LLMs learn causal relations in text via variational induction by detecting difference-makers in word sequences across diverse training data.
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Human visual interestingness is linearly decodable from final-layer embeddings in Qwen3-VL-8B and becomes progressively more structured across vision and language layers without explicit supervision.
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Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
LLMs learn causal relations in text via variational induction by detecting difference-makers in word sequences across diverse training data.
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Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers
Human visual interestingness is linearly decodable from final-layer embeddings in Qwen3-VL-8B and becomes progressively more structured across vision and language layers without explicit supervision.