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.

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.