server_param):
         server_args = ["--stage-overrides", stage_overrides] + server_args
     if extra:
         server_args = list(extra) + server_args
+    judge_params = _judge_server_params_for_test(test_name)
     with OmniServer(model, server_args, use_omni=use_omni, env_dict=_omni_server_env()) as server:
         server.test_name = test_name
+        server.judge_base_url = None
         print("OmniServer started successfully")
+        if judge_params is None:
+            yield server
+        else:
+            with _start_judge_server(judge_params) as judge:
+                server.judge_base_url = f"http://{judge.host}:{judge.port}"
                 yield server
         print("OmniServer stopping...")
 
@@ -260,6 +332,120 @@ def _resolve_num_warmups(params: dict[str, Any], *, default: int) -> int:
     return value
 
 
+def _is_finite_number(value: object) -> bool:
+    return isinstance(value, int | float) and not isinstance(value, bool) and math.isfinite(value)
+
+
+_OMNIINTERACT_AGGREGATE_MIN_IA_QTF1 = "omniinteract_aggregate_min_ia_qtf1"
+_OMNIINTERACT_AGGREGATE_SUBSETS = "omniinteract_aggregate_subsets"
+_OMNIINTERACT_AGGREGATE_GROUP = "omniinteract_aggregate_group"
+_OMNIINTERACT_AGGREGATE_COUNTS: dict[tuple[str, str], tuple[float, float, float]] = {}
+_OMNIINTERACT_MIN_EVALUATED_CASES = 1
+
+
+def _reset_omniinteract_aggregate_counts() -> None:
+    _OMNIINTERACT_AGGREGATE_COUNTS.clear()
+
+
+def _ia_qtf1_from_counts(tp: float, fp: float, fn: float) -> float:
+    precision = tp / (tp + fp) if tp + fp > 0 else 0.0
+    recall = tp / (tp + fn) if tp + fn > 0 else 0.0
+    return (2.0 * precision * recall / (precision + recall)) if precision + recall > 0 else 0.0
+
+
+def _assert_subset_evaluated(accuracy: dict[str, object]) -> None:
+    """Reject a subset whose clipped or cancelled cases left nothing to score.
+
+    Individual ineligible cases may be skipped. A subset with fewer than
+    ``_OMNIINTERACT_MIN_EVALUATED_CASES`` evaluated cases must not pass, or its
+    zero TP/FP/FN would be pooled as a successful accuracy result.
+    """
+
+    evaluated = accuracy.get("evaluated")
+    if not isinstance(evaluated, int) or isinstance(evaluated, bool):
+        raise AssertionError("OmniInteract accuracy evaluated count is missing")
+    if evaluated < _OMNIINTERACT_MIN_EVALUATED_CASES:
+        raise AssertionError(
+            f"OmniInteract subset evaluated {evaluated} cases (skipped={accuracy.get('skipped')}); "
+            f"at least {_OMNIINTERACT_MIN_EVALUATED_CASES} evaluated case is required"
+        )
+
+
+def _finite_accuracy_count(value: object, name: str) -> float:
+    assert _is_finite_number(value), f"OmniInteract accuracy summary {name} is missing"
+    return float(value)
+
+
+def _aggregate_accuracy_params_for_test(test_name: str) -> dict[str, object]:
+    config = _config_for_test(test_name)
+    if config is None:
+        return {}
+    min_ia_qtf1 = config.get(_OMNIINTERACT_AGGREGATE_MIN_IA_QTF1)
+    if min_ia_qtf1 is None:
+        return {}
+    if not _is_finite_number(min_ia_qtf1):
+        raise ValueError("omniinteract_aggregate_min_ia_qtf1 must be a finite number")
+    subsets: list[str] = []
+    raw_params = config.get("benchmark_params") or []
+    if not isinstance(raw_params, list):
+        raise TypeError(f"benchmark_params for {test_name} must be a list")
+    for item in raw_params:
+        if not isinstance(item, dict) or not item.get("omniinteract_evaluate"):
+            continue
+        subset = item.get("omniinteract_subsets")
+        if not isinstance(subset, str) or not subset:
+            raise ValueError("omniinteract_aggregate_min_ia_qtf1 requires omniinteract_subsets on each evaluated case")
+        subsets.append(subset)
+    if not subsets:
+        raise ValueError("omniinteract_aggregate_min_ia_qtf1 requires at least one evaluated OmniInteract subset")
+    if len(set(subsets)) != len(subsets):
+        raise ValueError("omniinteract_aggregate_subsets must not contain duplicates")
+    return {
+        _OMNIINTERACT_AGGREGATE_MIN_IA_QTF1: float(min_ia_qtf1),
+        _OMNIINTERACT_AGGREGATE_SUBSETS: subsets,
+        _OMNIINTERACT_AGGREGATE_GROUP: test_name,
+    }
+
+
+def _maybe_assert_aggregate_ia_qtf1(params: dict[str, object], acc_summary: dict[str, object]) -> None:
+    min_ia_qtf1 = params.get(_OMNIINTERACT_AGGREGATE_MIN_IA_QTF1)
+    if min_ia_qtf1 is None:
+        return
+    assert _is_finite_number(min_ia_qtf1), "omniinteract_aggregate_min_ia_qtf1 must be a finite number"
+    subsets = params.get(_OMNIINTERACT_AGGREGATE_SUBSETS)
+    if not isinstance(subsets, list | tuple) or not subsets:
+        raise ValueError("omniinteract_aggregate_min_ia_qtf1 requires omniinteract_aggregate_subsets")
+    if len(set(subsets)) != len(subsets):
+        raise ValueError("omniinteract_aggregate_subsets must not contain duplicates")
+    if not all(isinstance(item, str) and item for item in subsets):
+        raise ValueError("omniinteract_aggregate_subsets must be non-empty strings")
+    subset = params.get("omniinteract_subsets")
+    if not isinstance(subset, str) or not subset:
+        raise ValueError("omniinteract_aggregate_min_ia_qtf1 requires omniinteract_subsets")
+    if subset not in subsets:
+        raise ValueError(f"omniinteract_subsets {subset!r} is not in omniinteract_aggregate_subsets")
+    group = params.get(_OMNIINTERACT_AGGREGATE_GROUP)
+    if not isinstance(group, str) or not group:
+        raise ValueError("omniinteract_aggregate_min_ia_qtf1 requires omniinteract_aggregate_group")
+    tp = _finite_accuracy_count(acc_summary.get("Global_TP"), "Global_TP")
+    fp = _finite_accuracy_count(acc_summary.get("Global_FP"), "Global_FP")
+    fn = _finite_accuracy_count(acc_summary.get("Global_FN"), "Global_FN")
+    _OMNIINTERACT_AGGREGATE_COUNTS[(group, subset)] = (tp, fp, fn)
+    recorded = [_OMNIINTERACT_AGGREGATE_COUNTS.get((group, name)) for name in subsets]
+    present = [counts for counts in recorded if counts is not None]
+    if len(present) != len(subsets):
+        return
+    total_tp = sum(counts[0] for counts in present)
+    total_fp = sum(counts[1] for counts in present)
+    total_fn = sum(counts[2] for counts in present)
+    ia_qtf1 = _ia_qtf1_from_counts(total_tp, total_fp, total_fn)
+    print(
+        f"OmniInteract aggregate All Global IA-QTF1: {ia_qtf1:.6f} "
+        f"(TP={total_tp:.6f} / FP={total_fp:.6f} / FN={total_fn:.6f})"
+    )
+    assert ia_qtf1 >= float(min_ia_qtf1), f"OmniInteract aggregate All Global IA-QTF1 {ia_qtf1} is below {min_ia_qtf1}"
+
+
 def assert_result(result, params, num_prompt) -> None:
     assert result["completed"] == num_prompt, "Request failures exist"
     if params.get("dataset_name") == "omniinteract":
@@ -271,6 +457,23 @@ def assert_result(result, params, num_prompt) -> None:
             0,
         ), "OmniInteract requests did not all succeed"
         assert summary.get("artifacts_complete") is True, "OmniInteract artifacts are incomplete"
+        if params.get("omniinteract_evaluate"):
+            accuracy = summary.get("accuracy")
+            assert isinstance(accuracy, dict), "OmniInteract accuracy is missing"
+            assert accuracy.get("status") == "ok", "OmniInteract accuracy did not complete"
+            assert accuracy.get("failed") == 0, "OmniInteract accuracy reported failed cases"
+            _assert_subset_evaluated(accuracy)
+            acc_summary = accuracy.get("summary")
+            assert isinstance(acc_summary, dict), "OmniInteract accuracy summary is missing"
+            ia_qtf1 = acc_summary.get("IA_QTF1")
+            assert _is_finite_number(ia_qtf1), "OmniInteract All Global IA-QTF1 is missing"
+            min_ia_qtf1 = params.get("omniinteract_min_ia_qtf1")
+            if min_ia_qtf1 is not None:
+                assert _is_finite_number(min_ia_qtf1), "omniinteract_min_ia_qtf1 must be a finite number"
+                assert float(ia_qtf1) >= float(min_ia_qtf1), (
+                    f"OmniInteract All Global IA-QTF1 {ia_qtf1} is below {min_ia_qtf1}"
+                )
+            _maybe_assert_aggregate_ia_qtf1(params, acc_summary)
     baseline = params.get("baseline")
     hardware = result.get("Hardware")
     hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
@@ -304,8 +507,13 @@ def assert_result(result, params, num_prompt) -> None:
 )
 def test_performance_benchmark(omni_server, benchmark_params):
     test_name = benchmark_params["test_name"]
-    params = benchmark_params["params"]
+    params = dict(benchmark_params["params"])
     dataset_name = params.get("dataset_name", "")
+    if params.get("omniinteract_evaluate"):
+        judge_base_url = getattr(omni_server, "judge_base_url", None)
+        if not isinstance(judge_base_url, str) or not judge_base_url:
+            raise ValueError("omniinteract_evaluate requires top-level judge_server_params to start a judge")
+        params["omniinteract_judge_base_url"] = judge_base_url
 
     host = omni_server.host
     port = omni_server.port
@@ -349,6 +557,11 @@ def test_performance_benchmark(omni_server, benchmark_params):
         "eval_phase",
         "trust_remote_code",
         "expected_duplex_audio_turns_per_session",
+        "omniinteract_min_ia_qtf1",
+        _OMNIINTERACT_AGGREGATE_MIN_IA_QTF1,
+        _OMNIINTERACT_AGGREGATE_SUBSETS,
+        _OMNIINTERACT_AGGREGATE_GROUP,
+        "judge_server_params",
         "name",
         "enable_negative_prompt",
         "random_request_config",
@@ -403,7 +616,7 @@ def test_performance_benchmark(omni_server, benchmark_params):
             num_warmups=_resolve_num_warmups(params, default=2),
             benchmark_params_name=params.get("name") if isinstance(params.get("name"), str) else None,
         )
-        assert_result(result, params, num_prompt)
+        assert_result(result, {**params, **_aggregate_accuracy_params_for_test(test_name)}, num_prompt)
 
     # concurrency test
     for sweep_index, (concurrency, num_prompt) in enumerate(zip(max_concurrency_list, num_prompt_list)):
@@ -422,4 +635,4 @@ def test_performance_benchmark(omni_server, benchmark_params):
             num_warmups=_resolve_num_warmups(params, default=max(2, int(concurrency))),
             benchmark_params_name=params.get("name") if isinstance(params.get("name"), str) else None,
         )
-        assert_result(result, params, num_prompt)
+        assert_result(result, {**params, **_aggregate_accuracy_params_for_test(test_name)}, num_prompt)

tests/dfx/perf/tests/test_minicpmo_4_5_omniinteract.json

@@ -7,7 +7,7 @@
           "res": {
             "cuda": "H100"
           },
-          "num_cards": 1
+          "num_cards": 2
         }
       },
       "local_model",
@@ -21,6 +21,16 @@
         "--trust-remote-code"
       ]
     },
+    "judge_server_params": {
+      "model": "Qwen/Qwen2.5-7B-Instruct",
+      "use_omni": false,
+      "cuda_visible_devices": "1",
+      "extra_cli_args": [
+        "--served-model-name",
+        "Qwen2.5-7B-Instruct"
+      ]
+    },
+    "omniinteract_aggregate_min_ia_qtf1": 0.2,
     "benchmark_params": [
       {
         "dataset_name": "omniinteract",
@@ -40,6 +50,8 @@
         "omniinteract_ref_audio": "tests/assets/minicpmo_4_5/response_required_16k.wav",
         "omniinteract_output_dir": "tests/dfx/perf/results/omniinteract/1q1a",
         "omniinteract_max_video_duration_s": 600,
+        "omniinteract_evaluate": true,
+        "omniinteract_judge_model": "Qwen2.5-7B-Instruct",
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration"
       },
       {
@@ -60,6 +72,8 @@
         "omniinteract_ref_audio": "tests/assets/minicpmo_4_5/response_required_16k.wav",
         "omniinteract_output_dir": "tests/dfx/perf/results/omniinteract/1q1a_math",
         "omniinteract_max_video_duration_s": 600,
+        "omniinteract_evaluate": true,
+        "omniinteract_judge_model": "Qwen2.5-7B-Instruct",
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration"
       },
       {
@@ -80,6 +94,8 @@
         "omniinteract_ref_audio": "tests/assets/minicpmo_4_5/response_required_16k.wav",
         "omniinteract_output_dir": "tests/dfx/perf/results/omniinteract/1qna",
         "omniinteract_max_video_duration_s": 600,
+        "omniinteract_evaluate": true,
+        "omniinteract_judge_model": "Qwen2.5-7B-Instruct",
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration"
       }
     ]

tests/dfx/perf/tests/test_runner_metadata.py

@@ -694,6 +694,204 @@ def test_omniinteract_result_rejects_incomplete_artifacts():
         )
 
 
+def test_omniinteract_result_accepts_accuracy_at_floor():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    assert_result(
+        {
+            "completed": 4,
+            "omniinteract": {
+                "total": 4,
+                "success": 4,
+                "failed": 0,
+                "artifacts_complete": True,
+                "accuracy": {
+                    "status": "ok",
+                    "failed": 0,
+                    "evaluated": 4,
+                    "summary": {"IA_QTF1": 0.25},
+                },
+            },
+        },
+        {"dataset_name": "omniinteract", "omniinteract_evaluate": True, "omniinteract_min_ia_qtf1": 0.2},
+        4,
+    )
+
+
+def test_omniinteract_result_rejects_missing_accuracy():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    with pytest.raises(AssertionError, match="accuracy is missing"):
+        assert_result(
+            {
+                "completed": 4,
+                "omniinteract": {"total": 4, "success": 4, "failed": 0, "artifacts_complete": True},
+            },
+            {"dataset_name": "omniinteract", "omniinteract_evaluate": True},
+            4,
+        )
+
+
+def test_omniinteract_result_rejects_failed_accuracy():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    with pytest.raises(AssertionError, match="accuracy did not complete"):
+        assert_result(
+            {
+                "completed": 4,
+                "omniinteract": {
+                    "total": 4,
+                    "success": 4,
+                    "failed": 0,
+                    "artifacts_complete": True,
+                    "accuracy": {"status": "failed", "failed": 1, "summary": {"IA_QTF1": 0.9}},
+                },
+            },
+            {"dataset_name": "omniinteract", "omniinteract_evaluate": True, "omniinteract_min_ia_qtf1": 0.0},
+            4,
+        )
+
+
+def test_omniinteract_result_rejects_ia_qtf1_below_floor():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    with pytest.raises(AssertionError, match="IA-QTF1"):
+        assert_result(
+            {
+                "completed": 4,
+                "omniinteract": {
+                    "total": 4,
+                    "success": 4,
+                    "failed": 0,
+                    "artifacts_complete": True,
+                    "accuracy": {
+                        "status": "ok",
+                        "failed": 0,
+                        "evaluated": 4,
+                        "summary": {"IA_QTF1": 0.1},
+                    },
+                },
+            },
+            {"dataset_name": "omniinteract", "omniinteract_evaluate": True, "omniinteract_min_ia_qtf1": 0.2},
+            4,
+        )
+
+
+def _omniinteract_accuracy_result(
+    *,
+    tp: float,
+    fp: float,
+    fn: float,
+    ia_qtf1: float = 0.5,
+    evaluated: int = 4,
+    skipped: int = 0,
+) -> dict[str, object]:
+    return {
+        "completed": 4,
+        "omniinteract": {
+            "total": 4,
+            "success": 4,
+            "failed": 0,
+            "artifacts_complete": True,
+            "accuracy": {
+                "status": "ok",
+                "failed": 0,
+                "evaluated": evaluated,
+                "skipped": skipped,
+                "total": 4,
+                "summary": {"IA_QTF1": ia_qtf1, "Global_TP": tp, "Global_FP": fp, "Global_FN": fn},
+            },
+        },
+    }
+
+
+def _omniinteract_aggregate_params(subset: str, *, group: str) -> dict[str, object]:
+    return {
+        "dataset_name": "omniinteract",
+        "omniinteract_evaluate": True,
+        "omniinteract_subsets": subset,
+        "omniinteract_aggregate_min_ia_qtf1": 0.2,
+        "omniinteract_aggregate_subsets": ["1q1a", "1q1a_math", "1qna"],
+        "omniinteract_aggregate_group": group,
+    }
+
+
+def test_omniinteract_aggregate_waits_until_all_subsets():
+    from tests.dfx.perf.scripts.run_benchmark import _reset_omniinteract_aggregate_counts, assert_result
+
+    _reset_omniinteract_aggregate_counts()
+    assert_result(
+        _omniinteract_accuracy_result(tp=6.157502, fp=9, fn=8),
+        _omniinteract_aggregate_params("1q1a", group="wait"),
+        4,
+    )
+    assert_result(
+        _omniinteract_accuracy_result(tp=1.133541, fp=2, fn=2),
+        _omniinteract_aggregate_params("1q1a_math", group="wait"),
+        4,
+    )
+
+
+def test_omniinteract_aggregate_accepts_pooled_ia_qtf1_at_floor():
+    from tests.dfx.perf.scripts.run_benchmark import _reset_omniinteract_aggregate_counts, assert_result
+
+    _reset_omniinteract_aggregate_counts()
+    assert_result(
+        _omniinteract_accuracy_result(tp=6.157502, fp=9, fn=8),
+        _omniinteract_aggregate_params("1q1a", group="pass"),
+        4,
+    )
+    assert_result(
+        _omniinteract_accuracy_result(tp=1.133541, fp=2, fn=2),
+        _omniinteract_aggregate_params("1q1a_math", group="pass"),
+        4,
+    )
+    assert_result(
+        _omniinteract_accuracy_result(tp=0.0, fp=11, fn=18, ia_qtf1=0.0),
+        _omniinteract_aggregate_params("1qna", group="pass"),
+        4,
+    )
+
+
+def test_omniinteract_aggregate_rejects_pooled_ia_qtf1_below_floor():
+    from tests.dfx.perf.scripts.run_benchmark import _reset_omniinteract_aggregate_counts, assert_result
+
+    _reset_omniinteract_aggregate_counts()
+    assert_result(
+        _omniinteract_accuracy_result(tp=0.0, fp=1, fn=1, ia_qtf1=0.0),
+        _omniinteract_aggregate_params("1q1a", group="fail"),
+        4,
+    )
+    assert_result(
+        _omniinteract_accuracy_result(tp=0.0, fp=1, fn=1, ia_qtf1=0.0),
+        _omniinteract_aggregate_params("1q1a_math", group="fail"),
+        4,
+    )
+    with pytest.raises(AssertionError, match="aggregate All Global IA-QTF1"):
+        assert_result(
+            _omniinteract_accuracy_result(tp=0.0, fp=1, fn=1, ia_qtf1=0.0),
+            _omniinteract_aggregate_params("1qna", group="fail"),
+            4,
+        )
+
+
+def test_omniinteract_aggregate_rejects_entirely_skipped_subset():
+    from tests.dfx.perf.scripts.run_benchmark import _reset_omniinteract_aggregate_counts, assert_result
+
+    _reset_omniinteract_aggregate_counts()
+    assert_result(
+        _omniinteract_accuracy_result(tp=4.0, fp=0.0, fn=0.0, ia_qtf1=1.0),
+        _omniinteract_aggregate_params("1q1a", group="skipped-subset"),
+        4,
+    )
+    with pytest.raises(AssertionError, match="evaluated 0 cases"):
+        assert_result(
+            _omniinteract_accuracy_result(tp=0.0, fp=0.0, fn=0.0, ia_qtf1=1.0, evaluated=0, skipped=4),
+            _omniinteract_aggregate_params("1q1a_math", group="skipped-subset"),
+            4,
+        )
+
+
 def test_omni_tpot_baseline_accepts_measured_finite_sample():
     from tests.dfx.perf.scripts.run_benchmark import assert_result
 

vllm_omni/benchmarks/data_modules/omniinteract_dataset.py

@@ -22,6 +22,7 @@ from vllm.tokenizers import TokenizerLike
 from vllm.transformers_utils.repo_utils import hf_fs
 
 OMNIINTERACT_SUBSETS = ("1q1a", "1q1a_math", "1qna")
+OMNIINTERACT_SCENARIO_TAGS = ("realtime", "proactive", "nested", "interrupted", "1qna")
 DEFAULT_OMNIINTERACT_REPO = "lucky-lance/OmniInteract"
 DEFAULT_MAX_VIDEO_DURATION_S = 3600.0
 logger = logging.getLogger(__name__)
@@ -36,6 +37,18 @@ class OmniInteractCase:
     scene_type: str
 
 
+@dataclass(frozen=True)
+class OmniInteractEvaluationOptions:
+    judge_base_url: str
+    judge_model: str
+    judge_api_key: str
+    judge_timeout_s: float
+    judge_max_tokens: int
+    workers: int
+    output_dir: Path
+    skip_existing: bool = False
+
+
 @dataclass(frozen=True)
 class OmniInteractSessionOptions:
     output_root: Path
@@ -44,6 +57,7 @@ class OmniInteractSessionOptions:
     ref_audio: str
     require_response: bool = False
     max_video_duration_s: float = DEFAULT_MAX_VIDEO_DURATION_S
+    evaluation: OmniInteractEvaluationOptions | None = None
 
 
 @dataclass(frozen=True)
@@ -70,14 +84,31 @@ class OmniInteractDataset(BenchmarkDataset):
         subsets: Sequence[str] = OMNIINTERACT_SUBSETS,
         random_seed: int = 0,
         disable_shuffle: bool = False,
+        scenario_tags: Sequence[str] = (),
+        scenario_focus: bool = False,
+        video_list: str | Path | None = None,
     ) -> None:
         super().__init__(
             dataset_path=data_root or dataset_repo,
             random_seed=random_seed,
             disable_shuffle=disable_shuffle,
         )
-        self.root = resolve_omniinteract_root(data_root, dataset_repo)
+        self.video_list = Path(video_list).expanduser().resolve() if video_list else None
         self.subsets = tuple(subsets)
+        self.scenario_tags = normalize_scenario_tags(scenario_tags)
+        self.scenario_focus = bool(scenario_focus)
+        if self.video_list is not None:
+            if self.scenario_tags:
+                raise ValueError("OmniInteract video_list cannot be combined with scenario_tags")
+            if self.scenario_focus:
+                raise ValueError("OmniInteract video_list cannot be combined with scenario_focus")
+            if not self.video_list.is_file():
+                raise FileNotFoundError(f"--omniinteract-video-list does not exist: {self.video_list}")
+            self.root = None
+        else:
+            self.root = resolve_omniinteract_root(data_root, dataset_repo)
+            if self.scenario_focus and not self.scenario_tags:
+                raise ValueError("scenario_focus requires at least one scenario tag")
 
     def sample(
         self,
@@ -89,12 +120,18 @@ class OmniInteractDataset(BenchmarkDataset):
         **_: Any,
     ) -> list[SampleRequest]:
         del tokenizer
+        if self.video_list is not None:
+            cases = load_omniinteract_cases_from_video_list(self.video_list, num_prompts=num_requests)
+        else:
+            assert self.root is not None
             cases = discover_omniinteract_cases(
                 self.root,
                 self.subsets,
                 num_prompts=num_requests,
                 seed=self.random_seed,
                 disable_shuffle=self.disable_shuffle,
+                scenario_tags=self.scenario_tags,
+                scenario_focus=self.scenario_focus,
             )
         return [
             OmniInteractSampleRequest(
@@ -259,6 +296,182 @@ def _one_to_many_cases(root: Path) -> list[OmniInteractCase]:
     return cases
 
