Add 567-question single-video evaluation package and clips

single_video_567/README.md

@@ -0,0 +1,83 @@
+---
+language:
+- en
+task_categories:
+- visual-question-answering
+tags:
+- video-question-answering
+- evaluation
+- runningbench
+---
+
+# RunningBench single-video QA: 567 human-usable questions
+
+This folder is a portable evaluation release for the 567-question human-curated single-video subset. The 567 videos are **required**: upload the entire `videos/` directory alongside the QA file. A QA-only upload cannot reproduce this benchmark. This release is separate from the original 698-question single-video set and the 1,487-question mixed/video set.
+
+## Contents
+
+| Path | Contents |
+|---|---|
+| `data/single_567_human_eval.jsonl` | 567 questions, one JSON object per line |
+| `human_answer_repair_567/single_567_human_eval.jsonl` | Identical file at the path expected by the included runner |
+| `videos/<question_id>.mp4` | Exactly 567 pre-cut single-video clips |
+| `data/video_sha256.csv` | File size and SHA-256 for every clip |
+| `data/source_manifest.json` | Source hashes and correction counts |
+| `RunningBench_567题可用数据说明.md` | Human-curation scope and limitations |
+| `code/unified_eval.py` | Unified runner and scorer |
+| `code/model_matrix_567.csv` | Target model/configuration matrix from the multi-video evaluation |
+
+Each question has an `id`, `question`, option map (`A`, `B`, ...), answer letter list, video metadata and `media_file` pointing to `videos/<id>.mp4`. There are 325 single-select and 242 multi-select questions. The source comprises 562 complete clear human annotations and five repaired incomplete annotations. The 567 questions were selected as answer-usable; that does **not** claim that every video was manually verified. Compared with the corresponding original 698 rows, 120 answers, three question texts and three option sets changed. Use this release's answer as gold.
+
+## Download
+
+After the full folder is uploaded to `taryya/RunningBench` under `single_video_567/`:
+
+```bash
+hf download taryya/RunningBench --repo-type dataset \
+  --include 'single_video_567/*' --local-dir ./RunningBench
+cd RunningBench/single_video_567
+sha256sum -c <(awk -F, 'NR>1 {print $4 "  " $2}' data/video_sha256.csv)
+```
+
+If the upload uses a dedicated dataset repository, replace the repository ID and omit the `single_video_567/` prefix. On Windows, verify hashes with Python or another SHA-256 utility. The upload is complete only when `data/video_sha256.csv` lists 567 files and all 567 `videos/*.mp4` are present.
+
+## Evaluation rule
+
+Use the same single-video protocol for every model and thinking configuration:
+
+1. Supply one 60-second clip per question. Extract 64 JPEG frames in temporal order with `ffmpeg` at `fps=64/duration`, `scale='min(480,iw)':-2`, quality 3. The 480 limit applies to width; portrait height can exceed 480.
+2. Use the shared prompt and deterministic option shuffle in `code/unified_eval.py`, temperature 0, and the same 567 IDs for every full run. Preserve the mapping from shuffled letters back to source options.
+3. Parse the returned JSON answer into an option set. `exact` counts only an identical set; `overlap` is correct-option recall; `wellformed` checks the required answer count. Report `completed/567` and failed questions alongside accuracy. Do not mix a partial run with a full-run score.
+4. Give each model/configuration its own run ID and result JSONL. Record the exact checkpoint or provider model ID, backend version, generation budget, mode, corpus hash, prompt hash, frame settings and per-question output.
+5. For thinking-enabled models, preserve the complete raw response, extracted reasoning trace, `finish_reason`, usage and **every retry attempt**. Increase the output-token budget when `finish_reason=length`. A final response that still truncates, lacks a final answer, or lacks the expected visible reasoning is a failed/unscored question. Do not silently parse a partial answer. The included Qwen3-VL Thinking vLLM run uses `--reasoning-parser qwen3`.
+6. Models with a real `enable_thinking` switch should run the same checkpoint twice, explicitly on and off. Fixed Instruct and fixed Thinking checkpoints are distinct model variants. Historical base result files do not prove thinking was off.
+
+The comparison matrix lists 26 complete multi-video base models and 11 explicit thinking extensions. It is a **target list**, not a claim that all 37 configurations have already run on these 567 questions. Provider endpoints, access and weights must be resolved per model.
+
+Example with an OpenAI-compatible service and the included runner:
+
+```bash
+python code/unified_eval.py inventory --dataset single_567_human \
+  --root "$PWD" --single-clips "$PWD/videos" --work-dir "$PWD/work"
+python code/unified_eval.py run --dataset single_567_human \
+  --root "$PWD" --single-clips "$PWD/videos" --work-dir "$PWD/work" \
+  --run-id MODEL-MODE --model EXACT_MODEL_ID --thinking auto \
+  --base-url http://127.0.0.1:8000/v1 --max-tokens 8192 --max-tokens-cap 32768
+python code/unified_eval.py score --dataset single_567_human \
+  --root "$PWD" --work-dir "$PWD/work" --run-id MODEL-MODE
+```
+
+For switchable models set `--thinking true` or `--thinking false` explicitly. The backend must support `chat_template_kwargs.enable_thinking`; otherwise use a model-specific adapter that records the actual mode and preserves the trace. The bundled Floodgate adapter cannot verify `finish_reason` and is not suitable for the strict thinking/truncation rule without an extension.
+
+## Release checklist
+
+From the Hala server, the prepared upload folder is `~/datasets/RunningBench/single_video_567_hf_upload/` (567 video files, about 8.50 GB of video bytes). After Hugging Face authentication by a repository maintainer, upload **all** its contents to `single_video_567/` in the dataset repository:
+
+```bash
+hf auth login
+hf upload taryya/RunningBench \
+  ~/datasets/RunningBench/single_video_567_hf_upload single_video_567 \
+  --repo-type dataset --commit-message "Add 567-question single-video evaluation package and clips"
+```
+
+Do not upload the private server path as a string in place of the video bytes. Verify the published repository by downloading it to a clean directory and checking video count, hashes and `inventory` output (`questions=567`, `media=567/567`).

