Datasets:
Formats:
json
Languages:
English
Size:
< 1K
Tags:
physical-icl
physical-in-context-learning
video-generation
world-model
physics
demonstration-learning
License:
| license: other | |
| task_categories: | |
| - text-to-video | |
| - image-to-video | |
| language: | |
| - en | |
| pretty_name: Physical-ICL | |
| tags: | |
| - physical-icl | |
| - physical-in-context-learning | |
| - video-generation | |
| - world-model | |
| - physics | |
| - demonstration-learning | |
| - physics-iq | |
| size_categories: | |
| - n<1K | |
| configs: | |
| - config_name: h3_outcome_hidden_primary_v1 | |
| default: true | |
| data_files: | |
| - split: train | |
| path: data/h3_causal_icl_v1/training_inputs/train.jsonl | |
| - split: validation | |
| path: data/h3_causal_icl_v1/training_inputs/dev.jsonl | |
| - config_name: h3_outcome_hidden_all345 | |
| data_files: | |
| - split: train | |
| path: data/h3_causal_icl_v1/outcome_hidden_tasks_v1_all345/tasks/train.jsonl | |
| - split: validation | |
| path: data/h3_causal_icl_v1/outcome_hidden_tasks_v1_all345/tasks/dev.jsonl | |
| - config_name: h3_ordinary_pilot_v1 | |
| data_files: | |
| - split: train | |
| path: data/h3_causal_icl_v1/ordinary_pilot_v1/training_inputs/train320.jsonl | |
| # Physical-ICL | |
| **Physical-ICL** is a project-level dataset repository for studying physical in-context learning in video generation. | |
| The current version contains a preliminary subset built from Physics-IQ. Each sample is organized as a query video with candidate demonstration videos. These demonstrations are labeled by their relationship to the query, such as good, weak, opposite, or irrelevant demonstrations. | |
| All 345 task records and the first ordinary SFT (including final LoRA and 32 historical videos) are now included: [All 345 tasks](data/h3_causal_icl_v1/outcome_hidden_tasks_v1_all345/README.md) · [First SFT](data/h3_causal_icl_v1/ordinary_pilot_v1/README.md). | |
| ## H3 causal-ICL release — 2026-10-17 | |
| [data/h3_causal_icl_v1/](data/h3_causal_icl_v1/README.md) contains the current 5,341-pair causal rulebank, the completed outcome-hidden diagnostic (65 train / 2 dev, two independent 200-update LoRA models, 12 videos), exact model inputs, losses, quality flags and a browsable code mirror. Rubrics and dev evidence are separate from model inputs. Eight mechanism-positive training pairs have different specific responses and are explicitly flagged. This two-case diagnostic does not establish an overall causal-ICL gain. | |
| The explicit `h3_outcome_hidden_primary_v1` dataset-viewer configuration loads only its 65/2 task manifests; the older media collections remain available under their existing paths. Original training source videos are referenced, not duplicated. [Browse code](data/h3_causal_icl_v1/code/physical_icl_h3/README.md) · [12 generated videos](data/h3_causal_icl_v1/videos) · [Review notes](data/h3_causal_icl_v1/audit/preliminary_visual_notes.json). | |
| ## Current subset | |
| | Subset | Path | Description | | |
| |---|---|---| | |
| | Physics-IQ preliminary subset | `data/physiq_prelim/` | A preliminary physical ICL dataset constructed from Physics-IQ videos. | | |
| ## Repository structure | |
| data/ | |
| physiq_prelim/ | |
| gt_data/ | |
| task_0001/ | |
| episode_0001/ | |
| video.mp4 | |
| prompt/ | |
| init_frame.png | |
| prompt.txt | |
| demos/ | |
| good_demo_01.mp4 | |
| good_demo_01.png | |
| weak_demo_01.mp4 | |
| weak_demo_01.png | |
| opposite_demo_01.mp4 | |
| opposite_demo_01.png | |
| irrelevant_demo_01.mp4 | |
| irrelevant_demo_01.png | |
| summary.json | |
| case_summary.csv | |
| README.md | |
| export_warnings.txt | |
| storyboard_warnings.txt | |
| ## Sample format | |
| Each sample is stored under: | |
| data/physiq_prelim/gt_data/task_xxxx/episode_0001/ | |
| The files have the following meanings: | |