 
+def normalize_scenario_tags(tags: Sequence[str] | None) -> tuple[str, ...]:
+    """Validate and dedupe scenario tags while preserving caller order."""
+
+    if not tags:
+        return ()
+    normalized: list[str] = []
+    seen: set[str] = set()
+    invalid: list[str] = []
+    for raw in tags:
+        tag = str(raw or "").strip().lower()
+        if not tag:
+            continue
+        if tag not in OMNIINTERACT_SCENARIO_TAGS:
+            invalid.append(tag)
+            continue
+        if tag not in seen:
+            seen.add(tag)
+            normalized.append(tag)
+    if invalid:
+        raise ValueError(
+            "Unsupported OmniInteract scenario tags: "
+            f"{sorted(set(invalid))}; choose from {list(OMNIINTERACT_SCENARIO_TAGS)}"
+        )
+    return tuple(normalized)
+
+
+def _truthy(value: object) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, int | float):
+        return bool(value)
+    return str(value or "").strip().lower() in {"1", "true", "yes", "y", "t"}
+
+
+def _annotation_rows(root: object) -> list[dict[str, object]]:
+    if isinstance(root, list):
+        return [row for row in root if isinstance(row, dict)]
+    if not isinstance(root, dict):
+        return []
+    rows: list[dict[str, object]] = []
+    items = root.get("items")
+    if isinstance(items, list):
+        rows.extend(row for row in items if isinstance(row, dict))
+    answers = root.get("answers")
+    if isinstance(answers, list):
+        rows.extend(row for row in answers if isinstance(row, dict))
+    conversations = root.get("conversations")
+    if isinstance(conversations, list):
+        rows.extend(row for row in conversations if isinstance(row, dict))
+    return rows
+
+
+def case_scenario_tags(case: OmniInteractCase) -> frozenset[str]:
+    """Infer paper-table scenario tags from a case's scene type and annotation."""
+
+    tags: set[str] = set()
+    scene = str(case.scene_type or "").strip().lower()
+    if scene == "1qna":
+        tags.add("1qna")
+    elif scene == "nested":
+        tags.add("nested")
+
+    try:
+        payload = json.loads(case.annotation_path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeError, json.JSONDecodeError) as exc:
+        raise ValueError(f"Invalid OmniInteract annotation: {case.annotation_path}") from exc
+
+    for row in _annotation_rows(payload):
+        question_type = str(row.get("question_type") or row.get("label") or "").strip().lower()
+        if scene == "multi_turn":
+            if question_type == "realtime":
+                tags.add("realtime")
+            elif question_type == "proactive":
+                tags.add("proactive")
+        if _truthy(row.get("is_interrupted")) or _truthy(row.get("interrupted")):
+            tags.add("interrupted")
+    return frozenset(tags)
+
+
+def _select_with_scenario_tags(
+    cases: list[OmniInteractCase],
+    *,
+    num_prompts: int,
+    seed: int,
+    disable_shuffle: bool,
+    scenario_tags: Sequence[str],
+    scenario_focus: bool,
+) -> list[OmniInteractCase]:
+    tags = normalize_scenario_tags(scenario_tags)
+    if not tags:
+        ordered = list(cases)
+        if not disable_shuffle:
+            random.Random(seed).shuffle(ordered)
+        if num_prompts:
+            if num_prompts > len(ordered):
+                logger.warning(
+                    "Requested %d OmniInteract prompts but only %d are available; using all cases",
+                    num_prompts,
+                    len(ordered),
+                )
+            return ordered[:num_prompts]
+        return ordered
+
+    tag_sets = {case.video_path: case_scenario_tags(case) for case in cases}
+    if scenario_focus:
+        pool = [case for case in cases if tag_sets[case.video_path] & set(tags)]
+        missing = [tag for tag in tags if not any(tag in tag_sets[case.video_path] for case in pool)]
+        if missing:
+            raise ValueError(
+                f"No OmniInteract cases match scenario tags {missing} under the selected subsets (focus mode)"
+            )
+        if not pool:
+            raise ValueError(f"No OmniInteract cases match scenario tags {list(tags)}")
+        ordered = list(pool)
+        if not disable_shuffle:
+            random.Random(seed).shuffle(ordered)
+        if num_prompts:
+            if num_prompts > len(ordered):
+                logger.warning(
+                    "Requested %d OmniInteract prompts but only %d match tags %s; using all matches",
+                    num_prompts,
+                    len(ordered),
+                    list(tags),
+                )
+            return ordered[:num_prompts]
+        return ordered
+
+    rng = random.Random(seed)
+    buckets: dict[str, list[OmniInteractCase]] = {tag: [] for tag in tags}
+    for case in cases:
+        owned = tag_sets[case.video_path]
+        for tag in tags:
+            if tag in owned:
+                buckets[tag].append(case)
+    for tag, bucket in buckets.items():
+        if not bucket:
+            logger.warning(
+                "No OmniInteract cases available for scenario tag %r under the selected subsets",
+                tag,
+            )
+        elif not disable_shuffle:
+            rng.shuffle(bucket)
+
+    selected: list[OmniInteractCase] = []
+    selected_paths: set[Path] = set()
+    covered_tags: set[str] = set()
+    requested = set(tags)
+    for tag in tags:
+        if tag in covered_tags:
+            continue
+        for case in buckets[tag]:
+            if case.video_path in selected_paths:
+                continue
+            selected.append(case)
+            selected_paths.add(case.video_path)
+            covered_tags.update(tag_sets[case.video_path] & requested)
+            break
+
+    remaining = [case for case in cases if case.video_path not in selected_paths]
+    if not disable_shuffle:
+        rng.shuffle(remaining)
+
+    if num_prompts == 0:
+        return selected + remaining
+
+    if len(selected) > num_prompts:
+        logger.warning(
+            "OmniInteract cover tags need %d cases but --num-prompts=%d; truncating coverage set",
+            len(selected),
+            num_prompts,
+        )
+        return selected[:num_prompts]
+
+    return selected + remaining[: num_prompts - len(selected)]
+
+
 def discover_omniinteract_cases(
     root: Path,
     subsets: Sequence[str],
@@ -266,6 +479,8 @@ def discover_omniinteract_cases(
     num_prompts: int,
     seed: int = 0,
     disable_shuffle: bool = False,
+    scenario_tags: Sequence[str] = (),
+    scenario_focus: bool = False,
 ) -> list[OmniInteractCase]:
     invalid = set(subsets) - set(OMNIINTERACT_SUBSETS)
     if invalid:
@@ -276,6 +491,9 @@ def discover_omniinteract_cases(
         raise ValueError("OmniInteract subsets must not contain duplicates")
     if num_prompts < 0:
         raise ValueError("num_prompts must be non-negative")
+    tags = normalize_scenario_tags(scenario_tags)
+    if scenario_focus and not tags:
+        raise ValueError("scenario_focus requires at least one scenario tag")
     data_root, cases = _data_dir(root.resolve()), []
     for subset in subsets:
         subset_root = data_root / subset
@@ -285,15 +503,151 @@ def discover_omniinteract_cases(
         cases.extend(selected)
     if len({case.video_path for case in cases}) != len(cases):
         raise ValueError("OmniInteract dataset contains duplicate video paths")
-    if not disable_shuffle:
-        random.Random(seed).shuffle(cases)
-    if num_prompts:
-        if num_prompts > len(cases):
-            logger.warning(
-                "Requested %d OmniInteract prompts but only %d are available; using all cases", num_prompts, len(cases)
+    return _select_with_scenario_tags(
+        cases,
+        num_prompts=num_prompts,
+        seed=seed,
+        disable_shuffle=disable_shuffle,
+        scenario_tags=tags,
+        scenario_focus=scenario_focus,
     )
-        cases = cases[:num_prompts]
+
+
+def _subset_root_for_video(video_path: Path, subset: str) -> Path:
+    """Find the subset data root that owns an absolute video path."""
+
+    if subset not in OMNIINTERACT_SUBSETS:
+        raise ValueError(f"Unsupported OmniInteract subset: {subset!r}")
+    parts = video_path.resolve().parts
+    for index in range(len(parts) - 1, -1, -1):
+        if parts[index] != subset:
+            continue
+        candidate = Path(*parts[: index + 1])
+        if subset == "1qna":
+            if (candidate / "videos_bench").is_dir() and (candidate / "annotations").is_dir():
+                return candidate
+        elif (candidate / "video_json_map.json").is_file():
+            return candidate
+    raise ValueError(f"Could not locate OmniInteract subset root for {subset!r} from {video_path}")
+
+
+def _case_from_video_list_row(row: dict[str, object], *, line_no: int) -> OmniInteractCase:
+    """Rebuild an ``OmniInteractCase`` from one ``sampled_cases.jsonl`` row."""
+
+    subset = str(row.get("subset") or "")
+    video_text = str(row.get("video_path") or "")
+    if not subset or not video_text:
+        raise ValueError(f"OmniInteract video_list line {line_no} requires subset and video_path")
+    video_path = Path(video_text).expanduser().resolve()
+    if not video_path.is_file():
+        raise FileNotFoundError(f"OmniInteract video_list line {line_no} video missing: {video_path}")
+
+    subset_root = _subset_root_for_video(video_path, subset)
+    if subset == "1qna":
+        videos_root = (subset_root / "videos_bench").resolve()
+        annotations_root = (subset_root / "annotations").resolve()
+        if not video_path.is_relative_to(videos_root):
+            raise ValueError(f"OmniInteract video_list line {line_no} is outside videos_bench: {video_path}")
+        relative = video_path.relative_to(videos_root)
+        annotation = (subset_root / "annotations" / relative).with_suffix(".json").resolve()
+        if not annotation.is_relative_to(annotations_root):
+            raise ValueError(f"OmniInteract video_list line {line_no} unsafe annotation path: {relative}")
+        if not annotation.is_file():
+            raise FileNotFoundError(f"OmniInteract video_list line {line_no} annotation missing: {annotation}")
+        case = OmniInteractCase(
+            subset,
+            f"videos_bench/{relative.as_posix()}",
+            video_path,
+            annotation,
+            "1qna",
+        )
+    else:
+        by_path = {case.video_path.resolve(): case for case in _mapping_cases(subset_root, subset)}
+        case = by_path.get(video_path)
+        if case is None:
+            raise ValueError(f"OmniInteract video_list line {line_no} is not in {subset} mapping: {video_path}")
+
+    expected_name = str(row.get("output_name") or "")
+    if expected_name and expected_name != official_output_name(case):
+        raise ValueError(
+            f"OmniInteract video_list line {line_no} output_name mismatch: "
+            f"{expected_name!r} != {official_output_name(case)!r}"
+        )
+    return case
+
+
+def load_omniinteract_cases_from_video_list(
+    video_list: str | Path,
+    *,
+    num_prompts: int,
+) -> list[OmniInteractCase]:
+    """Load cases from a ``sampled_cases.jsonl``-style list, preserving order.
+
+    ``num_prompts`` caps the prefix of the list. ``0`` or a value larger than the
+    list length selects every row.
+    """
+
+    if num_prompts < 0:
+        raise ValueError("num_prompts must be non-negative")
+    path = Path(video_list).expanduser().resolve()
+    if not path.is_file():
+        raise FileNotFoundError(f"--omniinteract-video-list does not exist: {path}")
+
+    cases: list[OmniInteractCase] = []
+    seen: set[Path] = set()
+    with path.open(encoding="utf-8") as handle:
+        for line_no, raw in enumerate(handle, start=1):
+            text = raw.strip()
+            if not text:
+                continue
+            try:
+                row = json.loads(text)
+            except json.JSONDecodeError as exc:
+                raise ValueError(f"OmniInteract video_list line {line_no} is not valid JSON") from exc
+            if not isinstance(row, dict):
+                raise ValueError(f"OmniInteract video_list line {line_no} must be a JSON object")
+            case = _case_from_video_list_row(row, line_no=line_no)
+            if case.video_path in seen:
+                raise ValueError(f"OmniInteract video_list contains duplicate video_path: {case.video_path}")
+            seen.add(case.video_path)
+            cases.append(case)
+
+    if not cases:
+        raise ValueError(f"OmniInteract video_list is empty: {path}")
+    if num_prompts == 0 or num_prompts >= len(cases):
         return cases
+    return cases[:num_prompts]
+
+
+def official_output_name(case: OmniInteractCase) -> str:
+    """Match Lucky-Lance/OmniInteract ``collect_all_videos`` output_name layout.
+
+    ``1q1a`` / ``1q1a_math`` use the mapping-relative video path with ``/`` →
+    ``__``. ``1qna`` strips the ``videos_bench/`` prefix before the same rewrite.
+    """
+
+    relative = case.video_rel.replace("\\", "/")
+    if case.subset == "1qna":
+        prefix = "videos_bench/"
+        if relative.startswith(prefix):
+            relative = relative[len(prefix) :]
+    stem = Path(relative).with_suffix("").as_posix().replace("/", "__")
+    return f"{case.subset}/{stem}"
+
+
+def sampled_case_row(case: OmniInteractCase) -> dict[str, str]:
+    """One official MiniCPM-o ``--video_list`` row for a sampled OmniInteract case.
+
+    ``video_path`` reuses the absolute path already resolved from ``--dataset-path``
+    (local tree or Hub extract). Official
+    ``batch_inference_minicpmo.py --video_list`` accepts these rows as-is.
+    """
+
+    return {
+        "video_path": str(case.video_path.resolve()),
+        "output_name": official_output_name(case),
+        "subset": case.subset,
+    }
 