single_video_567/RunningBench_567题可用数据说明.md

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+# RunningBench 567 道可用题：数据说明
+
+## 概览
+
+本目录整理出一份可用于后续数据处理、模型评测或人工复核的 RunningBench QA 子集，共 **567 道题**。该子集从现有 698 道 QA 中筛选，所有题目均保留 `question_id`、视频标识、时间区间、题干、选项和答案。
+
+| 来源 | 数量 | 纳入条件 |
+|---|---:|---|
+| 人工完整且结论明确 | 562 | 标注记录 `complete=true`，人工 verdict 为 `clear`，且有最终答案 |
+| 不完整标注经补救 | 5 | 通过视频核验后恢复原答案，或修订题干/选项及答案后可用 |
+| **合计** | **567** | 两类 question_id 不重叠，已去重 |
+
+按答案选项数量统计，包含 **325 道单选题**和 **242 道多选题**。
+
+## 文件
+
+- `runningbench_567_human_usable.jsonl`：正式 567 题数据，每行一个 JSON 对象。
+- `runningbench_567_inclusion_ledger.csv`：逐题纳入台账，记录来源类别、补救状态和答案。
+- `runningbench_qa_human_corrected.jsonl`：完整的 698 题可追溯修订版；567 题子集从此文件筛出。
+- `README_修订说明.md`：整体人工答案筛选和修订统计。
+- `incomplete_annotation_repairs.csv`：不完整人工标注题的逐题补救记录及证据。
+- `video_case_checks.csv`：视频抽查的逐题结论和证据摘要。
+
+## JSONL 字段
+
+每一行是一道题，保留原 QA 的字段，主要包括：
+
+- `question_id`：题目唯一标识。
+- `category`：题目类别。
+- `segment`：视频片段描述及时间范围。
+- `video_id`：对应视频标识。
+- `start_second`、`end_second`：片段起止秒数。
+- `question`：题干。
+- `options`：选项对象，键通常为 A、B、C 等。
+- `answer`：正确选项数组；单选题数组长度为 1，多选题大于 1。
+- `usability_source`：本清单纳入来源，取值为 `complete_clear_human_annotation` 或 `repaired_incomplete_annotation`。
+- `repair_status`：补救题对应的修复状态；原本完整清晰的人工题为空字符串。
+
+## 筛选与修订口径
+
+1. 对完整清晰的人工记录，采用人工最终答案。全部 562 道满足清晰条件的题均保留；视频抽查仅覆盖部分题，不应将整份 567 题清单描述为“全部逐题视频验证”。
+2. 对不完整记录，只有视频证据足以确认答案，或能通过收窄题干、移除无法证实的选项并重设答案而修复的题，才补入清单。
+3. 不确定、无法判断、视频证据不足，或视频中没有正确选项的题，不纳入这 567 道。
+4. 原始 QA 和人工标注导出文件没有被覆盖；本数据是可追溯的衍生清单。
+
+## 使用建议
+
+- 训练或评测时用 `question_id` 作为稳定主键。
+- 若只需要单选题，筛选 `answer` 数组长度为 1（当前 325 道）。
+- 若将补救题与常规人工题分开分析，可按 `usability_source` 筛选，并结合 `repair_status` 查阅补救记录。
+- 发布或共享时保留本说明、纳入台账和修订记录，避免丢失答案来源与修订依据。
+
+## 可复现性
+
+筛选逻辑脚本为 `work/make_usable_567.ps1`。在包含 `outputs/human-answer-repair` 数据文件以及 `%TEMP%/rb-human-review` 人工导出和受保护 QA 源文件的工作环境中运行：
+
+```powershell
+./work/make_usable_567.ps1
+```
+
+脚本会重新生成 567 题 JSONL 和纳入台账，并检查总数及题目 ID 是否重复。

