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R. Urbaniak

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#artificial intelligence Preprint Oct 2026

Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models

Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a time misses context-dependent effects: a primary component can inhibit the activation of a backup, leading to issues with ranking components. Actual causality studies...

Sankaran Vaidyanathan, R. Urbaniak, Emily Bunnapradist et al. · 0 citations

#artificial intelligence Preprint Sep 2026

A Computationally Feasible Framework for Causal Probabilistic Explanation

Probabilistic Causal Impact (PCI) builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo.

R. Urbaniak, Sam Witty, Daniel Waxman et al. · 1 citation