[{"data":1,"prerenderedAt":372},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fwkOfpyoxOdkTLSvxxfnb9MGGx_C0_HL_MhxRkY5nwOk":245,"article-89":371},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? 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No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly 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generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},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":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},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":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},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":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},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",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language 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Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},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":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},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":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","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.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":23,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":313,"category":369,"tag":370},89,"2024-12-26","#Nature #LLM #Neuroscience","Y2lsBzmqexxaKNVtj68rMQ","article_res/cover/a726cc0f0274a6d9658b78c37b454ebb.jpeg","article_res/cover/e8f8349f81283aa22c6260d67a3de865.jpeg","Deep alignment of LLM with human brain language processing","Developing models that align more closely with human cognitive processing","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>A research article published earlier this year, titled \"Contextual feature extraction hierarchies converge in large language models and the brain,\" was successfully published in a sub-journal of the top international journal Nature after nearly a year of academic review in November this year.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008535\" data-ratio=\"0.9271844660194175\" data-s=\"300,640\" data-type=\"png\" data-w=\"824\" style=\"\" src=\"./assets/17423772459580.6811688428121041.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>By using intracranial electrophysiological recordings (intracranial electroencephalography, iEEG) from neurosurgical patients while they listened to speech, the study delved into the alignment relationship between LLMs and human brain language processing mechanisms.\u003C/p>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Research highlights\u003C/strong>\u003C/span>\u003C/h3>\u003Col style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">As LLMs improve their performance on benchmark tasks, they not only become more \"brain-like\" in accurately predicting neural responses but also demonstrate higher consistency with the hierarchical feature extraction pathways of the human brain—achieving efficient processing with fewer layers in the same encoding task.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">High-performing LLMs exhibit a shared strategy for hierarchical language processing, indicating a trend towards convergence on similar language processing mechanisms at a functional level.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">The role of contextual information is critical for both the performance of LLMs and their alignment with human brain activity. This finding underscores the central role of context processing in language understanding.\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Experimental methodology\u003C/strong>\u003C/span>\u003C/h3>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008536\" data-ratio=\"0.5370370370370371\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423772461460.7205528100584646.png\">\u003C/p>\u003Col style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">centered around each word, which served as the word response for each electrode.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">to serve as the word representation of the model.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">to quantify the degree of correspondence between LLM representations and neural responses.\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Research findings\u003C/strong>\u003Cspan style=\"text-align: center;color: rgba(0, 0, 0, 0.85);font-size: 16px;font-weight: 400;\">\u003C/span>\u003C/span>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003C/h3>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">1. Brain-related consistency and LLM performance consistency (Figure A)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008538\" data-ratio=\"0.6325678496868476\" data-s=\"300,640\" data-type=\"png\" data-w=\"958\" style=\"width: 470px;height: 297px;\" src=\"./assets/17423772459670.17238717587307528.