single_video_567/code/build_single_567_eval.py

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+#!/usr/bin/env python3
+"""Build the auditable 567-question single-video evaluation corpus."""
+
+import argparse
+import collections
+import csv
+import hashlib
+import json
+import os
+from pathlib import Path
+
+
+def read_jsonl(path):
+    with path.open(encoding="utf-8") as stream:
+        for line in stream:
+            if line.strip():
+                yield json.loads(line)
+
+
+def sha256(path):
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--root", type=Path, default=Path("~/datasets/RunningBench").expanduser())
+    parser.add_argument("--single-clips", type=Path,
+                        default=Path("~/datasets/seg600_verification/videoclips").expanduser())
+    args = parser.parse_args()
+    root = args.root.expanduser()
+    clips = args.single_clips.expanduser()
+    source = root / "human_answer_repair_567" / "runningbench_567_human_usable.jsonl"
+    corrected = root / "human_answer_repair_567" / "runningbench_qa_human_corrected.jsonl"
+    ledger = root / "human_answer_repair_567" / "runningbench_567_inclusion_ledger.csv"
+    reference = root / "segments_60s_gemini_verification" / "final" / "segments_700_verified.jsonl"
+    destination = root / "human_answer_repair_567" / "single_567_human_eval.jsonl"
+    manifest_path = root / "human_answer_repair_567" / "single_567_human_eval_manifest.json"
+
+    rows = list(read_jsonl(source))
+    original = {row["id"]: row for row in read_jsonl(reference)}
+    corrected_rows = list(read_jsonl(corrected))
+    corrected_by_id = {row["question_id"]: row for row in corrected_rows}
+    with ledger.open(encoding="utf-8-sig", newline="") as stream:
+        ledger_rows = list(csv.DictReader(stream))
+    if len(rows) != 567 or len(original) != 698 or len(corrected_rows) != 698 or len(ledger_rows) != 567:
+        raise ValueError("Unexpected source, reference, corrected, or ledger size")
+    ids = [row["question_id"] for row in rows]
+    if len(set(ids)) != 567:
+        raise ValueError("The 567 source contains duplicate question IDs")
+    if set(ids) != {row["question_id"] for row in ledger_rows}:
+        raise ValueError("567 source question IDs do not match the inclusion ledger")
+
+    missing_reference = [qid for qid in ids if qid not in original]
+    missing_media = [qid for qid in ids if not (clips / f"{qid}.mp4").is_file()]
+    if missing_reference or missing_media:
+        raise ValueError(f"Missing reference IDs={missing_reference[:10]} media={missing_media[:10]}")
+
+    output = []
+    changes = collections.Counter()
+    sources = collections.Counter()
+    n_select = collections.Counter()
+    for row in rows:
+        qid = row["question_id"]
+        prior = original[qid]
+        amended = corrected_by_id[qid]
+        if any(row[key] != amended[key] for key in ("question", "options", "answer")):
+            raise ValueError(f"567 row differs from corrected 698 gold for {qid}")
+        if row["video_id"] != prior["video"]:
+            raise ValueError(f"Video ID mismatch for {qid}: {row['video_id']} != {prior['video']}")
+        span = [float(row["start_second"]), float(row["end_second"])]
+        if span != [float(x) for x in prior["span_sec"]]:
+            raise ValueError(f"Video time span mismatch for {qid}: {span} != {prior['span_sec']}")
+        options = row["options"]
+        answer = row["answer"]
+        if not isinstance(options, dict) or not options:
+            raise ValueError(f"Invalid options for {qid}")
+        if not isinstance(answer, list) or not answer or len(answer) != len(set(answer)):
+            raise ValueError(f"Invalid answer for {qid}")
+        if any(letter not in options for letter in answer):
+            raise ValueError(f"Gold answer absent from options for {qid}")
+        if not row["question"].strip():
+            raise ValueError(f"Empty question for {qid}")
+        changes["question_changed"] += row["question"] != prior["question"]
+        changes["options_changed"] += options != prior["options"]
+        changes["answer_changed"] += set(answer) != set(prior["answer"])
+        sources[row["usability_source"]] += 1
+        n_select[len(answer)] += 1
+        output.append({
+            "id": qid,
+            "unit": "60s_segment",
+            "question": row["question"],
+            "options": options,
+            "answer": answer,
+            "question_type": prior.get("question_type"),
+            "category": row.get("category"),
+            "video": row["video_id"],
+            "span_sec": span,
+            "segment": row.get("segment"),
+            "media_file": f"{qid}.mp4",
+            "usability_source": row["usability_source"],
+            "repair_status": row.get("repair_status", ""),
+        })
+
+    temp = destination.with_suffix(".jsonl.part")
+    with temp.open("w", encoding="utf-8", newline="\n") as stream:
+        for row in output:
+            stream.write(json.dumps(row, ensure_ascii=False) + "\n")
+    os.replace(temp, destination)
+    manifest = {
+        "dataset": "single_567_human",
+        "questions": len(output),
+        "media_available": len(output),
+        "source": str(source),
+        "source_sha256": sha256(source),
+        "corrected_698": str(corrected),
+        "corrected_698_sha256": sha256(corrected),
+        "inclusion_ledger": str(ledger),
+        "inclusion_ledger_sha256": sha256(ledger),
+        "reference": str(reference),
+        "reference_sha256": sha256(reference),
+        "eval_jsonl": str(destination),
+        "eval_sha256": sha256(destination),
+        "usability_source_counts": dict(sources),
+        "answer_count_distribution": dict(sorted(n_select.items())),
+        "changes_from_original_698": dict(changes),
+        "note": "Gold/question/options come from the human usable subset, not the original 698."
+    }
+    manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(manifest, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()

single_video_567/code/model_matrix_567.csv

@@ -0,0 +1,27 @@
+model,multi_video_base,multi_video_think,thinking_mode,single_video_plan,backend_note
+InternVL3.5-241B-A28B,yes,no,unknown,base,resolve_model_and_endpoint
+Qwen3-VL-30B-A3B-Thinking,yes,yes,fixed_on,thinking,resolve_model_and_endpoint
+gemini-2.5-pro,yes,no,provider,base,resolve_provider_endpoint
+Qwen3-VL-235B-A22B-Instruct,yes,no,fixed_off,instruct,resolve_model_and_endpoint
+GLM-4.6V,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+Qwen3.5-27B,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+InternVL3.5-20B-A4B,yes,no,unknown,base,resolve_model_and_endpoint
+Kimi-VL-A3B-Thinking-2506,yes,yes,fixed_on,thinking,resolve_model_and_endpoint
+Gemma-3n-E4B-it,yes,no,fixed_off,base,resolve_model_and_endpoint
+InternVL3.5-14B,yes,no,unknown,base,resolve_model_and_endpoint
+Qwen3.5-35B-A3B,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+Gemma-4-31B-it,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+gemini-3.1-pro-preview,yes,no,provider,base,resolve_provider_endpoint
+Qwen2.5-VL-7B,yes,no,unknown,base,resolve_model_and_endpoint
+Qwen3-VL-8B,yes,no,unknown,instruct_and_thinking,pilot_uses_explicit_checkpoints
+GLM-4.1V-9B-Thinking,yes,yes,fixed_on,thinking,resolve_model_and_endpoint
+InternVL3.5-38B,yes,no,unknown,base,resolve_model_and_endpoint
+Qwen3.5-9B,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+Qwen2.5-VL-72B-Instruct,yes,no,fixed_off,instruct,resolve_model_and_endpoint
+InternVL3.5-8B,yes,no,unknown,base,resolve_model_and_endpoint
+Qwen3-VL-235B-A22B-Thinking,yes,yes,fixed_on,thinking,resolve_model_and_endpoint
+InternVL3.5-30B-A3B,yes,no,unknown,base,resolve_model_and_endpoint
+Gemma-4-26B-A4B-it,yes,no,fixed_off,base,resolve_model_and_endpoint
+gemini-3.8-flash,yes,no,provider,base,resolve_provider_endpoint
+Qwen3.5-122B-A10B,yes,yes,switchable,off_and_on,resolve_model_and_endpoint
+ERNIE-4.5-VL-28B-A3B,yes,yes,switchable,off_and_on,resolve_model_and_endpoint

