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The Hong Kong University of Science and Technology (Guangzhou)
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Why start from scratch when observational data can give you a head start? This paper shows how to design better causal experiments by actively learning *residual* biases, not the whole causal model.
LLMs can reason better on graphs if you teach them to selectively extract and denoise subgraphs, outperforming one-size-fits-all approaches in zero-shot settings.
