AI RESEARCH
Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence
arXiv CS.LG
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ArXi:2510.07500v2 Announce Type: replace We study black-box detection of machine-generated text under practical constraints: the scoring model (proxy LM) may mismatch the unknown source model, and per-input contrastive generation is costly. We propose SurpMark, a reference-based detector that summarizes a passage by the dynamics of its token surprisals. SurpMark discretizes surprisals into interpretable states, estimates a state-transition matrix for the test text, and scores it via a generalized Jensen-Shannon (GJS) gap between the test transitions and two fixed references (human vs.