Add nested manifest.json per config for direct main.py ingestion (co-exists with parquet+NDJSON); fix synthetic_video input_sequence by joining with text-config S_tokens

README.md

@@ -70,18 +70,22 @@ anonstreammem/substream-recollection/
 │   └── SoccerNet-NOTE.txt
 ├── text/
 │   ├── questions.parquet
-│   └── questions.json            # NDJSON copy of the 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
+│   ├── 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
@@ -97,6 +101,19 @@ 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
 
@@ -106,6 +123,25 @@ eh     = load_dataset("anonstreammem/substream-recollection", "easyhuman")["trai
 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:
 

metadata.json

@@ -114,6 +114,46 @@
       "encodingFormat": "application/x-parquet",
       "contentSize": "37225",
       "sha256": "5c4815297264c05f37957e6450077cae9f7ac8a48a0247a841c34abd827af6df"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "text-manifest-json",
+      "name": "text-manifest-json",
+      "description": "Nested-shape manifest.json for the text config — directly ingestible by the project's main.py via load_patternvideos_manifest. Each video entry contains a 'questions' list and 'sequences_used' = {S_tokens, S_lanes}. Co-exists with questions.parquet (for datasets.load_dataset) and questions.json (NDJSON).",
+      "contentUrl": "https://huggingface.co/datasets/anonstreammem/substream-recollection/resolve/main/text/manifest.json",
+      "encodingFormat": "application/json",
+      "contentSize": "9340055",
+      "sha256": "37d0a234526aa358734cc6d6b6de5e72728ec344eaab38c2f95cafcb01762487"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "synthetic_video-manifest-json",
+      "name": "synthetic_video-manifest-json",
+      "description": "Nested-shape manifest.json for the synthetic_video config — directly ingestible by the project's main.py. Each video entry carries the parent stream's S_tokens / S_lanes and per-question candidate metadata.",
+      "contentUrl": "https://huggingface.co/datasets/anonstreammem/substream-recollection/resolve/main/synthetic_video/manifest.json",
+      "encodingFormat": "application/json",
+      "contentSize": "5729546",
+      "sha256": "e35dcf7ee53bc65a31e2725662da53aa0143262e43a03d0b375e179afb01528c"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "natural_video-manifest-json",
+      "name": "natural_video-manifest-json",
+      "description": "Nested-shape manifest.json for the natural_video config — directly ingestible by the project's main.py. One video per question (1028 entries). No token/lane structure.",
+      "contentUrl": "https://huggingface.co/datasets/anonstreammem/substream-recollection/resolve/main/natural_video/manifest.json",
+      "encodingFormat": "application/json",
+      "contentSize": "605090",
+      "sha256": "f3f719f2812d8f433861a741440d13e836c6b008d516804259944e6c98b8d2ef"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "easyhuman-manifest-json",
+      "name": "easyhuman-manifest-json",
+      "description": "Nested-shape manifest.json for the easyhuman config — directly ingestible by the project's main.py. Per-video 'modality' field distinguishes text vs video rows.",
+      "contentUrl": "https://huggingface.co/datasets/anonstreammem/substream-recollection/resolve/main/easyhuman/manifest.json",
+      "encodingFormat": "application/json",
+      "contentSize": "1123982",
+      "sha256": "1fad69c8c36c476345e4ff478637cc55fde5a302e5fef9ce4e5a8da490b1eb67"
     }
   ],
   "recordSet": [