{"id":922,"date":"2026-02-10T01:30:19","date_gmt":"2026-02-09T16:30:19","guid":{"rendered":"https:\/\/itexplore.org\/jp\/columns\/ai-agents-brain-science-brain-to-text-trends\/"},"modified":"2026-02-10T01:30:19","modified_gmt":"2026-02-09T16:30:19","slug":"ai-agents-brain-science-brain-to-text-trends","status":"publish","type":"post","link":"https:\/\/itexplore.org\/jp\/columns\/ai-agents-brain-science-brain-to-text-trends\/","title":{"rendered":"AI\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3068\u8133\u79d1\u5b66\u3001\u30d6\u30ec\u30a4\u30f3\u30fb\u30c8\u30a5\u30fb\u30c6\u30ad\u30b9\u30c8\u306e\u6700\u65b0\u52d5\u5411"},"content":{"rendered":"<p>\u672c\u65e5\u306e\u6ce8\u76eeAI\u30fb\u30c6\u30c3\u30af\u30cb\u30e5\u30fc\u30b9\u3092\u3001\u5c02\u9580\u7684\u306a\u5206\u6790\u3068\u5171\u306b\u304a\u5c4a\u3051\u3057\u307e\u3059\u3002<\/p>\n<div class=\"wp-block-vk-blocks-alert vk_alert alert alert-warning has-alert-icon\">\n<div class=\"vk_alert_icon\">\n<div class=\"vk_alert_icon_icon\"><i class=\"fa-solid fa-triangle-exclamation\" aria-hidden=\"true\"><\/i><\/div>\n<div class=\"vk_alert_icon_text\"><span>Warning<\/span><\/div>\n<\/div>\n<div class=\"vk_alert_content\">\n<p>\u3053\u306e\u8a18\u4e8b\u306fAI\u306b\u3088\u3063\u3066\u81ea\u52d5\u751f\u6210\u30fb\u5206\u6790\u3055\u308c\u305f\u3082\u306e\u3067\u3059\u3002AI\u306e\u6027\u8cea\u4e0a\u3001\u4e8b\u5b9f\u8aa4\u8a8d\u304c\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3001\u91cd\u8981\u306a\u5224\u65ad\u3092\u4e0b\u3059\u969b\u306f\u5fc5\u305a\u30ea\u30f3\u30af\u5148\u306e\u4e00\u6b21\u30bd\u30fc\u30b9\u3092\u3054\u78ba\u8a8d\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/div>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">Agyn: \u30c1\u30fc\u30e0\u30d9\u30fc\u30b9\u306e\u81ea\u5f8b\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u305f\u3081\u306e\u30de\u30eb\u30c1\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30b7\u30b9\u30c6\u30e0<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p><strong>Agyn<\/strong>\u306f\u3001\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306b\u304a\u3051\u308b\u554f\u984c\u89e3\u6c7a\u3092\u3001\u500b\u3005\u306e\u30bf\u30b9\u30af\u3067\u306f\u306a\u304f\u3001\u30c1\u30fc\u30e0\u6d3b\u52d5\u3068\u3057\u3066\u6349\u3048\u308b\u65b0\u3057\u3044\u30de\u30eb\u30c1\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30b7\u30b9\u30c6\u30e0\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u30b7\u30b9\u30c6\u30e0\u306f\u3001<strong>LLM<\/strong>\u306e\u80fd\u529b\u3092\u6d3b\u7528\u3057\u3064\u3064\u3001\u5354\u8abf\u3001\u8abf\u67fb\u3001\u5b9f\u88c5\u3001\u30ec\u30d3\u30e5\u30fc\u3068\u3044\u3063\u305f\u5f79\u5272\u3092\u5c02\u9580\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u5272\u308a\u5f53\u3066\u3001\u5b9f\u969b\u306e\u958b\u767a\u30c1\u30fc\u30e0\u306e\u3088\u3046\u306a\u69cb\u9020\u3068\u30d7\u30ed\u30bb\u30b9\u3092\u6a21\u5023\u3057\u307e\u3059\u3002<\/p>\n<p><strong>Agyn<\/strong>\u306f\u3001\u4eba\u9593\u306b\u3088\u308b\u4ecb\u5165\u306a\u3057\u306b\u3001\u5206\u6790\u304b\u3089\u30d7\u30eb\u30ea\u30af\u30a8\u30b9\u30c8\u4f5c\u6210\u3001\u30ec\u30d3\u30e5\u30fc\u307e\u3067\u306e\u4e00\u9023\u306e\u958b\u767a\u30d7\u30ed\u30bb\u30b9\u3092\u5b9f\u884c\u3057\u3001SWE-bench 500\u306772.4%\u306e\u30bf\u30b9\u30af\u89e3\u6c7a\u7387\u3092\u9054\u6210\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u306f\u3001\u7d44\u7e54\u69cb\u9020\u3068\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u306e\u91cd\u8981\u6027\u3092\u793a\u5506\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2602.01465\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> Modeling software engineering as a team activity with specialized AI agents significantly improves autonomous issue resolution.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Nikita Benkovich, Vitalii Valkov<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p><strong>Agyn<\/strong> introduces a novel multi-agent system that models issue resolution in software engineering as a team activity, rather than a monolithic or pipeline process.