AVC-1.8M · An open video–language dataset

Aligned Video
Captions 1.8M

1.8 million ten-second video windows, each carrying a stack of temporally aligned text layers — speech transcripts, on-screen text, dense visual descriptions, fused captions, and structured entities — plus a modality-attributed retrieval & QA benchmark that tells you which layer holds the evidence for every query.

1,825,901
canonical 10-second windows
28,893
YouTube videos · ~5,070 hours · 18.2M 1 fps frames
10+
aligned text layers per window, from ASR to structured entities
4,237
human-reviewed benchmark items — 2,195 queries + 2,042 QA pairs

Anatomy of a window

Every window is a stack of aligned text

Two real windows from the test split. Ten frames, one for each second — and beneath them, the text layers that describe the same ten seconds from different sources. Queries are attributed to the layer that generated them.

Frame at second 0: pot of liquid boiling on a stovetop Frame at second 1 Frame at second 2: on-screen text appears over the pot Frame at second 3 Frame at second 4 Frame at second 5 Frame at second 6: on-screen text fading out Frame at second 7 Frame at second 8: red spatula stirring the pot Frame at second 9: spatula stirring rice mixture

Howto & Style — a cooking window where speech, screen text, and visuals all carry signal 1mbyCXQIYxU__s140__e150

ASR Now, we're going to bring this to a boil on medium heat just to reduce the liquid. Then we're going to give it a stir,
SCENE TEXT Bring to a boil on medium heat to reduce liquid
VISUAL The video shows a close-up of a stainless steel pot with a brown liquid inside. The pot is on a black stovetop burner with a flame underneath. The liquid is bubbling and steaming, and there is a red spatula in the pot …
FUSED A close-up shows a pot of rice and brown liquid bubbling and steaming on a stovetop burner, with a red spatula stirring the mixture. The person cooking explains that they are bringing the rice to a boil on medium heat …
ENTITY character: Narrator — chef, informative

Attributed benchmark queries

asr-born
“how to reduce liquid by boiling on medium heat”
ocr-born
“how to bring to a boil on medium heat to reduce liquid”
visuals-born
“cooking rice on stovetop with red spatula”
dense-born
“how to cook rice on a stovetop with stirring instructions”
Frame at second 0: close-up of a black scooter handlebar Frame at second 1 Frame at second 2: hand operating the handlebar controls with text overlays Frame at second 3 Frame at second 4 Frame at second 5 Frame at second 6: removable battery pack with labels Frame at second 7 Frame at second 8 Frame at second 9: battery pack labeled 15000mah

Science & Technology — a product demo where the evidence lives in the on-screen text lgwKFOTjbCU__s820__e830

ASR 6 included handlebar will provide you with options to accelerate brake and to go back. It comes with a 15,000 mah battery that
SCENE TEXT Maximum speed 6 km/h · Press the right button to go · Left button brake · Airwheel ROBOT · Removable large capacity lithium battery (can be used independently as power bank) · 15000mah battery …
VISUAL The video clip shows a close-up of a black scooter handlebar with a white text overlay that reads “Zero line embedded patent handlebar”. The handlebar has a black button in the center, a grey lever on the left side …
FUSED This clip demonstrates the Airwheel ROBOT scooter, focusing on its specialized handlebar with embedded patent technology and intuitive controls for acceleration, braking, and reversing …
ACTIVITIES A hand pressing the buttons and levers on the scooter handlebar, demonstrating the functions of the controls …

Attributed benchmark queries

asr-born
“device with handlebar controls for accelerate brake and reverse”
ocr-born
“Airwheel removable 15000mAh lithium battery power bank”
visuals-born
“Airwheel Robot scooter handlebar controls”
dense-born
“removable 15000mah lithium battery used as power bank for scooter”
More windows: when only one layer carries the signalfour regimes
Speech-heavy window frame at 1s Speech-heavy window frame at 3s Speech-heavy window frame at 5s Speech-heavy window frame at 7s Speech-heavy window frame at 9s

Autos & Vehicles — speech carries the content; no scene text NqnUqUDCp0A__s370__e380

