min1321/extreme-when-bench

ExtremeWhenBench

Hour-scale natural-language temporal grounding benchmark.

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. Code repository: https://github.com/naver-ai/ExtremeWhenBench.

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:

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

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	https://video-mme.github.io/
LVBench	67	https://lvbench.github.io/
MLVU	38	https://github.com/JUNJIE99/MLVU

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 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, 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

@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.