Email: "AlphaGo Moment" for Model Architecture Discovery...? b

Subject:
"AlphaGo Moment" for Model Architecture Discovery...? b
Received:
7/30/2025, 3:20:36 AM
From:
"The AI Timeline" <weekly@mail.bycloud.ai>
To:
desert.glitter.81@getmynews.app
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&lt;/style&gt;&lt;/head&gt;&lt;body class="a" style="margin:0px auto;padding:0px;word-wrap:normal;word-spacing:normal;background-color:#dedede;"&gt;&lt;div role="article" aria-roledescription="email" aria-label="email_name" lang="en" style="font-size:1rem"&gt;&lt;div style="display:none;max-height:0px;overflow:hidden;"&gt; dive into this week&#39;s most popular AI research papers. &#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204; &lt;/div&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" align="center" cellpadding="0" class="gg"&gt;&lt;tr&gt;&lt;td align="center" valign="top"&gt;&lt;table role="none" width="670" border="0" cellspacing="0" cellpadding="0" class="aa" style="width:670px;table-layout:fixed;"&gt;&lt;tr&gt;&lt;td class="bodyWrapper" align="center" valign="top" style="padding:7px 7px 7px 7px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="border-width:0px 0px 0px 0px;border-style: solid; border-color: #2a2a2a;border-radius:10px 10px 0px 0px;background-color:#ffffff;" class="c"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr id="header"&gt;&lt;td style="padding:15px 15px 0px 15px;"&gt;&lt;div style="padding-top:0px;padding-right:0px;padding-bottom:20px;padding-left:0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td class="f" align="right" valign="top"&gt;&lt;p&gt; July 29, 2025 &nbsp; | &nbsp; &lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3Ezice0-CcXQlzz-ZCCPojvsRTSlsyi1-sip5RQifZY6NR-JYTtSpo-xhZnjrDuXmbunkD5RaS2naQzMLVYg-bIjnFTb_hu0uo_HUUAWpdtDrmdxayBhf3lE53Mxtba_WViqxvn0ulA3S0XdXH5zNXYU6a5A1MdD_8H6wZlhT38yKa25gkxEVhG5k23hS_GChY92dA-npNy49F_6XBJOqn5lsc8splb7IdWD6sWWvY7eE8SiUI0Hll9fZwvnkD64cBJjbCvKu2r4AfzN2qnWcvwwI0048I8-F1-rbpIVMLKpAKxxjL_BjZz3akdkYubxZb139VxhstDzFxZZSTMcE6bpIxUvFNW2nPDIF-QajqaRG7aFc_3HQUfa8K4VeI7zHXyC0eLbuhaTtNPqsnrpxzxPRWCVT1vHiJG8JMYeKZxTMMq4pC0dXWLf2D0vCuP2Cu3efVD8WWzrEwVhSyzz6YoZ1J14UoONewtjEnAWY5jIVCOSaLeU7fFaYw11mYd6kM2TkTOGOfIhG6YZNFyjIqkLVBMfEm9CgRoL68pKW_6XCPTfUjPSSMge2Y9jkk7-LmuJBaNwSwANkKFkX7Aeb0iqZtTyVwtCUWN-uemEmHrP8FGZGuWsSSc2bR3VmD9KvaUr_QSOc3jvX_v-4S6Bh6TpqFW0CLRTzzP3MgrkXD5t_g/4im/UYmj5S4nTfKie84sa8X2EA/h0/h001.5pvRXDZjsYiSGQYH7adKiiINkxNSdjdpWcSpbZzteFE"&gt;Read Online&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" valign="top" style="padding:15px 0;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top"&gt;&lt;h1 style="text-align:left;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-weight:Bold;font-size:32px;color:#2A2A2A;padding:2px 0;line-height:38px;"&gt; &quot;AlphaGo Moment&quot; for Model Architecture Discovery...? &lt;/h1&gt;&lt;p style="text-align:left;font-family:'Helvetica',Arial,sans-serif;font-weight:normal;font-size:20px;color:#3E3E3E;padding:5px 0;line-height:24px;"&gt; Plus more on Group Sequence Policy Optimization and Autoregressive models in Data-Constrained Settings &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="height:0px;width:0px;"&gt;&lt;div style="height:1px;" data-open-tracking="true"&gt; &lt;img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4im/UYmj5S4nTfKie84sa8X2EA/ho.gif" alt="" width="1" height="1" border="0" style="height:1px !important;width:1px !important;border-width:0 !important;margin-top:0 !important;margin-bottom:0 !important;margin-right:0 !important;margin-left:0 !important;padding-top:0 !important;padding-bottom:0 !important;padding-right:0 !important;padding-left:0 !important;"/&gt; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr id="content-blocks"&gt;&lt;td class="email-card-body" align="center" valign="top" style="padding-bottom:28px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td id="nov-18-th-nov-24-th-33-latest-ai-re" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h6 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:87.5%;"&gt;&lt;i&gt;July 21st ~ July 27th&lt;/i&gt;&lt;br&gt;&lt;i&gt;#66 Latest AI Research Explained Simply&lt;/i&gt;&lt;/h6&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="industry-news-in-1-line" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"&gt;🗞️ Industry News in 1 Line&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt;♥ 5.2k&lt;/span&gt;&lt;/span&gt; Chinese