single_video_567/code/run_qwen3vl8b_batch.sh

@@ -0,0 +1,89 @@
+#!/usr/bin/env bash
+# Submit with: sbatch run_qwen3vl8b_batch.sh
+# Runs the Instruct and Thinking 8B checkpoints on human-curated single-video QA only.
+#SBATCH --job-name=rb-qwen3vl8b-pilot
+#SBATCH --partition=batch
+#SBATCH --nodelist=hala
+#SBATCH --gres=gpu:a6000:1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=80G
+#SBATCH --time=04:00:00
+#SBATCH --output=/home/yuedong_tan/datasets/RunningBench/unified_eval/work/logs/%x-%j.out
+
+set -euo pipefail
+
+ROOT=/home/yuedong_tan/datasets/RunningBench/unified_eval
+PY="$ROOT/.venv_clean/bin/python"
+VLLM="$ROOT/.venv_clean/bin/vllm"
+PORT=$((18000 + SLURM_JOB_ID % 10000))
+EVAL_DATASET=single_567_human
+EVAL_SKIP="${RB_SKIP:-0}"
+EVAL_LIMIT="${RB_LIMIT:-2}"
+RUN_TAG_SUFFIX="${RB_RUN_TAG_SUFFIX:-human567-pilot}"
+mkdir -p "$ROOT/work/logs"
+export HF_HOME="${HF_HOME:-/home/yuedong_tan/.cache/huggingface}"
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export VLLM_USE_FLASHINFER_SAMPLER=0
+
+serve_and_eval() {
+    local model="$1"
+    local run_id="$2"
+    local max_tokens="$3"
+    local max_tokens_cap="$4"
+    local log="$ROOT/work/logs/${run_id}-${SLURM_JOB_ID}-server.log"
+    local parser_args=()
+    if [[ "$model" == *Thinking ]]; then
+        parser_args=(--reasoning-parser qwen3)
+    fi
+    echo "Starting $model on $(hostname), port $PORT"
+    "$VLLM" serve "$model" \
+        --host 127.0.0.1 --port "$PORT" \
+        --dtype bfloat16 --max-model-len 49152 \
+        --limit-mm-per-prompt '{"image":64,"video":0}' \
+        --max-num-seqs 1 --gpu-memory-utilization 0.90 \
+        "${parser_args[@]}" >"$log" 2>&1 &
+    SERVER_PID=$!
+    trap 'kill "${SERVER_PID:-}" 2>/dev/null || true' EXIT
+    local ready=0
+    for ((i=0; i<120; i++)); do
+        if curl -fsS "http://127.0.0.1:$PORT/health" >/dev/null 2>&1; then
+            ready=1
+            break
+        fi
+        if ! kill -0 "$SERVER_PID" 2>/dev/null; then
+            echo "Server exited; see $log"
+            tail -80 "$log"
+            return 1
+        fi
+        sleep 15
+    done
+    if [[ "$ready" != 1 ]]; then
+        echo "Server readiness timed out; see $log"
+        tail -80 "$log"
+        return 1
+    fi
+    "$PY" "$ROOT/unified_eval.py" run --dataset "$EVAL_DATASET" \
+        --skip "$EVAL_SKIP" --limit "$EVAL_LIMIT" \
+        --run-id "$run_id" --model "$model" --thinking auto \
+        --max-tokens "$max_tokens" --max-tokens-cap "$max_tokens_cap" \
+        --workers 1 --rps 0.2 \
+        --base-url "http://127.0.0.1:$PORT/v1"
+    "$PY" "$ROOT/unified_eval.py" score --dataset "$EVAL_DATASET" --run-id "$run_id"
+    kill "$SERVER_PID" 2>/dev/null || true
+    for ((i=0; i<15; i++)); do
+        if ! kill -0 "$SERVER_PID" 2>/dev/null; then
+            break
+        fi
+        sleep 1
+    done
+    kill -9 "$SERVER_PID" 2>/dev/null || true
+    wait "$SERVER_PID" 2>/dev/null || true
+    SERVER_PID=
+    trap - EXIT
+}
+
+"$PY" -m pip show vllm transformers torch | grep -E 'Name:|Version:'
+serve_and_eval Qwen/Qwen3-VL-8B-Instruct "qwen3vl8b-instruct-$RUN_TAG_SUFFIX" 2048 8192
+serve_and_eval Qwen/Qwen3-VL-8B-Thinking "qwen3vl8b-thinking-$RUN_TAG_SUFFIX" 8192 32768
+"$PY" "$ROOT/unified_eval.py" compare --dataset single_567_human

