[{"data":1,"prerenderedAt":205},["ShallowReactive",2],{"DlFXI4Eibt_Bn9lrEZz1TYbHCWFZj3IvqwHQSEW-Exc":3,"12hN5QjXHFABkkf6csYgb7cSJPMqbDY7icDLvk5rn5I":194},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"hot":39,"new":78,"banner":118,"data":143,"cache":193},[8,9,10],"Agent","OpenAI","LLM",[12,14,17,20,23,25,27,30,33,36],{"title":8,"total":13},39,{"title":15,"total":16},"Google",44,{"title":18,"total":19},"Nvidia",13,{"title":21,"total":22},"Claude",11,{"title":9,"total":24},35,{"title":10,"total":26},85,{"title":28,"total":29},"DeepSeek",9,{"title":31,"total":32},"OCR",1,{"title":34,"total":35},"Chat",7,{"title":37,"total":38},"Generator",116,[40,48,55,64,71],{"id":41,"publish_date":42,"is_original":4,"collection":5,"cover_url":43,"cover_url_1_1":44,"title":45,"summary":46,"author":47},557,"2022-04-29","article_res/cover/7a9b1375ed9bb298154981bae42b794d.jpeg","article_res/cover/afa281dd52bc0454e6735daa8e6b0706.jpeg","Translation and summary of Messari Report [2.8 Kristin Smith, Blockchain Association and Katie Haun, a16z]","We need unity and speed right now.","Translation",{"id":49,"publish_date":50,"is_original":4,"collection":5,"cover_url":51,"cover_url_1_1":52,"title":53,"summary":54,"author":47},531,"2022-05-25","article_res/cover/e8362057f8fa189594c60afdfaaeb6e5.jpeg","article_res/cover/8ea08d0d6fa7eee6b57ed4ec61b61ad6.jpeg","Decentralized Society: Finding Web3’s Soul / Decentralized Society: Finding the Soul of Web3 -7","Decentralization through Pluralism When analyzing ecosystems, it's desirable to measure how decentralized it is.",{"id":56,"publish_date":57,"is_original":32,"collection":58,"cover_url":59,"cover_url_1_1":60,"title":61,"summary":62,"author":63},127,"2024-11-14","#Google #AI Game #World Model #AI Story","article_res/cover/0233a875b7ec2debf59779e311547569.jpeg","article_res/cover/6ffddb6ae4914b3c699493311aa9f198.jpeg","Google Launches \"Unbounded\": A Generative Infinite Character Life Simulation Game","Unbounded: A Generative Infinite Game of Character Life Simulation","Renee's Entrepreneurial Journey",{"id":13,"publish_date":65,"is_original":32,"collection":66,"cover_url":67,"cover_url_1_1":68,"title":69,"summary":70,"author":63},"2025-02-14","#Deep Dive into LLMs #Andrej Karpathy #LLM #Tool Use #Hallucination","article_res/cover/11e858ad6b74dfa80f923d549b62855c.jpeg","article_res/cover/615e1b320f1fc163edc1d2d154a6de33.jpeg","Andrej Karpathy's in-depth explanation of LLM (Part 4): Hallucinations","hallucinations, tool use, knowledge/working memory",{"id":72,"publish_date":73,"is_original":4,"collection":5,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":47},579,"2022-04-07","article_res/cover/39387376ba28447af1eb40576b9df215.jpeg","article_res/cover/02727ede8551ed49901d0abe6d6305b7.jpeg","Messari Report Translation and Summary 【1-7 Surviving the Winter】","I’d be more cautious here: 10 year and 10 hour thinking only.",[79,87,95,103,111],{"id":80,"publish_date":81,"is_original":32,"collection":82,"cover_url":83,"cover_url_1_1":84,"title":85,"summary":86,"author":63},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":112,"publish_date":105,"is_original":32,"collection":113,"cover_url":114,"cover_url_1_1":115,"title":116,"summary":117,"author":63},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[119,127,135],{"id":120,"publish_date":121,"is_original":32,"collection":122,"cover_url":123,"cover_url_1_1":124,"title":125,"summary":126,"author":63},160,"2024-10-04","#Philosophy","article_res/cover/496990c49211e8b7f996b7d39c18168e.jpeg","article_res/cover/14dbaa1ade9cb4316d5829423a900362.jpeg","Time","The fungus of the morning does not know the waxing and waning of the moon, and the cicada does not know the seasons; this is a short life. To the south of the state of Chu there is a dark spirit which regards five hundred years as spring and five hundred years as autumn. In ancient times there was a great tree called the Ming which regarded eight thousand years as spring and eight thousand years as autumn; this is a long life.",{"id":128,"publish_date":129,"is_original":32,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":63},98,"2024-12-17","#AI Video Generator #Sora #Pika","article_res/cover/3b86e85d03fff4f356a3e4cf2bb329c9.jpeg","article_res/cover/5fa5c20ad0b40f8f544d257c0ef02938.jpeg","Pika 2.0 video generation officially released: effect comparison with Sora","今天，我们推出了Pika 2.0模型。卓越的文字对齐效果。惊人的视觉表现。