Add natural_video_density config (880 density-band questions); add media_included flag; fix misfiled easyhuman media paths; correct SoccerNet reconstruction note; refresh Croissant metadata

LICENSES/SoccerNet-NOTE.txt

@@ -14,6 +14,10 @@ To reconstruct these clips:
      - `source_match`: full SoccerNet match label.
      - `source_half`: 1 or 2.
      - `source_video_path`: relative path under `soccernet/<league>/<season>/<match>/`.
+       NOTE: 54 of the 78 rows record a `_224p.mkv` filename, but the download
+       command above fetches `_720p.mkv`. The resolutions share one timeline, so
+       substitute the suffix: `1_224p.mkv` -> `1_720p.mkv`. All 78 rows resolve
+       after that substitution. Clip timings are resolution-independent.
      - `clip_start_in_source_seconds`, `clip_end_in_source_seconds`: clip span.
      - `event_type`, `event_position_in_source_seconds`: anomaly metadata
        (positive rows only).

README.md

@@ -23,6 +23,8 @@ configs:
     data_files: natural_video/questions.parquet
   - config_name: easyhuman
     data_files: easyhuman/questions.parquet
+  - config_name: natural_video_density
+    data_files: natural_video_density/questions.parquet
 dataset_info:
   - config_name: text
     splits:
@@ -40,190 +42,56 @@ dataset_info:
     splits:
       - name: train
         num_examples: 672
+  - config_name: natural_video_density
+    splits:
+      - name: train
+        num_examples: 880
 ---
 
 # 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:
+A controlled benchmark for membership recall, designed to test properties of memory beyond accuracy in LLMs. 
+Each row is a `(stream, probe, label)` tuple: the model sees a long input stream and a short candidate, and answers whether the candidate (or in the case of natural video, the referenced action) occurred inside the stream.
 
 | config | rows | content |
 | --- | --- | --- |
-| `text` | 7,640 | text-modality questions for the synthetic substream benchmark. |
-| `synthetic_video` | 6,065 | 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.
+| `text` | 7,640 | synthetic substream questions, text modality, L=8…4096 |
+| `synthetic_video` | 6,065 | the same streams rendered as video |
+| `natural_video` | 1,028 | EPIC-Kitchens-100 clips + SoccerNet provenance rows, L=8…1024 |
+| `natural_video_density` | 880 | density-band questions over the `natural_video` clips, L=16…1024 |
+| `easyhuman` | 672 | 3-belt EasyHuman videos (224) and their text counterparts (448) |
 
 ## 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
+ds = load_dataset("anonstreammem/substream-recollection", "natural_video")["train"]
 ```
 
-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, ...}`).
+Each config also ships `questions.json` (NDJSON) and `manifest.json` (nested, for
+direct `main.py` ingestion).
 
-Video files are referenced by relative `video_path` / `clip_path` in each
-parquet. To resolve them locally, snapshot the repo:
+`video_path` and `clip_path` are **relative to the config directory**:
 
 ```python
 from huggingface_hub import snapshot_download
-root = snapshot_download(
-    "anonstreammem/substream-recollection",
-    repo_type="dataset",
-    allow_patterns=["synthetic_video/**", "natural_video/**", "text/**"],
-)
+root = snapshot_download("anonstreammem/substream-recollection", repo_type="dataset")
+path = f"{root}/natural_video/{row['video_path']}"
 ```
 
-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` |
+## Licences
 
-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.
+Synthetic and EasyHuman content is CC BY 4.0. EPIC-Kitchens-100 derived clips are
+CC BY-NC 4.0. SoccerNet rows are provenance-only under the SoccerNet NDA. Per-row
+`license` column; full texts in `LICENSES/`.
 
 ## Citation
 
-Anonymous, "Substream Recollection," 2026 (anonymized for review).
+Anonymous, "Good Memory Has ECC: Evaluating the Memory of Vision-Language Models Beyond Accuracy," 2026 (anonymized for review).
 
-## Changelog
+Cite the original sources for the natural videos:
 
-- **v2.1.0 (2026-07):** removed 55 rows in a data-quality pass (40 `text`, 15 `synthetic_video`); associated probe clips removed and row counts updated. No other rows changed.
+- Damen, D. et al. "Rescaling Egocentric Vision: Collection, Pipeline and
+  Challenges for EPIC-KITCHENS-100." IJCV, 2022.
+- Giancola, S., Amine, M., Dghaily, T., Ghanem, B. "SoccerNet: A Scalable Dataset
+  for Action Spotting in Soccer Videos." CVPR Workshops, 2018.

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