ASR Well, we'll talk about the motor, but it's a two door. And we want to make sure that we're not weighing this thing down with too much weight. We're going to add some stuff. It's going to add weight. That's going to happen. But I think we got to really pay attention. If there's
SCENE TEXT (empty)
VISUAL The video clip shows two men standing in a garage, one in a blue hoodie and the other in a grey hoodie. They are standing in front of a red Jeep Wrangler. The man in the grey hoodie is pointing at the front of the Jeep, while the man in the blue hoodie is listening intently. The garage has a white ceiling and a metal wall with a tire hanging on it. There are also some tools and other items visible in the background.
FUSED In a garage setting, two men stand in front of a red Jeep Wrangler, discussing the impact of modifications on the vehicle's weight. One man points to the Jeep as they talk about being careful not to overload it, emphasizing the importance of monitoring added weight during upgrades. The background includes a hanging tire and various tools, highlighting the workshop environment. The conversation focuses on balancing …
Scene-text-heavy window frame at 1s Scene-text-heavy window frame at 3s Scene-text-heavy window frame at 5s Scene-text-heavy window frame at 7s Scene-text-heavy window frame at 9s

Science & Technology — on-screen text carries the content kBCz54D57AM__s520__e530

ASR settled down a little with iOS eight. In 2014, it introduced some highly requested features like interactive notifications, audio and video messages, widgets, a
SCENE TEXT iOS 8 Released September 17, 2014 Features Supported devices Interactive Notifications iPhone Handoff 4S, 5, 5C, 5S, 6 Audio Messages iPod touch Video Messages 5th, 6th Gen Health App iPad Apple Music 2nd, 3rd, 4th Gen Widgets iPad mini QuickType Keyboard 1st Gen, 2, 3 TimeLapse Recording iPad Air Smart Photo Editing 1st Gen, 2 Hey Siri CarPlay Apple Pay
VISUAL The video clip features a man in a blue shirt and grey blazer standing on a stage, presenting a slide with the title “iOS 8” and a list of features and supported devices. The slide shows three iPhone screens displaying various apps and features, including the Health app, the Music app, and the Camera app. The man is speaking about the features of iOS 8, highlighting the highly requested features like interactive …
FUSED A man in a blue shirt and grey blazer stands on stage, presenting a slide titled “iOS 8” that highlights the major features and supported devices of Apple's 2014 software release. The slide showcases three iPhone screens with apps such as Health, Music, and Camera. As he speaks, he describes new features including interactive notifications, audio and video messaging, widgets, and more, emphasizing the updates that …
Visuals-heavy window frame at 1s Visuals-heavy window frame at 3s Visuals-heavy window frame at 5s Visuals-heavy window frame at 7s Visuals-heavy window frame at 9s

Travel & Events — no speech; visuals carry the scene T60_zAvnAsU__s820__e830

ASR (empty)
SCENE TEXT touropia
VISUAL The video clip opens with a wide shot of a coastal town, showcasing a picturesque harbor filled with boats. The town is nestled against a lush green hillside, with a prominent church tower standing tall in the center. The sky is a clear blue, and the water is a vibrant turquoise. The camera then pans down to a closer view of the harbor, revealing a row of white boats docked along a stone pier. People can be seen …
FUSED A sweeping view reveals a charming coastal town with a vibrant turquoise harbor, white boats docked along a stone pier, and a lush hillside crowned by a prominent church tower. People stroll leisurely along the waterfront, enjoying the sunny, tranquil atmosphere. The word “touropia” appears on screen, suggesting a travel or tourism focus. No speech is heard in this serene, picturesque scene.
Music-flagged window frame at 1s Music-flagged window frame at 3s Music-flagged window frame at 5s Music-flagged window frame at 7s Music-flagged window frame at 9s

Pets & Animals — no speech, no scene text; music present tN1cL8CBv5Y__s280__e290

ASR (empty)
SCENE TEXT (empty)
VISUAL The video shows a close-up of a hamster cage. The cage is filled with blue and purple paper bedding. There are various toys and accessories in the cage, including a red ball with white polka dots, a small orange bowl, a wooden bridge, and a small wooden house. A person's hand is seen reaching into the cage and moving some of the toys around. The person is wearing a green shirt with a tattoo on their arm.
FUSED A close-up shot captures a person arranging colorful toys and accessories inside a hamster cage filled with blue and purple bedding. The individual, wearing a green shirt and sporting a tattoo, carefully moves items like a red polka-dotted ball, an orange bowl, and wooden structures to prepare a comfortable space for their pet. No speech or on-screen text is present in this clip.
ASR transcript · ~88% of windows Frame OCR + VLM scene text · ~65% Dense visual description Narrative / activity / background / audio summaries Fused caption Entities · 4.0M characters · 2.9M objects · 1.8M backgrounds · 17 vertical schemas Audio metadata · incl. 90,060 music captions Content flags

Windows are sampled across categories from English-language YouTube videos in Panda-70M. Alongside the text layers, the dataset ships 193 precomputed embedding matrices (six image encoders × three poolings, audio encoders, and text encoders over 13+ text views and entity search texts) and lexical BM25 indexes — so most experiments never touch the raw media.

