Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection

• URL: http://arxiv.org/abs/2602.01240v1
• Date: Sun, 01 Feb 2026 14:02:51 GMT
• Title: Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection
• Abstract summary: Zero-shot methods detect LLM-generated text by computing statistical signatures using a surrogate model.<n>Existing approaches typically employ a fixed surrogate for all inputs regardless of the unknown source.<n>We propose a prototype-based framework that learns text-detector affinity through two-stage training.
• Score: 15.739137990102991
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Zero-shot methods detect LLM-generated text by computing statistical signatures using a surrogate model. Existing approaches typically employ a fixed surrogate for all inputs regardless of the unknown source. We systematically examine this design and find that detection performance varies substantially depending on surrogate-source alignment. We observe that while no single surrogate achieves optimal performance universally, a well-matched surrogate typically exists within a diverse pool for any given input. This finding transforms robust detection into a routing problem: selecting the most appropriate surrogate for each input. We propose DetectRouter, a prototype-based framework that learns text-detector affinity through two-stage training. The first stage constructs discriminative prototypes from white-box models; the second generalizes to black-box sources by aligning geometric distances with observed detection scores. Experiments on EvoBench and MAGE benchmarks demonstrate consistent improvements across multiple detection criteria and model families.

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