{"article":{"annotation":{"article_id":"longharness-tests-evidence-management-for-long-context-agents","available_at":"2026-10-13T00:52:07.251918Z","geography_revision":"ef6ee0010dda5fe7b240fd514749b43e214c57da73222d2e40ec83b276dd0977","id":"9bf61481db4d4e67b2aa3c9fcfcd62ce3aaa06dceb0cccdf17ce693fcd8b2526","input_sha256":"87e3921b6fcd6213a9e6987e6cb05442d41991cdd0467c791d54299e3d47ef3c","limitations":["Rule-derived subjects are candidates, not verified editorial labels or calibrated model probabilities.","Coverage is distinct from event location. 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Das System verwaltet quellengebundene Belege in umfangreichen Dokumentensammlungen; wenn neue Belege eintreffen, aktualisiert es sowohl den Belegbestand als auch vorläufige Schlussfolgerungen.","Bei der Auswertung der ersten Version von OfficeQA Pro berichtet der Artikel mit GLM-5.3-Flash eine Exact-Match-Rate von 60,2 %. Die Autoren beschreiben einen deutlichen Anstieg gegenüber der direkten Modellnutzung; das Ergebnis gilt jedoch nur für den konstruierten Benchmark sowie das getestete Modell und Abrufverfahren. Es belegt keine gleiche Leistung in allen realen Workflows mit langem Kontext."],"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":"النسبة المئوية","de":"Prozent","en":"Percent","es":"Porcentaje","fr":"Pourcentage","pt":"Percentual","tr":"Yüzde"},"type":"number","unit":"percent"},{"key":"scope","label":{"ar":"النطاق","de":"Umfang","en":"Scope","es":"Alcance","fr":"Périmètre","pt":"Escopo","tr":"Kapsam"},"language":"en","type":"text"}],"description":{"ar":"OfficeQA Pro V1؛ معيار يضم نحو 100 مليون رمز؛ تطابق تام","de":"OfficeQA Pro V1; Benchmark mit etwa 100 Millionen Token; Exact Match","en":"OfficeQA Pro V1; benchmark of about 100 million tokens; exact match","es":"OfficeQA Pro V1; benchmark de unos 100 millones de tokens; coincidencia exacta","fr":"OfficeQA Pro V1; benchmark d’environ 100 millions de tokens; 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correspondência exata","tr":"OfficeQA Pro V1; yaklaşık 100 milyon tokenlık benchmark; exact match"}}],"rows":[{"metric":"GLM-5.3-Flash exact-match rate on OfficeQA Pro V1","scope":"OfficeQA Pro V1; benchmark of about 100 million tokens; exact match","value":60.2}],"sourceUrls":["https://arxiv.org/html/2610.13075v1"],"title":{"ar":"نتيجة البحث","de":"Forschungsergebnis","en":"Research result","es":"Resultado del estudio","fr":"Résultat de l’étude","pt":"Resultado da pesquisa","tr":"Araştırma sonucu"},"type":"table","updatedAt":"2026-10-13T00:17:05Z"}],"version":1},"data_downloads":[{"formats":{"csv":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents/data/reported-result-1?lang=de&format=csv","json":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents/data/reported-result-1?lang=de&format=json"},"id":"reported-result-1"}],"data_types":["table"],"editorial_illustration":{"alt":"Konzeptionelle Darstellung von Technologie, Prozessoren und verbundenen Informationen.","asset_id":"newsverum-v1-technology","avif_variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.avif","width":480},{"height":480,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-720.avif","width":720},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.avif","width":960},{"height":800,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1200.avif","width":1200},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.avif","width":1536}],"credit":"Newsverum","documentary":false,"height":1024,"kind":"symbolic-editorial-illustration","language":"de","scope":"topic","url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.webp","width":480},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.webp","width":960},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","width":1536}],"width":1536},"event_id":"event-44b33fab-2f6c-5df1-b327-172be892c694","event_period":"2026-10-09","event_period_basis":"event-or-reference-period","id":"longharness-tests-evidence-management-for-long-context-agents","image":null,"importance":0.6,"language":"de","modified_at":"2026-10-13T00:51:40+00:00","publication_basis":"publication-record","published_at":"2026-10-13T00:51:40+00:00","reading_minutes":1,"region":"world","section_illustration":{"alt":"Konzeptionelle Darstellung von Technologie, Prozessoren und verbundenen Informationen.","asset_id":"newsverum-v1-technology","avif_variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.avif","width":480},{"height":480,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-720.avif","width":720},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.avif","width":960},{"height":800,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1200.avif","width":1200},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.avif","width":1536}],"credit":"Newsverum","documentary":false,"height":1024,"kind":"symbolic-editorial-illustration","language":"de","scope":"section","url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.webp","width":480},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.webp","width":960},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","width":1536}],"width":1536},"slug":"longharness-tests-evidence-management-for-long-context-agents","sources":[{"checkedAt":"2026-10-13","name":"arXiv preprint by researchers affiliated with Fudan University, Shanghai AI Laboratory, Peking University and other Chinese institutions","publishedAt":"2026-10-09","url":"https://arxiv.org/html/2610.13075v1"}],"summary":"Das Framework unterstützt Agentenantworten, indem es Belege und Schlussfolgerungen in langen Dokumenten gemeinsam aktualisiert.","text":"Das Framework unterstützt Agentenantworten, indem es Belege und Schlussfolgerungen in langen Dokumenten gemeinsam aktualisiert.\n\nForschende mit Verbindungen zu chinesischen Einrichtungen wie der Fudan University, dem Shanghai AI Laboratory und der Peking University stellten LongHarness vor. 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