<\/p>\n<p>Leveraging the capabilities of <strong>LLMs<\/strong>, the system assigns specialized agents to roles such as coordination, research, implementation, and review, replicating the structure and processes of a real development team.<\/p>\n<p><strong>Agyn<\/strong> operates autonomously through the entire development lifecycle, from analysis to pull request creation and review, achieving a 72.4% task resolution rate on SWE-bench 500. This highlights the significance of organizational structure and agent infrastructure.<\/p>\n<\/blockquote>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">BrainFuse: \u73fe\u5b9f\u7684\u306a\u751f\u7269\u5b66\u7684\u30e2\u30c7\u30ea\u30f3\u30b0\u3068\u30b3\u30a2AI\u65b9\u6cd5\u8ad6\u3092\u7d71\u5408\u3059\u308b\u7d71\u4e00\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p><strong>BrainFuse<\/strong>\u306f\u3001\u751f\u7269\u5b66\u7684\u306a\u30cb\u30e5\u30fc\u30e9\u30eb\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u52fe\u914d\u30d9\u30fc\u30b9\u5b66\u7fd2\u3092\u7d71\u5408\u3059\u308b\u7d71\u4e00\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u30b7\u30b9\u30c6\u30e0\u306f\u3001\u8a73\u7d30\u306a\u795e\u7d4c\u30c0\u30a4\u30ca\u30df\u30af\u30b9\u3092\u5fae\u5206\u53ef\u80fd\u306a\u5b66\u7fd2\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306b\u7d71\u5408\u3057\u3001GPU\u4e0a\u3067\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u53ef\u80fd\u306a\u30a4\u30aa\u30f3\u30c1\u30e3\u30cd\u30eb\u30c0\u30a4\u30ca\u30df\u30af\u30b9\u3092\u6700\u59273,000\u500d\u9ad8\u901f\u5316\u3057\u307e\u3059\u3002<\/p>\n<p><strong>BrainFuse<\/strong>\u306f\u3001\u795e\u7d4c\u79d1\u5b66\u30bf\u30b9\u30af\u3068AI\u30bf\u30b9\u30af\u306e\u4e21\u65b9\u3067\u305d\u306e\u80fd\u529b\u3092\u767a\u63ee\u3057\u3001\u7d0438,000\u500b\u306e\u30cb\u30e5\u30fc\u30ed\u30f3\u30681\u5104\u500b\u306e\u30b7\u30ca\u30d7\u30b9\u3092\u6301\u3064\u30e2\u30c7\u30eb\u3092\u4f4e\u6d88\u8cbb\u96fb\u529b\u3067\u30cb\u30e5\u30fc\u30ed\u30e2\u30eb\u30d5\u30a3\u30c3\u30af\u30c1\u30c3\u30d7\u306b\u30c7\u30d7\u30ed\u30a4\u53ef\u80fd\u3067\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u6b21\u4e16\u4ee3\u306e\u751f\u7269\u5b66\u7684\u30a4\u30f3\u30b9\u30d4\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u53d7\u3051\u305f\u30a4\u30f3\u30c6\u30ea\u30b8\u30a7\u30f3\u30c8\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u304c\u52a0\u901f\u3055\u308c\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2601.21407\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> BrainFuse unifies biophysical neural simulation and gradient-based learning, enabling advanced bio-inspired AI systems and accelerating cross-disciplinary research.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Baiyu Chen, Yujie Wu, Siyuan Xu, Peng Qu, Dehua Wu, Xu Chu, Haodong Bian, Shuo Zhang, Bo Xu, Youhui Zhang, Zhengyu Ma, Guoqi Li<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p><strong>BrainFuse<\/strong> is a unified infrastructure that integrates biophysical neural simulation with gradient-based learning, bridging the gap between neuroscience and artificial intelligence.<\/p>\n<p>The system integrates detailed neuronal dynamics into a differentiable learning framework and accelerates customizable ion-channel dynamics by up to 3,000x on GPUs.<\/p>\n<p>Demonstrating capabilities in both neuroscience and AI tasks, <strong>BrainFuse<\/strong> can deploy models with approximately 38,000 neurons and 100 million synapses on neuromorphic hardware with low power consumption, accelerating the development of next-generation bio-inspired intelligent systems.