 
 def case_manifest(case: OmniInteractCase, output_dir: Path) -> dict[str, Any]:

vllm_omni/benchmarks/omniinteract.py

@@ -32,6 +32,7 @@ from vllm_omni.benchmarks.data_modules.omniinteract_dataset import (
     OmniInteractCase,
     OmniInteractPreparedInput,
     case_manifest,
+    sampled_case_row,
 )
 
 # The duplex client library (vllm_omni.clients) is imported lazily inside the
@@ -48,7 +49,7 @@ PCM16_SAMPLE_RATE = 16_000
 PCM16_BYTES_PER_SAMPLE = 2
 OUTPUT_SAMPLE_RATE = 24_000
 SUCCESS_ARTIFACTS = (".done", "output.wav", "wav_transcript.json", "events.json", "result.json")
-BATCH_ARTIFACTS = ("batch_summary.json", "official_eval_manifest.jsonl")
+BATCH_ARTIFACTS = ("batch_summary.json", "official_eval_manifest.jsonl", "sampled_cases.jsonl")
 ARTIFACT_LOCK_FILE = ".omniinteract.lock"
 _INPUT_CHUNK_MS = 200
 # Minimum new played audio between two incremental playback.ack sends for one
@@ -816,6 +817,7 @@ def write_batch_artifacts(
         for case, result in zip(cases, results, strict=True)
         if result.success and result.eligible_for_official_eval
     ]
+    sampled_rows = [sampled_case_row(case) for case in cases]
     ineligible = [result for result in results if result.success and not result.eligible_for_official_eval]
     if ineligible:
         reasons = Counter(
@@ -831,6 +833,12 @@ def write_batch_artifacts(
         root / "official_eval_manifest.jsonl",
         "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows),
     )
+    # Every sampled case (including failed / ineligible) so official MiniCPM-o
+    # ``--video_list`` can replay the same video set without re-deriving ids.
+    _atomic_write_text(
+        root / "sampled_cases.jsonl",
+        "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in sampled_rows),
+    )
 
 
 def _websocket_url(config: OmniInteractBenchmarkConfig) -> str:

vllm_omni/benchmarks/omniinteract_judge.py

@@ -0,0 +1,495 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""Text-only LLM judge for OmniInteract unified evaluation.
+
+The three prompt templates follow the official English judge protocol in
+Lucky-Lance/OmniInteract ``eval/evaluation/llm_judge.py``
+(commit de304cef35fd9a50a5caadb5090c34cfbf0dd868). They correspond to the
+OmniInteract paper appendix (arXiv:2605.26485) Listing A.1 (early-stage),
+Listing A.2 (interrupted partial quality), and Listing A.3 (core-stage).
+A local OpenAI-compatible judge is not the paper's GPT-4o judge, so scores
+are protocol-compatible rather than official paper-table numbers.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import threading
+import time
+from collections.abc import Mapping
+from dataclasses import dataclass
+
+import regex as re
+import requests
+
+_JUDGE_MAX_ATTEMPTS = 3
+_RETRYABLE_HTTP_STATUS = frozenset({502, 503, 504})
+_SCORE_FLOAT_PATTERN = re.compile(
+    r"""score["']?\s*[:=]\s*([-+]?\d*\.?\d+(?:[eE][-+]?\d+)?)""",
+    re.IGNORECASE,
+)
+
+# Official English templates from OmniInteract llm_judge.py.
+# Paper appendix: Listing A.1 early, A.2 interrupted-partial, A.3 core.
+# Source: Lucky-Lance/OmniInteract@de304cef35fd9a50a5caadb5090c34cfbf0dd868
+
+EARLY_SYSTEM_PROMPT = (
+    "You are a streaming voice assistant evaluation judge. "
+    "Judge only based on the given text. "
+    "Output must be parseable JSON with no other text."
+)
+
+EARLY_USER_TEMPLATE = """Determine whether the early output between start and t_a is an early hallucination.
+
+[scene_type] {scene_type}
+[slot] slot_id={slot_id},
+  turn_index={turn_index},
+  step_index={step_index},
+  boundary_type={boundary_type},
+  is_interrupted={is_interrupted}
+[question] {question}
+[current_gt_answer] {gt_answer}
+[full_chunk_context] {full_context}
+[early_actual_text] {actual_text}
+
+Rules:
+1. Greetings, confirmations, waiting, brief observations, and follow-up phrases -> Neutral.
+2. If the model starts substantively answering, guessing unseen info, revealing
+   future steps, or making definitive factual claims -> FP.
+3. For 1QnA first step, reciting the full procedure before acting -> FP.
+4. score is interaction quality 0-1 when Neutral; 0 when hallucination.
+
+Output JSON:
+{{"flag":"Neutral|FP_Hallucination",
+ "score":float 0-1,
+ "rationale":"one sentence"}}"""
+
+CORE_SYSTEM_PROMPT = (
+    "You are a strict streaming voice assistant core-answer evaluation judge. "
+    "Judge only based on the given text and reference answer. "
+    "Output must be parseable JSON with no other text."
+)
+
+CORE_USER_TEMPLATE = """Score the core output after t_a.
+
+[scene_type] {scene_type}
+[slot] slot_id={slot_id},
+  turn_index={turn_index},
+  step_index={step_index},
+  boundary_type={boundary_type},
+  is_interrupted={is_interrupted}
+[question] {question}
+[current_gt_answer] {gt_answer}
+[future_gt_answers_or_steps]
+  {future_answers}
+[full_chunk_context] {full_context}
+[core_actual_text] {actual_text}
+
+Rules:
+1. score 0-1: correctness and coverage of core_actual_text vs gt_answer.
+2. Off-topic, factual errors, or missing key answer -> low score.
+3. 1QnA: reward only current-step info; penalize spoiling future steps or skipping the current step.
+4. If score > 0, extract the earliest contiguous substring from core_actual_text
+   that establishes the answer as trigger_phrase.
+5. trigger_phrase must be a verbatim substring; empty if score == 0.
+
+Output JSON:
+{{"score":float 0-1,
+ "trigger_phrase":"substring or empty",
+ "spoiler":true|false,
+ "rationale":"one sentence"}}"""
+
+INTERRUPT_PARTIAL_SYSTEM_PROMPT = (
+    "You are a strict evaluator for interrupted voice-assistant answers. "
+    "Judge only from the provided text. "
+    "Return valid JSON only, with no extra text."
+)
+
+INTERRUPT_PARTIAL_USER_TEMPLATE = """Evaluate the quality of the assistant output
+that was already spoken before or around an interruption.
+
+[Task]
+The assistant was answering, but the interaction was interrupted before
+completion. The assistant was not required to complete the full original answer.
+Score whether the content already spoken is relevant, correct, and useful for
+the current ground-truth answer.
+
+[Question] {question}
+[Ground Truth Answer] {gt_answer}
+[Assistant Output Already Spoken]
+{actual_text}
+
+Scoring Rules:
+1. Score from 0 to 1.
+2. Do not penalize incompleteness: a partial answer can receive a high score if the spoken part is correct and useful.
+3. Score high when the spoken content overlaps with, paraphrases, or conveys useful parts of the ground truth.
+4. Score low for acknowledgments or prefaces without substantive answer content.
+5. Score low for wrong-question, irrelevant, or generic-filler output.
+6. hallucination=true if the output contains clear incorrect facts, wrong target content, or unsupported content.
+7. Ignore overflow duration when scoring quality; spill is measured separately.
+
+Output JSON:
+{{"score":float 0-1,
+ "hallucination":true|false,
+ "rationale":"one sentence"}}"""
+
+
+class JudgeRequestError(RuntimeError):
+    """Raised when the configured judge endpoint cannot produce a response."""
+
+
+@dataclass(frozen=True)
+class EarlyJudgment:
+    category: str
+    score: float
+    rationale: str
+    raw: str
+    parse_source: str
+
+
+@dataclass(frozen=True)
+class CoreJudgment:
+    score: float
+    trigger_phrase: str
+    spoiler: bool
+    rationale: str
+    raw: str
+    parse_source: str
+
+
+@dataclass(frozen=True)
+class PartialJudgment:
+    score: float
+    hallucination: bool
+    rationale: str
+    raw: str
+    parse_source: str
+
+
+def _clamp_unit_interval(value: float) -> float:
+    return max(0.0, min(1.0, value))
+
+
+def _json_unit_score(value: object) -> tuple[float | None, bool]:
+    """Parse one JSON score into a finite unit-interval value.
+
+    The second flag is true when the value is a non-finite float (NaN or
+    infinity). Callers must treat that as a parse failure and award no credit.
+    A value that is not a float returns ``(None, False)`` so existing
+    fallbacks stay available.
+    """
+
+    if isinstance(value, bool) or not isinstance(value, int | float | str):
+        return None, False
+    try:
+        parsed = float(value)
+    except ValueError:
+        return None, False
+    if not math.isfinite(parsed):
+        return None, True
+    return _clamp_unit_interval(parsed), False
+
+
+def _required_json_unit_score(parsed: Mapping[str, object]) -> tuple[float | None, bool]:
+    """Require an explicit finite ``score`` field on a parsed JSON object.
+
+    Returns ``(score, False)`` for a present, finite unit-interval value
+    (including an explicit ``0`` / ``0.0``). Returns ``(None, True)`` when
+    ``score`` is missing, has an invalid type, or is non-finite — callers must
+    treat that as ``llm_parse_failed`` and must not invent a valid zero.
+    """
+
+    if "score" not in parsed:
+        return None, True
+    score, non_finite = _json_unit_score(parsed["score"])
+    if non_finite or score is None:
+        return None, True
+    return score, False
+
+
+def _text(value: object) -> str:
+    return str(value or "").strip()
+
+
+def _bool(value: object) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, int | float):
+        return bool(value)
+    return _text(value).lower() in {"1", "true", "yes", "y", "t"}
+
+
+def _first_json_object(text: str) -> dict[str, object] | None:
+    decoder = json.JSONDecoder()
+    position = text.find("{")
+    while position >= 0:
+        try:
+            value, _ = decoder.raw_decode(text[position:])
+        except json.JSONDecodeError:
+            position = text.find("{", position + 1)
+            continue
+        if isinstance(value, dict) and all(isinstance(key, str) for key in value):
+            return value
+        position = text.find("{", position + 1)
+    return None
+
+
+def _score_float_from_text(text: str) -> float | None:
+    """Parse a unit-interval score from text; ignore unrelated leading numbers."""
+
+    match = _SCORE_FLOAT_PATTERN.search(text)
+    if match is None:
+        return None
+    try:
+        value = float(match.group(1))
+    except ValueError:
+        return None
+    if 0.0 <= value <= 1.0:
+        return value
+    return None
+
+
+def _chat_url(base_url: str) -> str:
+    base = base_url.rstrip("/")
+    if base.endswith("/chat/completions"):
+        return base
+    if base.endswith("/v1"):
+        return f"{base}/chat/completions"
+    return f"{base}/v1/chat/completions"
+
+
+class OmniInteractJudge:
+    """OpenAI-compatible client implementing the OmniInteract judge contract."""
+
+    def __init__(
