Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models

Excerpt

arXiv:2610.06603v1 Announce Type: cross Abstract: Text from multiple sources can become interleaved into a single sequence when attribution metadata is lost, such as overlapping speech transcripts, document reading flows, or concurrent agent streams. We formalize this challenge as Word-Level Text Unmixing: given an interleaved lexical stream and source count K, recover the original source sequences while preserving every word occurrence and its within-source order exactly. Directly generating se