还有✨场景成分✨",{"id":136,"publish_date":137,"is_original":32,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":63},71,"2025-01-14","#Nvidia #World Foundation Model #Cosmos #Physical AI #Embodied AI","article_res/cover/feddf8c832dfb45d28804291f6a42a9e.jpeg","article_res/cover/d6bc2f1186d96b78228c2283a17a3645.jpeg","NVIDIA's Cosmos World Model","Cosmos World Foundation Model Platform for Physical AI",[144,163,188],{"title":8,"items":145},[146,147,155],{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},{"id":148,"publish_date":149,"is_original":32,"collection":150,"cover_url":151,"cover_url_1_1":152,"title":153,"summary":154,"author":63},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":156,"publish_date":157,"is_original":32,"collection":158,"cover_url":159,"cover_url_1_1":160,"title":161,"summary":162,"author":63},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"title":9,"items":164},[165,172,180],{"id":166,"publish_date":157,"is_original":32,"collection":167,"cover_url":168,"cover_url_1_1":169,"title":170,"summary":171,"author":63},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":173,"publish_date":174,"is_original":4,"collection":175,"cover_url":176,"cover_url_1_1":177,"title":178,"summary":179,"author":63},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":181,"publish_date":182,"is_original":4,"collection":183,"cover_url":184,"cover_url_1_1":185,"title":186,"summary":187,"author":63},417,"2023-08-24","#OpenAI","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"title":10,"items":189},[190,191,192],{"id":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},{"id":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},true,{"code":4,"msg":5,"data":195},{"id":196,"publish_date":197,"is_original":32,"collection":198,"articles_id":199,"cover_url":200,"cover_url_1_1":201,"title":202,"summary":203,"author":63,"content":204},26,"2025-02-26","#AI Research #Nvidia","hWE_9_ghf7B21qOvoeA8gA","article_res/cover/a49545e2ad00521f5e7e1c5670e6003b.jpeg","article_res/cover/c9e76abe1708cc4a775f6bea0a3afc96.jpeg","Stanford and NVIDIA's Evo 2 - Large AI Model for Biomolecular Sciences","Genome modeling and design across all domains of life with Evo 2","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 is a powerful new AI model that provides deep analysis across species for DNA, RNA, and proteins.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010284\" data-ratio=\"0.5314814814814814\" data-s=\"300,640\" data-type=\"jpeg\" data-w=\"1080\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769970500.738494909434114.jpeg\">\u003C/section>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Capable of understanding the genetic code across all domains of life.\u003C/span>\u003C/span>\u003C/h2>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 is the largest publicly available AI model for genomic data globally, built on the NVIDIA DGX Cloud platform through a collaboration between the non-profit biomedical research institute Arc Institute and Stanford University.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 was trained on a large dataset of nearly 9 trillion nucleotides, which are the basic building blocks of DNA and RNA. Evo 2 can be applied to biomolecular research, including predicting the shape and function of proteins based on genetic sequences, identifying new molecules for healthcare and industrial applications, and evaluating how genetic mutations affect their functions.\u003C/span>\u003C/p>\u003Cblockquote style='box-sizing: border-box;margin: 20px 0px;;padding: 10px 10px 10px 20px;border-style: none none none solid;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0.05);width: auto;height: auto;box-shadow: rgba(0, 0, 0, 0) 0px 0px 0px 0px;display: block;overflow: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cp style=\"box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;font-weight: normal;\">\u003Cspan leaf=\"\">\"Evo 2 represents a significant milestone in generative genomics. By advancing our understanding of these fundamental building blocks of life, we can pursue healthcare and environmental science solutions that are unimaginable today.\"\u003C/span>\u003C/p>\u003Cp style=\"box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;font-weight: normal;\">\u003Cspan leaf=\"\">-- Patrick Hsu (Co-founding Core Investigator at the Arc Institute and Assistant Professor of Bioengineering at UC Berkeley)\u003C/span>\u003C/p>\u003C/blockquote>\u003Cblockquote style='box-sizing: border-box;margin: 20px 0px;;padding: 10px 10px 10px 20px;border-style: none none none solid;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0.05);width: auto;height: auto;box-shadow: rgba(0, 0, 0, 0) 0px 0px 0px 0px;display: block;overflow: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cp style=\"box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;font-weight: normal;\">\u003Cspan leaf=\"\">\"Designing new biology has traditionally been a tedious, unpredictable, and manual process. With Evo 2, we make the bio-design of complex systems more accessible to researchers, thus enabling new beneficial advances that would otherwise take an extensive amount of time.