tech giant Alibaba has announced its entry into wearables with the &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j23RiQHylPXEQIpw8v5qnRFftxYxPGHnfke26jCq0gxUgEuxI3XX5T6Lm4jFsUhrwL1CWpyWFf7NeefX3lkLE8AhImw256pMk4vmiHbQowAz-SX628Xu_x5vDggA5DsL5YQ_vaauDTXyQhgUpOVhJ-LX2_4o4Ft15SLhbpIcH9RA1/4im/UYmj5S4nTfKie84sa8X2EA/h1/h001.bH8EKpRlj4Y0V-CRuuIdVqbHv7f4254vpT_nq3SAkis" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Quark AI Glasses&lt;/span&gt;&lt;/a&gt;, which directly embed the firm&#39;s advanced Qwen AI assistant into the hardware. These glasses are going to be released in late 2025 and will offer hands-free calling, music streaming, real-time language translation, and meeting transcription. The glasses also feature a built-in camera. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt;♥ 1.5k&lt;/span&gt;&lt;/span&gt; Anthropic has published a new blog post on &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoSVmY_SAk44X112T7RvnIPy8IzDDasKlhGZp3Nv3zr7n4OQsYva7d4cA71iuL4D48tkTV8p4md0Pj_SxxsVuHzT9G5RLqYOVT2XYYpCUmorOLEkDIQLcPi2p8hfhT4YV0Q/4im/UYmj5S4nTfKie84sa8X2EA/h2/h001.WG8E2DA4tZ4MC1KF1IYKbknTvyLRG1X7wSb9NTKe0kI" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;autonomous auditing agents&lt;/span&gt;&lt;/a&gt; that not only scale the search for AI alignment failures but also solve a key validation problem in safety research. These &quot;auditing agents&quot; successfully uncovered intentionally planted flaws in testing and are now being used to help vet the safety of frontier models like Claude 4. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt;♥ 11k&lt;/span&gt;&lt;/span&gt; Tencent has just open-sourced &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.Yrjeomdvv1ZwvzDWkvi_ZCDiF9XSf7fWA8aDkxfduK7HIm0Fm-KN_dp6mMLlOJ5-Zq8m45wnG9S_RdoP04hN2jXWKPRPcaPqbSbk85DefdNyqlfz_gA24hUeAtFhZ4JPjwQP4hS60SQVIng4rKpeaQ/4im/UYmj5S4nTfKie84sa8X2EA/h3/h001.xNyAuk2xHScvyuw_WNzUrzjyiaJlz4_GbGbQvbQD-Ls" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Hunyuan3D&lt;/span&gt;&lt;/a&gt;, the industry&#39;s first model that can generate entire interactive 3D worlds from a single sentence or image. This new model can transform workflows in game development and VR, and because it&#39;s fully open-source, you can &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJQ07eKDN4RHUXlDibn1bFW1IRVudZnA-MQJQv5QAb7iGkjYx4sndS0S2xbIxGh8RDcDXhKK3uaQBY-vTZ4ZcT0SRpTb1bhDTpRIskD_xlAPaIVoJMCNXclnb6vhS5dixrg/4im/UYmj5S4nTfKie84sa8X2EA/h4/h001.N7gzIlX4zGsMpR4MmEoV5KfQLmH_-fuArC3APfFDXDU" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;dive into the code&lt;/span&gt;&lt;/a&gt; or &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWjHyI47naFpoR9oVedMsKlMFFT615kjonfv9oyjNnVS-zMoPW4L90cH7IpWd5H6Zgs11Fegsmeszvxonn6Jg4w8pBQev-oKWtzIFznRa0M05Wun6TjjfqjZxc_7GdLeudQ/4im/UYmj5S4nTfKie84sa8X2EA/h5/h001.z0HgLF0aXeaG40DUIEt2Vg_1lLR7f7DlLtKM2DKU1w0" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;download model weights&lt;/span&gt;&lt;/a&gt; for yourself. &lt;/p&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:500px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/028250c2-b760-427c-917c-167e744109d4/teaser.png?t=1753801324" alt="" height="auto" width="500" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="transparent" style="background-color:transparent;border-color:#2C81E5;border-style:solid;border-width:5px;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;Move Fast, Ship Smart with Korbit AI&lt;/span&gt;&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWpXJwGTuBL2KcuE-UoEFliAxzayxlMj91HMsxmeVc26mVVDh08D0BHF_ggsYEQ965o6dHzgqaCRo6KpqE1HPuqQiLR9RmO4dnyLpPAJoe5wu/4im/UYmj5S4nTfKie84sa8X2EA/h6/h001.FS3vYwu6Qs3ANoJnF3k9fZiWDHo7WC9PCqID9LVv-uI" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/69f832b8-7d3a-425d-8799-6d4413cfaec8/Deliver_better_code_faster__ByCloudNL___1_.jpg?t=1753797862" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;At &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWpXJwGTuBL2KcuE-UoEFliBPDETyYL5bEOPVea9N4Dt-WXqBMcYu5mJ4i97FDJNCs0FX40no0fJSXiVqiKYHZmoiwKtDfInrDLPqCuS3uPow/4im/UYmj5S4nTfKie84sa8X2EA/h7/h001.qIf_LuJpjWUNeI5uqqUDfPniaNSk-I5zCJdCz3K3vCk" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Korbit&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt;, we know speed and quality go hand‑in‑hand. Our AI‑powered code review and engineering insights platform gives your