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The average of brain-related consistency across all electrodes shows a high degree of alignment between the performance of LLMs and their neural alignment. It can be seen from the figure that models with poorer performance (blue/purple) have lower average correlations, while models with better performance (yellow) have higher correlations. The shaded area represents the standard error between electrodes, showing a consistent trend.\u003C/section>\u003C/li>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">2. Peak correlation and LLM performance (Figure B)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008539\" data-ratio=\"0.9965870307167235\" data-s=\"300,640\" data-type=\"png\" data-w=\"586\" style=\"width: 309px;height: 308px;\" src=\"./assets/17423772459700.8971072919231899.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The peak correlation for each model was calculated based on the highest correlation score across all layers for each electrode, then averaged over all electrodes.\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The results showed a significant positive correlation between average peak correlation and LLM performance (Pearson r = 0.92, p = 2.24 × 10⁻⁵).\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">: The significance level of the results is indicated using asterisks (\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">3. The relationship between the distribution of peak correlation layers and distance (Figure C)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008540\" data-ratio=\"0.6341991341991342\" data-s=\"300,640\" data-type=\"png\" data-w=\"924\" style=\"\" src=\"./assets/17423772461140.744752311729703.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The study calculated the locally smoothed peak hierarchical distribution based on the distance of electrodes from the posterior part of the superior temporal gyrus (pmHG).\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The results showed that the peak hierarchy gradually increased with the distance of electrodes from pmHG. Better-performing models (yellow) peaked at positions closer to the lower layers of the model, showing a lower distribution compared to poorer-performing models (blue/purple).\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">4. The average peak hierarchy and model performance (Figure D)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008541\" data-ratio=\"0.9967105263157895\" data-s=\"300,640\" data-type=\"png\" data-w=\"608\" style=\"width: 273px;height: 272px;\" src=\"./assets/17423772461130.8476027427405093.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">By analyzing the average peak layer across all electrodes for each model, we found that it is significantly negatively correlated with LLM performance (Pearson r = −0.81, p = 0.0013).\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">This suggests that better-performing models tend to reach the highest brain correlation at lower layers (closer to the input).\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Key findings\u003C/strong>\u003C/span>\u003C/h3>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">The research results indicate that the performance of large language models not only affects their performance in language tasks but also directly determines the degree of alignment with the human brain's language processing mechanisms.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">Models with better performance reach peaks of brain-related relevance at lower levels, implying that high-performance LLMs have more effective feature extraction capabilities and hierarchical processing mechanisms closer to the human brain.\u003C/p>\u003C/section>\u003C/li>\u003C/ul>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">The Importance of Contextual Information: A New Perspective on the Alignment between Large Language Models and the Brain\u003C/strong>\u003C/span>\u003C/h3>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>reveals how the length and content of the context window affect model performance and neural alignment.\u003C/p>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">1. The impact of the context window on hierarchical alignment (Figure A)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008545\" data-ratio=\"1.0597014925373134\" data-s=\"300,640\" data-type=\"png\" data-w=\"536\" style=\"width: 331px;height: 351px;\" src=\"./assets/17423772470290.2643915430371264.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">influence.