TLDR

pages/_publications/lczero-planning.html

@@ -1,6 +1,6 @@
 ---
-title: LCZero Planning
-tldr: TLDR
+title: Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents
+tldr: We propose contrastive sparse autoencoders (CSAE), a novel framework for studying pairs of game trajectories. Using CSAE, we are able to extract and interpret concepts that are meaningful to the chess-agent plans. We primarily focused on a qualitative analysis of the CSAE features before proposing an automated feature taxonomy. Furthermore, to evaluate the quality of our trained CSAE, we devise sanity checks to wave spurious correlations in our results.
 tags:
   - Chess
   - XAI
@@ -11,9 +11,9 @@
 editedOn: 2024-06-05
 authors:
   - "[[Yoann Poupart]]"
-readingTime:
+readingTime: 25
 image: /assets/publications/images/lczero-planning_thumbnail.png
-description: TL;DR> TLDR
+description: TL;DR> We propose contrastive sparse autoencoders (CSAE), a novel framework for studying pairs of game trajectories. Using CSAE, we are able to extract and interpret concepts that are meaningful to the chess-agent plans. We primarily focused on a qualitative analysis of the CSAE features before proposing an automated feature taxonomy. Furthermore, to evaluate the quality of our trained CSAE, we devise sanity checks to wave spurious correlations in our results.
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