team:&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ul style="font-weight:normal;list-style-type:disc;margin-bottom:12px !important;margin-top:12px !important;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;&lt;b&gt;Instant, Context‑Aware Feedback&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; on every PR - catch critical issues before they hit production&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;&lt;b&gt;Automated PR Summaries&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; so reviewers spend time fixing, not guessing&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;&lt;b&gt;High Signal‑to‑Noise Reviews&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; tuned to your codebase and standards&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;&lt;b&gt;Actionable Team Insights&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; on review velocity, compliance, and quality trends&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;Join hundreds of engineering leaders already using Korbit to unblock bottlenecks, enforce coding standards, and upskill their devs in real time.&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" style="padding:0px 28px;text-align:center;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;&lt;b&gt;Ready to see Korbit in action?&lt;/b&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWpXJwGTuBL2KcuE-UoEFliCZeHe1bpMLf5DmZ9rVQ3nCHwif6xCkroEaHZKlam5NduSUbkMmLiLDehwBVYdv9bw58OtmVHYUAWqRPomphfdl/4im/UYmj5S4nTfKie84sa8X2EA/h8/h001.odgSJNhRDIYYfQPK3tlAqKyKz5bDouAvwX4e2wK85xQ" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt;&lt;span style="font-weight:600;"&gt;&lt;b&gt;Start your free trial today.&lt;/b&gt;&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;&lt;sub&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoGymQ3NNPtd5dE5MV_8UgjIDFPVXngz8pvQBldSW42yhUe_Qiq6DgEPMEBuPL9yfRpXelTiuu2kS8pLFvsoem_XoZoy_n13sTKUhZIbl0VH6/4im/UYmj5S4nTfKie84sa8X2EA/h9/h001.C8MW7gKWfMxAqmVp5tEFjxqTpU09maIGg9CA2-NWuII" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Advertise with The AI Timeline! &lt;/span&gt;&lt;/a&gt;&lt;/sub&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="group-sequence-policy-optimization" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"&gt;Group Sequence Policy Optimization&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;i&gt;Zheng&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; et al. [&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Qwen Team, Alibaba Inc.&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt;]&lt;/i&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 1.4K &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; LLM Reasoning &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; bycloud’s pick &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="introduction-to-group-sequence-poli" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Introduction to Group Sequence Policy Optimization (GSPO)&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Reinforcement learning helps large language models tackle complex problems like advanced math and coding. However, existing methods like GRPO become unstable with massive models, which ends up causing catastrophic failures during training. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; The biggest problem is that GRPO uses token-level importance ratios that create noisy gradients, especially in long responses or sparse models like Mixture-of-Experts (MoE). &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; In this paper, the researchers have introduced Group Sequence Policy Optimization (GSPO), a new algorithm that replaces token-level adjustments with sequence-level optimization, promising stability and efficiency. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="how-group-sequence-policy-optimizat" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;How Group Sequence Policy Optimization (GSPO) works&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; GSPO rethinks reinforcement learning by aligning optimization with how rewards are given: at the sequence level, not per token. For a group of responses to the same query, a single importance ratio is calculated based on the entire sequence likelihood. This ratio, normalized by response length, measures how much the current policy deviates from the old one. Rewards are then compared relatively within the group, similar to GRPO, but applied uniformly to all tokens in a response. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Unlike GRPO, which weights each token’s gradient by its individual importance ratio, GSPO assigns equal weight to every token in a sequence. This avoids the high variance from fluctuating token-level ratios, which destabilizes training. For cases needing token-level rewards (like multi-turn dialogues), GSPO-token, a variant, adjusts advantages per token while mathematically matching the sequence-level approach. Additionally, GSPO clips entire responses, not tokens, ensuring only representative samples guide updates. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/26db47a7-ba26-4996-8805-d8dc5ad8c10b/image.png?t=1753798109" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt; Group Sequence Policy Optimization (GSPO) Algorithm&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; This sequence-first design naturally handles challenges like MoE volatility, where experts activate inconsistently across updates. By focusing on overall sequence likelihood instead of token-level probabilities, GSPO sidesteps the noise that cripples GRPO in sparse models. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="results-and-implications-of-group-s" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Results and implications of Group Sequence Policy Optimization (GSPO)&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; GSPO outperforms GRPO in stability, efficiency, and benchmark performance. Tests on a Qwen3-30B model showed higher training rewards and superior results on coding benchmarks (AIME’24, LiveCodeBench, CodeForces) using the same compute. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Notably, it stabilized MoE training without GRPO’s workaround (Routing Replay), which forced expert consistency at extra cost. GSPO also clipped over 100× more tokens than GRPO yet trained faster, proving token-level gradients in GRPO are inefficiently noisy. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/89bf560b-f222-4611-b8dd-d830416faac8/image.png?t=1753798179" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; These gains contributed to the improved Qwen3 models. GSPO’s sequence-level logic also simplifies infrastructure: it could allow inference engines (like vLLM) to supply likelihoods directly, skipping recomputation during training. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKm8zndrKPrfbOmRSbDeizYC41ChrzZakmT6ay6Cr4A_-_gNoNwuav-4oWgUsLN5VHRs0UR0dQwPURqhoRrVvXEI/4im/UYmj5S4nTfKie84sa8X2EA/h10/h001.QFDvibiVl6FqprcKzrGnShfh1Y_rvI70nk50kQSgS_U" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Read Full Paper &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="diffusion-beats-autoregressive-in-d" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"&gt;Diffusion Beats Autoregressive in Data-Constrained Settings&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;i&gt;Prabhudesai&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; et al. [&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Carnegie Mellon University&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt;]&lt;/i&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 977 &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; DiffusionLM &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="introduction-to-data-constrained-la" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Introduction to Data-Constrained Language Modeling&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; In the age of AI, data is often referred to as “New Oil” and rightfully so. Training large language models frequently hits a roadblock as high-quality data is becoming scarce. While computing resources grow steadily, datasets like those from healthcare or robotics remain limited. This creates a challenge for standard autoregressive (AR) models, which predict text left-to-right. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; They struggle when trained repeatedly on small datasets, saturating quickly after a few epochs. However, new research reveals that &lt;b&gt;masked diffusion models&lt;/b&gt;, which corrupt and reconstruct text in random orders, excel where data is sparse but compute is abundant. Let’s explore why. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="inner-workings-of-masked-diffusion" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Inner Workings of Masked Diffusion&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Unlike AR models that follow a fixed left-to-right sequence, masked diffusion treats text generation like solving a jigsaw puzzle. For each training example, it: &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Randomly masks tokens&lt;/b&gt; (e.g., replacing words with &lt;code&gt;[MASK]&lt;/code&gt;), &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Trains the model to reconstruct&lt;/b&gt; the original text from these masked versions. &lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; This process exposes the model to countless prediction tasks. Imagine predicting the word “apple” in the sentence “An ___ a day keeps the doctor away” versus “An ___ fell from the tree.” The context changes dynamically. This randomness acts as &lt;b&gt;implicit data augmentation&lt;/b&gt;, letting the model learn robust patterns from repeated data. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/f4035b45-3212-43b0-955e-316e136377a6/image.png?t=1753797918" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Meanwhile, AR models see only one task: predicting the next word in a rigid sequence. Their fixed approach limits their ability to extract new insights from recycled data. Diffusion’s flexibility, enabled by &lt;b&gt;bidirectional attention&lt;/b&gt; (seeing all unmasked tokens), allows deeper learning over hundreds of epochs without overfitting. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-scaling-insights" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Evaluation and Scaling Insights&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; In tests with datasets as small as 25M–100M unique tokens, diffusion models surpassed AR models once compute exceeded a critical threshold. Key findings: &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ul style="font-weight:normal;list-style-type:disc;margin-bottom:12px !important;margin-top:12px !important;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Efficiency&lt;/b&gt;: AR models peaked at ~50 epochs before overfitting, while diffusion models improved consistently for &lt;b&gt;500+ epochs&lt;/b&gt;. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Scaling Law&lt;/b&gt;: The compute needed for diffusion to outperform AR follows a power law: &lt;code&gt;C ∝ U²·¹⁷⁴&lt;/code&gt; (where &lt;i&gt;U&lt;/i&gt; = unique tokens). For example, with 100M tokens, diffusion wins after ~1.5e²⁰ FLOPs. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Downstream Gains&lt;/b&gt;: On benchmarks like SciQ and Lambada, diffusion achieved up to &lt;b&gt;10% higher accuracy&lt;/b&gt; than AR models when both reused limited data. &lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/511ef1a4-0a9b-4cfb-9525-a2b43ac68773/image.png?t=1753797949" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Results for the best auto-regressive and diffusion models trained in different data-constrained settings.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; This signals a paradigm shift: for data-scarce domains (e.g., specialized medical text), diffusion could become the go-to approach. Future work may explore hybrid AR-diffusion architectures to balance compute and data efficiency. As datasets plateau, innovations like this will be vital to keep pushing AI forward. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="embed-gen-img-top"&gt;&lt;td align="center" valign="top" style="padding:12px 27px 12px 27px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="o" style="padding:12px 12px 12px 12px;;background-color:#FFFFFF;border-color:#F1F1F1;border-radius:5px 5px 5px 5px;border-width:1px 1px 1px 1px;"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"&gt;&lt;tr&gt;&lt;td class="embed-img" align="center" valign="top" style="vertical-align:middle;padding:0px 0px 12px 0px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpfS3x25gz_c9CiNQJ0FEcVBWBFPlCHf-HDYx1IFH9991SkyuwcBuFMIagrHQwmnhaflk2m9ntoKcK_Hciacq6PRNitfLsJEFXP8GaYkOvV2d/4im/UYmj5S4nTfKie84sa8X2EA/h11/h001.Y_eXZyzfrS9rR_tfN_nLUXuQHD9x6V6zbbNJXYzD-ZQ" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://diffusion-scaling.github.io/static/images/preview.png" width="576" style="height:auto;display:block;" class="w100pc"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="cc"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="l"&gt;&lt;p&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpfS3x25gz_c9CiNQJ0FEcVBWBFPlCHf-HDYx1IFH9991N3ysW3W9R3rsqAy2tBiKZNwfWfUSxlORqjjjhuehPPIaVknrGc_sijqyMjxYV3Gz/4im/UYmj5S4nTfKie84sa8X2EA/h12/h001.ejbUidcL9cyR0gwA4W5lsy1FkT49fWXfZsP2K5P_pJw" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"&gt; Diffusion Beats Autoregressive in Data-Constrained Settings &lt;tr&gt;&lt;td align="left" valign="top" class="m"&gt;&lt;p style="font-size:13px;line-height:19px;color:#2D2D2D;"&gt; Diffusion models significantly outperform autoregressive models when compute is abundant but data is scarce, achieving lower validation loss and superior downstream performance. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"&gt;&lt;p style="word-break:break-word;"&gt;diffusion-scaling.github.io&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92Z1eMICJwe19AgaPYzgGhpkGA18VN8Ej5sK4xYoTfS0P5gE5U3cjLOm7Skrhzc1A56iXlV0zjDmWa8qkZSVyMSC/4im/UYmj5S4nTfKie84sa8X2EA/h13/h001.Xt9s6zHjSCX3WAWimW3qgt-lgBHTbB-TfTaOBIOCDJM" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Read Full Paper &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="alpha-go-moment-for-model-architect" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"&gt;AlphaGo Moment for Model Architecture Discovery&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;i&gt;Liu et al. [Shanghai Jiao Tong University, SII, Taptap, GAIR]&lt;/i&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 2.9K &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; LLM Training&lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="embed-gen-img-top"&gt;&lt;td align="center" valign="top" style="padding:12px 27px 12px 27px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="o" style="padding:12px 12px 12px 12px;;background-color:#FFFFFF;border-color:#F1F1F1;border-radius:5px 5px 5px 5px;border-width:1px 1px 1px 1px;"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"&gt;&lt;tr&gt;&lt;td class="embed-img" align="center" valign="top" style="vertical-align:middle;padding:0px 0px 12px 0px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJQT3U6YOGmXmSelcmjmo28RunBXpBO9T75OHkqNcOGVLQuW3JwVmVstTnciJ4iXQkYGRmkrCqTvuyg8UKqPfrE9o9SApNNRYn1zUrbYH8XUx/4im/UYmj5S4nTfKie84sa8X2EA/h14/h001.OkOl4Ar3xB6NSRA-fLjIpy-agkMGjTuuAG4oRtGqSUo" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://beehiiv-images-production.s3.amazonaws.com/uploads/asset/file/059bd7b4-4906-4d59-a5bb-7028e488c510/Alpha_and_ASI.png?t=1753797501" width="576" style="height:auto;display:block;" class="w100pc"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="cc"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="l"&gt;&lt;p&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJQT3U6YOGmXmSelcmjmo28RunBXpBO9T75OHkqNcOGVL9aHDRxWeDatd7l4ZaamvLhLcsA2h6F-ZkWEr63opiU5lXKMjmRJ-fEc4aUMs_3o6/4im/UYmj5S4nTfKie84sa8X2EA/h15/h001.L5PVCNRhthEd0mqGDDbYTUWc_aIRoVsJ8d3GKjswyFI" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"&gt; Neural Network Research Data Gallery &lt;tr&gt;&lt;td align="left" valign="top" class="m"&gt;&lt;p style="font-size:13px;line-height:19px;color:#2D2D2D;"&gt; View cutting-edge machine learning models and algorithms with step-by-step analysis and logical reasoning for complex research problems. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"&gt;&lt;p style="word-break:break-word;"&gt;gair-nlp.github.io/ASI-Arch&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="introduction-to-asiarch" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Introduction to ASI-ARCH&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; AI capabilities are advancing rapidly, and many human researchers can’t keep up. This creates a bottleneck: while AI systems grow exponentially stronger, research progress remains limited by human cognitive capacity. The paper introduces ASI-ARCH, the first &lt;b&gt;artificial superintelligence system for AI research&lt;/b&gt;. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; It tackles neural architecture discovery, a field where breakthroughs like Transformers historically required years of human effort. ASI-ARCH overcomes this by enabling AI to autonomously generate, test, and refine novel architectures without human-defined constraints, shifting from optimization to true innovation. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/830473bf-3601-4469-8cac-69a98358c776/scaling.png?t=1753797299" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="inner-workings-of-asiarch" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Inner workings of ASI-ARCH&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; ASI-ARCH operates through a closed-loop system with three specialized agents. First, the &lt;b&gt;Researcher&lt;/b&gt; proposes new architectures. It selects parent designs from top-performing historical candidates and modifies them using insights from a dynamic knowledge base. This includes both human literature summaries and the system’s own experimental data. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Most importantly, the Researcher also implements code directly, avoiding gaps between design and execution. Before proceeding, a novelty check ensures ideas are original, while sanity tests prevent flawed implementations like quadratic complexity. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/9f9fac36-5897-4b8f-b0b3-a0bec33b489f/pipeline.png?t=1753797353" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;ASI-Arch autonomous research framework demonstrating AI’s capability to conduct end-to-end scientific discovery, from hypothesis generation to empirical validation.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Next, the &lt;b&gt;Engineer&lt;/b&gt; trains and evaluates proposals. Unlike traditional methods that discard failing code, ASI-ARCH self-debugs: if training crashes, error logs are fed back to the Engineer for iterative fixes. A real-time monitor halts inefficient runs, like models taking 3× longer than peers. After training, an LLM-as-judge scores architectures qualitatively, weighing novelty, complexity, and efficiency alongside quantitative metrics like loss reduction. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; Finally, the &lt;b&gt;Analyst&lt;/b&gt; distills insights. It compares each architecture’s performance against its “parent” and “siblings” in an evolutionary tree, mimicking scientific ablation studies. These insights feed back into the Researcher’s knowledge base. To manage computational costs, ASI-ARCH uses a two-stage strategy: lightweight exploration (20M parameters) identifies promising candidates, followed by rigorous verification at full scale (340M+ parameters). &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-results-of-asiarch" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Evaluation and results of ASI-ARCH&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; In testing, ASI-ARCH ran 1,773 autonomous experiments over 20,000 GPU hours. It discovered 106 state-of-the-art linear attention architectures, surpassing human-designed baselines like DeltaNet and Mamba2. Key results showed consistent improvements: on language tasks like WikiText perplexity and commonsense reasoning (BoolQ, ARC), ASI-ARCH’s top models achieved up to a 48.51 average score versus 47.84 for the best baseline. Notably, five standout architectures, including PathGateFusionNet and ContentSharpRouter, demonstrated unique innovations like hierarchical routing and parallel sigmoid gates. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/c54312be-ee1d-4793-b781-347c1541a61a/combined_trend_analysis.png?t=1753797326" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Performance indicators showing steady improvement in benchmark scores and consistent reduction in loss values, with composite fitness scores demonstrating rapid initial improvement followed by gradual plateau.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; A key finding was the &lt;b&gt;scaling law for scientific discovery&lt;/b&gt;: the number of breakthroughs grew linearly with compute (Fig. 1). This transforms research from a human-limited to computation-scalable process. Analysis revealed that top architectures relied heavily on empirical insights from past experiments (44.8% of design choices) rather than just prior literature. However, limitations exist. The system favored established components like gating mechanisms, and its fitness function’s sigmoid transform capped score gains from large improvements. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/053a5527-5d82-49cb-9e80-ab0500e38091/performance_table.png?t=1753797339" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Performance comparison of 5 selected novel linear attention architectures discovered by ASI-Arch.