datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arc-agi3-kimi-k2.7-ar25
ARC-AGI-3 ar25 — Agent Trajectories (kimi-k2.7)
Gameplay trajectories from the harness×model pair kimi-k2.7 playing the
ARC-AGI-3 game ar25, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-kimi-k2.7-ar25.arc-agi3-agy-gemini3.1pro-tr87
ARC-AGI-3 tr87 — Agent Trajectories (agy-gemini3.1pro)
Gameplay trajectories from the harness×model pair agy-gemini3.1pro playing the
ARC-AGI-3 game tr87, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-agy-gemini3.1pro-tr87.arc-agi3-agy-gemini3.1pro-g50t
ARC-AGI-3 g50t — Agent Trajectories (agy-gemini3.1pro)
Gameplay trajectories from the harness×model pair agy-gemini3.1pro playing the
ARC-AGI-3 game g50t, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-agy-gemini3.1pro-g50t.arc-agi3-agy-gemini3.1pro-su15
ARC-AGI-3 su15 — Agent Trajectories (agy-gemini3.1pro)
Gameplay trajectories from the harness×model pair agy-gemini3.1pro playing the
ARC-AGI-3 game su15, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-agy-gemini3.1pro-su15.kepler-arc-agi-3-traces
Kepler 1.0 ARC-AGI-3 trace corpus
Run artifacts from Kepler 1.0, an open-source agent harness for the 25 public
ARC-AGI-3 games. A stock CLI coding agent
encodes its theory of each game as an executable world_model.py, certifies it
against the full recorded interaction history, plans inside the certified
model, and acts through a guarded channel that voids the plan on the first
misprediction.
Project page ·
Code ·
Paper ·
Integrity record
The canonical release contains two… See the full description on the dataset page: https://huggingface.co/datasets/cveinnt/kepler-arc-agi-3-traces.arc-agi-2-grids
ARC-AGI-2 Grids — training + analysis corpus (NVARC-compatible)
Companion dataset for the Kaggle ARC Prize 2026 (ARC-AGI-2) solver built on
sorokin/qwen3_4b_grids15_sft139 + per-task rank-256 LoRA (NVARC lineage).
Everything here is generated from public canonical data only (1,000
training / 120 evaluation tasks); no hidden competition data is included.
Contents
Path
Rows
Description
train/train_tasks.jsonl
1,000
canonical training tasks (full I/O)… See the full description on the dataset page: https://huggingface.co/datasets/Nabidnur/arc-agi-2-grids.arc_agi_1_augmentedarc-agi-1-ruleloopvit-rules
ARC-AGI-1 RuleLoopViT Rules
This dataset contains one canonical, task-specific English rule for each of the
400 official ARC-AGI-1 training tasks. Rules were inferred only from official
demonstration input/output pairs. Official test inputs, test outputs, and test
traces were excluded from rule authoring.
Each row includes:
a concise standalone core_rule_text;
a five-section full_rule_text;
the corresponding structured sections;
augmentation-aware references for colors… See the full description on the dataset page: https://huggingface.co/datasets/omrisap/arc-agi-1-ruleloopvit-rules.arc_agi_2_augmentedarc-agi-2-jepa-episodes
ARC-AGI-2 masked-demonstration episodes (JEPA-ready)
A task-level reasoning dataset built from the official ARC-AGI-2 tasks. Every row is an episode:
a set of demonstration pairs (context), one held-out test_input, and the target_output the model must
produce by inferring the rule shared by the demonstrations. It is not an input grid -> output grid
dataset; the unit of learning is the task rule.
Built 2026-09-25 with scripts/build_arc2_episodes.py (seed 20260925). Everything… See the full description on the dataset page: https://huggingface.co/datasets/koushikz1/arc-agi-2-jepa-episodes.