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#Product updates
Oct 15Thu
  1. Google Research75

    Analyzing technical documents with Deep Review

    Google announced the public release of Deep Review, an agentic AI system that provides feedback and technical verification on complex documents, initially for computer science papers and now extended to legal, finance, and other domains. It evolved from the Paper Assistant Tool, which was piloted with STOC 2026, ICML 2026, NeurIPS 2026, and ICLR 2027, offering opt-in pre-submission feedback to authors of more than 16,000 manuscripts. Every user gets two complimentary reviews per week, with higher limits via a Gemini API key, and uploaded documents are not used to train Google models.

    Why it matters: It documents a concrete agentic review pipeline and pilot results, useful for readers weighing AI-assisted verification of technical documents.

Oct 8Thu
  1. GitHub Blog · AI & ML58

    Secret protection must scale with software

    GitHub reports that one in three pull requests now involves an AI agent, up from fewer than one in 10 a year ago, while a new secret appears in public code roughly every two seconds. GitHub introduces a ModernBERT-based classifier built with Microsoft Applied Sciences that extends push protection to unstructured secrets, evaluating candidate batches in under two milliseconds and potentially more than doubling the secrets it can prevent. The feature enters private preview and will be available to organizations with GitHub Secret Protection on Enterprise Cloud and GitHub Teams, and will ship with GitHub Enterprise Server 3.23 in public preview.

  2. Microsoft Research71

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia introduces Harnessed Agentic RL, a paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, and open-sources Agent Lightning v1.0, a roughly 3,500-line framework with an API Gateway, Rollout Controller, and Customized Trainer built on verl. Agents run as standard Kubernetes jobs without commercial sandbox services, and Collocated Async RL shares GPUs between rollout and updates, achieving about a 2x end-to-end speedup over synchronous RL. An end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples.

    Why it matters: It offers a concrete, reproducible recipe for training deployed agents in place, with code size, infrastructure, and benchmark numbers stated explicitly.