{"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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Sistem, uzun belge koleksiyonlarında kaynak bağlantılı kanıtları yönetiyor; yeni kanıt geldikçe hem kanıt havuzunu hem de geçici sonuçları güncelliyor.","OfficeQA Pro’nun ilk sürümündeki değerlendirmede GLM-5.3-Flash ile tam eşleşme oranı yüzde 60,2 olarak raporlandı. Makale, doğrudan model kullanımına kıyasla belirgin artış bildiriyor; ancak sonuç, oluşturulan benchmark ve kullanılan model/arama düzeniyle sınırlı. Uzun bağlamdaki her gerçek iş akışında aynı performansın elde edileceğini göstermez."],"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; exact match","pt":"OfficeQA Pro V1; benchmark com cerca de 100 milhões de tokens; correspondência exata","tr":"OfficeQA Pro V1; yaklaşık 100 milyon tokenlık benchmark; exact match"},"id":"reported-result-1","methodology":{"ar":"تقتصر القيمة على تجربة المعيار المذكورة في الورقة؛ وليست نتيجة ميدانية أو ضماناً عاماً للأداء.","de":"Der Wert ist auf das im Artikel beschriebene Benchmark-Experiment beschränkt; er ist kein Feldergebnis und keine allgemeine Leistungsgarantie.","en":"The value is limited to the benchmark experiment reported in the paper; it is not a field result or general performance guarantee.","es":"El valor se limita al experimento de benchmark descrito en el artículo; no es un resultado de campo ni una garantía general.","fr":"La valeur se limite à l’expérience de benchmark décrite dans l’article; ce n’est ni un résultat de terrain ni une garantie générale.","pt":"O valor se limita ao experimento de benchmark descrito no artigo; não é um resultado de campo nem uma garantia geral.","tr":"Değer, makalenin belirttiği benchmark deneyiyle sınırlıdır; saha sonucu veya genel performans garantisi değildir."},"rowTranslations":[{"metric":{"ar":"معدل التطابق التام لـ GLM-5.3-Flash على OfficeQA Pro V1","de":"Exact-Match-Rate von GLM-5.3-Flash auf OfficeQA Pro V1","en":"GLM-5.3-Flash exact-match rate on OfficeQA Pro V1","es":"Tasa de coincidencia exacta de GLM-5.3-Flash en OfficeQA Pro V1","fr":"Taux d’exact match de GLM-5.3-Flash sur OfficeQA Pro V1","pt":"Taxa de correspondência exata do GLM-5.3-Flash no OfficeQA Pro V1","tr":"OfficeQA Pro ilk sürümünde GLM-5.3-Flash tam eşleşme oranı"},"scope":{"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; exact match","pt":"OfficeQA Pro V1; benchmark com cerca de 100 milhões de tokens; 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=tr&format=csv","json":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents/data/reported-result-1?lang=tr&format=json"},"id":"reported-result-1"}],"data_types":["table"],"editorial_illustration":{"alt":"Teknoloji, işlemci ve bağlantılı bilginin kavramsal illüstrasyonu.","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":"tr","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":"tr","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":"Teknoloji, işlemci ve bağlantılı bilginin kavramsal illüstrasyonu.","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":"tr","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":"Çerçeve, uzun belgelerde kanıtları ve sonuçları birlikte güncelleyerek ajan yanıtlarını destekliyor.","text":"Çerçeve, uzun belgelerde kanıtları ve sonuçları birlikte güncelleyerek ajan yanıtlarını destekliyor.\n\nÇin’deki Fudan, Shanghai AI Laboratory ve Peking University dâhil kurumlarla bağlantılı araştırmacılar LongHarness’i tanıttı. Sistem, uzun belge koleksiyonlarında kaynak bağlantılı kanıtları yönetiyor; yeni kanıt geldikçe hem kanıt havuzunu hem de geçici sonuçları güncelliyor.\n\nOfficeQA Pro’nun ilk sürümündeki değerlendirmede GLM-5.3-Flash ile tam eşleşme oranı yüzde 60,2 olarak raporlandı. Makale, doğrudan model kullanımına kıyasla belirgin artış bildiriyor; ancak sonuç, oluşturulan benchmark ve kullanılan model/arama düzeniyle sınırlı. Uzun bağlamdaki her gerçek iş akışında aynı performansın elde edileceğini göstermez.","title":"LongHarness uzun bağlamlı ajanlarda kanıt yönetimini sınadı","topics":[],"translations":[{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=tr","language":"tr","url":"https://newsverum.com/tr/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=en","language":"en","url":"https://newsverum.com/en/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=ar","language":"ar","url":"https://newsverum.com/ar/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=fr","language":"fr","url":"https://newsverum.com/fr/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=es","language":"es","url":"https://newsverum.com/es/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=de","language":"de","url":"https://newsverum.com/de/news/longharness-tests-evidence-management-for-long-context-agents"},{"api_url":"https://newsverum.com/api/v1/articles/longharness-tests-evidence-management-for-long-context-agents?lang=pt","language":"pt","url":"https://newsverum.com/pt/news/longharness-tests-evidence-management-for-long-context-agents"}],"url":"https://newsverum.com/tr/news/longharness-tests-evidence-management-for-long-context-agents"},"version":"1"}