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代理工具

Antigravity CLI 1.1.6 Switches to Markdown-Defined Custom Agents, Bringing Tool Inheritance and Command Policies Under Version Control

Google Antigravity CLI adds a mechanism for creating main agents and subagents with `agent.md`, with frontmatter controls for MCP inheritance, visibility, and command execution policies. The new release also improves code search streaming and agent monitoring, though whether file-based policies can provide a reliable security boundary still requires testing.

最新模型

Microsoft Launches MAI-Image-2.5-Pro and MAI-Voice-2-Flash, Using In-House Models to Separate the Quality and Cost Curves

Microsoft is bringing its highest-quality image generation model and a high-throughput voice model into public preview in Foundry simultaneously, targeting precision editing and real-time voice agents, respectively. The company has published cost and latency data from several production environments, but cross-provider comparisons still lack consistent, reproducible testing conditions.

最新模型

SGLang-Omni Integrates MOSS-Transcribe-Diarize, Bringing Multi-Speaker Transcription to Continuous Batching

SGLang-Omni has added a deployment path for MOSS-Transcribe-Diarize that returns segment-level timestamps, text, and speaker labels in a single response through an OpenAI-compatible endpoint. This makes the open 0.9B model easier to deploy in online services, although the three evaluation datasets used for the official performance results are not currently fully public.

AI 基礎設施

NVIDIA ModelExpress Transfers Weights GPU-to-GPU, Cutting Scale-Out Time for an 806 GiB Model to Under Two Minutes

NVIDIA has detailed ModelExpress’s complete transfer path, allowing new inference replicas to copy weights and compilation caches from existing GPUs, with P2P RDMA as the preferred method. In an eight-B200 test, the company cut DeepSeek-V4-Pro’s time to API readiness from eight minutes to 1 minute 44 seconds, though the result depends heavily on identical GPU models, high-speed networking, and compatible tensor configurations.

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OpenForgeRL Brings Real-World Agent Frameworks into Reinforcement Learning, Decoupling Training and Execution with an Agent Server

Researchers from Microsoft Research and other institutions have introduced OpenForgeRL, enabling Codex, OpenClaw, and GUI agents to undergo end-to-end reinforcement learning within their native execution frameworks. The system uses a model-call proxy server and Kubernetes to isolate rollouts, but its code, data, and weights have yet to be fully released.