Original Summary

TL;DR: detectors mostly catch chat training , not "AI". A base model paraphrasing reads human. Then each detector wants something different, and the fix for one breaks another. Open source pipeline + every measurement in the repo. Setup: one 1,990-word AI essay (Spanish), one change at a time, a human control (Unamuno, 1914) and the AI original in every session. Grammarly ZeroGPT GPTZero AI original 45% 45.3% After the pipeline 0% 0% What I learned: Base vs chat: Qwen3-4B-Base with the HIP adapter (Xu et al. 2026). Every chat-model "humanize" recipe I tried stayed at 64–100% in Grammarly. Sampling matters: the same paragraph gives 0% on one run and 100% on the next, so the final stage samples candidates and keeps the one that passes. Prompt-induced failure: I seeded each paragraph with the original's first two words to keep it in Spanish. That made the model rebuild the stock opening. 22 candidates: 100%. Different seed words: 0%. Detectors disagree: Grammarly wants long sentences; ZeroGPT punishes a long sentence containing one textbook clause (34% → 52% after joining). Rhythm + selection satisfies both. Fidelity guards: tries that lose a key concept, change a quotation or invent a reference get rejected (the model once wrote a citation that doesn't exist). CPU only, ~30–60 s per paragraph on an M4, no API. Spanish passes all three; English already passes ZeroGPT (100% → 0% on two essays), GPTZero's English model is next. Built for text you sign, not for cheating on assignments. Repo + full evidence: https://github.com/ervin-mo/humanizar-es I'd love numbers from other languages and detectors. Try it: it's a skill for Claude Code, Codex, OpenCode or Antigravity. Tell your agent: "Install the skill from github.com/ervin-mo/humanizar-es and humanize this text" . It installs itself and asks before downloading the model (~4.6 GB). Or by hand: git clone https://github.com/ervin-mo/humanizar-es && cd humanizar-es && python3 install.py . Disclosure: I built this (free, MIT). English isn't my first language, so I used an LLM to help me write this post.   submitted by   /u/Lumpy-Car-4086 [link]   [comments]

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  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/8 09:53:10