Marek Šuppa, Ivan Vykopal, Jaroslav Kopčan · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.31636 · cited by 0 works (OpenAlex) · open-access PDF
Pipa agen multibahasa seperti FineWeb2 menerapkan satu resep di ribuan bahasa frame, meninggalkan mode kegagalan dari bahasa individu yang tidak terduga. Kami menggunakan LLM sekali, sebagai corpus debugger daripada klasifier: agen menggunakan alat mengeksplorasi corpus melalui API yang hanya dapat dibaca, dan mengkompilasi setiap temuan ke dalam tes yang dapat dijalankan, yang kemudian disahkan oleh penutur asli. Pada bagian Slovak 26.5M-dokumen agen yang valid dari FineWeb2, tiga berjalan biaya $2.21-\$2.84 masing-masing menemukan tujuh temuan di atas empat fenomena. Yang terbesar adalah peternakan konten yang dihasilkan oleh mesin dari 1.408.437 dokumen (5.32%) yang melewati filter pipa, 86.6% dari itu melampaui ambang batas yang lebih ketat. Label Blinded mendukung 6 dari 7 temuan (rate defect cost
Machine translation (Meta M2M100); the English original follows.
Multilingual pipelines such as FineWeb2 apply one recipe across a thousand languages, leaving the failure modes of individual languages unanticipated. We use an LLM once, as a corpus debugger rather than a classifier: a tool-using agent explores the corpus through a read-only, budgeted API and compiles each finding into an executable test, which a native speaker then validates. On the 26.5M-document Slovak portion of FineWeb2, three runs costing \$2.21-\$2.84 each found seven findings over four phenomena. The largest is a machine-generated content farm of 1,408,437 documents (5.32%) that passed the pipeline's filters, 86.6% of it passing even stricter thresholds. Blinded labels support 6 of the 7 findings (defect rates .53-1.00 against .08 in a random control) and do not support one. The compiled suite scans the full corpus in 0.7 s of wall-clock time without a GPU, against \$12k-\$90k per LLM classification pass. The best static sampling baseline validates more selectors than any agent run, at under half the cost. The agent contributes exact prevalence, a precision estimate checked against sampled documents, an account of why the pipeline missed each slice, and a reusable test. We release the framework, Slovak test suite, validation data and agent trajectories.
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Topics: Computer science, Debugger, Pipeline (software), Audit, Debugging, Executable
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