PLOT localizes causal variables in neural networks by fitting optimal transport couplings between abstract and neural intervention effect geometries, enabling fast handles or guided search.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
PLOT localizes causal variables in neural networks by fitting optimal transport couplings between abstract and neural intervention effect geometries, enabling fast handles or guided search.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.