Update README.md

README.md

@@ -4,51 +4,52 @@ license: gpl-3.0
 
 # CausalARC: Abstract Reasoning with Causal World Models
 
-Jacqueline R. M. A. Maasch^1, John Kalantari^2, Kia Khezeli^2
-1. Cornell Tech, New York, NY
-2. YRIKKA, New York, NY
+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
+
+
+**Note: This page is currently under construction. See our official project page here: <a href="https://jmaasch.github.io/carc/"
+     target="_blank">https://jmaasch.github.io/carc/</a> **
+
+## Overview
+
+Reasoning requires adaptation to novel problem settings 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 causal world model, formally expressed as a structural causal model (SCM). 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.
+
 
-<p>
-  <b>Note: This page is currently under construction. See our official project page here: <a href="https://jmaasch.github.io/carc/"
-     target="_blank">https://jmaasch.github.io/carc/</a> </b>
-  <br>
-  <br>
-  Reasoning requires adaptation to novel problem settings 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 causal world model, formally expressed as a structural causal model (SCM). 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.
-</p>
-<br>
 <p style="text-align:center">
   <img style="vertical-align:middle" src='https://jmaasch.github.io/carc/static/images/pch.png' width="30%" class="center">
   <br>
-  <small>Pearl Causal Hierarchy: observing factual realities (L1), exerting actions to 
+  <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.
-      </small>
-</p>
-<br>
-<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. 
+  </i>
 </p>
-<br>
+
+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 style="text-align:center">
   <img style="vertical-align:middle" src='https://jmaasch.github.io/carc/static/images/demo.png' width="100%" class="center">
   <br>
-  <small> The CausalARC testbed. <b>(A)</b> First, SCM <i>M</i> is manually transcribed in Python code. <b>(B)</b>
+  <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.
-      </small>
+  </i>
 </p>