single_video_567/code/unified_eval.py

@@ -0,0 +1,561 @@
+#!/usr/bin/env python3
+"""Evaluate both RunningBench QA sets with the original 64-frame protocol.
+
+Uses an OpenAI-compatible vision chat endpoint (for example, a local vLLM server).
+No model is downloaded or started by this script.
+"""
+
+import argparse
+import base64
+import collections
+import hashlib
+import json
+import os
+import random
+import subprocess
+import sys
+import tarfile
+import threading
+import time
+import urllib.error
+import urllib.request
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+
+VERSION = "runningbench-unified-v6"
+DATASETS = {
+    "single_698": "segments_60s_gemini_verification/final/segments_700_verified.jsonl",
+    "single_567_human": "human_answer_repair_567/single_567_human_eval.jsonl",
+    "mixed_1487": "qa_fix_gold_recovery_and_adjudication/data/runningbench_v2_kept.jsonl",
+}
+PROMPT = """Answer this multiple-choice question about egocentric walking/running footage.
+The frames below are sampled in order from the clip(s) the question refers to; each clip is
+introduced by its label.
+
+Watch before deciding. Do not answer from the wording of the options alone.
+
+QUESTION: {question}
+
+OPTIONS:
+{options}
+
+Select exactly {n} option{plural}.
+Return ONLY JSON: {{"answer": [{example}]}}"""
+MULTI_UNITS = {"cross_video", "cross_recording", "anonymous_multi_clip"}
+
+
+def jsonl(path):
+    with open(path, encoding="utf-8") as fh:
+        for line in fh:
+            if line.strip():
+                yield json.loads(line)
+
+
+def file_hash(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as fh:
+        for chunk in iter(lambda: fh.read(1024 * 1024), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def question_id(q, dataset):
+    return q["id"] if dataset.startswith("single_") else q["review_id"]
+
+
+def load_questions(root, dataset):
+    path = root / DATASETS[dataset]
+    questions = list(jsonl(path))
+    ids = [question_id(q, dataset) for q in questions]
+    if len(ids) != len(set(ids)):
+        raise ValueError(f"Duplicate question IDs in {path}")
+    return path, questions
+
+
+def shuffled_view(options, seed):
+    real = sorted(options)
+    order = list(real)
+    random.Random(seed).shuffle(order)
+    shown = {real[i]: options[order[i]] for i in range(len(real))}
+    back = {real[i]: order[i] for i in range(len(real))}
+    return shown, back
+
+
+def parse_answer(raw, letters):
+    import re
+
+    result = None
+    try:
+        result = json.loads(raw)
+    except (TypeError, ValueError):
+        match = re.search(r"\{.*\}", raw or "", re.S)
+        if match:
+            try:
+                result = json.loads(match.group(0))
+            except ValueError:
+                pass
+    answer = result.get("answer") if isinstance(result, dict) else None
+    if answer is None:
+        answer = re.findall(r'"([A-J])"', raw or "") or re.findall(r"\b([A-J])\b", raw or "")
+    if isinstance(answer, str):
+        answer = [answer]
+    if not isinstance(answer, list):
+        return []
+    return sorted({x.strip()[0].upper() for x in answer if isinstance(x, str) and x.strip()} & set(letters))
+
+
+def allocate(durations, total, floor):
+    """Match model_evaluation_harness/code/extract_frames.py."""
+    n = len(durations)
+    if not n:
+        raise ValueError("Question has no clips")
+    if n * floor >= total:
+        return [floor] * n
+    summed = sum(durations) or 1.0
+    raw = [d / summed * total for d in durations]
+    base = [max(floor, int(x)) for x in raw]
+    while sum(base) > total:
+        i = max(range(n), key=lambda j: (base[j] - floor, raw[j]))
+        if base[i] <= floor:
+            break
+        base[i] -= 1
+    remainder = total - sum(base)
+    order = sorted(range(n), key=lambda j: -(raw[j] - int(raw[j])))
+    for i in range(remainder):
+        base[order[i % n]] += 1
+    return base
+
+
+def video_duration(path):
+    import re
+
+    result = subprocess.run(
+        ["ffmpeg", "-i", str(path)], capture_output=True, timeout=120,
+    )
+    match = re.search(r"Duration:\s*(\d+):(\d\d):(\d\d(?:\.\d+)?)",
+                      result.stderr.decode("utf-8", "replace"))
+    if not match:
+        return 60.0
+    return int(match.group(1)) * 3600 + int(match.group(2)) * 60 + float(match.group(3))
+
+
+class MediaResolver:
+    def __init__(self, root, single_clips, cache):
+        self.root, self.single_clips, self.cache = root, single_clips, cache
+        self.tars = {}
+
+    def close(self):
+        for archive in self.tars.values():
+            archive.close()
+
+    def clips(self, dataset, question):
+        if dataset.startswith("single_"):
+            path = self.single_clips / f"{question['id']}.mp4"
+            if not path.is_file():
+                raise FileNotFoundError(path)
+            return [("CLIP_1", path)]
+
+        bundle = question["bundle"]
+        archive = self.tars.get(bundle)
+        if archive is None:
+            path = self.root / "annotation_bundles" / f"{bundle}.tar"
+            archive = tarfile.open(path, "r")
+            self.tars[bundle] = archive
+        resolved = []
+        for clip in question["clips"]:
+            member_name = f"{bundle}/{clip['path']}"
+            member = archive.getmember(member_name)
+            if not member.isfile():
+                raise ValueError(f"Not a media file: {member_name}")
+            target = self.cache / bundle / question["review_id"] / Path(clip["path"]).name
+            if not target.is_file() or target.stat().st_size != member.size:
+                target.parent.mkdir(parents=True, exist_ok=True)
+                temp = target.with_suffix(target.suffix + ".part")
+                with archive.extractfile(member) as source, open(temp, "wb") as destination:
+                    while chunk := source.read(1024 * 1024):
+                        destination.write(chunk)
+                os.replace(temp, target)
+            resolved.append((clip["label"], target))
+        return resolved
+
+
+def extract_question_frames(dataset, qid, clips, cache, budget=64, floor=4, width=480):
+    durations = [video_duration(path) for _, path in clips]
+    quotas = allocate(durations, budget, floor)
+    base = cache / dataset / f"f{budget}_m{floor}_w{width}" / qid
+    all_clips = []
+    for i, ((label, source), duration, quota) in enumerate(zip(clips, durations, quotas)):
+        directory = base / f"c{i:02d}"
+        directory.mkdir(parents=True, exist_ok=True)
+        marker = directory / "frames.json"
+        expected = {"version": VERSION, "source": str(source), "source_size": source.stat().st_size,
+                    "quota": quota, "width": width}
+        frames = []
+        if marker.is_file():
+            try:
+                previous = json.loads(marker.read_text(encoding="utf-8"))
+                if previous["config"] == expected:
+                    frames = [Path(p) for p in previous["frames"]]
+                    if not frames or not all(p.is_file() for p in frames):
+                        frames = []
+            except (KeyError, ValueError):
+                pass
+        if not frames:
+            for stale in directory.glob("frame_*.jpg"):
+                stale.unlink()
+            fps = quota / max(duration, 0.1)
+            # Intentionally identical to the original mixed-set harness: width <= 480,
+            # with aspect ratio preserved. Portrait frames can have height > 480.
+            command = ["ffmpeg", "-y", "-loglevel", "error", "-threads", "1", "-i", str(source),
+                       "-vf", f"fps={fps:.6f},scale='min({width},iw)':-2", "-frames:v", str(quota),
+                       "-q:v", "3", str(directory / "frame_%03d.jpg")]
+            subprocess.run(command, check=True, capture_output=True, timeout=900)
+            frames = sorted(directory.glob("frame_*.jpg"))
+            if not frames:
+                raise RuntimeError(f"ffmpeg generated no frames from {source}")
+            marker.write_text(json.dumps({"config": expected, "frames": [str(p) for p in frames]}), encoding="utf-8")
+        all_clips.append((label, frames))
+    return all_clips, quotas
+
+
+class RateLimiter:
+    def __init__(self, requests_per_second):
+        self.interval = 1 / max(requests_per_second, 0.01)
+        self.last = 0.0
+        self.lock = threading.Lock()
+
+    def wait(self):
+        with self.lock:
+            delay = self.interval - (time.monotonic() - self.last)
+            if delay > 0:
+                time.sleep(delay)
+            self.last = time.monotonic()
+
+
+class Client:
+    def __init__(self, base_url, model, api_key, thinking, max_tokens, max_tokens_cap, rps):
+        self.url = base_url.rstrip("/") + "/chat/completions"
+        self.model, self.api_key, self.thinking = model, api_key, thinking
+        self.max_tokens, self.max_tokens_cap = max_tokens, max_tokens_cap
+        self.limiter = RateLimiter(rps)
+
+    def ask(self, prompt, clips):
+        content = [{"type": "text", "text": prompt}]
+        for label, frames in clips:
+            content.append({"type": "text", "text": f"--- {label} ---"})
+            for path in frames:
+                data = base64.b64encode(path.read_bytes()).decode("ascii")
+                content.append({"type": "image_url", "image_url": {"url": "data:image/jpeg;base64," + data}})
+        body = {"model": self.model, "messages": [{"role": "user", "content": content}],
+                "temperature": 0.0}
+        if self.thinking != "auto":
+            body["chat_template_kwargs"] = {"enable_thinking": self.thinking == "true"}
+        attempts = []
+        budget = self.max_tokens
+        while True:
+            body["max_tokens"] = budget
+            payload = json.dumps(body).encode("utf-8")
+            last = None
+            result = None
+            for request_attempt in range(4):
+                self.limiter.wait()
+                request = urllib.request.Request(
+                    self.url, data=payload, method="POST",
+                    headers={"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"})
+                try:
+                    with urllib.request.urlopen(request, timeout=900) as response:
+                        result = json.load(response)
+                    break
+                except urllib.error.HTTPError as exc:
+                    detail = exc.read(1000).decode("utf-8", "replace")
+                    last = f"HTTP {exc.code}: {detail}"
+                    if exc.code not in (429, 500, 502, 503, 504):
+                        break
+                except (urllib.error.URLError, ValueError) as exc:
+                    last = f"{type(exc).__name__}: {exc}"
+                if request_attempt < 3:
+                    time.sleep(min(30, 2 ** (request_attempt + 1)))
+            if result is None:
+                if attempts:
+                    return {"error": f"Request failed after truncated response: {last}", "attempts": attempts}
+                raise RuntimeError(f"Model request failed: {last}")
+            choice = result["choices"][0]
+            item = {"message": choice["message"], "finish_reason": choice.get("finish_reason"),
+                    "usage": result.get("usage"), "max_tokens": budget}
+            attempts.append(item)
+            if item["finish_reason"] != "length":
+                return {**item, "attempts": attempts,
+                        "error": None if item["finish_reason"] == "stop"
+                        else f"Unverified finish_reason={item['finish_reason']!r}"}
+            if budget >= self.max_tokens_cap:
+                return {**item, "attempts": attempts,
+                        "error": f"Output truncated at max_tokens_cap={self.max_tokens_cap}"}
+            budget = min(self.max_tokens_cap, budget * 2)
+
+
+class FloodgateClient:
+    def __init__(self, root, model, max_tokens, rps):
+        sys.path.insert(0, str(root / "model_evaluation_harness" / "code"))
+        from floodgate import Floodgate, RateLimiter
+
+        self.client = Floodgate(limiter=RateLimiter(rps))
+        self.model, self.max_tokens = model, max_tokens
+
+    def ask(self, prompt, clips):
+        parts = [{"text": prompt}]
+        for label, frames in clips:
+            parts.append({"text": f"--- {label} ---"})
+            for path in frames:
+                parts.append({"inlineData": {"mimeType": "image/jpeg",
+                                              "data": base64.b64encode(path.read_bytes()).decode("ascii")}})
+        raw = self.client.generate(self.model, parts, max_tokens=self.max_tokens,
+                                   timeout=600, attempts=4, temperature=0.0)
+        return {"message": {"content": raw, "reasoning_content": None},
+                "finish_reason": None, "usage": None, "max_tokens": self.max_tokens,
+                "attempts": [], "error": None}
+
+
+def prompt_for(question, qid):
+    shown, back = shuffled_view(question["options"], qid)
+    n = len(question["answer"])
+    letters = sorted(shown)
+    prompt = PROMPT.format(question=question["question"], options="\n".join(f"{letter}. {shown[letter]}" for letter in letters),
+                           n=n, plural="s" if n > 1 else "", example=", ".join(f'"{letter}"' for letter in letters[:n]))
+    return prompt, letters, back
+
+
+def check_run_config(path, config):
+    if path.is_file():
+        previous = json.loads(path.read_text(encoding="utf-8"))
+        if previous != config:
+            raise ValueError(f"Run configuration changed; choose another --run-id: {path}")
+    else:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(config, indent=2, ensure_ascii=False), encoding="utf-8")
+
+
+def run(args, dataset, root, work):
+    corpus_path, questions = load_questions(root, dataset)
+    if args.limit:
+        questions = questions[args.skip:args.skip + args.limit]
+    elif args.skip:
+        questions = questions[args.skip:]
+    run_dir = work / "runs" / args.run_id
+    config = {"version": VERSION, "dataset": dataset, "corpus_sha256": file_hash(corpus_path),
+              "prompt_sha256": hashlib.sha256(PROMPT.encode()).hexdigest(), "model": args.model,
+              "backend": args.backend,
+              "thinking": args.thinking, "max_tokens": args.max_tokens,
+              "max_tokens_cap": args.max_tokens_cap, "frames_per_question": args.frames,
+              "min_frames_per_clip": args.min_frames, "max_width": args.max_width,
+              "frame_extractor": "ffmpeg fps=k/duration; scale=min(max_width,iw):-2", "temperature": 0.0}
+    if args.command == "run":
+        check_run_config(run_dir / f"{dataset}.config.json", config)
+    outfile = run_dir / f"{dataset}.jsonl"
+    done = set()
+    if outfile.is_file():
+        for record in jsonl(outfile):
+            if not record.get("error"):
+                done.add(record["question_id"])
+    questions = [q for q in questions if question_id(q, dataset) not in done]
+    if args.command == "run" and args.backend == "openai" and not args.base_url:
+        raise ValueError("--base-url is required for run")
+    client = None
+    if args.command == "run":
+        if args.backend == "floodgate":
+            if args.thinking != "auto":
+                raise ValueError("Floodgate backend does not support --thinking true/false")
+            client = FloodgateClient(root, args.model, args.max_tokens, args.rps)
+        else:
+            client = Client(args.base_url, args.model, args.api_key, args.thinking,
+                            args.max_tokens, args.max_tokens_cap, args.rps)
+    resolver = MediaResolver(root, Path(args.single_clips).expanduser(), work / "cache" / "media")
+    prepared = []
+    try:
+        for question in questions:
+            qid = question_id(question, dataset)
+            try:
+                clips = resolver.clips(dataset, question)
+                frames, quotas = extract_question_frames(dataset, qid, clips, work / "cache" / "frames",
+                                                         args.frames, args.min_frames, args.max_width)
+                prepared.append((question, frames, quotas))
+                if args.command == "prepare":
+                    print(f"PREPARED {dataset} {qid} clips={len(frames)} frames={sum(len(f) for _, f in frames)} quotas={quotas}")
+            except Exception as exc:
+                prepared.append((question, exc, []))
+                print(f"MEDIA_ERROR {dataset} {qid} {type(exc).__name__}: {exc}", flush=True)
+    finally:
+        resolver.close()
+    if args.command == "prepare":
+        print(f"PREPARE_DONE dataset={dataset} questions={len(questions)} ok={sum(not isinstance(f, Exception) for _, f, _ in prepared)}")
+        return
+
+    def evaluate(item):
+        question, frames, quotas = item
+        qid = question_id(question, dataset)
+        if isinstance(frames, Exception):
+            return {"question_id": qid, "dataset": dataset, "error": f"media:{type(frames).__name__}: {frames}"}
+        prompt, letters, back = prompt_for(question, qid)
+        started = time.time()
+        try:
+            response = client.ask(prompt, frames)
+            message = response.get("message") or {}
+            raw = message.get("content") or ""
+            reasoning = message.get("reasoning_content") or message.get("reasoning")
+            if not reasoning and "<think>" in raw and "</think>" in raw:
+                reasoning = raw.split("<think>", 1)[1].rsplit("</think>", 1)[0]
+            thinking_required = args.thinking == "true" or "thinking" in args.model.lower()
+            trace_present = bool(reasoning) or "<think>" in raw or "Thinking Process:" in raw
+            base = {"question_id": qid, "dataset": dataset, "unit": question.get("unit"),
+                    "model": args.model, "thinking": args.thinking,
+                    "raw_message": message, "raw": raw, "reasoning_content": reasoning,
+                    "thinking_trace_required": thinking_required,
+                    "thinking_trace_present": trace_present,
+                    "finish_reason": response.get("finish_reason"), "usage": response.get("usage"),
+                    "max_tokens_used": response.get("max_tokens"), "attempts": response.get("attempts"),
+                    "latency_s": round(time.time() - started, 1)}
+            if response.get("error"):
+                return {**base, "error": response["error"]}
+            if "<think>" in raw and "</think>" not in raw:
+                return {**base, "error": "Unclosed thinking trace; final answer unavailable"}
+            if thinking_required and not trace_present:
+                return {**base, "error": "Thinking trace missing from model response"}
+            answer_text = raw.rsplit("</think>", 1)[-1]
+            displayed = parse_answer(answer_text, letters)
+            if not displayed:
+                return {**base, "error": "No parseable final answer"}
+            prediction = sorted({back[x] for x in displayed if x in back})
+            gold = sorted(question["answer"])
+            return {**base, "question_type": question.get("question_type"), "n_select": len(gold),
+                    "n_options": len(question["options"]), "n_clips": len(frames),
+                    "n_frames": sum(len(f) for _, f in frames), "frame_quotas": quotas,
+                    "pred": prediction, "gold": gold,
+                    "exact": prediction == gold, "overlap": len(set(prediction) & set(gold)) / max(len(gold), 1),
+                    "n_pred": len(prediction)}
+        except Exception as exc:
+            return {"question_id": qid, "dataset": dataset, "model": args.model,
+                    "thinking": args.thinking, "error": f"{type(exc).__name__}: {str(exc)[:400]}"}
+
+    counts = collections.Counter()
+    outfile.parent.mkdir(parents=True, exist_ok=True)
+    with open(outfile, "a", encoding="utf-8") as output, ThreadPoolExecutor(max_workers=args.workers) as pool:
+        for future in as_completed([pool.submit(evaluate, item) for item in prepared]):
+            record = future.result()
+            output.write(json.dumps(record, ensure_ascii=False) + "\n")
+            output.flush()
+            counts["error" if record.get("error") else "ok"] += 1
+            if sum(counts.values()) % 10 == 0:
+                print(f"PROGRESS {dataset} {sum(counts.values())}/{len(prepared)} ok={counts['ok']} error={counts['error']}", flush=True)
+    print(f"RUN_DONE dataset={dataset} ok={counts['ok']} error={counts['error']} output={outfile}")
+
+
+def score(root, work, run_id, dataset):
+    _, questions = load_questions(root, dataset)
+    path = work / "runs" / run_id / f"{dataset}.jsonl"
+    if not path.is_file():
+        print(f"NO_RESULTS {dataset} {path}")
+        return
+    latest = {row["question_id"]: row for row in jsonl(path)}
+    rows = [row for row in latest.values() if not row.get("error") and "exact" in row]
+    print(f"SCORE {dataset} complete={len(rows)}/{len(questions)} errors={sum(bool(row.get('error')) for row in latest.values())}")
+    groups = {"overall": rows}
+    if dataset == "mixed_1487":
+        groups["multi_video_531"] = [r for r in rows if r["unit"] in MULTI_UNITS]
+    for unit in sorted({r["unit"] for r in rows}):
+        groups[unit] = [r for r in rows if r["unit"] == unit]
+    for name, members in groups.items():
+        if members:
+            correct = sum(r["exact"] for r in members)
+            overlap = sum(r["overlap"] for r in members) / len(members)
+            wellformed = sum(r["n_pred"] == r["n_select"] for r in members) / len(members)
+            print(f"  {name}: n={len(members)} exact={correct}/{len(members)} ({100*correct/len(members):.2f}%) "
+                  f"overlap={100*overlap:.2f}% wellformed={100*wellformed:.2f}%")
+
+
+def compare(root, work, datasets):
+    """Show completed coverage and scores across recorded model/mode runs."""
+    all_questions = {name: load_questions(root, name)[1] for name in datasets}
+    print("run_id\tmodel\tthinking\tdataset\tgroup\tcompleted\ttotal\texact\toverlap_pct\twellformed_pct")
+    for run_dir in sorted((work / "runs").glob("*")):
+        if not run_dir.is_dir():
+            continue
+        for dataset in datasets:
+            config_path = run_dir / f"{dataset}.config.json"
+            result_path = run_dir / f"{dataset}.jsonl"
+            if not config_path.is_file() or not result_path.is_file():
+                continue
+            config = json.loads(config_path.read_text(encoding="utf-8"))
+            results = {r["question_id"]: r for r in jsonl(result_path)}
+            questions = all_questions[dataset]
+            groups = {"overall": questions}
+            if dataset == "mixed_1487":
+                groups["multi_video_531"] = [q for q in questions if q["unit"] in MULTI_UNITS]
+            for group, members in groups.items():
+                valid = [results[question_id(q, dataset)] for q in members
+                         if question_id(q, dataset) in results
+                         and not results[question_id(q, dataset)].get("error")
+                         and "exact" in results[question_id(q, dataset)]]
+                exact = sum(r["exact"] for r in valid)
+                overlap = sum(r["overlap"] for r in valid) / len(valid) if valid else 0
+                wellformed = sum(r["n_pred"] == r["n_select"] for r in valid) / len(valid) if valid else 0
+                print(f"{run_dir.name}\t{config['model']}\t{config['thinking']}\t{dataset}\t{group}\t"
+                      f"{len(valid)}\t{len(members)}\t{exact}\t{100*overlap:.2f}\t{100*wellformed:.2f}")
+
+
+def inventory(root, single_clips, datasets):
+    for dataset in datasets:
+        _, questions = load_questions(root, dataset)
+        units = collections.Counter(q.get("unit") for q in questions)
+        choices = collections.Counter(len(q["answer"]) for q in questions)
+        print(f"{dataset}: questions={len(questions)} units={dict(units)} answer_count={dict(choices)}")
+        if dataset.startswith("single_"):
+            missing = [q["id"] for q in questions if not (single_clips / f"{q['id']}.mp4").is_file()]
+            print(f"  media={len(questions)-len(missing)}/{len(questions)} missing={missing[:10]}")
+        else:
+            bundles = collections.Counter(q["bundle"] for q in questions)
+            missing = [b for b in bundles if not (root / "annotation_bundles" / f"{b}.tar").is_file()]
+            print(f"  bundles={dict(bundles)} missing={missing}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("command", choices=["inventory", "prepare", "run", "score", "compare"])
+    parser.add_argument("--dataset", choices=[*DATASETS, "both"], default="both")
+    parser.add_argument("--root", default="~/datasets/RunningBench")
+    parser.add_argument("--single-clips", default="~/datasets/seg600_verification/videoclips")
+    parser.add_argument("--work-dir", default="~/datasets/RunningBench/unified_eval/work")
+    parser.add_argument("--run-id", default="qwen3vl8b-instruct-v1")
+    parser.add_argument("--model", default="Qwen/Qwen3-VL-8B-Instruct")
+    parser.add_argument("--backend", choices=["openai", "floodgate"], default="openai")
+    parser.add_argument("--base-url", default=None)
+    parser.add_argument("--api-key", default=os.environ.get("OPENAI_API_KEY", "EMPTY"))
+    parser.add_argument("--thinking", choices=["auto", "true", "false"], default="auto")
+    parser.add_argument("--max-tokens", type=int, default=2048)
+    parser.add_argument("--max-tokens-cap", type=int, default=32768)
+    parser.add_argument("--frames", type=int, default=64)
+    parser.add_argument("--min-frames", type=int, default=4)
+    parser.add_argument("--max-width", type=int, default=480)
+    parser.add_argument("--workers", type=int, default=2)
+    parser.add_argument("--rps", type=float, default=0.4)
+    parser.add_argument("--skip", type=int, default=0)
+    parser.add_argument("--limit", type=int, default=0)
+    args = parser.parse_args()
+    if args.max_tokens < 1 or args.max_tokens_cap < args.max_tokens:
+        parser.error("--max-tokens-cap must be >= positive --max-tokens")
+    root = Path(args.root).expanduser()
+    work = Path(args.work_dir).expanduser()
+    selected = ["single_698", "mixed_1487"] if args.dataset == "both" else [args.dataset]
+    if args.command == "inventory":
+        inventory(root, Path(args.single_clips).expanduser(), selected)
+    elif args.command == "score":
+        for dataset in selected:
+            score(root, work, args.run_id, dataset)
+    elif args.command == "compare":
+        compare(root, work, selected)
+    else:
+        for dataset in selected:
+            run(args, dataset, root, work)
+
+
+if __name__ == "__main__":
+    main()

