SYNCR

SYNCR is a simulator-grounded framework for cross-video reasoning: questions that cannot be answered from any single video, but require aligning events, matching identities, comparing motion, or integrating partial observations across several videos.

Because the videos are produced in simulation, every answer is derived from environment state rather than from human annotation. The same generators produce both an evaluation benchmark and a training set over disjoint videos, so supervision can be studied without contaminating evaluation.

Contents

Path	Items	Description
test/metadata.jsonl	4,000	Evaluation benchmark, 500 questions for each of 8 tasks
train/sft_all8_answer_only.json	15,960	Training set, chat-formatted, answer-only targets
habitat_data.tar	—	Habitat clips used by both splits
kubric_data.tar	—	Kubric clips used by both splits

The evaluation set draws on 4,827 distinct video files and the training set on 14,956, with no video shared between the two splits. (These are unions over tasks; per-task counts sum to more, because some videos are used by more than one task.)

Loading

The benchmark and the training set have different schemas, so they are published as two configs:

from datasets import load_dataset

test  = load_dataset("CrossVideoReasoning/SYNCR", split="test")               # 4,000 questions
train = load_dataset("CrossVideoReasoning/SYNCR", "training", split="train")  # 15,960 examples

Getting the videos

Video paths in the annotations are relative to the repository root and resolve under videos/:

videos/clvr/video_validation/video_11194.mp4
videos/clvr/video_train/video_05825.mp4
videos/habitat_data/route_plan/...
videos/kubric_data/scene_144/cam2.mp4

1. Habitat and Kubric — extract the archives in this repository:

mkdir -p videos && for f in *_data.tar; do tar -xf "$f" -C videos/; done

2. CLEVRER — these clips are not redistributed here. Download them from the official CLEVRER release at http://clevrer.csail.mit.edu and place them so the paths above resolve:

mkdir -p videos/clvr
unzip video_validation.zip -d videos/clvr/     # 1,987 clips used by the benchmark
unzip video_train.zip      -d videos/clvr/     # 5,879 clips used by the training set

Which sources each split needs:

Split	CLEVRER	Habitat	Kubric	Total distinct
Evaluation	1,987 (video_validation)	1,086	1,754	4,827
Training	5,879 (video_train)	4,958	4,119	14,956

Tasks

Eight tasks in four reasoning families, 500 evaluation questions each.

Family	Task	Source	Videos/question	Chance
Temporal Alignment	Multi-Angle Synchronization	Kubric	3	25%
Temporal Alignment	Sequential Ordering	CLEVRER	4	25%
Spatial Tracking	Object Re-identification	Habitat	2	25%
Spatial Tracking	Spatial Measurement	Kubric	2	25%
Comparative Reasoning	Numerical Comparison	CLEVRER	2	20%
Comparative Reasoning	Kinematic Comparison	CLEVRER	2	25%
Holistic Synthesis	Object Counting	Habitat	3	25%
Holistic Synthesis	Route Planning	Habitat	3	25%

Numerical Comparison presents five options; every other task presents four. Correct answers are balanced across option positions (975 each for A–D, plus 100 at E for the five-option task).

Schema

Each row of test/metadata.jsonl:

Field	Type	Description
category	string	Reasoning family (Temporal_Alignment, Spatial_Tracking, Comparative_Reasoning, Holistic_Synthesis)
task	string	Task name
question	string	The question text
options	list[string]	Answer choices, each prefixed A), B), …
answer	string	The correct option, verbatim
videos	list[string]	Video paths, in the order the question refers to them as Video 1, Video 2, …
video_ranges	list[[float, float]]	Start/end seconds per video, where the question is scoped to a window (1,000 questions)
reasoning	string	Simulator-derived derivation of the answer, where available (1,000 questions)

Video order is significant. videos[0] is "Video 1" in the question text, and so on.

Evaluation protocol

Models answer zero-shot with the videos supplied in order and labelled Video 1:, Video 2:, … We score the parsed final option with deterministic decoding where supported.

Two notes that materially affect measured accuracy:

• Allow enough generation budget. Some models emit long chains of thought before the answer and, if truncated, produce no parseable option — which scores as wrong and understates the model. This is most pronounced on Synchronization. A budget of 8,192 new tokens is sufficient for the models we tested; 2,048 is not.
• Score unparseable answers explicitly. Report them separately rather than silently counting them as incorrect, particularly for base models.

Training set

train/sft_all8_answer_only.json is a chat-formatted mixture over all eight tasks (15,960 examples), with targets containing only the answer line. Each message list interleaves text and video content blocks; the video field carries the same relative paths as the benchmark.

Video sources and licensing

Videos are rendered from three simulators, each with its own upstream terms:

• CLEVRER — Yi et al., ICLR 2020 — http://clevrer.csail.mit.edu
• Kubric — Greff et al., CVPR 2022
• Habitat — Savva et al., ICCV 2019 / Szot et al., NeurIPS 2021

The annotations in this repository are released under CC BY 4.0. CLEVRER clips are not redistributed here and remain under their original terms. Rendered Habitat and Kubric clips remain subject to the licenses of those simulators and their scene assets; consult those before redistribution.

Changes from the previous release

This release supersedes the earlier version of this dataset and is not a drop-in replacement:

• The evaluation set is now 4,000 questions (500 per task), replacing the previous 8,163-example single split, and a 15,960-example training split has been added.
• Tasks were renamed and regenerated: CLEVRER collision comparison → Numerical Comparison, Kubric spatial tracking → Spatial Measurement, Habitat agent/object tracking → Object Re-identification.
• Video paths are now repository-relative rather than absolute.

Results reported in the accompanying paper correspond to this version.