\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">* indicates\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">** indicates\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">*** indicates\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The results show that,\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Each point in the figure represents a correlation result, with 95% confidence intervals indicated by error bars, and significance levels marked by asterisks (∗, ∗∗, ∗∗∗):\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">2. The relationship between context content and model performance (Figure B)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008546\" data-ratio=\"1.2103004291845494\" data-s=\"300,640\" data-type=\"png\" data-w=\"466\" style=\"width: 263px;height: 318px;\" src=\"./assets/17423772461140.09278291405790617.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">There is a significant positive correlation between the influence of context content on model representation and its benchmark evaluation performance (Spearman r = 0.66,\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">and enhance their feature representation capabilities.\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">3. The relationship between context content and brain similarity (Figure C)\u003C/strong>\u003C/span>\u003C/h4>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008547\" data-ratio=\"1.1906779661016949\" data-s=\"300,640\" data-type=\"png\" data-w=\"472\" style=\"width: 253px;height: 301px;\" src=\"./assets/17423772459790.920115006792295.png\">\u003C/p>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">There was also a significant positive correlation between the model's contextual content and its average peak brain similarity (Spearman r = 0.84,\u003C/section>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The horizontal error bars in the figure indicate the standard error of the mean brain similarity across electrodes, further emphasizing the central role of context processing in brain alignment.\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch4 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">4. Distribution of regional context effects (Figures D and E)\u003C/strong>\u003C/span>\u003C/h4>\u003Cul style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">In the brain maps drawn on the FreeSurfer platform, electrodes were colored according to their influence on the peak brain similarity, showing the distribution of context effects across different brain regions.\u003C/p>\u003C/section>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008551\" data-ratio=\"0.48518518518518516\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"\" src=\"./assets/17423772484530.24081117218324355.png\">\u003C/p>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">The contextual effect differences of four major language-related brain regions were analyzed, and a bar chart was created to display the average contextual effect values for each region:\u003C/p>\u003C/section>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008552\" data-ratio=\"1.2758620689655173\" data-s=\"300,640\" data-type=\"png\" data-w=\"406\" style=\"width: 204px;height: 260px;\" src=\"./assets/17423772469300.9709219146620391.png\">\u003C/p>\u003C/li>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;list-style-type: square;padding-left: 25px;color: rgb(0, 0, 0);\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The color of each bar is consistent with the brain map's color scheme, and error bars represent the standard error.\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">: The differences between regions were analyzed using the Wilcoxon rank-sum test, and the significance levels are indicated by asterisks (∗, ∗∗, ∗∗∗).\u003C/section>\u003C/li>\u003C/ul>\u003C/ul>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Meaning\u003C/strong>\u003C/span>\u003C/h3>\u003Col style='margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">revealing the central role of contextual information in language understanding and processing.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">High-performance LLMs can better utilize contextual information, not only improving their task performance but also aligning more closely with the cognitive mechanisms of the human brain. This provides important inspiration for the future optimization of models.\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">\u003Cp style=\"cursor: pointer;color: rgb(0, 0, 0);line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">Different brain regions show varying responses to contextual information, indicating the hierarchical nature of language processing and the synergistic functions of regional areas.