One Tokenizer achieves zero-gap multimodal integration by mapping all inputs to a unified token vocabulary, allowing native LLMs to perform deep cross-modal reasoning without modular encoders or fusion layers, and outperforming encoder-based baselines on DNA-text tasks.
Context-aware regularization with markovian in- tegration for attention-based nucleotide analysis.arXiv preprint arXiv:2507.09378,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.GN 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Mind the Gap No More: Achieving Zero-Gap Multimodal Integration via One Tokenizer
One Tokenizer achieves zero-gap multimodal integration by mapping all inputs to a unified token vocabulary, allowing native LLMs to perform deep cross-modal reasoning without modular encoders or fusion layers, and outperforming encoder-based baselines on DNA-text tasks.