How much text does a prompt-free human-vs-machine detector need, and what does it key on?

What it found

The mimic-contexts n-gram detector (TF-IDF character and word n-grams, fitted on five families) is scored on poetry, reviews and wiki after cutting each text to its first N characters: AUROC 0.65 at 50, 0.75 at 100, 0.84 at 400 (the length the imitation-game loop shows its detector) and 0.93 at 1,500; refitting on the short text recovers only 0.02–0.07. Stripping punctuation changes AUROC by +0.003 and shuffling words by −0.014, so the detector reads vocabulary, not syntax or formatting (lowercasing and whitespace views are no-ops for this vectoriser). Preliminary: plain-prompt RAID text only, one detector, one split, no LLM involved.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Tests

More in Adversarial human imitation

• If a human-vs-machine detector's threshold is set for a 5% false-positive rate, does that rate hold on a kind of text it never saw?If a detector's threshold is set for a 5% false-positive rate, does it hold on an unseen text type? Leave-one-family-out with the mimic-contexts n-gram detector: realised rate 0.00 to 0.54 (abstracts 0.54, books 0.18), and recipes catch 1.7% of machine text. Preliminary, plain-prompt RAID text only.
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• Does a kernel MMD test catch the same wrong digital twins as a classifier two-sample test?Does a kernel MMD test catch the same wrong digital twins as a classifier two-sample test? Stylised pendulum: raw-trajectory MMD is blind to a Coulomb-friction error (power 0.05 to 0.06) that boosted trees catch from n = 200, but MMD on second differences detects a +25% noise-level error at 0.97 from n = 100.
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Other research topics

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.