+        self,
+        base_url: str,
+        model: str,
+        *,
+        api_key: str = "EMPTY",
+        timeout_s: float = 60.0,
+        max_tokens: int = 512,
+    ) -> None:
+        if not base_url.strip():
+            raise ValueError("judge base URL is required")
+        if not model.strip():
+            raise ValueError("judge model is required")
+        self.base_url = base_url
+        self.model = model
+        self.api_key = api_key or "EMPTY"
+        self.timeout_s = timeout_s
+        self.max_tokens = max_tokens
+        self._thread_local = threading.local()
+
+    def _session(self) -> requests.Session:
+        session = getattr(self._thread_local, "session", None)
+        if session is None:
+            session = requests.Session()
+            self._thread_local.session = session
+        return session
+
+    def _generate(self, system_prompt: str, user_prompt: str) -> str:
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            "temperature": 0.0,
+            "stream": False,
+            "max_tokens": self.max_tokens,
+        }
+        last_error: Exception | None = None
+        for attempt in range(_JUDGE_MAX_ATTEMPTS):
+            try:
+                response = self._session().post(
+                    _chat_url(self.base_url),
+                    headers=headers,
+                    json=payload,
+                    timeout=self.timeout_s,
+                )
+            except requests.Timeout as exc:
+                last_error = JudgeRequestError(f"judge request failed: {exc}")
+                if attempt + 1 < _JUDGE_MAX_ATTEMPTS:
+                    time.sleep(0.5 * (2**attempt))
+                    continue
+                raise last_error from exc
+            except requests.RequestException as exc:
+                raise JudgeRequestError(f"judge request failed: {exc}") from exc
+            if response.status_code in _RETRYABLE_HTTP_STATUS and attempt + 1 < _JUDGE_MAX_ATTEMPTS:
+                time.sleep(0.5 * (2**attempt))
+                continue
+            if not response.ok:
+                raise JudgeRequestError(f"judge returned HTTP {response.status_code}: {response.text[:500]}")
+            try:
+                body = response.json()
+                choices = body["choices"]
+                message = choices[0]["message"]
+                return str(message["content"])
+            except (KeyError, IndexError, TypeError, ValueError) as exc:
+                raise JudgeRequestError(f"unexpected judge response: {response.text[:500]}") from exc
+        assert last_error is not None
+        raise last_error
+
+    def judge_early(
+        self,
+        slot: Mapping[str, object],
+        full_context: str,
+        actual_text: str,
+    ) -> EarlyJudgment:
+        raw = self._generate(
+            EARLY_SYSTEM_PROMPT,
+            EARLY_USER_TEMPLATE.format(
+                scene_type=_text(slot.get("scene_type")),
+                slot_id=slot.get("slot_id"),
+                turn_index=slot.get("turn_index"),
+                step_index=slot.get("step_index"),
+                boundary_type=_text(slot.get("boundary_type")),
+                is_interrupted=str(_bool(slot.get("is_interrupted"))).lower(),
+                question=_text(slot.get("question_text")),
+                gt_answer=_text(slot.get("gt_answer")),
+                full_context=full_context.strip(),
+                actual_text=actual_text.strip(),
+            ),
+        )
+        parsed = _first_json_object(raw)
+        flag = _text(parsed.get("flag")) if parsed else raw.strip()
+        normalized = flag.lower().replace("-", "_").replace(" ", "")
+        if "hallucination" in normalized:
+            return EarlyJudgment(
+                category="hallucination",
+                score=0.0,
+                rationale=_text(parsed.get("rationale")) if parsed else "",
+                raw=raw,
+                parse_source="llm_json" if parsed else "llm_parse_failed",
+            )
+        if "neutral" in normalized:
+            if parsed is None:
+                return EarlyJudgment(
+                    category="neutral",
+                    score=0.0,
+                    rationale="",
+                    raw=raw,
+                    parse_source="llm_parse_failed",
+                )
+            score, score_failed = _required_json_unit_score(parsed)
+            if score_failed:
+                return EarlyJudgment(
+                    category="unparsed",
+                    score=0.0,
+                    rationale=_text(parsed.get("rationale")),
+                    raw=raw,
+                    parse_source="llm_parse_failed",
+                )
+            return EarlyJudgment(
+                category="neutral",
+                score=score if score is not None else 0.0,
+                rationale=_text(parsed.get("rationale")),
+                raw=raw,
+                parse_source="llm_json",
+            )
+        return EarlyJudgment(
+            category="unparsed",
+            score=0.0,
+            rationale=_text(parsed.get("rationale")) if parsed else "",
+            raw=raw,
+            parse_source="llm_parse_failed",
+        )
+
+    def judge_core(
+        self,
+        slot: Mapping[str, object],
+        full_context: str,
+        actual_text: str,
+        future_answers: str,
+    ) -> CoreJudgment:
+        raw = self._generate(
+            CORE_SYSTEM_PROMPT,
+            CORE_USER_TEMPLATE.format(
+                scene_type=_text(slot.get("scene_type")),
+                slot_id=slot.get("slot_id"),
+                turn_index=slot.get("turn_index"),
+                step_index=slot.get("step_index"),
+                boundary_type=_text(slot.get("boundary_type")),
+                is_interrupted=str(_bool(slot.get("is_interrupted"))).lower(),
+                question=_text(slot.get("question_text")),
+                gt_answer=_text(slot.get("gt_answer")),
+                future_answers=future_answers.strip(),
+                full_context=full_context.strip(),
+                actual_text=actual_text.strip(),
+            ),
+        )
+        parsed = _first_json_object(raw)
+        if parsed is not None:
+            score, json_score_failed = _required_json_unit_score(parsed)
+            if json_score_failed:
+                return CoreJudgment(
+                    score=0.0,
+                    trigger_phrase="",
+                    spoiler=False,
+                    rationale=_text(parsed.get("rationale")),
+                    raw=raw,
+                    parse_source="llm_parse_failed",
+                )
+            return CoreJudgment(
+                score=score if score is not None else 0.0,
+                trigger_phrase=_text(parsed.get("trigger_phrase")),
+                spoiler=_bool(parsed.get("spoiler")),
+                rationale=_text(parsed.get("rationale")),
+                raw=raw,
+                parse_source="llm_json",
+            )
+        fallback = _score_float_from_text(raw)
+        return CoreJudgment(
+            score=fallback if fallback is not None else 0.0,
+            trigger_phrase="",
+            spoiler=False,
+            rationale="",
+            raw=raw,
+            parse_source="llm_float_text" if fallback is not None else "llm_parse_failed",
+        )
+
+    def judge_interrupted_partial(
+        self,
+        slot: Mapping[str, object],
+        actual_text: str,
+    ) -> PartialJudgment:
+        raw = self._generate(
+            INTERRUPT_PARTIAL_SYSTEM_PROMPT,
+            INTERRUPT_PARTIAL_USER_TEMPLATE.format(
+                question=_text(slot.get("question_text")),
+                gt_answer=_text(slot.get("gt_answer")),
+                actual_text=actual_text.strip(),
+            ),
+        )
+        parsed = _first_json_object(raw)
+        if parsed is None:
+            return PartialJudgment(
+                score=0.0,
+                hallucination=False,
+                rationale="",
+                raw=raw,
+                parse_source="llm_parse_failed",
+            )
+        score, score_failed = _required_json_unit_score(parsed)
+        if score_failed:
+            return PartialJudgment(
+                score=0.0,
+                hallucination=False,
+                rationale=_text(parsed.get("rationale")),
+                raw=raw,
+                parse_source="llm_parse_failed",
+            )
+        return PartialJudgment(
+            score=score if score is not None else 0.0,
+            hallucination=_bool(parsed.get("hallucination")),
+            rationale=_text(parsed.get("rationale")),
+            raw=raw,
+            parse_source="llm_json",
+        )

