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      "source": "OpenAI News",
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      "source_url": "https://openai.com/news/",
      "category": "AI 模型",
      "tags": [],
      "published": "Sat, 01 Aug 2026 00:00:00 GMT",
      "published_at": "2026-08-01T08:00:00+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.",
      "original_summary": "OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.",
      "localized": false,
      "stale": false
    },
    {
      "id": "8fd53ef199dde2ba",
      "title": "Scaling Kubernetes pods with KEDA based on Amazon SQS queue depth",
      "original_title": "Scaling Kubernetes pods with KEDA based on Amazon SQS queue depth",
      "url": "https://www.cncf.io/blog/2026/07/31/scaling-kubernetes-pods-with-keda-based-on-amazon-sqs-queue-depth",
      "source": "CNCF Blog",
      "source_id": "cncf-blog",
      "source_url": "https://www.cncf.io/blog/",
      "category": "云原生",
      "tags": [],
      "published": "Fri, 31 Jul 2026 11:00:00 +0000",
      "published_at": "2026-07-31T19:00:00+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "In event-driven Kubernetes architectures, CPU and memory utilization often fail to reflect real system pressure. A worker pod may sit idle from a CPU perspective while thousands of messages pile up in an Amazon SQS queue",
      "original_summary": "In event-driven Kubernetes architectures, CPU and memory utilization often fail to reflect real system pressure. A worker pod may sit idle from a CPU perspective while thousands of messages pile up in an Amazon SQS queue",
      "localized": false,
      "stale": false
    },
    {
      "id": "0b6f4583ad52e7cc",
      "title": "TokTier: Exact Stateful Tokenization for Agentic LLM Serving",
      "original_title": "TokTier: Exact Stateful Tokenization for Agentic LLM Serving",
      "url": "https://arxiv.org/abs/2607.29678v1",
      "source": "arXiv AI / ML / NLP",
      "source_id": "arxiv-ai",
      "source_url": "https://arxiv.org/",
      "category": "AI 模型",
      "tags": [],
      "published": "2026-07-31T17:56:30Z",
      "published_at": "2026-08-01T01:56:30+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "LLM serving systems cache prompt KV state, yet most front ends still re-tokenize the full request text on every call. The cost lands on coding agents, which resubmit a long transcript after each small tool result, and re",
      "original_summary": "LLM serving systems cache prompt KV state, yet most front ends still re-tokenize the full request text on every call. The cost lands on coding agents, which resubmit a long transcript after each small tool result, and re",
      "localized": false,
      "stale": false
    },
    {
      "id": "f8ec64126bdac17b",
      "title": "Advancing responsible AI across Europe",
      "original_title": "Advancing responsible AI across Europe",
      "url": "https://openai.com/index/advancing-responsible-ai-across-europe",
      "source": "OpenAI News",
      "source_id": "openai-news",
      "source_url": "https://openai.com/news/",
      "category": "安全",
      "tags": [],
      "published": "Fri, 31 Jul 2026 15:00:00 GMT",
      "published_at": "2026-07-31T23:00:00+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "OpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe. The work will continue as the EU AI Act advances.",
      "original_summary": "OpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe. The work will continue as the EU AI Act advances.",
      "localized": false,
      "stale": false
    },
    {
      "id": "96cad8fa6f664f8b",
      "title": "ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction",
      "original_title": "ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction",
      "url": "https://arxiv.org/abs/2607.29677v1",
      "source": "arXiv AI / ML / NLP",
      "source_id": "arxiv-ai",
      "source_url": "https://arxiv.org/",
      "category": "研究",
      "tags": [],
      "published": "2026-07-31T17:55:58Z",
      "published_at": "2026-08-01T01:55:58+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "Enterprise workflows increasingly rely on agents for \\emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence",
      "original_summary": "Enterprise workflows increasingly rely on agents for \\emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence",
      "localized": false,
      "stale": false
    },
    {
      "id": "966cd163f8692d8c",
      "title": "Building abundant intelligence",
      "original_title": "Building abundant intelligence",
      "url": "https://openai.com/index/building-abundant-intelligence",
      "source": "OpenAI News",
      "source_id": "openai-news",
      "source_url": "https://openai.com/news/",
      "category": "AI 模型",
      "tags": [],
      "published": "Fri, 31 Jul 2026 15:00:00 GMT",
      "published_at": "2026-07-31T23:00:00+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.",
      "original_summary": "A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.",
      "localized": false,
      "stale": false
    },
    {
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      "title": "Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering",
      "original_title": "Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering",
      "url": "https://arxiv.org/abs/2607.29675v1",
      "source": "arXiv AI / ML / NLP",
      "source_id": "arxiv-ai",
      "source_url": "https://arxiv.org/",
      "category": "研究",
      "tags": [],
      "published": "2026-07-31T17:55:02Z",
      "published_at": "2026-08-01T01:55:02+08:00",
      "fetched_at": "2026-08-03T18:06:03.787734+08:00",
      "summary": "Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored. We study differentially private re",
      "original_summary": "Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored. We study differentially private re",
      "localized": false,
      "stale": false
    }
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