Composition

What 1.8M windows look like

Windows span fifteen source categories, so retrieval systems meet cooking tutorials, news, gaming, and music on equal footing. The benchmark keeps its four evidence modalities in near-equal balance by construction.

10-second windows by source category 1,825,901 total
Film & Animation252,636
Travel & Events162,462
Education141,474
Pets & Animals139,317
Nonprofits & Activism131,193
Comedy123,535
People & Blogs122,446
Science & Technology120,137
Autos & Vehicles119,838
Entertainment112,966
Music107,122
Sports84,976
Howto & Style75,891
Gaming70,638
News & Politics61,270
Benchmark items by generating modality 4,237 total
asr1,073550 queries · 523 QA
ocr972515 queries · 457 QA
visuals1,100569 queries · 531 QA
dense1,092561 queries · 531 QA

Of the 2,195 retrieval queries, 695 are graded hard and 1,500 normal.

What the archived clips look like

Fifteen categories, sampled from the benchmark

Frames are sampled with a fixed seed from the 4,128 archived benchmark clips, at most one per parent video within a category. Eleven automatically sampled frames were replaced after a manual screen for close-up identifiable faces and sensitive content; the sample manifest plus the replacement log with reasons ship in evals/qualitative/ in the container.

Autos & Vehicles
Autos & Vehicles — window aLQFZkc0KiM__s830__e840 Autos & Vehicles — window Uspcs3mE9kc__s660__e670 Autos & Vehicles — window xGZk41qMiUs__s50__e60 Autos & Vehicles — window BY8gfx1Vp84__s800__e810 Autos & Vehicles — window RwhFmjgKXwA__s870__e880 Autos & Vehicles — window dmENTebOUa0__s40__e50 Autos & Vehicles — window sOllO_GVczo__s200__e210 Autos & Vehicles — window GYM1bdi3h2c__s220__e230
Comedy
Comedy — window g_0Ruf1mrV8__s630__e640 Comedy — window AjNdkxQtCFg__s810__e820 Comedy — window yLUvvfw_06s__s620__e630 Comedy — window QPr-Cmlbaas__s160__e170 Comedy — window qf0TjmMiRvM__s600__e610 Comedy — window FI_cMPgcW_w__s350__e360 Comedy — window kbo28SZZQyg__s90__e100 Comedy — window swbIR4kxITM__s160__e170
Education
Education — window o62sk4600m8__s2890__e2900 Education — window DK8n5pz2qqw__s50__e60 Education — window mJsiwzvcl9k__s20__e30 Education — window sYKfzEV_jxE__s490__e500 Education — window L9vBaCzCgGA__s70__e80 Education — window CbnCVDK504s__s2450__e2460 Education — window 87VhNA12jB4__s510__e520 Education — window LC5I8BsO18I__s240__e250
Entertainment
Entertainment — window CT8l0novTMg__s550__e560 Entertainment — window b8UcoIwU9nY__s320__e330 Entertainment — window N6G2_5sMhuM__s120__e130 Entertainment — window el0PVB0UaWQ__s770__e780 Entertainment — window FNIitZt3pyc__s70__e80 Entertainment — window zXRVhKMaZJU__s20__e30 Entertainment — window BWTK7MCPWqc__s870__e880 Entertainment — window HjkoK6f86_U__s460__e470
Film & Animation
Film & Animation — window F8ckE1y1Hfs__s70__e80 Film & Animation — window VP7dUZdfo-I__s2950__e2960 Film & Animation — window wCwJqlt7lMg__s20__e30 Film & Animation — window 4-WqlMKq9n8__s990__e1000 Film & Animation — window 5stVcWlkoHo__s20__e30 Film & Animation — window 3ClTJmcNWR8__s1370__e1380 Film & Animation — window KCADH3x56eE__s300__e310 Film & Animation — window C5TCaYeWhJs__s230__e240
Gaming
Gaming — window _yEq7thLB7U__s90__e100 Gaming — window cdt-zOSIyhU__s50__e60 Gaming — window fdQ_rBuMaYI__s160__e170 Gaming — window 4jMva9xBd8Y__s40__e50 Gaming — window 3uTNJEK9Z6o__s260__e270 Gaming — window VLaVpUsb9Fo__s190__e200 Gaming — window erMfN8Lpnb0__s160__e170 Gaming — window ZPBzCLeJQls__s60__e70
Howto & Style
Howto & Style — window LfZda-MN39A__s200__e210 Howto & Style — window 6ciPZTtMEIg__s180__e190 Howto & Style — window d6Oupo8upK8__s200__e210 Howto & Style — window FCjcO43e1BI__s60__e70 Howto & Style — window y_BY8qykYrc__s40__e50 Howto & Style — window hnHpty7eYgg__s230__e240 Howto & Style — window hT49jg2BLlc__s40__e50 Howto & Style — window v9Iv1ZyacZg__s20__e30
Music
Music — window cur_cx6mkmk__s550__e560 Music — window X9sWxTq5rH0__s450__e460 Music — window xejx48lZVhQ__s30__e40 Music — window TkX1fMITvlk__s810__e820 Music — window RMekmQPVtuU__s0__e10 Music — window 45v9g7KX-Ko__s30__e40 Music — window Blx8oLimWDA__s50__e60 Music — window hDFjsHfwees__s2100__e2110
News & Politics
News & Politics — window AxihKpo50WA__s30__e40 News & Politics — window Yt8FtU4-rSY__s80__e87 News & Politics — window 0KGXPWX86Y8__s90__e100 News & Politics — window -_5h-hcktzk__s60__e70 News & Politics — window oOw9J4Uo7fw__s150__e160 News & Politics — window RD-Z9xEhR4I__s500__e510 News & Politics — window R4vKYWV0VuQ__s340__e350 News & Politics — window E7kystDepSo__s30__e40
Nonprofits & Activism
Nonprofits & Activism — window zJ9t7b9GUaQ__s440__e450 Nonprofits & Activism — window AF9qvqmG3CE__s650__e660 Nonprofits & Activism — window Ihs4VFZWwn4__s880__e890 Nonprofits & Activism — window STTBCV_XMPc__s410__e420 Nonprofits & Activism — window bz_NgKnVKxE__s620__e630 Nonprofits & Activism — window -idjbjkrHeM__s130__e140 Nonprofits & Activism — window 5YVp6eAXPx0__s300__e310 Nonprofits & Activism — window hwhpw9nOPiM__s540__e550
People & Blogs
People & Blogs — window OalKTKnYjHc__s720__e730 People & Blogs — window Ap-TqKina44__s110__e120 People & Blogs — window ar1n3pJDFPE__s150__e160 People & Blogs — window IKuHX95tplQ__s700__e710 People & Blogs — window 5Bw3zAS1ufk__s620__e630 People & Blogs — window Ghl3pJWt-0I__s280__e290 People & Blogs — window iMfo0dfLmTA__s350__e360 People & Blogs — window kHzmBP4H0lA__s240__e250
Pets & Animals
Pets & Animals — window GLXpU5YqlU0__s1130__e1140 Pets & Animals — window aDbIAy9sMHk__s1730__e1740 Pets & Animals — window DusXyqCFrTs__s460__e470 Pets & Animals — window pH-QjEPbvUI__s0__e10 Pets & Animals — window HkagDd8GYCw__s280__e290 Pets & Animals — window USJfeVTenhs__s120__e130 Pets & Animals — window F14nHmyu2cM__s1740__e1750 Pets & Animals — window R8rCRd84AS0__s70__e80
Science & Technology
Science & Technology — window ljl4sjHnxxE__s0__e10 Science & Technology — window AjStZFnCT4c__s150__e160 Science & Technology — window hoQKLOrAU4E__s530__e540 Science & Technology — window 8u00umH_uew__s770__e778 Science & Technology — window zxGqGCtPxn4__s450__e460 Science & Technology — window ZKjKpYunh3s__s610__e620 Science & Technology — window j4URtOcKWEg__s490__e500 Science & Technology — window SqbTrbxWT1o__s110__e120
Sports
Sports — window OSMEfpp-XOE__s360__e370 Sports — window INKvTuLn65I__s670__e680 Sports — window RvNqWs997Zo__s210__e220 Sports — window CzcXA4m6ZNg__s110__e120 Sports — window mBrZLPB_yks__s610__e620 Sports — window HEhDr5DsNc4__s230__e240 Sports — window NhHxwA3zLIM__s300__e310 Sports — window d269CK8E0Cs__s30__e40
Travel & Events
Travel & Events — window WP3JxIg_h2g__s680__e690 Travel & Events — window oPVJYbcSXLk__s170__e180 Travel & Events — window fbvKtDyL-Fc__s930__e940 Travel & Events — window NQkOTujHZls__s160__e170 Travel & Events — window J7RZd4s7h0w__s120__e130 Travel & Events — window CvZFpadnFu8__s200__e210 Travel & Events — window VKyCW70oLgk__s170__e180 Travel & Events — window o5GxQxejNQs__s600__e610

Get the data

One public container. No registration, no keys.