vllm_omni/benchmarks/patch/patch.py

@@ -54,6 +54,7 @@ from vllm_omni.benchmarks.data_modules.daily_omni_dataset import (
 from vllm_omni.benchmarks.data_modules.omniinteract_dataset import (
     DEFAULT_OMNIINTERACT_REPO,
     OmniInteractDataset,
+    OmniInteractEvaluationOptions,
     OmniInteractPreparedInput,
     OmniInteractSampleRequest,
     OmniInteractSessionOptions,
@@ -92,6 +93,7 @@ from vllm_omni.benchmarks.omniinteract import (
 from vllm_omni.benchmarks.omniinteract import (
     write_batch_artifacts as write_omniinteract_batch_artifacts,
 )
+from vllm_omni.benchmarks.omniinteract_eval import evaluate_batch as evaluate_omniinteract_batch
 from vllm_omni.metrics import definitions as defs
 from vllm_omni.metrics.utils import coerce_bool, coerce_positive_float_scalar, coerce_positive_int_scalar
 
@@ -429,6 +431,39 @@ def _finalize_omniinteract_batch(
     return compact_summary
 
 
+async def _evaluate_omniinteract_batch(
+    input_requests: list[SampleRequest],
+    outputs: list[RequestFuncOutput],
+) -> dict[str, object] | None:
+    rows = [
+        (sample, output)
+        for sample, output in zip(input_requests, outputs, strict=True)
+        if isinstance(sample, OmniInteractSampleRequest)
+    ]
+    if not rows:
+        return None
+    options = rows[0][0].omniinteract_options
+    if not isinstance(options, OmniInteractSessionOptions) or options.evaluation is None:
+        return None
+    cases, results = [], []
+    for sample, output in rows:
+        result = getattr(output, "omniinteract_case_result", None)
+        if sample.omniinteract_case is None or not isinstance(result, OmniInteractCaseResult):
+            raise RuntimeError("OmniInteract benchmark output lost its dataset identity")
+        cases.append(sample.omniinteract_case)
+        results.append(result)
+    try:
+        return await asyncio.to_thread(
+            evaluate_omniinteract_batch,
+            cases,
+            results,
+            options.evaluation,
+        )
+    except Exception as exc:  # noqa: BLE001 - post-hoc accuracy must not fail a finished benchmark
+        logger.exception("OmniInteract evaluation failed")
+        return {"status": "failed", "error": str(exc)}
+
+
 def _prepare_omniinteract_batch(input_requests: list[SampleRequest]) -> None:
     roots: set[Path] = set()
     for sample in input_requests:
@@ -556,14 +591,32 @@ def get_samples(args, tokenizer, **kwargs):
             subsets=tuple(getattr(args, "omniinteract_subsets")),
             random_seed=args.seed,
             disable_shuffle=getattr(args, "disable_shuffle", False),
+            scenario_tags=tuple(getattr(args, "omniinteract_scenario_tags", None) or ()),
+            scenario_focus=bool(getattr(args, "omniinteract_scenario_focus", False)),
+            video_list=getattr(args, "omniinteract_video_list", None),
+        )
+        output_root = Path(getattr(args, "omniinteract_output_dir"))
+        evaluation = None
+        if bool(getattr(args, "omniinteract_evaluate", False)):
+            evaluation_output = getattr(args, "omniinteract_eval_output_dir", None)
+            evaluation = OmniInteractEvaluationOptions(
+                judge_base_url=str(getattr(args, "omniinteract_judge_base_url", "http://127.0.0.1:8000")),
+                judge_model=str(getattr(args, "omniinteract_judge_model", "")),
+                judge_api_key=str(getattr(args, "omniinteract_judge_api_key", "EMPTY")),
+                judge_timeout_s=float(getattr(args, "omniinteract_judge_timeout_s", 60.0)),
+                judge_max_tokens=int(getattr(args, "omniinteract_judge_max_tokens", 512)),
+                workers=int(getattr(args, "omniinteract_eval_workers", 8)),
+                output_dir=Path(evaluation_output) if evaluation_output else output_root / "evaluation",
+                skip_existing=bool(getattr(args, "omniinteract_eval_skip_existing", False)),
             )
         options = OmniInteractSessionOptions(
-            output_root=Path(getattr(args, "omniinteract_output_dir")),
+            output_root=output_root,
             timeout_s=float(getattr(args, "omniinteract_timeout_s")),
             media_timeout_s=float(getattr(args, "omniinteract_media_timeout_s")),
             ref_audio=str(getattr(args, "omniinteract_ref_audio")),
             require_response=bool(getattr(args, "omniinteract_require_response")),
             max_video_duration_s=float(getattr(args, "omniinteract_max_video_duration_s")),
+            evaluation=evaluation,
         )
         requests = dataset.sample(
             tokenizer,
@@ -3478,6 +3531,9 @@ async def benchmark(
     benchmark_duration = time.perf_counter() - benchmark_start_time
 
     omniinteract_summary = _finalize_omniinteract_batch(input_requests, outputs)
+    omniinteract_evaluation = await _evaluate_omniinteract_batch(input_requests, outputs)
+    if omniinteract_summary is not None and omniinteract_evaluation is not None:
+        omniinteract_summary["accuracy"] = omniinteract_evaluation
 
     metrics: Any
     actual_output_lens: list[int] | int

vllm_omni/entrypoints/cli/benchmark/cli_args.py

@@ -32,6 +32,13 @@ def _positive_finite_float(value: str) -> float:
     return parsed
 
 
+def _positive_int(value: str) -> int:
+    parsed = int(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError(f"must be a positive integer, got {value!r}")
+    return parsed
+
+
 def _existing_file(value: str) -> str:
     path = Path(value).expanduser()
     if not path.is_file():
@@ -42,6 +49,7 @@ def _existing_file(value: str) -> str:
 def add_omniinteract_cli_args(parser: argparse.ArgumentParser) -> None:
     from vllm_omni.benchmarks.data_modules.omniinteract_dataset import (
         DEFAULT_MAX_VIDEO_DURATION_S,
+        OMNIINTERACT_SCENARIO_TAGS,
         OMNIINTERACT_SUBSETS,
     )
 
@@ -49,6 +57,35 @@ def add_omniinteract_cli_args(parser: argparse.ArgumentParser) -> None:
     group.add_argument(
         "--omniinteract-subsets", nargs="+", choices=OMNIINTERACT_SUBSETS, default=list(OMNIINTERACT_SUBSETS)
     )
+    group.add_argument(
+        "--omniinteract-scenario-tags",
+        nargs="+",
+        choices=OMNIINTERACT_SCENARIO_TAGS,
+        default=None,
+        help=(
+            "Scenario tags used when sampling cases: realtime, proactive, nested, interrupted, 1qna. "
+            "Default behavior covers each requested tag with at least one case (when available), "
+            "then fills remaining --num-prompts from the rest of the selected subsets."
+        ),
+    )
+    group.add_argument(
+        "--omniinteract-scenario-focus",
+        action="store_true",
+        help=(
+            "Only run cases matching --omniinteract-scenario-tags. "
+            "Without this flag, tags are used for coverage-first sampling."
+        ),
+    )
+    group.add_argument(
+        "--omniinteract-video-list",
+        type=_existing_file,
+        help=(
+            "JSONL of sampled OmniInteract cases (video_path, output_name, subset), "
+            "typically sampled_cases.jsonl from a prior run. Preserves list order. "
+            "Cannot be combined with --seed, --dataset-path, or --omniinteract-scenario-tags. "
+            "With this flag, --num-prompts caps the prefix of the list (0 or oversized = all)."
+        ),
+    )
     group.add_argument(
         "--omniinteract-timeout-s", type=_positive_finite_float, default=900.0, help="Complete session timeout."
     )
@@ -76,6 +113,50 @@ def add_omniinteract_cli_args(parser: argparse.ArgumentParser) -> None:
         default=Path("omniinteract-output"),
         help="Directory for case and evaluator artifacts.",
     )
+    group.add_argument(
+        "--omniinteract-evaluate",
+        action="store_true",
+        help="Run the text-only OmniInteract judge after generation and print an accuracy report.",
+    )
+    group.add_argument(
+        "--omniinteract-judge-base-url",
+        default="http://127.0.0.1:8000",
+        help="Base URL of an already-running OpenAI-compatible judge server.",
+    )
+    group.add_argument("--omniinteract-judge-model", help="Model name exposed by the judge server.")
+    group.add_argument(
+        "--omniinteract-judge-api-key",
+        default="EMPTY",
+        help="Bearer token for the judge server.",
+    )
+    group.add_argument(
+        "--omniinteract-judge-timeout-s",
+        type=_positive_finite_float,
+        default=60.0,
+        help="Timeout for each judge request.",
+    )
+    group.add_argument(
+        "--omniinteract-judge-max-tokens",
+        type=_positive_int,
+        default=512,
+        help="Maximum completion tokens for each judge request.",
+    )
+    group.add_argument(
+        "--omniinteract-eval-workers",
+        type=_positive_int,
+        default=8,
+        help="Maximum number of concurrent judge requests.",
+    )
+    group.add_argument(
+        "--omniinteract-eval-output-dir",
+        type=Path,
+        help="Accuracy artifact directory; defaults to OUTPUT_DIR/evaluation.",
+    )
+    group.add_argument(
+        "--omniinteract-eval-skip-existing",
+        action="store_true",
+        help="Reuse successful per-case evaluation artifacts.",
+    )
 
 
 def add_multi_stage_cli_args(parser: argparse.ArgumentParser) -> None:
@@ -421,6 +502,22 @@ def preprocess_serve_args(args: argparse.Namespace) -> None:
             raise ValueError("OmniInteract requires --endpoint /v1/realtime")
         if not getattr(args, "omniinteract_ref_audio", None):
             raise ValueError("OmniInteract requires --omniinteract-ref-audio")
+        if getattr(args, "omniinteract_evaluate", False) and not getattr(args, "omniinteract_judge_model", None):
+            raise ValueError("OmniInteract evaluation requires --omniinteract-judge-model")
+        if getattr(args, "omniinteract_video_list", None):
+            explicit = getattr(args, "explicit_keys", ())
+            if "seed" in explicit:
+                raise ValueError("--omniinteract-video-list cannot be combined with --seed")
+            if "dataset_path" in explicit:
+                raise ValueError("--omniinteract-video-list cannot be combined with --dataset-path")
+            if getattr(args, "omniinteract_scenario_tags", None):
+                raise ValueError("--omniinteract-video-list cannot be combined with --omniinteract-scenario-tags")
+            if getattr(args, "omniinteract_scenario_focus", False):
+                raise ValueError("--omniinteract-video-list cannot be combined with --omniinteract-scenario-focus")
+        if getattr(args, "omniinteract_scenario_focus", False) and not getattr(
+            args, "omniinteract_scenario_tags", None
+        ):
+            raise ValueError("--omniinteract-scenario-focus requires --omniinteract-scenario-tags")
         if getattr(args, "ignore_eos", False):
             raise ValueError("OmniInteract does not support --ignore-eos")
         if getattr(args, "profile", False):