\"\u003C/span>\u003C/p>\u003Cp style=\"box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;font-weight: normal;\">\u003Cspan leaf=\"\">-- Brian Hie (Assistant Professor of Chemical Engineering at Stanford University, Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and Innovation Fellow at the Arc Institute)\u003C/span>\u003C/p>\u003C/blockquote>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Broad applications in biomolecular science\u003C/span>\u003C/span>\u003C/h2>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 can provide deep insights into DNA, RNA, and proteins. Trained across multiple species, including plants, animals, and bacteria, this model can be applied in scientific fields such as healthcare, agricultural biotechnology, and materials science.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010285\" data-ratio=\"0.6375\" data-s=\"300,640\" data-type=\"gif\" data-w=\"640\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769972360.7093648985654881.gif\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 adopts a novel model architecture capable of processing genomic sequences up to one million tokens long. This broad view of the genome may uncover insights into the connections between distant parts of the genome and their implications for cellular function, gene expression, and disease mechanisms.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">A human gene contains tens of thousands of nucleotides — thus, for AI models to analyze such complex biological systems, they need to process as large a portion of the genetic sequence as possible at once.\u003C/span>\u003C/p>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Healthcare and drug discovery\u003C/span>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 can help researchers understand which genetic variations are associated with specific diseases and design new molecules precisely targeting these regions to treat diseases. For example, researchers at Stanford University and the Arc Institute found that in tests on the BRCA1 gene (a gene linked to breast cancer), Evo 2 could predict with 90% accuracy whether previously unidentified mutations would affect gene function.\u003C/span>\u003C/p>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Agriculture\u003C/span>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">This model can help scientists develop crop varieties that are more climate-resilient or have higher nutritional density by providing deep insights into plant biology, thus addressing global food shortages. In other scientific fields, Evo 2 can also be applied to biofuel design or the engineering of proteins that degrade oil or plastic.\u003C/span>\u003C/p>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Overview of the Evo2 Model Architecture, Training Process, Datasets, and Evaluation\u003C/span>\u003C/span>\u003C/h2>\u003Cul style='box-sizing: border-box;margin: 8px 0px;;list-style-type: disc;padding: 0px 0px 0px 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;' class=\"list-paddingleft-1\">\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Evo 2 models DNA sequences, enabling the application of the central dogma across molecular and cellular scales.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.7834586466165413\" data-s=\"300,640\" data-type=\"png\" data-w=\"665\" type=\"block\" data-imgfileid=\"100010286\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769970650.42820858447438237.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Evo 2 is trained on data covering all domains of life, containing trillions of nucleotide sequences, with each UMAP point representing a single genome.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010287\" data-ratio=\"0.9742857142857143\" data-s=\"300,640\" data-type=\"png\" data-w=\"700\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769971290.218341549770404.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">A two-stage training strategy is adopted to optimize model performance while scaling up to one million base pairs, capturing a wide range of biological patterns.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.2296983758700696\" data-s=\"300,640\" data-type=\"png\" data-w=\"862\" type=\"block\" data-imgfileid=\"100010288\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769972580.4787218482686142.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Innovative data augmentation and weighting methods focus on functional gene elements during the pre-training phase and emphasize long sequence composition during the mid-term training phase.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.4826446280991736\" data-s=\"300,640\" data-type=\"png\" data-w=\"605\" type=\"block\" data-imgfileid=\"100010289\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769970990.36780498715851295.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">The tokens used to train Evo 2 are divided into 40B and 7B, corresponding to the short-term pre-training phase and the long-context mid-term training phase, respectively.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"1.1097560975609757\" data-s=\"300,640\" data-type=\"png\" data-w=\"246\" type=\"block\" data-imgfileid=\"100010290\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769972390.0026518265036119537.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">The schematic diagram of the new multi-hybrid StripedHyena 2 architecture shows the efficient module layout of short-term explicit (SE), mid-term regularization (MR), and long-term implicit (LI) hyena operators.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.21830985915492956\" data-s=\"300,640\" data-type=\"png\" data-w=\"852\" type=\"block\" data-imgfileid=\"100010291\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769978140.6592481509735981.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Under 1024 GPUs and a scale of 40B, the iteration time of StripedHyena 2, StripedHyena 1, and Transformer was compared, showing significant throughput improvements.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.6424242424242425\" data-s=\"300,640\" data-type=\"png\" data-w=\"330\" type=\"block\" data-imgfileid=\"100010292\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769979130.31314536707837304.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">The validation perplexity of Evo 2's mid-term training compares model size and context length, demonstrating performance advantages as size and context length increase.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010293\" data-ratio=\"0.9726443768996961\" data-s=\"300,640\" data-type=\"png\" data-w=\"329\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769973950.26491063948428195.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">The revised \"needle-in-a-haystack\" task evaluated Evo 2's recall ability in long contexts (up to 1 million sequence lengths), demonstrating that the model can achieve effective recall in a 1-million-token context.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010294\" data-ratio=\"1.0478011472275335\" data-s=\"300,640\" data-type=\"png\" data-w=\"523\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769970770.9068032767194816.png\">\u003C/section>\u003C/li>\u003C/ul>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">The mechanistic interpretability of Evo2 reveals features at the DNA, RNA, protein, and organism levels.\u003C/span>\u003C/span>\u003C/h2>\u003Cul style='box-sizing: border-box;margin: 8px 0px;;list-style-type: disc;padding: 0px 0px 0px 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;' class=\"list-paddingleft-1\">\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Sparse autoencoders (SAEs) were trained on Evo 2 to extract SAE features related to interpretable biological functions, which can be used for annotation, discovery, and guiding sequence generation.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010295\" data-ratio=\"0.45107794361525705\" data-s=\"300,640\" data-type=\"png\" data-w=\"603\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769990530.8192637185736189.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Phage-related features in the E. coli K12 MG1655 genome preferentially activate for RefSeq-annotated prophages (on the left and upper right) and trigger on phage-derived spacers within CRISPR arrays (lower right).\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010296\" data-ratio=\"0.3539140022050717\" data-s=\"300,640\" data-type=\"png\" data-w=\"907\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769983230.6725638307460649.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Feature activations related to open reading frames (ORFs), intergenic sites, tRNAs, and rRNAs are shown in a 100-kb region of E. coli K12 MG1655.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010297\" data-ratio=\"0.1527777777777778\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769972390.5907117952591237.