[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-tencent-hunyuan-hyra-1":3,"topics-all":36,"news-related-4d9f11bb-5795-45e5-a90b-7eb29756da24":55},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"4d9f11bb-5795-45e5-a90b-7eb29756da24","腾讯混元发布 Hyra-1.0：用递归自我改进重写研究智能体的范式","7 月 21 日,腾讯混元正式发布 Hyra-1.0（Hunyuan Research Agent），这是国内首个面向研究与工程任务的递归自我改进（Recursive Self-Improvement，RSI）智能体。\n\nHyra 的设计遵循 The Bitter Lesson：把外部框架做轻、把动作空间做宽。给定任务描述后，它会持续运行一个探索循环——基于历史经验（含过往 solution 的运行日志、评估器反馈、源代码）不断提出更好的方案，直到主动结束或预算耗尽，最终返回历史最优解。这种“经验复用 + 自动反思”的模式，让 Hyra 能在公开 benchmark 之外，在产品系统、AI 研发流水线乃至自然科学与工业场景里持续进化。\n\n官方样例展示了这种能力的优势：仅凭一张 2D 参考图生成可渲染的 3D 模型，Hyra 多轮迭代修改建模代码和渲染参数，由基于 rubrics 的 VLM judge 从轮廓、比例、结构完整性、材质等维度打分，产物比 Claude Code goal 模式更接近参考图像、更符合人类审美。\n\n过去一年 RSI 与自动研究几乎由海外主导：DeepMind 的 AlphaEvolve 用 48 次标量乘法完成 4×4 复数矩阵相乘，Together AI 把 11 维 kissing number 下界推到 604，Karpathy 的 autoresearch 给出精简的训练自循环范式。腾讯这次入场，意味着国内厂商开始系统性补齐这一前沿方向——而 RSI 的真正考验是“在生产环境里跑得久、稳、安全”，Hyra 把验证直接挂在研发流水线和工业场景里，这种姿态本身比纯 demo 更值得关注。","https:\u002F\u002Ffinance.sina.com.cn\u002Ftech\u002Fdigi\u002F2026-07-21\u002Fdoc-iniiptrh9371935.shtml","d46ec0a7-501b-4ef8-9c89-2391b2701b3b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"6ad31a14-c0da-42df-81fd-564281f768db","agentic-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",{"id":18,"name":19,"slug":19,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":28},"0cd8ef97-6f0d-4b6f-b73d-952ed7417286","en","Hyra-1.0 rewrites research agents with recursive self-improvement","On July 21, Tencent Hunyuan officially released Hyra-1.0 (Hunyuan Research Agent) — the first domestic Recursive Self-Improvement (RSI) agent targeting research and engineering tasks. Hyra's design follows The Bitter Lesson: keep external frameworks light, make the action space wide. Given a task description, it runs an exploration loop continuously — using historical experience (including the run logs of past solutions, evaluator feedback, and source code) to propose better solutions until it actively ends or the budget runs out, finally returning the historically best solution. This \"experience reuse + automatic reflection\" pattern lets Hyra evolve continuously not only on public benchmarks but also in product systems, AI R&D pipelines, and even natural science and industrial scenarios. The official demo shows the advantage of this capability: given only a 2D reference image to generate a renderable 3D model, Hyra iterates across multiple rounds modifying modeling code and rendering parameters, scored by a rubrics-based VLM judge on silhouette, proportion, structural integrity, material and other dimensions. The result is closer to the reference image and more human-aesthetic than Claude Code's goal mode. Over the past year, RSI and automated research have been mostly led overseas: DeepMind's AlphaEvolve did 4×4 complex matrix multiplication in 48 scalar multiplications; Together AI pushed the 11-dimensional kissing number lower bound to 604; Karpathy's autoresearch proposed a streamlined training self-loop paradigm. Tencent's entry this time means domestic vendors are starting to systematically fill in this frontier — and the real test of RSI is \"running long, steady and safe in production\". Hyra hangs verification directly onto R&D pipelines and industrial scenarios, an attitude itself more worth watching than a pure demo.","tencent-hunyuan-hyra-1","2026-07-21T06:30:00Z","2026-07-21T06:05:25.133092Z","2026-08-19T02:08:40.142862Z",true,"agent",284,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"2ada2e69-25c2-4951-9e49-2b24a043393e","腾讯 Marvis 把 Agent 拽到端侧:混元要做 PC 集群,应用宝做了「系统级」分诊","tencent-marvis-on-device-agent","2026-07-23T20:30:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"9d775769-35ec-4107-bcec-601427fda303","云迹科技 WAIC 首发「人机共生世界价值模型」:把具身智能从「规则控制」推向「价值驱动」","yunji-waic-value-driven-embodied","2026-07-18T00:15:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"2bbd5141-7e38-41bb-9a44-4972af73602d","阶跃星辰推出\"全球首个智能体原生 OS\"Step AOS:把 LLM 当 OS 公民,MCP 拆碎系统调用","step-aos-agent-native-os","2026-07-14T02:30:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"f3c2ed01-6860-418a-852a-2c74e99acdb1","香港发布HKGAI V3大模型：单次无干预运行28小时的生产力级超级智能体","hkgai-v3-hk-28-hour-super-agent","2026-06-04T07:00:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"de9a670a-f11b-4baf-9252-b073fa55eb35","AI4S迎来发展分水岭：奥明星程完成超亿元融资，构建AI科学家能力体系","ai4s-funding-2026-billions-china","2026-04-23T17:03:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"44aa8908-1cef-48d6-b224-7de12a8d4afd","NeoHorse-1：让 Agent 执行轨迹进入自我改进回路","neohorse-1-agentic-post-training-rsi","2026-09-09T07:19:25+00:00"]