| | File or folder | Description | | |
| |---|---| | |
| | `video.mp4` | Query target video. In the current version, this uses the full Physics-IQ video when available. | | |
| | `prompt/init_frame.png` | Query initial frame, extracted from the first frame of the full query video. | | |
| | `prompt/prompt.txt` | Text prompt for the query video. | | |
| | `demos/*.mp4` | Candidate demonstration videos. These use 5-second Physics-IQ testing clips. | | |
| | `demos/*.png` | 3x3 event-aware storyboard images generated from the corresponding demo video. | | |
| ## Demo types | |
| Demo files are named by their coarse relationship to the query: | |
| | Filename pattern | Meaning | | |
| |---|---| | |
| | `good_demo_XX.mp4` | A suitable positive demonstration. | | |
| | `weak_demo_XX.mp4` | A weakly related demonstration. | | |
| | `opposite_demo_XX.mp4` | A demonstration showing an opposite or contrastive physical outcome. | | |
| | `irrelevant_demo_XX.mp4` | An unrelated or different-category control demonstration. | | |
| The corresponding `.png` file is a 3x3 storyboard extracted from the same demo video. For example: | |
| good_demo_01.mp4 | |
| good_demo_01.png | |
| Low-quality generated demonstrations are not included in the current version. | |
| ## Metadata files | |
| | File | Description | | |
| |---|---| | |
| | `data/physiq_prelim/summary.json` | Machine-readable metadata for all samples and demonstrations. | | |
| | `data/physiq_prelim/case_summary.csv` | Human-readable case-level summary. | | |
| | `data/physiq_prelim/export_warnings.txt` | Export warnings, if any. | | |
| | `data/physiq_prelim/storyboard_warnings.txt` | Storyboard generation warnings, if any. | | |
| ## Metadata schema | |
| Each item in `summary.json` corresponds to one query sample. The main fields are: | |
| | Field | Description | | |
| |---|---| | |
| | `case_id` | Unique case identifier. | | |
| | `task_name` | Task folder name. | | |
| | `gt_path` | Path to the query video. | | |
| | `image` | Path to the query initial frame. | | |
| | `prompt` | Query prompt. | | |
| | `query_scenario` | Physics-IQ scenario name for the query. | | |
| | `query_macro_group` | Coarse physical category. | | |
| | `query_event_tag` | Fine-grained event tag. | | |
| | `demos` | Candidate demonstrations for this query. | | |
| | `available_demo_types` | Available demo types for this query. | | |
| Each demo entry contains: | |
| | Field | Description | | |
| |---|---| | |
| | `demo_type` | One of `good`, `weak`, `opposite`, or `irrelevant`. | | |
| | `demo_path` | Path to the demo video. | | |
| | `demo_image_path` | Path to the 3x3 storyboard image. | | |
| | `demo_scenario` | Physics-IQ scenario name for the demo. | | |
| | `demo_relation` | More detailed relation label. | | |
| | `physical_similarity` | Physical similarity label. | | |
| | `visual_similarity` | Visual similarity label. | | |
| ## Usage example | |
| Load the metadata: | |
| import json | |
| from pathlib import Path | |
| root = Path("data/physiq_prelim") | |
| with open(root / "summary.json", "r", encoding="utf-8") as f: | |
| items = json.load(f) | |
| sample = items[0] | |
| query_video = root.parent.parent / sample["gt_path"] | |
| query_image = root.parent.parent / sample["image"] | |
| query_prompt = sample["prompt"][0] | |
| good_demos = [ | |
| d for d in sample["demos"] | |
| if d["demo_type"] == "good" | |
| ] | |
| For video-capable models, use `demo_path`. For image-only models, use `demo_image_path`. | |
| ## Notes | |
| - Query videos use full Physics-IQ videos when available. | |
| - Demo videos use 5-second Physics-IQ testing clips. | |
| - Demo storyboard images are generated using event-aware 3x3 frame sampling. | |
| - The current version does not include low-quality generated demonstrations. | |
| - This repository is intended for research and preliminary experiments. | |