Update README.md

README.md

@@ -33,20 +33,24 @@ pretty_name: CausalARC – Abstract Reasoning with Causal World Models
   <sup>2</sup> YRIKKA, New York, NY -->
 </p>
 
+<b>NeurIPS 2025 Workshop: Bridging Language, Agent, and World Models for Reasoning and Planning (LAW)</b>
+<i>Spotlight Poster</i>
 
-<b>Note: This page is currently under construction. See our official project page here: <a href="https://jmaasch.github.io/carc/"
+
+<b>See our official project page here: <a href="https://jmaasch.github.io/carc/"
      target="_blank">https://jmaasch.github.io/carc/</a> </b>
 
 # Overview
 
-Reasoning requires adaptation to novel problem settings under limited data and distribution shift. 
+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 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.
+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">