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">In E. coli K12 MG1655, a region contains the tufB gene and a tRNA array ending with thrT (on the left), as well as the rpoB-rpoC site (on the right), showing characteristic activations related to α-helix, β-sheet, and tRNA. The figure also overlays structural predictions from AlphaFold 3 (AF3): on the left is the complex of EF-Tu with thrT tRNA, and on the right is the complex of RpoB with RpoC.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010298\" data-ratio=\"0.16574074074074074\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769984640.5826536906822293.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">A feature in the human genome is more likely to be activated after a frameshift mutation than after mutations of less harmful types.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010300\" data-ratio=\"0.3950617283950617\" data-s=\"300,640\" data-type=\"png\" data-w=\"648\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769973280.7310415213105235.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">In the human genome, a series of features are activated at DNA motifs corresponding to transcription factor binding sites.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010301\" data-ratio=\"0.3318077803203661\" data-s=\"300,640\" data-type=\"png\" data-w=\"874\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769971330.571776113802883.png\">\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cspan leaf=\"\">Features associated with exons, introns, and their boundaries in the human genome can be used to annotate the woolly mammoth genome.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010302\" data-ratio=\"0.16203703703703703\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" style=\"height: auto !important;\" src=\"https://res.cooltool.vip/article_res/assets/17423769971160.5904126321714114.png\">\u003C/section>\u003C/li>\u003C/ul>\u003Csection>\u003Cspan leaf=\"\">\u003Cbr>\u003C/span>\u003C/section>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Summary\u003C/span>\u003C/span>\u003C/h2>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">All forms of life encode information through DNA. Although tools for genome sequencing, synthesis, and editing have revolutionized biological research, the intelligent construction of new biological systems also requires a deep understanding of the immense complexity embedded in genomes.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 —— a biofoundation model trained on 9.3 trillion curated DNA base pairs spanning all domains of life. Evo 2 has 7B and 40B parameters, providing an unprecedented one-million-token context window and single-nucleotide resolution, capable of accurately predicting the functional impact of genetic variants from non-coding pathogenic mutations to clinically relevant BRCA1 variants based solely on DNA sequences without task-specific fine-tuning.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 can autonomously learn various biological features such as exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. In addition to its predictive capabilities, Evo 2 can generate whole-genome sequences for mitochondrial, prokaryotic, and eukaryotic organisms, with better naturalness and coherence than previous methods. With search-guided inference, Evo 2 achieves controllable generation of epigenomic structures and demonstrates the effect of inference-time scaling in biology for the first time.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Evo 2 is fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, aiming to accelerate the exploration and design of biological complexity. Github link🔗: http://github.com/ArcInstitute/evo2\u003C/span>\u003C/p>\u003Csection style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">\u003Cbr>\u003C/span>\u003C/section>\u003Csection style='box-sizing: border-box;margin: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">✍️Finally, to be honest, I didn't understand this article at all. All the above was interpreted by ChatGPT.\u003C/span>\u003C/section>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010299\" data-ratio=\"0.7627551020408163\" data-s=\"300,640\" data-type=\"png\" data-w=\"784\" style=\"width: 350px;height: auto !important;\" type=\"block\" src=\"https://res.cooltool.vip/article_res/assets/17423769986380.42032865557003496.png\">\u003C/section>\u003Csection>\u003Cspan leaf=\"\">\u003Cbr>\u003C/span>\u003C/section>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",1752585424653]