---
license: cc-by-4.0
language:
  - en
pretty_name: Substream Recollection
size_categories:
  - 10K<n<100K
task_categories:
  - video-classification
  - visual-question-answering
  - question-answering
tags:
  - long-context
  - memory
  - benchmark
  - video-llm
configs:
  - config_name: text
    data_files: text/questions.parquet
  - config_name: synthetic_video
    data_files: synthetic_video/questions.parquet
  - config_name: natural_video
    data_files: natural_video/questions.parquet
  - config_name: easyhuman
    data_files: easyhuman/questions.parquet
dataset_info:
  - config_name: text
    splits:
      - name: train
        num_examples: 7680
  - config_name: synthetic_video
    splits:
      - name: train
        num_examples: 6080
  - config_name: natural_video
    splits:
      - name: train
        num_examples: 1028
  - config_name: easyhuman
    splits:
      - name: train
        num_examples: 672
---

# Substream Recollection

A controlled benchmark for substream-membership recall in long-context VLMs and
LLMs. Each row is a `(stream, probe, label)` tuple: the model sees a long input
stream and a short probe, and must answer "yes" or "no" — did the probe occur
inside the stream?

The dataset is organized into four top-level configs keyed by modality + source:

| config | rows | content |
| --- | --- | --- |
| `text` | 7,680 | text-modality questions for the synthetic substream benchmark. |
| `synthetic_video` | 6,080 | rendered synthetic substream videos. |
| `easyhuman` | 672 | rendered 3-belt EasyHuman videos (224 video rows at L=256) plus their text-modality counterparts (448 text rows at L=256 + L=1024). The `modality` column distinguishes `text` vs `video`. Pattern-based, so no entropy ground truth on this config. |
| `natural_video` | 1,028 | EPIC-Kitchens-100 derived clips and SoccerNet provenance metadata. |

## Directory layout

```
anonstreammem/substream-recollection/
├── README.md
├── metadata.json                 # Croissant 1.0 covering all 3 RecordSets
├── LICENSES/
│   ├── EPIC-Kitchens-100-CC-BY-NC-4.0.txt
│   ├── synthetic-and-easyhuman.txt
│   └── SoccerNet-NOTE.txt
├── text/
│   ├── questions.parquet
│   ├── questions.json            # NDJSON copy of the parquet
│   └── manifest.json             # nested-shape manifest for main.py
├── synthetic_video/
│   ├── questions.parquet
│   ├── questions.json
│   ├── manifest.json
│   └── videos/
│       └── L_8_frames/ … L_1024_frames/
├── easyhuman/
│   ├── questions.parquet
│   ├── questions.json
│   └── manifest.json
└── natural_video/
    ├── questions.parquet
    ├── questions.json
    ├── manifest.json
    └── videos/
        ├── nat_8_frames/         # was exact1fps_short
        ├── nat_16_frames/        # was exact1fps_B1
        ├── nat_64_frames/        # was exact1fps_B2
        ├── nat_128_frames/       # was exact1fps_B3
        ├── nat_512_frames/       # was exact1fps_B4
        └── nat_1024_frames/      # was exact1fps_B5
```

Inside each `synthetic_video/videos/L_<L>_frames/<bucket>/` you'll find the
parent videos (e.g. `video_1_v0.mp4`) plus a `clips/` subfolder with the probe
clips. EasyHuman uses a flatter layout with no inner bucket directory.

## Loading

Each config ships in three formats so downstream consumers can pick whichever is
most convenient:

- `<config>/questions.parquet` — the canonical flat per-question table for
  `datasets.load_dataset(...)` and pandas/Arrow workflows.
- `<config>/questions.json` — NDJSON copy of the parquet (one row per line).
- `<config>/manifest.json` — nested `{"videos": [...]}`-shape manifest, directly
  ingestible by the project's `main.py` via
  `datasets.patternvideos_manifest.load_patternvideos_manifest`. No adapter
  required.

### Flat parquet via `datasets`

```python
from datasets import load_dataset

text   = load_dataset("anonstreammem/substream-recollection", "text")["train"]
synthv = load_dataset("anonstreammem/substream-recollection", "synthetic_video")["train"]
eh     = load_dataset("anonstreammem/substream-recollection", "easyhuman")["train"]
natv   = load_dataset("anonstreammem/substream-recollection", "natural_video")["train"]
```

### Nested manifest for direct `main.py` ingestion

```bash
huggingface-cli download anonstreammem/substream-recollection \
    --repo-type dataset --local-dir ./data
python main.py ./data/synthetic_video/manifest.json \
    --asset-root ./data/synthetic_video \
    --model internvl-3-5 --bucket-filter UNIFORM_EVAL_L008_ELOW \
    --limit 1 --limit_questions 3
```

The manifest groups rows by their parent video (`video_path` for video-modality
configs; `(stream_id, length_L, entropy_band)` for text-modality rows). Each
video entry carries `sequences_used = {S_tokens, S_lanes}` so the loader can
serve sequence-mode evaluation without any extra columns. Per-question entries
follow the loader's native binary format
(`{candidate: {sequence, sequences, clip_path, clip_start, clip_end, present},
answer: "yes"|"no", question_time, ...}`).

