{"article":{"annotation":{"article_id":"multi-agent-session-alternation-tests-machine-learning-engineering","available_at":"2026-10-13T00:52:16.046437Z","geography_revision":"ef6ee0010dda5fe7b240fd514749b43e214c57da73222d2e40ec83b276dd0977","id":"cbbb7175821fee0a13e298e7658e769e66dc065fcb79ddbe76a37e62f8a3318e","input_sha256":"6512fc4c0224e8bbcd7e6c2fe728a9633e4e5132f1585ebabdad6bd6aba48848","limitations":["Rule-derived subjects are candidates, not verified editorial labels or calibrated model probabilities.","Coverage is distinct from event location. 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Jede Sitzung endet nach einer begrenzten Zahl von Experimenten; der Kontext wird zwischen den Sitzungen zurückgesetzt, während Code, Modelle und frühere Ergebnisse in einem gemeinsamen Arbeitsbereich erhalten bleiben.","Bei 75 Kaggle-Aufgaben aus MLE-bench berichten die Autoren eine Any-Medal-Rate von 78,2 % und geben an, dass der Workflow die stärkste Open-Source-Referenz mit geringerem Zeitbudget übertraf. Das Ergebnis gilt für diesen Wettbewerbsdatensatz und diese Agentenkonfigurationen; es garantiert keinen gleichen Vorteil bei anderen Modellversionen oder realen Technikprojekten."],"sections":[]},"corrections":[],"country":"CN","data":{"blocks":[{"columns":[{"key":"metric","label":{"ar":"المقياس","de":"Messgröße","en":"Measure","es":"Medida","fr":"Mesure","pt":"Medida","tr":"Ölçüt"},"language":"en","type":"text"},{"key":"value","label":{"ar":"القيمة المعلنة","de":"Berichteter Wert","en":"Reported value","es":"Valor comunicado","fr":"Valeur rapportée","pt":"Valor informado","tr":"Bildirilen değer"},"type":"number","unit":"percent"},{"key":"scope","label":{"ar":"نطاق التجربة","de":"Versuchsumfang","en":"Experimental scope","es":"Alcance experimental","fr":"Périmètre expérimental","pt":"Escopo experimental","tr":"Deney kapsamı"},"language":"en","type":"text"}],"description":{"ar":"75 مهمة Kaggle؛ تقييم MLE-bench","de":"75 Kaggle-Aufgaben; MLE-bench-Auswertung","en":"75 Kaggle tasks; 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