D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection
AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and expression origin may differ. We cast mixed-origin detection as dimension-to-composition source attribution, inferring content origin and expression origin before composing them into four collaboration types. We propose Dimension-to-Composition Routing (D2C-Routing), which routes content-side and expression-side evidence to supervised dimension heads before a learned gated compo
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- PossiblePossibly related (embedding) · 52%Can you teach yourself to detect AI writing? Maybe - The Conversation →
- PossiblePossibly related (embedding) · 48%The fanfiction community is at war with AI — and itself →
- PossiblePossibly related (embedding) · 48%Substack adds an AI detector to help spot blogs written by no one →
- PossiblePossibly related (embedding) · 48%Nature sub-journal study: Over 880,000 texts show AI writing assistants are accelerating linguistic homogenization - finance.biggo.com →
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
- FuzzyOverlapping authors or contributors · 62%TauricResearch/TradingAgents →
“Shared author/contributor keys: xiao”
- LinkedLinked via arxiv author · 85%Xin Cheng →
“D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection”
- LinkedLinked via arxiv author · 85%Fuwei Zhang →
“D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection”