--- license: cc-by-4.0 task_categories: - video-text-to-text language: - en size_categories: - 1K Short-clip vs hour-long temporal grounding

**2,273 open-form natural-language questions** over **194 hour-long videos** (mean 75.7 min, max 9 hr) sourced from LVBench, MLVU, and VideoMME. The median GT event is 9 s — matched to Charades-STA's 7.1 s — so the same event grain now sits inside a search space ~**153× larger**. Companion to *Natural-Language Temporal Grounding in Hour-Long Videos is a Search Problem: A Benchmark and Empirical Decomposition* — [arXiv:2606.12300](https://arxiv.org/abs/2606.12300). Code repository: ****. ## Status **Annotations available now.** The reference evaluation script and the lmms-eval task plugin will be released at the GitHub repository linked above together with the camera-ready version of the paper. ## Introduction At hour-scale, the binding constraint is **search, not recognition**. Video-LLMs are bottlenecked not by localizing a nearby event, but — given a natural-language query — by finding the right region of a long video. Short-video benchmarks can't distinguish these failure modes; hour-scale grounding makes the distinction central and lets the task decompose into a **search** stage and a **localize** stage — structurally the *retrieve-then-read* split that reshaped open-domain QA. ## Results Best per-model mIoU on Charades-STA vs. ExtremeWhenBench (same task). Open Video-LLMs collapse 5–120×, and a frame-level CLIP retriever overtakes every open Video-LLM under our compute budget: | Model | Charades-STA | ExtremeWhenBench | Ratio | | ------------------------------ | -----------: | ---------------: | ----: | | **Qwen3.5-9B** | **0.579** | **0.110** | 5.3× | | InternVL3.5-8B | 0.359 | 0.003 | 120× | | LLaVA-OneVision-7B | 0.226 | 0.003 | 75× | | LLaVA-NextVideo-7B | 0.089 | 0.001 | 89× | | GPT-5.4 (64f) | 0.299 | 0.013 | 23× | | Gemini-2.5-flash (1k f) | 0.308 | 0.053 | 5.8× | | Gemini-3.5-flash (auto-fps) | 0.466 | 0.115 | 4.1× | | **CLIP ViT-L/14-336** (retrieval) | 0.332 | **0.269** | 1.2× | CLIP outperforms every open Video-LLM on ours while sitting in the middle of the pack on Charades-STA — the order flips when search dominates. A failure taxonomy attributes **85%** of Video-LLM errors to search; a retrieve-then-ground hybrid recovers **6.7×** over the monolithic Video-LLM. Scaling frames alone doesn't close the gap — Qwen3.5-9B keeps climbing out to N=2,048 frames but remains far below the retrieval baseline:

Frame-count sweep, Qwen3.5-9B

Queries are **open-form natural language**, not template-bound: MATTR 0.78 vs. 0.60 (TVBench) and 0.54 (Charades-STA); 1,578 unique 4-gram stems (~25× per-question gap over TVBench). ## Loading ```python from datasets import load_dataset ds = load_dataset("min1321/extreme-when-bench", split="test") print(ds[0]) ``` ## Schema | Column | Type | Description | | ------------------ | -------------- | ---------------------------------------------- | | `qid` | string | Stable question id | | `video_id` | string | Source-corpus video id | | `source_corpus` | string | `LVBench` / `MLVU` / `VideoMME` | | `question` | string | The grounding question | | `correct_interval` | list\[float, float] | Ground-truth `[start_s, end_s]` | | `duration_s` | float | Length of the GT interval (s) | | `video_duration_s` | float | Length of the full source video (s) | | `event_summary` | string | Short paraphrase of the event | | `category` | string | Coarse event category | | `youtube_url` | string | YouTube URL (empty for MLVU) | Predictions are `[start, end]` intervals in seconds. Metrics: **mIoU**, **R@0.3 / R@0.5 / R@0.7**, **parse-failure rate** (parse failures count as IoU = 0). ## Source videos This dataset ships annotations only — download the videos from their source corpora: | Corpus | Videos | Where to get it | | --------- | -----: | ------------------------------------------------- | | VideoMME | 89 | | | LVBench | 67 | | | MLVU | 38 | | The `source_corpus`, `youtube_url`, and `video_duration_s` columns give you the per-video lookup. ## Evaluation Two evaluation paths will be released at the [companion GitHub repo](https://github.com/naver-ai/ExtremeWhenBench) together with the camera-ready (see **Status** above): - **Reference vLLM script** — async OpenAI client that streams video URLs to a vLLM serve and parses `[start, end]` predictions. Matches the paper's Table 4 (Qwen3.5-9B, `num_frames=768`, `enable_thinking=False`): mIoU 0.0469. - **lmms-eval task plugin** — drop-in for [EvolvingLMMs-Lab/lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval), with a small adapter patch (`+49` lines) that lets the `openai` adapter send video as a URL so vLLM can decode server-side (mIoU 0.0485 on the same setup). We plan to upstream the patch + task as a PR to `EvolvingLMMs-Lab/lmms-eval`. Reported numbers we will reproduce: | Path | mIoU | | ------------------------------------------ | ------ | | Paper Table 4 (reported) | 0.053 | | Reference vLLM script | 0.0469 | | lmms-eval task | 0.0485 | ## Citation ```bibtex @article{seo2026extremewhenbench, title = {Natural-Language Temporal Grounding in Hour-Long Videos is a Search Problem: A Benchmark and Empirical Decomposition}, author = {Seo, Sukmin and Kim, Geewook}, journal = {arXiv preprint arXiv:2606.12300}, year = {2026} } ``` ## License Annotations: CC-BY-4.0. Copyright (c) 2026-present NAVER Cloud Corp. Source videos are governed by the licenses of LVBench, MLVU, and VideoMME respectively; this dataset does not redistribute them.