{"arxiv_id":"2608.29453","incoming":{"papers":0,"sentences":0,"groups":[],"unlabelled_sentences":0},"outgoing":{"papers":2,"sentences":3,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2410.21276","title":"GPT-4o System Card","url":"/paper/2410.21276","section":"Model Configuration","sentence":"Experiments are run with gpt-4o [ref] , qwen3.7-plus [ref] , and Claude Sonnet 5 [ref] .","intent_p":0.97,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":1,"sentences":2,"quotes":[{"arxiv_id":"2311.16452","title":"Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine","url":"/paper/2311.16452","section":"Introduction","sentence":"Large language models (LLMs) have shown strong performance across a wide range of clinical tasks, approaching or exceeding physician performance [ref] .","intent_p":0.91,"contests_p":null}]}],"unlabelled_sentences":0},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2608.29453"}}