The full distribution lives in a public Azure Blob Storage container with anonymous read access — no SAS tokens, no sign-up. Machine-readable metadata is served as MLCommons Croissant at the container root.

container root
https://publicavcdist.blob.core.windows.net/avc-1-8m/

AVC-1.8M v2 · September 19, 2026

Eight adopted annotation layers, completed embedding matrices, and the adopted 4B, Omni-3B and 270M router adapters. The original v1 benchmark stays frozen.

Download manifest and checksums · v2 datasheet · Known limitations

The separate candidates/ bundle contains 18,527 F5 items labeled machine-filtered, not human-audited. These are not additions to the benchmark. The validator accepted 1 of 200 source-support negative controls; this is calibration, not a human accuracy claim.

download the v2 bundles and verify checksums
curl -fLO https://publicavcdist.blob.core.windows.net/avc-1-8m/v2/releases/2026-09-19/pull.sh
bash pull.sh

No source videos, audio or frames are included. Per-layer/model license terms apply. The manifest states bundle sizes before download; the interactive inference demo is currently stopped.

Quick check

Confirm the container is reachable and skim the layout docs:

shell
# dataset metadata (Croissant 1.0, with Responsible-AI fields)
curl -s https://publicavcdist.blob.core.windows.net/avc-1-8m/croissant.json | head

# layout + usage docs at the container root
curl -s https://publicavcdist.blob.core.windows.net/avc-1-8m/README.md

Download

Grab individual files with wget/curl, or whole trees with azcopy:

shell
# the benchmark itself is tiny — start here (~4 MB each)
wget https://publicavcdist.blob.core.windows.net/avc-1-8m/test_question_answers.jsonl
wget https://publicavcdist.blob.core.windows.net/avc-1-8m/test_search_queries.jsonl

# per-window caption records for the test split (~184 MB)
wget https://publicavcdist.blob.core.windows.net/avc-1-8m/dist/captions_and_meta-test.jsonl

# bulk-download a subtree (see the size table before recursing!)
azcopy copy 'https://publicavcdist.blob.core.windows.net/avc-1-8m/dist' . --recursive

Load it with mlcroissant

python
import mlcroissant as mlc

ds = mlc.Dataset("https://publicavcdist.blob.core.windows.net/avc-1-8m/croissant.json")

# record sets: "retrieval_queries", "qa_pairs"
for record in ds.records(record_set="retrieval_queries"):
    print(record)
    break

What's where — and how heavy it is

The text side of the dataset (captions, benchmark, QA) is light; embeddings and raw media are the heavy part. Full-corpus video files are not redistributed — every record carries its source YouTube URL and time range, so clips can be fetched from the source when needed; the 4,128 test-corpus MP4s are archived in test_vids/ so benchmark evaluation never depends on continued source availability. Check before you recurse:

PathContentsWeight
croissant.json · README.md Machine-readable metadata and layout docs at the container root Tiny
test_question_answers.jsonl · test_search_queries.jsonl The benchmark: 2,042 QA pairs and 2,195 retrieval queries with the window's full caption context inline — self-contained for judging Tiny (~4 MB each)
dist/ Per-window caption records for train / val / test (1,631,378 / 94,362 / 88,566 windows) plus generated training pools: ~11.6M QA pairs and ~11.6M retrieval queries Light–moderate (single-file downloads; test captions ~184 MB)
evals/ The frozen retrieval corpus: 88,566-window db strings in 11 modality combinations, plus precomputed query- and corpus-side embeddings per encoder Heavy (embeddings dominate)
closedqa/ Per-model closed-QA outputs for reference baselines Light
test_vids/ 4,128 raw test-window MP4s (<vid>__s<start>__e<end>.mp4) Heavy

What the records look like

One real record from each of the main files. The dist/ examples all come from the cooking window shown in the anatomy section above, so you can trace a single window from captions to queries to benchmark.

