Can a small open code model write a cursor-stroke generator that fools a human-vs-bot detector?
research/mimic-codeloop
A 1.5B code model rewrites the cursor generator against the mimic-cursor detector: only 25 to 29% of its candidates run, and the reference detector stays at AUROC 1.000, tied with the component search at the same 24-candidate budget (one seed, preliminary negative).
What it found
Replaces mimic-cursor's component search with Qwen2.5-Coder-1.5B-Instruct, run on CPU, which reads the detector's score (and, in one condition, its feature gaps) and rewrites a Python stroke generator: 6 rounds of 4 candidates, the component search's per-round budget. Data, detector, reference detector and copy test are those of mimic-cursor (DUX, 46 people). The reference detector stays at AUROC 1.000 on held-out people in both conditions, as does the component search at 24 candidates (0.999 to 1.000, 8 runs). Only 7 of 24 (blind) and 6 of 24 (feedback) candidates were usable; the rest were rejected as banned tokens (likely imports), failed to load or crashed. Limitations: one seed per condition (three were planned), one small model, 24 candidates, no human templates available to the model, a sandbox whose import ban may account for some rejections. Preliminary and negative; a larger model on a GPU is an owner step.
A figure from its own output
Paper, code and results
More in Adversarial human imitation
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- Do prompts that fool a prompted LLM detector also fool detectors that use no prompt?Do the mimic-loop imitator prompts fool detectors they never saw? No: matched on length, all 26 testable prompts are still caught (n-gram AUROC 0.83 to 0.96, Binoculars-style 0.84 to 1.00); low raw scores come from text length.
- Can a learned Markov chain over a person's webcam frames fool a detector?Can a Markov chain over a person's frames fool a detector? No: chains with 8, 32 or 128 states, fitted on one person or on everyone, emitting state centres or real frames, are caught at AUROC 1.000 (one cell 0.999), where stitching the same real frames in 50-frame blocks scored 0.57 to 0.65.
- Does fitting a webcam-signal generator to one person fool a detector that has seen that person?Does fitting to one person help? Copying a person's own earlier windows is caught at AUROC 0.59 to 0.65 on their later windows; copying other people is 0.58 to 0.91, and 0.93 to 0.97 once the detector knows the person; a fitted parametric generator is caught at 1.000 either way.
- Does giving a Markov chain dwell times or a second-order memory let it fool a webcam-signal detector?Does giving a chain over a person's frames dwell times or second-order memory help? No: semi-Markov and second-order chains (8 or 32 states, own or pooled fit, centre or real-frame emission) are all caught at AUROC 0.999 to 1.000.
- Why is block-stitched real webcam signal still caught: the joins, or mixing people?Why is block-stitched real webcam signal still caught? With 50-frame blocks random stitching gives AUROC 0.887, one person's windows only 0.687, join-matched 0.796, both 0.588 (= whole-window replay 0.597); at 5 frames all are caught (8 seeds).
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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.