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    <news:news>
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        <news:language>hr</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935: dobici CoT treninga slijevaju se u jače predviđanje akcije, a ne u dublje rezoniranje agenata</news:title>
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        <news:language>hr</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806: EVAF konsolidira pamćenje agenata u parametre modela za postojanost cilja nakon brisanja konteksta</news:title>
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        <news:name>24 AI</news:name>
        <news:language>hr</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502: reasoning modeli troše više tokena na zadatke koje pogriješe, suprotno od ljudi koji odustaju</news:title>
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    <news:news>
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        <news:language>hr</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>en</news:language>
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        <news:name>24 AI</news:name>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935: CoT training gains land in stronger action prediction, not deeper agent reasoning</news:title>
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    <news:news>
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        <news:name>24 AI</news:name>
        <news:language>en</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806: EVAF consolidates agent memory into model parameters for goal persistence after context deletion</news:title>
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    <news:news>
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        <news:name>24 AI</news:name>
        <news:language>en</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502: reasoning models spend more tokens on tasks they fail, opposite to humans who disengage</news:title>
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    <loc>https://24-ai.news/en/news/2026-06-28/github-copilot-agentic-harness-benchmark/</loc>
    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>en</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: MAI-Code-1-Flash, Microsoft&apos;s coding model, now generally available in Copilot Business and Enterprise plans</news:title>
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    <news:news>
      <news:publication>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
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    <news:news>
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        <news:language>de</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935: CoT-Trainingsgewinne fließen in stärkere Aktionsvorhersage, nicht in tieferes Agenten-Reasoning</news:title>
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    <news:news>
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        <news:language>de</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806: EVAF konsolidiert Agentenspeicher in Modellparameter für Zielbeständigkeit nach Kontextlöschung</news:title>
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    <loc>https://24-ai.news/de/news/2026-06-28/arxiv-reasoning-tokens-failure-divergence/</loc>
    <news:news>
      <news:publication>
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        <news:language>de</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502: Reasoning-Modelle verbrauchen mehr Tokens bei Fehlern – im Gegensatz zu Menschen, die aufgeben</news:title>
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    <loc>https://24-ai.news/de/news/2026-06-28/github-copilot-agentic-harness-benchmark/</loc>
    <news:news>
      <news:publication>
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        <news:language>de</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: Copilot Agentic Harness erreicht Vendor-Niveau mit geringerem Token-Verbrauch über 20+ Frontier-Modelle</news:title>
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    <loc>https://24-ai.news/de/news/2026-06-28/github-mai-code-1-flash-copilot-ga/</loc>
    <news:news>
      <news:publication>
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        <news:language>de</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: MAI-Code-1-Flash, Microsofts Coding-Modell, jetzt allgemein verfügbar in Copilot Business und Enterprise</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>AMD: Resource Manager 自动抢占空闲 GPU 工作负载，将资源归还集群共享池</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935: CoT 训练收益流向更强的动作预测，而非更深的智能体推理</news:title>
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    <news:news>
      <news:publication>
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        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806: EVAF 将智能体记忆固化入模型参数，在上下文清除后保持目标持久性</news:title>
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    <news:news>
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        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502: 推理模型在出错任务上消耗更多 token，与人类放弃的行为相反</news:title>
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    <loc>https://24-ai.news/zh/news/2026-06-28/github-copilot-agentic-harness-benchmark/</loc>
    <news:news>
      <news:publication>
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        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: Copilot 智能体 harness 在 20 余款前沿模型上达到供应商 harness 水平，且 token 消耗更低</news:title>
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    <loc>https://24-ai.news/zh/news/2026-06-28/github-mai-code-1-flash-copilot-ga/</loc>
    <news:news>
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        <news:language>zh</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: MAI-Code-1-Flash，微软编程模型，现已在 Copilot Business 和 Enterprise 方案中正式发布</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ja</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>AMD: Resource Managerが非アクティブなGPUワークロードを自動的にプリエンプションしクラスタの共有プールにリソースを返還</news:title>
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    <news:news>
      <news:publication>
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        <news:language>ja</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935：CoTトレーニングの恩恵はより深い推論ではなくより強い行動予測として現れる</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ja</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806：EVAFがエージェントの記憶をモデルパラメータに統合しコンテキスト削除後も目標を維持</news:title>
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    <news:news>
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        <news:name>24 AI</news:name>
        <news:language>ja</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502：推論モデルは失敗したタスクにより多くのトークンを消費し、諦める人間とは逆の傾向を示す</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ja</news:language>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: Copilot agenticハーネスが20以上のフロンティアモデルにわたってベンダーハーネス水準の性能を達成し、より少ないトークン消費を実現</news:title>
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    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ja</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: MAI-Code-1-Flash、MicrosoftのコーディングモデルがCopilot BusinessおよびEnterpriseプランで一般提供開始</news:title>
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    <loc>https://24-ai.news/ko/news/2026-06-28/amd-rocm-gpu-workload-preemption/</loc>
    <news:news>
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      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>AMD: Resource Manager, 비활성 GPU 워크로드를 자동 선점하여 클러스터 공유 풀에 자원 반환</news:title>
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    <loc>https://24-ai.news/ko/news/2026-06-28/arxiv-cot-training-action-prediction/</loc>
    <news:news>
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        <news:name>24 AI</news:name>
        <news:language>ko</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26935: CoT 훈련의 이득은 깊은 에이전트 추론이 아닌 행동 예측 강화로 귀결</news:title>
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  </url>
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        <news:name>24 AI</news:name>
        <news:language>ko</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26806: EVAF, 컨텍스트 삭제 후에도 목표를 유지하는 에이전트 파라메트릭 메모리 통합</news:title>
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  </url>
  <url>
    <loc>https://24-ai.news/ko/news/2026-06-28/arxiv-reasoning-tokens-failure-divergence/</loc>
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        <news:name>24 AI</news:name>
        <news:language>ko</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>arXiv:2606.26502: 추론 모델은 틀린 문제에 더 많은 토큰 소비 — 포기하는 사람과 반대 패턴</news:title>
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  </url>
  <url>
    <loc>https://24-ai.news/ko/news/2026-06-28/github-copilot-agentic-harness-benchmark/</loc>
    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ko</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: Copilot 에이전트 하니스, 20개 이상의 프론티어 모델에서 벤더 하니스 수준 달성 및 토큰 사용량 절감</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://24-ai.news/ko/news/2026-06-28/github-mai-code-1-flash-copilot-ga/</loc>
    <news:news>
      <news:publication>
        <news:name>24 AI</news:name>
        <news:language>ko</news:language>
      </news:publication>
      <news:publication_date>2026-06-28T00:00:00Z</news:publication_date>
      <news:title>GitHub: MAI-Code-1-Flash, 마이크로소프트의 코딩 모델, 이제 Copilot Business 및 Enterprise 플랜에서 일반 제공</news:title>
    </news:news>
  </url>
</urlset>
