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A small, fast text classifier can answer the easy cases itself and pass only the uncertain ones to a large language model. Version 1.0 of this work estimated how much time such a two-stage setup saves; this version measures it while both models run on one consumer graphics card. The two-stage setup made the average dec...
A small, fast text classifier can answer the easy cases itself and pass only the uncertain ones to a large language model. Version 1.0 of this work estimated how much time such a two-stage setup saves; this version measures it while both models run on one consumer graphics card. The two-stage setup made the average dec...
Language models can write research hypotheses much faster than anyone can test them. Earlier studies have judged such hypotheses by expert ratings, benchmarks with known answers or laboratory experiments. We report a small, fully documented case from a different angle: what happens to a handful of model-generated predi...
This archive accompanies the manuscript "Positional-prefix subsampling of archive FASTQ for telomeric-repeat estimation: a preregistered negative benchmark, a rare catastrophic-failure benchmark, and a submission-format marker confirmed prospectively". It contains the protocols, analysis plans and SHA-256 stage locks w...
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