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Nov 11Wed
  1. Databricks Blog66

    Databricks has announced that Genie Ontology is now officially available.

    Databricks announces the official availability of Genie Ontology, where agents continuously learn business context from dashboards, SQL queries, notebooks, and semantic models, and key definitions are managed by humans through Unity Catalog.

    Why it matters: The original text provides pass rates, time consumption, and token comparisons for benchmarks co-built with Turing, allowing judgment on the impact of the enterprise context layer on the accuracy of agents.

Nov 10Tue
  1. Berkeley AI Research22

    BAIR at CoRL 2026

    BAIR team presented multiple research achievements at the Robot Learning Conference (CoRL) held from November 9 to 12, 2026, in Austin, Texas, including 33 main conference papers, 4 breakthrough posters, 10 oral presentations, and 7 thematic speeches, among which Professor Negar Mehr will deliver an early career keynote speech. The conference covers topics such as robotic vision-language-action models, embodied agents, fine-grained manipulation alignment, and hindsight reward editing.

Nov 6Fri
  1. Apple Machine Learning Research

    Apple's Amortizing Maximum Inner Product Search accelerates retrieval

    Apple's Amortizing Maximum Inner Product Search, presented at ICML 2026, learns support functions by training small neural networks to understand typical query matching patterns at a fixed cost, achieving faster precise retrieval during inference. This method utilizes ICNN to learn support functions, and KeyNet directly outputs the best matching key vector, both trained on supervised datasets constructed based on actual query distributions, significantly improving retrieval efficiency in RAG scenarios.

  2. Microsoft Research

    PDD Kit officially validated full-stack web applications written by AI intelligent agents

    The PDD Kit translates user intent into formal specifications and verifies whether the code written by the agent for each input conforms to these specifications, supplemented by testing. The team officially validated the Rust-written Pet Store login logic using the Chopin environment on GitHub Next with the PDD Kit, and the Verus tool discovered and fixed existing bugs, proving no login bugs under the specification.

Oct 27Tue
  1. LangChain Deep Agents releases

    LangChain Deep Agents deepagents-code==0.2.2 release: Security and performance improvements

    The deepagents-code==0.2.2 version of LangChain Deep Agents has been released, including security and performance improvements. New features include GPT-6.1 Terra support, Chinese text input to chat, plugin update detection, etc.; bug fixes include project .env file trust value settings, terminal UI link limits, Git option limits, etc.; performance has been improved by reusing parent process context, optimizing server startup processes, and streamlining transcription rendering, etc. It is recommended to upgrade as soon as possible.

Oct 22Thu
Oct 21Wed
Oct 14Wed
  1. Google DeepMind

    Gemini 4 Argon brings breakthrough progress to robotics technology

    Gemini 4 Argon has achieved major breakthroughs in physical understanding, code generation, and visual semantic reasoning, achieving a pure reasoning score of 87% on the Embodied Reasoning (ERQA) benchmark test, surpassing all leading models. Argon can be applied to all stages of the robot's lifecycle, including mobile manipulation practice using MuJoCo, zero-shot robot control, and closed-loop steering of video world models.

Oct 13Tue
Oct 1Thu
  1. Amazon Science

    Graph Center Agent: How to Utilize Graph Structures for Agent Reasoning

    Amazon Science has released Graph Center Agent technology, which combines a continuous synchronized graph (digital twin) representing the network with device attributes and connections, with cascade graph algorithms (decomposition, aggregation, tracing) and agent orchestration to achieve minute-level root cause analysis of complex network failures, achieving 95% accuracy in a demonstration at NTT DOCOMO's MWC.

May 8Fri
  1. Berkeley AI Research

    Adapting to parallel reasoning: the next paradigm for efficient reasoning extension

    Adapting to parallel reasoning allows models to decide on their own when to decompose and parallelize independent subtasks, how many threads to start, and how to coordinate. Existing methods such as fixed parallelism, heuristic structured search, and Monte Carlo tree search determine the parallel structure externally. Recent advancements like ParaThinker and GroupThink attempt to let models adaptively select parallel strategies, but decisions are still constrained externally.