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This paper introduces Brain2Semantics2Text, a non-invasive speech decoding method that maps MEG responses to semantic embeddings to reconstruct sentence-level text without word-level alignment.
This paper presents a training-free graph-based framework for reading order inference in complex document layouts, using language model signals and a max-regret inference rule. The method significantly outperforms existing baselines on historical manuscripts and multi-column benchmarks, achieving 95% edge accuracy on wrap-around Glossa layouts.