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  1. Microsoft Official Blog72

    Microsoft opens the Copilot harness through Foundry, supporting the development of multi-model intelligent agents

    Microsoft opens the GitHub team's Copilot harness built by GitHub through Microsoft Foundry, providing a fully managed way to build intelligent agents that can reason, call tools, and complete long tasks.

    Why it matters: Microsoft opens the multi-model harness supporting multiple Copilot products internally to Foundry, allowing readers to judge the trade-offs of building their own orchestration layer based on this.

  1. Microsoft AI News67

    Microsoft released the MAI Image 2.7 image editing model, ranking 3rd on Arena Edit and 2nd on Artificial Analysis.

    Microsoft AI released the image editing model MAI Image 2.7, launching as a preview version. It ranks 3rd in Arena Edit and 2nd in the Artificial Analysis image editing chart, making Microsoft AI the 2nd place lab.

    Why it matters: Microsoft announced the rankings of MAI Image 2.7 on two image editing lists and provided the improvement magnitude on 10 use cases compared to the previous generation.

  1. Hugging Face Blog66

    Microsoft and Hugging Face use ThinkingBox database validation for RL reward training agents

    Microsoft and Hugging Face release ThinkingBox-Training, directly using ThinkingBox's deterministic verification of database final states as reinforcement learning rewards, training Qwen3.8-27B for 50 steps with GRPO

    Why it matters: Microsoft and Hugging Face directly use database final state verification as reinforcement learning rewards and have publicly released task filtering and training code, which can be transferred to other stateful intelligent agent training

  2. Microsoft AI News78

    Microsoft releases medical diagnostic system MAI-DxO and step-by-step diagnostic benchmark SDBench

    Microsoft's Health MAI team released the AI diagnostic system MAI-DxO and its accompanying benchmark SDBench, with the results published in Science. On 304 NEJM complex cases, MAI-DxO achieved an 83% diagnostic accuracy rate, with doctors achieving 36% with external information and 22% without; on MIMIC-IV-ED's emergency common cases, it was 80%, with doctors at 47%.

    Why it matters: The original text provides a step-by-step diagnostic benchmark and a quantitative comparison of multi-agent orchestration, allowing readers to understand the path of medical AI evaluation shifting from exam questions to interactive diagnosis.

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