dist/captions_and_meta-{train,val,test}.jsonlone record per window
{
  "id": "1mbyCXQIYxU__s140__e150",
  "split": "test",
  "source_url": "http://youtube.com/watch?v=1mbyCXQIYxU",
  "source_time_range": "02:20-02:30",
  "source_video_category": "Howto & Style",
  "fused_caption": "A close-up shows a pot of rice and brown liquid bubbling and steaming on a stovetop burner, with a red spatula stirring the mixture. The person cooking explains that they are bringing the rice to a boil on medium heat to reduce the liquid …",
  "visual_caption": "The video shows a close-up of a stainless steel pot with a brown liquid inside. The pot is on a black stovetop burner with a flame underneath …",
  "narrative_caption": "The video clip shows a person cooking rice on a stovetop, bringing the rice to a boil and stirring it.",
  "activities_caption": "The person is cooking rice by bringing it to a boil on a stovetop and stirring it with a red spatula.",
  "background_caption": "The background of the video is a kitchen countertop with a black stovetop burner …",
  "speech_transcript": "Now, we're going to bring this to a boil on medium heat just to reduce the liquid. Then we're going to give it a stir,",
  "vlm_primary_scene_text": "Bring to a boil on medium heat to reduce liquid",
  "inferred_video_category": "Food & Drink",
  "inferred_video_subcategory": "Cooking",
  "inferred_title": "How to Cook Rice: Reduce Liquid & Stir",
  "inferred_5_word_summary": "Bring rice to a boil, stir"
}
dist/retrieval_queries-{train,val,test}.jsonl~11.6M generated training queries + val/test pools
{
  "id": "49d28db5-02cc-5c6f-b2b1-10450679d880",
  "window_id": "1mbyCXQIYxU__s140__e150",
  "source_url": "http://youtube.com/watch?v=1mbyCXQIYxU",
  "source_time_range": "02:20-02:30",
  "source_video_category": "Howto & Style",
  "split": "test",
  "generation_modality_source": "asr",
  "text": "how to reduce liquid by boiling on medium heat"
}
dist/question_answer_pairs-{train,val,test}.jsonl~12.9M generated QA pairs
{
  "id": "d9dbc72b-d4f6-5241-a98f-09cdf2fed829",
  "window_id": "1mbyCXQIYxU__s140__e150",
  "source_url": "http://youtube.com/watch?v=1mbyCXQIYxU",
  "source_time_range": "02:20-02:30",
  "source_video_category": "Howto & Style",
  "split": "test",
  "generation_modality_source": "visuals",
  "question": "What type of pot is being used to cook the rice?",
  "answer": "A stainless steel pot."
}
dist/benchmark-retrieval_queries.jsonl2,195 validated benchmark queries (QA twin: benchmark-question_answer_pairs.jsonl)
{
  "id": "9eb8c431-944b-5865-909f-fe07817d41e9",
  "window_id": "1mbyCXQIYxU__s140__e150",
  "source_url": "http://youtube.com/watch?v=1mbyCXQIYxU",
  "source_time_range": "02:20-02:30",
  "source_video_category": "Howto & Style",
  "generation_modality_source": "asr",
  "llm_validation_reason": "The query specifically asks about the reason for stirring while reducing liquid on medium heat, which matches the clip's content showing and explaining this cooking process. It is not a verbatim copy of the text or transcript …",
  "llm_context_source_difficulty": "hard",
  "text": "why do you stir while reducing liquid on medium heat"
}
evals/retrieval/db_strings.jsonlretrieval-corpus text in 11 modality combinations
{
  "id": "-2fmOkN6KH4__s0__e10",
  "text": {
    "dvd": "The video clip starts with a black screen with the words \"Everything Wrong With DEATH WISH\" in white text. The text is in a bold, sans-serif font …",
    "asr": "What? The only 11 seconds",
    "ocr": "Everything Wrong With DEATH WISH In 13 minutes or less Spoilers!! (duh.) 0 00:00:00 Movie Sin Counter Movie Sin Timer WTF? Only 11 seconds of one logo …",
    "fuse": "The clip opens with a bold title card reading Everything Wrong With DEATH WISH and playful warnings about spoilers, set against a dramatic sunset …",
    "asr+ocr": "…", "dvd+asr": "…", "dvd+ocr": "…", "fuse+asr": "…",
    "fuse+ocr": "…", "dvd+asr+ocr": "…", "all": "…"
  }
}
// dvd = dense video description. Matching .npy embeddings per encoder × text
// combo live in evals/retrieval/db_embeddings/; query-side embeddings alongside.
Versioning. v1 is frozen — files at the current paths will not change. Additions (re-captions, extra query-side embeddings, new result CSVs) land under a v2/ prefix, and croissant.json is updated additively. Pin paths with confidence.

