---
license: mit
language:
  - en
pretty_name: ConceptARC Rule Annotations
tags:
  - arc
  - concept-arc
  - abstract-reasoning
  - rule-learning
  - evaluation
size_categories:
  - 10K<n<100K
configs:
  - config_name: model_evaluations
    data_files: "evaluation_rows.parquet"
    default: true
  - config_name: human_rule_annotations
    data_files: "human_rule_annotations.parquet"
  - config_name: ground_truth_rules
    data_files: "ground_truth_rules.parquet"
---

# ConceptARC Rule Annotations

Model outputs, natural-language rules and human judgements of those rules on the 480 tasks of the [ConceptARC](https://github.com/victorvikram/ConceptARC) benchmark. This is the data behind the paper **Distinguishing Performance From Competence in Evaluations of Humanlike Abstract Reasoning** (NeurIPS 2026, Evaluations and Datasets Track).

- **Paper:** [arXiv:2510.02125](https://arxiv.org/abs/2510.02125)
- **Project page and interactive viewer:** [claasbeger.github.io/performance-competence-gap](https://claasbeger.github.io/performance-competence-gap/)
- **Authors:** Claas Beger, Ryan Yi, Shuhao Fu, Kaleda Denton, Arseny Moskvichev, Sarah W. Tsai, Sivasankaran Rajamanickam, Melanie Mitchell
- **Contact:** claasbeger@santafe.edu

## Summary

Accuracy alone can overstate or understate how well a model reasons with the abstractions a benchmark was designed to test. For each ConceptARC task we collected the model's output grid together with the natural-language rule it stated, and a team of annotators judged whether that rule captures the intended abstraction. The same judgements were made for rules written by human participants.

Models were evaluated with textual and visual inputs, at low and medium reasoning effort, and with and without Python tools. ConceptARC covers 16 spatial and semantic concepts with 10 tasks per concept, and each task has 3 test inputs (480 test items in total).

## Contents

| Subset / file | Rows | Contents |
|---|---|---|
| `model_evaluations` (`evaluation_rows.parquet`, `.csv`) | 7,680 | One row per model attempt on one test input: 16 runs × 480 items. Covers o3 (low and medium effort, with and without tools) and Claude Sonnet 4 and Gemini 2.5 Pro (medium effort, with and without tools), each with textual and visual inputs. |
| `human_rule_annotations` (`human_rule_annotations.parquet`, `.csv`) | 3,049 | One row per human participant rule, from the study of Moskvichev et al. (2023). Rules were only collected for correct outputs. |
| `ground_truth_rules` (`ground_truth_rules.parquet`, `.csv`) | 160 | The intended rule for each ConceptARC task, as revised by the authors. Columns: `concept`, `puzzle`, `rule`. |
| `corpus/` | 160 files | ConceptARC task definitions (training demonstrations and test inputs and outputs), one JSON file per task under `corpus/<Concept>/<Task>.json`. |

### Model evaluation columns

| Column | Description |
|---|---|
| `modality` | `Textual` (grids given as numbers) or `Visual` (grids given as images). |
| `model` | `o3`, `claude-sonnet-4` or `gemini-2.5-pro`. |
| `run_folder` | Run configuration, e.g. `mediumeffort_autosummary_withtools` (reasoning effort, and whether Python tools were available). |
| `source_file` | Original log file the row came from. |
| `concept`, `puzzle`, `test_idx` | Concept group, task (e.g. `Center3`) and test input (1–3). Join `puzzle` with `corpus/<concept>/<puzzle>.json`. |
| `effort`, `tools_used` | Reasoning effort setting, and whether the model actually called a tool. |
| `answer` | The model's predicted output grid. |
| `is_correct`, `err` | Whether `answer` exactly matches the ground-truth grid, and any parsing error. This is a grid check only and says nothing about the rule. |
| `Rule` | The rule the model stated for its solution. |
| `summary` | The model's reasoning summary as logged. |
| `programming_calls` | Code the model ran, when tools were enabled. |
| `Rule_correct_label`, `Rule_correct` | Human judgement of `Rule` (see below). |
| `rule_evaluation_reasoning` | The annotators' notes on the judgement, where recorded. |
| `starred` | `1` if an annotator starred the row. |

### Human rule columns

| Column | Description |
|---|---|
| `concept_group`, `Task`, `Test` | Concept, task file and test input (1–3). |
| `VerbalDescription` | The rule written by the participant. |
| `Rule_correct_label`, `Rule_correct` | Human judgement of the rule (see below). |
| `starred` | `1` if an annotator starred the row. |

### Rule judgements

| `Rule_correct_label` | `Rule_correct` | Meaning |
|---|---|---|
| Correct - Intended | 1 | The rule works on the demonstrations and captures the abstraction the task was designed to test. |
| Correct - Unintended | 1 | The rule works on the demonstrations but does not capture the intended abstraction (for example a surface-level shortcut). |
| Incorrect | 0 | The rule does not work on the demonstrations. |
| Nonresponsive | -1 | No usable rule was given. |
| Unclear | -2 | The rule was too unclear to judge confidently. |
| Literal | -3 | Human rules only: the rule describes the literal process of producing the output grid (for example clicking "copy" or selecting one specific colour) rather than the transformation. |

The paper's figures group Nonresponsive, Unclear and Literal together as "Not Classified". Judgements were made by the authors. One annotator gave an initial judgement for each item, and ambiguous cases were discussed as a group until consensus was reached.

## Loading

```python
from datasets import load_dataset

models = load_dataset("ClaasBeger/ConceptARC_Rule_Annotations", "model_evaluations", split="train")
humans = load_dataset("ClaasBeger/ConceptARC_Rule_Annotations", "human_rule_annotations", split="train")
rules = load_dataset("ClaasBeger/ConceptARC_Rule_Annotations", "ground_truth_rules", split="train")
```

## Citation

```bibtex
@inproceedings{beger2026distinguishing,
  title     = {Distinguishing Performance From Competence in Evaluations of Humanlike Abstract Reasoning},
  author    = {Beger, Claas and Yi, Ryan and Fu, Shuhao and Denton, Kaleda and Moskvichev, Arseny and Tsai, Sarah W. and Rajamanickam, Sivasankaran and Mitchell, Melanie},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2026}
}
```

Please also cite ConceptARC:

```bibtex
@article{moskvichev2023conceptarc,
  title   = {The {ConceptARC} Benchmark: Evaluating Understanding and Generalization in the {ARC} Domain},
  author  = {Moskvichev, Arseny and Odouard, Victor Vikram and Mitchell, Melanie},
  journal = {Transactions on Machine Learning Research},
  year    = {2023}
}
```