<\/p>\n<\/blockquote>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">MEG-XL: \u9577\u6587\u8108\u4e8b\u524d\u5b66\u7fd2\u306b\u3088\u308b\u30c7\u30fc\u30bf\u52b9\u7387\u306e\u826f\u3044\u30d6\u30ec\u30a4\u30f3\u30fb\u30c8\u30a5\u30fb\u30c6\u30ad\u30b9\u30c8<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p><strong>MEG-XL<\/strong>\u306f\u3001\u81e8\u5e8a\u7684\u306a\u30d6\u30ec\u30a4\u30f3\u30fb\u30c8\u30a5\u30fb\u30c6\u30ad\u30b9\u30c8\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u306e\u305f\u3081\u306b\u958b\u767a\u3055\u308c\u305f\u3001\u30c7\u30fc\u30bf\u52b9\u7387\u306e\u826f\u3044\u30e2\u30c7\u30eb\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u30e2\u30c7\u30eb\u306f\u3001\u5f93\u6765\u306e\u6570\u79d2\u3067\u306f\u306a\u304f\u30012.5\u5206\u9593\u306eMEG\uff08\u8133\u78c1\u56f3\uff09\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3067\u4e8b\u524d\u5b66\u7fd2\u3055\u308c\u3066\u304a\u308a\u3001\u3053\u308c\u306f\u7d04191,000\u30c8\u30fc\u30af\u30f3\u306b\u76f8\u5f53\u3057\u307e\u3059\u3002<\/p>\n<p><strong>MEG-XL<\/strong>\u306f\u3001\u5c11\u91cf\u306e\u30c7\u30fc\u30bf\uff08\u4f8b\uff1a1\u6642\u9593\uff09\u3067\u3001\u5f93\u6765\u306e\u30e2\u30c7\u30eb\uff08\u4f8b\uff1a50\u6642\u9593\uff09\u3068\u540c\u7b49\u306e\u6027\u80fd\u3092\u767a\u63ee\u3057\u3001\u7279\u306b\u9577\u6587\u8108\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u512a\u308c\u305f\u5358\u8a9e\u30c7\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u80fd\u529b\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2602.02494\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> Long-context pre-training in MEG-XL significantly enhances data efficiency and performance for brain-to-text applications by leveraging extended neural context.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Dulhan Jayalath, Oiwi Parker Jones<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p><strong>MEG-XL<\/strong> is a data-efficient model designed for clinical brain-to-text interfaces, which require minimal training data from paralyzed patients.<\/p>\n<p>The model is pre-trained using 2.5 minutes of MEG (magnetoencephalography) context per sample, significantly longer than prior work, capturing extended neural context equivalent to approximately 191,000 tokens.<\/p>\n<p><strong>MEG-XL<\/strong> achieves supervised performance with a fraction of the data and outperforms existing brain foundation models, demonstrating that long-context pre-training effectively exploits extended neural context for improved word decoding.<\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>AI\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306e\u30c1\u30fc\u30e0\u9023\u643a\u306b\u3088\u308b\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u958b\u767a\u52b9\u7387\u5316\u3001\u751f\u7269\u5b66\u7684\u30e2\u30c7\u30ea\u30f3\u30b0\u3068AI\u3092\u7d71\u5408\u3059\u308bBrainFuse\u3001\u9577\u6587\u8108\u3092\u6d3b\u7528\u3057\u305f\u30c7\u30fc\u30bf\u52b9\u7387\u306e\u826f\u3044\u30d6\u30ec\u30a4\u30f3\u30fb\u30c8\u30a5\u30fb\u30c6\u30ad\u30b9\u30c8\u30e2\u30c7\u30ebMEG-XL\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n","protected":false},"author":1,"featured_media":853,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"vkexunit_cta_each_option":"","footnotes":""},"categories":[3],"tags":[8,17,16,155,154,153,15,97],"class_list":{"0":"post-922","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-columns","8":"tag-ai","10":"tag-llm","11":"tag-155","12":"tag-154","13":"tag-153","15":"tag-97"},"_links":{"self":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts\/922","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/comments?post=922"}],"version-history":[{"count":0,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts\/922\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/media\/853"}],"wp:attachment":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/media?parent=922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/categories?post=922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/tags?post=922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}