arXiv CS
open access
Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models
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 so
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another#1327731
- Word-Level Text Unmixing via Evidence-Preserving Ownership Routing with Language Models#1215699
- TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputs#998930
- ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings#482110
- Semantic Fibers and Cross-Gram Interference: A Calculus of Safety Drift in Overcomplete Representations#1084775
Record · ID 1194478
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive