Does adding a zero-shot perplexity score to the n-gram detector help it catch machine text in families it never saw?

What it found

The mimic-contexts n-gram detector and its Binoculars-style score (Qwen2.5-0.5B) are uncorrelated within each class (−0.02 human, 0.02 machine), yet combining them barely helps on the held-out families poetry, reviews and wiki: n-gram AUROC 0.926, mean of z-scores 0.935 (paired gain +0.009, 95% interval −0.010 to +0.026), fitted logistic stacks −0.003 and −0.019. It helps on wiki (0.892 to 0.935) and hurts on poetry and mpt-chat, where the zero-shot score is weak. Preliminary: 720 held-out texts, three chat models, a small scorer, one split; no LLM was run.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Tests

More in Adversarial human imitation

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.