Datasets:

Vincwng
/

Physical-ICL

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 · First SFT.

H3 causal-ICL release — 2026-10-17

data/h3_causal_icl_v1/ 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 · 12 generated videos · Review notes.

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.