Benchmark & evaluation

Every query knows where its evidence lives

Most video-retrieval benchmarks can't tell you why a system failed. AVC-1.8M's benchmark items are modality-attributed (generation-source-attributed, in the release docs' terminology): each of the 2,195 retrieval queries and 2,042 QA pairs is labeled with the text layer that generated it — asr, ocr, visuals, or dense — together with a difficulty grade. Scores decompose by evidence source, so “our model is weak on on-screen text” becomes a measurable claim rather than a hunch. All items are LLM-validated and human-reviewed, and the review quality is itself audited: a 200-item media-viewing audit found 94.5% of sampled benchmark items fully supported by the source video (189/200; Wilson 95% CI [.904, .969]); a 277-item caption-layer audit found 94.6% of sampled captions free of major errors (262/277, with zero major errors in the visual and fused layers); and a 100+100-item human adjudication of the validation judge confirmed 99/100 of judge-accepted items. Audits are single-annotator; the batches, review apps, and scored artifacts ship in evals/media_audit/, evals/caption_audit/, and evals/judge_audit/.

Official protocol

  • Rank the full 88,566-window test corpus for each query.
  • Report R@k, MRR, and NDCG; graded relevance credits the ±10 s neighboring windows of the gold window. Neighbor credit is resolved by matching (video_id, start_time) against the canonical corpus index, so truncated terminal windows are handled correctly.
  • Report per-modality splits alongside the overall number.
  • Reference values for the graded protocol ship as code/results/protocol_ablation_v1.2.csv, generated by code/evaluation/protocol_ablation.py (stdlib-only).

Bring your own embedding

  • Embed the fixed corpus strings (evals/retrieval/db_strings.jsonl — 11 modality-combination text views per window) and the query strings.
  • Rank against the fixed window ids; no re-crawling, no media processing required.
  • Compare against the shipped baseline embeddings in evals/.

A taste of the benchmark

Twelve of the 2,195 retrieval queries — three per modality. Click a column header to sort. The full files are dist/benchmark-retrieval_queries.jsonl and dist/benchmark-question_answer_pairs.jsonl.

QueryModalityDifficultyCategoryWindow
How many planets are in our solar system?asrhardEducationx8ULq9M-aEk__s50__e60
child winning and talking to mommy about eggsasrhardEntertainmentKy62ZtebT_Y__s290__e300
YouTube yearbook from 2016asrnormalEntertainmentN6G2_5sMhuM__s150__e160
old guard outdated textbooks science revolutionocrhardEducationP6Cr_jRVQ6c__s200__e210
Airwheel removable 15000mAh lithium battery power bankocrhardScience & TechnologylgwKFOTjbCU__s820__e830
how to select packages in Cygwin setupocrnormalEducationZVGumut8EJY__s340__e350
child playing on red and blue bouncy castle indoorsvisualshardMusicK5UDgP31D74__s40__e50
man in grey t-shirt standing in park with serious expressionvisualshardEntertainmentJ6Zd2OT6Nds__s70__e80
two women tense conversation in cluttered officevisualsnormalGaming72eSGJVUsDQ__s240__e250
speaker holding black object with 'Cata' sign on platformdensehardNonprofits & ActivismcEhk5qCAAVA__s650__e660
video comparing Mazda Miata appearance to a happy fishdensehardAutos & Vehicles7epWd-nLPSs__s250__e260
man presenting on teamwork and collaboration on stagedensenormalNonprofits & ActivismWbGi7KdxYhU__s30__e40

Training-scale pools of ~11.6M generated queries and ~11.6M QA pairs (attributed the same way) ship in dist/ for fine-tuning and ablations. Evaluation code is public in the container under code/evaluation/ (scorers plus the protocol-ablation generator).

What the benchmark reveals

Leaderboard conclusions depend on how you slice

Rankings and absolute scores move when you change the evidence layer the queries come from, the text you index, or whether retrieval has to do its job before answering.

Ranking agreement across 38 retrieval configurations
speech vs visual slicesτ = 0.07nearly uncorrelated
overall vs fusion sliceτ = 0.83tracks the aggregate

Kendall tau over the same 38 configurations. A system that looks strong overall can look weak on speech-born or visually born queries alone.

What moves the score
index-text swing43 ptsR@5, encoder fixed
BM25 vs dense0.811vs 0.759 R@5
retrieve-then-answer0.30vs 0.90 oracle

Under matched indexed text, BM25 tops every dense single index overall. End-to-end accuracy sits far below the same answerer given oracle context — retrieval is the dominant bottleneck in the tested pipelines.

Can you train on it? Now measured.

Two fine-tuning pilots, both from released data alone — routers on the query pools, and one epoch of LoRA on caption pairs. Numbers below are from evals/distilled_router/ and evals/finetune_vlm/.

Query routing from the train pool
0.6B router acc.805vs ~.25 zero-shot
routed e2e R@5.764vs .759 flat index
270M on CPU161 msper query

Students fine-tuned on 500K released train-pool queries learn to send each query to the right index. Zero-shot, the same models route essentially everything to one index (~.25 accuracy, chance level).

