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A beautiful reminder that nature’s greatest stories are often told without words. 🐦❤️ A mother’s love, a tiny life, and a bond that makes every moment magical. Follow @Aistzone for more extraordinary AI-generated bird stories #AIWildlife# #BirdLovers# #NatureBeauty# #Aistzone#

Gemini 4 Carbon is now rolling out to developers, enterprises, scientists, and consumers! We built it to be a true partner for your hardest coding, research, and engineering workflows. Here's a fun example where Carbon read research papers on how songbirds sing and built an interactive model of a bird’s vocal organ…tuning the live audio physics against real bird recordings so you can hear 6 species sing or play it like a music synthesizer.

Our strongest frontier model, Gemini 4 Carbon, is now available. Built for deeply complex, long-horizon workflows that push a frontier model’s intelligence, Carbon delivers frontier performance in agentic workflows across complex knowledge work, software engineering and web development, cybersecurity, and scientific reasoning. While Gemini 4 Argon remains the ideal model for the majority of software engineering and knowledge work, Carbon steps in for your hardest tasks that push a model to its limits — from complex, multi-step debugging to open-ended scientific research. Available starting today: — Consumers: use it in the @GeminiApp and AI Mode in Search for Google AI Ultra subscribers — Developers and Enterprises: build via the API in @GoogleAIStudio and @AndroidStudio, Google @Antigravity for Google AI Ultra subscribers, and Gemini for business — Scientists and Researchers: apply for a chance to get 6 months free via @Antigravity — Cybersecurity Defenders: apply to join the Fairwind Program for access to Carbon for cyber Watch how Carbon brings reasoning and coding together, turning avian research into an interactive 3D simulation of a songbird’s vocal organ that synthesizes the sounds of six bird species live.

万千绿翅鸭腾空！青海都兰湿地现磅礴“鸟浪”奇观A Grand “Bird Wave”Emerges as Thousands of Green-winged Teals Soar over Dulan Wetland in #Qinghai# Province

EvoOntology：给数据智能体一个会成长的语义层 数据智能体（Data Agent）面对异构数据时，能“看见”的往往只有表名、列名和文件路径；指标如何定义、实体如何关联、业务上有何约束，这些语义都藏在数据之外。让智能体每次从零探索，代价高且易出语义错误；把人工维护的语义层全量注入提示词，又难以扩展、随数据漂移而失效。这就是论文中的 agent-data gap。 中国人民大学 ruc-datalab 提出的 EvoOntology（arXiv:2609.15779）给出了新解法：首个面向数据智能体的自进化本体层（Self-Evolving Ontology Layer）。核心洞察是把本体当作“可训练的智能体状态”：不更新模型权重，而让语义知识随真实使用持续积累、验证、版本化演进，每次变更可检查、可比较、可回滚。 本体以三层结构封装为 MCP 服务器，供智能体运行时主动查询： · Schema Layer 定义本体的表示边界（节点族、语义关系、引用模式）； · Content Layer 是类型化语义图，由 Terms / Mappings / Constraints / Evidence 四类节点构成，每个语义对象都有工作负载证据支撑，落地验证后才提交； · Tool Layer 仅暴露 browse_semantics 与 resolve_semantics 两个 MCP 工具加紧凑会话 manifest，智能体按需检索当前步骤所需语义，而非全量注入上下文。 本体遵循 Build → Use → Evolve → Evaluate → Publish 生命周期：构建器智能体从真实工作负载推导候选概念并对照原始数据验证；交互轨迹被持续记录；进化时诊断重复行为、归因到具体层级并生成局部补丁；候选版本只有在相同数据、相同智能体、相同解码设置与交互预算的配对评估中可复现地胜过父版本，才会发布。 在 BIRD、DDR-10K、InsightBench 三个基准、四个 LLM 骨干（GPT-5.5 / GPT-5.6-sol / Claude-Sonnet-5 / Claude-Opus-4.8）上，EvoOntology 一致优于无本体 ReAct 与静态初始本体。其中 DDR-Bench 上仅“自进化”环节就贡献了 +7.7（81.8 → 89.5），验证了持续演进相对静态语义层的增量价值。 论文： 代码：