Update README.md

README.md

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-license: gpl-3.0
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+---
+license: gpl-3.0
+---
+
+<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><br>
+<p style="text-align:center">
+  <img style="vertical-align:middle" src='https://jmaasch.github.io/carc/static/images/pch.png' width="40%" class="center">
+  <br>
+  <small>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><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. 
+</p>
+<br><br>
+<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>
+    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>
+</p>