Vision captioning → frames-only QA
zero-shot 8B.363frames only
+1 epoch LoRA.434+7.1 pts
Nova Pro / GPT-4.1.409 / .469published frames-only

One epoch of LoRA on 50K released frame-to-caption pairs, paired on 2,035 items (McNemar exact p = 1.05e-15). Past Nova Pro; within 3.5 points of GPT-4.1. Every evidence-source slice improves.

The instructive twist — why the aligned stack matters: the same tuned model, trained to imitate the visual-description layer alone, gets worse at supporting retrieval (R@5 .363 → .217 when its re-captions replace the released ones), because it learned that layer's convention: it largely stopped transcribing on-screen text (84% → 28% of captions) and undershot the layer's own specificity (fewer proper nouns, fewer shots covered). It's convention, not lost capability — the same model's QA on on-screen-text questions improved. Single-layer imitation trades cross-modal coverage for in-layer style; the multi-layer stack is what lets you both measure that and avoid it.

Worked example. Query “Ana de Armas Bond 25 cast” retrieves its window at rank 1 with the released caption (which transcribes the on-screen name cards) and falls to rank 53,184 with the tuned re-caption that describes only “four people around a wooden table.” Caption specificity is retrieval's oxygen. Full set in worked_examples.json.

Scope: one model, one target layer, one epoch, 50K-sample pilots; measured on this benchmark, not a general claim about fine-tuning.

License & terms

CC BY-NC-SA 4.0 for everything we made

All annotations, captions, entities, embeddings, and benchmark files are released under CC BY-NC-SA 4.0. Source media remain governed by their original platform terms.

Takedown

If a source video is removed from its platform, or you are a rights holder or subject of a video and want it excluded, contact <REDACTED DURING PAPER REVIEW> with the video id. Derived records for removed or objected-to source videos are removed from the distribution.

FAQ

Common questions

Do I need credentials or an account to download?

No. The container allows anonymous public reads — every URL on this page works from a plain curl with no tokens, SAS signatures, or registration.

Can I train on it?

Yes. The train split (1,631,378 windows) ships with generated training pools of ~11.6M retrieval queries and ~11.6M QA pairs, under CC BY-NC-SA 4.0 (non-commercial, share-alike, with attribution). Two released pilots measure the utility directly: routers fine-tuned on the query pools beat the strongest flat index end-to-end, and one epoch of LoRA on 50K caption pairs lifts an open 8B VLM's frames-only QA by 7 points (evals/distilled_router/, evals/finetune_vlm/).

How do I evaluate a new encoder?

Embed the fixed corpus strings in evals/retrieval/db_strings.jsonl and the benchmark queries with your encoder, then rank against the fixed 88,566 window ids and report R@k / MRR / NDCG per modality split. No media processing is required — the corpus text views and baseline embeddings are precomputed.

Can I download the videos?

The dataset does not redistribute the full 1.8M-window media. Every caption, query, and QA record carries the source YouTube URL and time range, so you can fetch any clip from the source. The 4,128 test-corpus window MP4s are archived in test_vids/, so the benchmark remains fully reproducible even if source videos disappear. Media files stay governed by their original platform terms.

Why are some windows missing layers?

Coverage follows content: ~88% of windows have speech (ASR), ~65% carry detected scene text, and 90,060 have music captions — silent or text-free windows simply have nothing to transcribe. Missing layers are empty fields, not dropped rows, so masking is a simple empty-string check.

What if my video is in the dataset?

Email <REDACTED DURING PAPER REVIEW> with the video id and we'll remove its derived records from the distribution. See Takedown.

Changelog

Versions

AUG 2026

evals & code additions (v1 unchanged)

Create-only additions on top of the frozen v1 paths: quality-audit artifacts (evals/media_audit/, evals/caption_audit/, evals/judge_audit/); routing-experiment artifacts (evals/distilled_router/; fine-tuning pilot artifacts under evals/finetune_vlm/ — result CSVs, per-item QA judgments for both model conditions, 88K tuned re-captions, paired caption statistics, the 55-query rank-delta list, and worked examples; the curated visual sample with its manifest and replacement log (evals/qualitative/); released evaluation code and the corrected protocol-ablation table (code/); and an errata-and-authority note in the container README. All create-only; v1 paths untouched.

AUG 2026

v1 — initial public distribution (frozen)

Full caption records for 1.8M windows, the modality-attributed benchmark, the 88,566-window evaluation corpus with precomputed embeddings, closed-QA baseline outputs, and 4,128 raw test-window videos. Paths under v1 will not change; future additions land under v2/.