Hanshuai Cui

Web platforms must classify new pages before anyone labels them, large language model labels per page are noisy, and links mean opposite things across sites: online stores often join same-category products, while university sites mostly join pages of different classes. ZeroPolar calls the sign with which links enter a prediction structural polarity, reads that sign without true labels from the excess agreement of LLM labels and text-prototype predictions along edges, and applies one signed propagation step. With one GPT-4o label per page, ZeroPolar-T reaches the highest eleven-graph mean, 73.99%, 6.01 points above LLM-BP.

Web platforms must classify new pages before anyone labels them, large language model labels per page are noisy, and links mean opposite things across sites: online stores often join same-category products, while university sites mostly join pages of different classes. ZeroPolar calls the sign with which links enter a prediction structural polarity, reads that sign without true labels from the excess agreement of LLM labels and text-prototype predictions along edges, and applies one signed propagation step. With one GPT-4o label per page, ZeroPolar-T reaches the highest eleven-graph mean, 73.99%, 6.01 points above LLM-BP.

Web platforms screen posts for large-language-model text at a very low false-positive rate, and invisible edits such as homoglyphs and zero-width spaces push machine text into the human score range. NormDetect normalizes each document with Unicode rules, then anchors a small per-section classifier head to the normalized Binoculars zero-shot score, with the blend weight chosen on training folds under a human false-positive cap. On eight RAID sections and eleven attacks with the Falcon-7B scoring pair, it recalls 55.5% at a 0.1% calibrated false-positive rate, 5.0 points above normalized Binoculars, while every section's human false-positive bound stays at most 1%.

Web platforms screen posts for large-language-model text at a very low false-positive rate, and invisible edits such as homoglyphs and zero-width spaces push machine text into the human score range. NormDetect normalizes each document with Unicode rules, then anchors a small per-section classifier head to the normalized Binoculars zero-shot score, with the blend weight chosen on training folds under a human false-positive cap. On eight RAID sections and eleven attacks with the Falcon-7B scoring pair, it recalls 55.5% at a 0.1% calibrated false-positive rate, 5.0 points above normalized Binoculars, while every section's human false-positive bound stays at most 1%.