\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Cp>\u003Cbr>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,275,283,291,294,302,310],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":23,"collection":262,"summary_md5":263,"cover_url":264,"cover_url_1_1":265},303,"bf8d770e888dfe0a813804faaf0a2a40","2024-04-02","bc27fa490c4d0d525bac812fc0793534","#AI Avatar #AI Animation","b638c5bd27770bb18f3a040362454ae6","article_res/cover/da8ffab03c7280a4f183d66e8b8e82e7.jpeg","article_res/cover/a5d0c0d01e08ee54a5e3e2b4d218a8d3.jpeg",{"id":267,"title_md5":268,"publish_date":269,"author_md5":270,"is_original":4,"collection":271,"summary_md5":272,"cover_url":273,"cover_url_1_1":274},484,"af76b06a6a201755883f5b64a78911eb","2023-04-10","cfab1ba8c67c7c838db98d666f02a132","#Stable Diffusion #AI Image Generator","5d615395f499310d37a158d7addf51f7","article_res/cover/73624a211e1da591990a68332c54be3e.jpeg","article_res/cover/8d10285c2263513b4ec9b69fcc93a2c1.jpeg",{"id":276,"title_md5":277,"publish_date":278,"author_md5":261,"is_original":23,"collection":279,"summary_md5":280,"cover_url":281,"cover_url_1_1":282},479,"94d0e788828142f395db30cf022e1dd9","2023-04-15","#LLM","9583b09a71d003174bff244d4df6f62b","article_res/cover/888b62a0c15cac082662d260c0b8ba59.jpeg","article_res/cover/c6c9b1528d75c495c6830ee1ed75b06e.jpeg",{"id":284,"title_md5":285,"publish_date":286,"author_md5":261,"is_original":23,"collection":287,"summary_md5":288,"cover_url":289,"cover_url_1_1":290},226,"0cf292403dc275825546721918f6ee4a","2024-07-07","#AI Video Generator #AI Animation","c01d1cc504b314f88dc408afa77b21b8","article_res/cover/0da632cc597be863db7e82eadf492a6b.jpeg","article_res/cover/abcdbae958b5b74bf445be4352796ac9.jpeg",{"id":21,"title_md5":292,"publish_date":22,"author_md5":261,"is_original":23,"collection":5,"summary_md5":293,"cover_url":24,"cover_url_1_1":25},"11aaec56e2b3e420ea476e019c0487b3","3624c8cab3a8417fd95bcdb265d6edb0",{"id":295,"title_md5":296,"publish_date":297,"author_md5":261,"is_original":23,"collection":298,"summary_md5":299,"cover_url":300,"cover_url_1_1":301},262,"601aa040433376a99796b5ef89201583","2024-05-28","#Google #Imagen3","f8a439af78fd9f137a260b56088b5d3d","article_res/cover/3fe335712c303d1908ea5f856858e907.jpeg","article_res/cover/b41734e00060ac4011afcb91fc23c02b.jpeg",{"id":303,"title_md5":304,"publish_date":305,"author_md5":261,"is_original":23,"collection":306,"summary_md5":307,"cover_url":308,"cover_url_1_1":309},235,"3bcfd2777475451f0ba64108d579d824","2024-06-25","#LLM #ControlNet","a68d17db4c32e7a9d0e52547762205c1","article_res/cover/59570bf9128ad980c576347da9ac0b5d.jpeg","article_res/cover/d3cf47b4520c2ac1753d6debb218d793.jpeg",{"id":185,"title_md5":311,"publish_date":167,"author_md5":261,"is_original":23,"collection":186,"summary_md5":312,"cover_url":187,"cover_url_1_1":188},"e73415acabc9bb396be72a35c59fc6de","8886f8e44abb23e325ac5af11848a6f8",{"related":314,"small":354},[315,323,331,339,347],{"id":316,"publish_date":317,"is_original":23,"collection":318,"cover_url":319,"cover_url_1_1":320,"title":321,"summary":322,"author":28},149,"2024-10-17","#State of AI Report 2024","article_res/cover/3b8743ca9c648e09b59e160b16c4b196.jpeg","article_res/cover/4773993643e3fc5a8b782c4d0761636d.jpeg","\"State of AI Report 2024\" (1) - AlphaGeometry, Synthetic Data, RAG","Foundation models demonstrate their ability to break out of language as multimodal research.",{"id":324,"publish_date":325,"is_original":4,"collection":326,"cover_url":327,"cover_url_1_1":328,"title":329,"summary":330,"author":28},408,"2023-09-21","#AI Agents #LLM #AI Agent","article_res/cover/897f57a35c9bd3bb8e8c738dae940108.jpeg","article_res/cover/f83dfe7f16aef354ff7e6e1372afd074.jpeg","Playing Werewolf with LLM Agents (Continued)","We observed that LLMs exhibit some strategic behaviors not explicitly preprogrammed in the game rules or prompts.",{"id":332,"publish_date":333,"is_original":23,"collection":334,"cover_url":335,"cover_url_1_1":336,"title":337,"summary":338,"author":28},114,"2024-11-30","#SITUATIONAL AWARENESS #AGI","article_res/cover/23af40c46a5942e4f50eb700282c82fa.jpeg","article_res/cover/2d8568027a8fe3c668db5c417f3035c0.jpeg","From GPT-4 to AGI: The Exponential Growth of OOMs in Computing","The models, they just want to learn. You have to understand this. The models, they just want to learn. - Ilya Sutskever",{"id":340,"publish_date":341,"is_original":4,"collection":342,"cover_url":343,"cover_url_1_1":344,"title":345,"summary":346,"author":28},270,"2024-05-20","#AI Agent","article_res/cover/df3dfd94230da278a801bb28d27ae25b.jpeg","article_res/cover/377a030c5b17b3ad7fe9125d9b3346e6.jpeg","Cognosys - AI Automated Workflow","Meet the AI that simplifies tasks and speeds up your workflow. Get things done faster and smarter, without the fuss.",{"id":348,"publish_date":349,"is_original":4,"collection":5,"cover_url":350,"cover_url_1_1":351,"title":352,"summary":353,"author":28},333,"2024-01-28","article_res/cover/f38b77fc0c87f5839b5ca3717ddecca8.jpeg","article_res/cover/7bb18a2ed5306675b8dc15f025c157f9.jpeg","DAO in \"CRYPTO THESES 2024\"","In the crypto-utopian vision of the future, DAOs would be benevolent, AI-driven protocol governors.",[355,361,367],{"title":10,"list":356},[357,358,359,360],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":362},[363,364,365,366],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":368},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646411122]