single_video_567/data/source_manifest.json

@@ -0,0 +1,32 @@
+{
+  "dataset": "single_567_human",
+  "questions": 567,
+  "media_available": 567,
+  "source": "/home/yuedong_tan/datasets/RunningBench/human_answer_repair_567/runningbench_567_human_usable.jsonl",
+  "source_sha256": "776820e6bdc741ceaf3b74d6a10478c2fc61cabdc6fed81827621158deb0ec55",
+  "corrected_698": "/home/yuedong_tan/datasets/RunningBench/human_answer_repair_567/runningbench_qa_human_corrected.jsonl",
+  "corrected_698_sha256": "bd58c8138acbacbafd455cb95c004d644bebf11b09353f6d2adb2ca30481055f",
+  "inclusion_ledger": "/home/yuedong_tan/datasets/RunningBench/human_answer_repair_567/runningbench_567_inclusion_ledger.csv",
+  "inclusion_ledger_sha256": "0aaced6ebda9ba9bfd09fb762279dd4c693f053a7e07692583b358ca5a7d4515",
+  "reference": "/home/yuedong_tan/datasets/RunningBench/segments_60s_gemini_verification/final/segments_700_verified.jsonl",
+  "reference_sha256": "d43bcb780123e4b607e792f899206db0ebfbb840395e45f8de205b06bd3e34fa",
+  "eval_jsonl": "/home/yuedong_tan/datasets/RunningBench/human_answer_repair_567/single_567_human_eval.jsonl",
+  "eval_sha256": "fdd9c292d7aaf0f221ff2d635f37b48906458f6dfdfc66cce59ba0a9313159ce",
+  "usability_source_counts": {
+    "complete_clear_human_annotation": 562,
+    "repaired_incomplete_annotation": 5
+  },
+  "answer_count_distribution": {
+    "1": 325,
+    "2": 84,
+    "3": 107,
+    "4": 46,
+    "5": 5
+  },
+  "changes_from_original_698": {
+    "question_changed": 3,
+    "options_changed": 3,
+    "answer_changed": 120
+  },
+  "note": "Gold/question/options come from the human usable subset, not the original 698."
+}

single_video_567/data/video_sha256.csv

@@ -0,0 +1,568 @@
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