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      <news:title>arXiv:2607.14642: MCPEvol-Bench, GPT-5.4와 Claude 모두 MCP 도구 변경 시 정확도를 잃는다는 것을 보여주다</news:title>
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      <news:title>Google Research：拡散モデルの創造性はscore関数の「平滑化」で説明できる</news:title>
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      <news:publication_date>2026-07-15T00:00:00Z</news:publication_date>
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      <news:publication_date>2026-07-15T00:00:00Z</news:publication_date>
      <news:title>arXiv:2607.12463: 함수 인식 FIM 중간 훈련으로 코딩 에이전트 SWE-Bench 점수 최대 +5.4 향상</news:title>
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      <news:title>arXiv:2607.12385：PM-Bench, 에이전트의 「전망적 기억」측정——최고 모델 GPT-5.4도 F1 65.1%에 그쳐</news:title>
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      <news:title>vLLM：TML Inkling Day-0 지원——GB200에서 1조 파라미터 모델이 380 tokens/s 달성</news:title>
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  </url>
</urlset>