Video files are referenced by relative `video_path` / `clip_path` in each
parquet. To resolve them locally, snapshot the repo:

```python
from huggingface_hub import snapshot_download
root = snapshot_download(
    "anonstreammem/substream-recollection",
    repo_type="dataset",
    allow_patterns=["synthetic_video/**", "natural_video/**", "text/**"],
)
```

Then `Path(root) / row["video_path"]` resolves to the actual mp4.

## Schema (updated 2026-05-04)

Common columns across all four parquets:

- `question_id` (str): canonical question id (note: not globally unique on its own — see "Identifiers" below)
- `stream_id` (str): id of the parent stream / video this question targets
- `split` (str): `substream` / `easyhuman` / `natural`
- `length_L` (int): normalized parent-stream length (8…4096)
- `entropy_band` (str): `low` / `medium` / `max-entropy` / `easyhuman` / `natural`
- `question_variant` (str): `sequential` / `spatial` / `easyhuman_binary` / `binary_natural`
- `question_text` (str): natural-language probe shown to the model
- `answer` (str): ground-truth `"yes"` or `"no"`
- `candidate_sequence` (list[str], nullable): probe substream as a list of token strings (S_tokens); null on natural rows
- `candidate_clip_start` / `candidate_clip_end` (float, nullable): probe-clip time bounds in seconds when applicable
- `candidate_tag` (str, nullable): EasyHuman category tag (e.g. `x_present`, `mistake_absent`); null on substream/natural
- `candidate_present` (bool, nullable): ground-truth substream-membership; null on natural
- `license` (str): per-row license — `CC-BY-4.0`, `CC-BY-NC-4.0`, or `SoccerNet-NDA`

Synthetic-only columns (present on `text` and `synthetic_video`; null on `natural_video`; absent on `easyhuman`):

- `h_hat_overall` (float, nullable): per-stream empirical Lempel-Ziv entropy in bits/token (paper-canonical: `entropy_overall.empirical_bits.S_tokens`)
- `h_hat_prefix` (float, nullable): per-question prefix empirical LZ entropy in bits/token (`entropy_prefix.S_tokens`)
- `candidate_sequence_lanes` (list[str], nullable): probe lane track (S_lanes), aligned 1:1 with `candidate_sequence`

Lane-track column (present on all configs; carries the parent stream's S_lanes):

- `input_sequence_lanes` (str, nullable): comma-joined parent-stream `S_lanes`, aligned 1:1 with `input_sequence` (or with the parent stream's S_tokens for video-modality rows). Null on `natural_video`.

Per-config additional columns:

- `text/questions.parquet`: `input_sequence` (str) — comma-joined input symbols (the text-modality payload)
- `synthetic_video/questions.parquet`: `video_path` (str), `clip_path` (str) — repo-relative paths
- `easyhuman/questions.parquet`: `modality` (str: `text` or `video`), `input_sequence` (str, nullable; populated on text rows), `video_path` / `clip_path` (str, nullable; populated on video rows)
- `natural_video/questions.parquet`: `video_path` (str), `source_dataset` (str: `epic-kitchens-100` or `soccernet`), `source_class` (str: e.g. `wash_plate`, `red_card`), `source_provenance` (str, JSON: SoccerNet rows only)

### Identifiers

`question_id` is preserved from the source pipeline and is **not** globally
unique on its own (the same question id appears across multiple buckets/lengths
when the same probe is reused). The composite key
`(question_id, length_L, entropy_band, question_variant)` is unique per row.

## SoccerNet rows

The 78 SoccerNet-derived rows (event class `red_card`) appear in
`natural_video/questions.parquet` but the underlying mp4s are **not**
redistributed here — the SoccerNet source is NDA-gated. Use the
`source_provenance` JSON column to pull the originals from
[https://www.soccer-net.org/data](https://www.soccer-net.org/data) after
signing the SoccerNet NDA. See `LICENSES/SoccerNet-NOTE.txt`.

## Licenses

| split / source | license | per-row tag | file |
| --- | --- | --- | --- |
| Synthetic streams (`text`, `synthetic_video`) | CC BY 4.0 | `CC-BY-4.0` | `LICENSES/synthetic-and-easyhuman.txt` |
| EasyHuman (`easyhuman` config) | CC BY 4.0 | `CC-BY-4.0` | `LICENSES/synthetic-and-easyhuman.txt` |
| EPIC-Kitchens-100 derived clips (`natural_video`, `source_dataset=epic-kitchens-100`) | CC BY-NC 4.0 (research use only) | `CC-BY-NC-4.0` | `LICENSES/EPIC-Kitchens-100-CC-BY-NC-4.0.txt` |
| SoccerNet derived rows (`natural_video`, `source_dataset=soccernet`; provenance only) | NDA-gated source; not redistributed | `SoccerNet-NDA` | `LICENSES/SoccerNet-NOTE.txt` |

The top-level `license: cc-by-4.0` tag on this card refers to the
Anonymous-Authors-owned splits (synthetic + EasyHuman) and to this card,
metadata, and code only. EPIC-Kitchens-100 derivatives remain CC BY-NC 4.0.

## Citation

Anonymous, "Substream Recollection," 2026 (anonymized for review).