README.md

---
license: gpl-3.0
language:
- en
tags:
- world-models
- arc
- abstraction-reasoning-corpus
- reasoning
- abstract-reasoning
- logical-reasoning
- causal-reasoning
- counterfactual-reasoning
- evaluation
- benchmark
- structural-causal-models
- causal-discovery
- test-time-training
- few-shot-learning
- in-context-learning
task_categories:
- question-answering
pretty_name: CausalARC – Abstract Reasoning with Causal World Models
---



<p align="center">
  <img src='https://jmaasch.github.io/carc/static/images/header.png' width="100%" class="center">
  <!--
  Jacqueline R. M. A. Maasch<sup>1</sup>, John Kalantari<sup>2</sup>, Kia Khezeli<sup>2</sup>
  <sup>1</sup> Cornell Tech, New York, NY
  <sup>2</sup> YRIKKA, New York, NY -->
</p>

<p align="center">
  <b>NeurIPS 2025 LAW Workshop ★ Spotlight Paper </b>
  <br>
  <b>Amazon AGI Trusted AI Symposium 2026 ★ Poster</b>
  <br>
  <b>See our official project page here: <a href="https://jmaasch.github.io/carc/"
       target="_blank">https://jmaasch.github.io/carc/</a> </b>
</p>

# Overview

On-the-fly reasoning often requires adaptation to novel problems under limited data and distribution shift. 
This work introduces CausalARC: an experimental testbed for AI reasoning in low-data and out-of-distribution regimes, 
modeled after the Abstraction and Reasoning Corpus (ARC). Each CausalARC reasoning task is sampled from a fully specified 
<i>causal world model</i>, formally expressed as a structural causal model. 
Principled data augmentations provide observational, interventional,  and counterfactual feedback about the world model in 
the form of few-shot, in-context learning demonstrations. As a proof-of-concept, we illustrate the use of CausalARC for four 
language model evaluation settings: (1) abstract reasoning with test-time training, (2) counterfactual reasoning with 
in-context learning, (3) program synthesis, and (4) causal discovery with logical reasoning. Within- and between-model 
performance varied heavily across tasks, indicating room for significant improvement in language model reasoning.

<p align="center">
  <img src='https://jmaasch.github.io/carc/static/images/pch.png' width="35%" class="center">
  <i>Pearl Causal Hierarchy: observing factual realities (L1), exerting actions to 
    induce interventional realities (L2), and imagining alternate counterfactual realities (L3) 
    [<a href="https://dl.acm.org/doi/pdf/10.1145/3501714.3501743?casa_token=hJAJZQLNGbEAAAAA:exuQk37fuXGMkpOVJEKACgnupjkP-adDQGhv2YzfBN9MfoERkAcHQRDgT3myWfccfqucQd8h63Q"
     target="_blank">1</a>]. Lower levels generally underdetermine higher levels.
  </i>
</p>

This work extends and reconceptualizes the ARC setup to support causal reasoning evaluation under limited data and distribution shift. 
Given a fully specified SCM, all three levels of the Pearl Causal Hierarchy (PCH) are well-defined: any observational (L1), interventional 
(L2), or counterfactual (L3) query can be answered about the environment under study [2]. This 
formulation makes CausalARC an open-ended playground for testing reasoning hypotheses at all 
three levels of the PCH, with an emphasis on abstract, logical, and counterfactual reasoning. 

<p align="center">
  <img src='https://jmaasch.github.io/carc/static/images/demo.png' width="100%" class="center">
  <i> The CausalARC testbed. <b>(A)</b> First, SCM <i>M</i> is manually transcribed in Python code. <b>(B)</b>
    Input-output pairs are randomly sampled, providing observational (L1) learning signals about the
    world model. <b>(C)</b> Sampling from interventional submodels <i>M</i>' of <i>M</i> yields interventional (L2)
    samples (x', y'). Given pair (x, y), performing multiple interventions while holding the exogenous
    context constant yields a set of counterfactual (L3) pairs. <b>(D)</b> Using L1 and L3 pairs as in-context
    demonstrations, we can automatically generate natural language prompts for diverse reasoning tasks.
  </i>
</p>