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; ASI-ARCH demonstrates that AI can autonomously drive architectural innovation, with emergent principles, like AlphaGo’s “Move 37”, surpassing human intuition. They also open-sourced all code and architectures, with the team invites broader exploration. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="embed-gen-img-l"&gt;&lt;td align="center" valign="top" style="padding:12px 27px 12px 27px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="o" style="padding:12px 12px 12px 12px;;background-color:#FFFFFF;border-color:#F1F1F1;border-radius:5px 5px 5px 5px;border-width:1px 1px 1px 1px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td width="40%" align="center" valign="top" class="mob-stack"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJTmXbsLwcARQJlmoe_4WCUHYqrfoQ0Zsk1t8GZ05h1Q1Po29IKg_IH1Qy54E1idp9VLtzo5xLmsvpmHVmjlgUSNwhD3r413M9EbnMxdOW_Gt/4im/UYmj5S4nTfKie84sa8X2EA/h16/h001.cD_ynw4MlPjbnRx8seGpscz3Wi0KvX9lRQiwHlP_tZU" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://opengraph.githubassets.com/8f95588d377cd4006b9632bd8aa2f598f7c1762be52ea250df75b0b6f53cca55/GAIR-NLP/ASI-Arch" width="230" style="height:auto;display:block;" class="w100pc"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;td width="3%" style="font-size:16px;line-height:16px;" class="mob-stack"&gt;&nbsp;&lt;/td&gt;&lt;td width="57%" align="left" valign="middle" class="mob-stack"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="left" valign="middle" class="l"&gt;&lt;p&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJTmXbsLwcARQJlmoe_4WCUHYqrfoQ0Zsk1t8GZ05h1Q1g27ubirb_dta9HTrDAWm0OE--n__Zyb-jeqrCf3Xrx7KcUyWDpMQ6LSmJx76V_gH/4im/UYmj5S4nTfKie84sa8X2EA/h17/h001.rkiKoXU46JkTrs2m1cYvDtR5oW1TZm0DHozh_WGp52Y" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"&gt; GitHub - GAIR-NLP/ASI-Arch: AlphaGo Moment for Model Architecture Discovery. &lt;tr&gt;&lt;td align="left" valign="top" class="m"&gt;&lt;p style="font-size:13px;line-height:19px;color:#2D2D2D;"&gt; AlphaGo Moment for Model Architecture Discovery. Contribute to GAIR-NLP/ASI-Arch development by creating an account on GitHub. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"&gt;&lt;p style="word-break:break-word;"&gt;github.com/GAIR-NLP/ASI-Arch&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="current-critics-on-claims-of-artifi" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;"&gt;Current Critics on Claims of &quot;Artificial Superintelligence&quot; &lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="mso-line-height-alt:150.0%;"&gt; SJTU researchers claim this as &quot;Artificial Superintelligence&quot; for neural architecture search, but &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j22S5tt9K4bMT_gXfiqlfJR-y1HAZFbYKdW3bPGZQOOzScM4eXyDrgqlUrp5LgwJ_i0Egrneuz7QIBUMw3tGU_RdFwlenYSFevqAZBRJRu0NF/4im/UYmj5S4nTfKie84sa8X2EA/h18/h001.JFD7QHX1qnrK3mdOXpwDu-SbcZjdgIcWIoM3IYGuKS4" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;critics on alphaXiv are pushing back&lt;/span&gt;&lt;/a&gt; due to the following reasons: &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ul style="font-weight:normal;list-style-type:disc;margin-bottom:12px !important;margin-top:12px !important;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt; Potential&lt;b&gt; AI-generated sections&lt;/b&gt; detected throughout the paper &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;Methodological contradiction&lt;/b&gt;: critiques benchmarks while using them &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;b&gt;&quot;&lt;/b&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCRhG_ted2B4O7KMIIr45riEAl7RjY0HRfJtKcQ1DZ8wvdR6agn0AH3om5MNdQJqpZASGKztEFnjVBCD4XF3avnNSOh_F0Ko5OXL5DMizXScWqYLK1qjXFxqqPE1WulEBZA/4im/UYmj5S4nTfKie84sa8X2EA/h19/h001.ZYcKAH7IvdpMoHzDC7oPT5MQwMra5N9B72Q9gp0X9sU" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;&lt;b&gt;Buzzword maxxing&lt;/b&gt;&lt;/span&gt;&lt;/a&gt;&lt;b&gt;&quot;&lt;/b&gt; with inflated terminology &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"&gt; Community calls the work’s title &quot;shameless&quot; and &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCdQ1Bvvn9toB_yiwgalEYxQrJ0Drz-KKklsy3NUTkOBrtsoDZQ_EQ9bMv9OT5AClgkGRBZshnr2Vea7zBFJtanahkMro6H5He8g-Iz7uIj8hu8jqejHxcAvdjl7ciQOE3A/4im/UYmj5S4nTfKie84sa8X2EA/h20/h001.6eqA3RuI1cyooHdG_Y7k4auNXVrXWKUKUn_D5TAVpmw" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;inconsistent&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92a8aHLFr0CrMS_9G6YrFPZddUZ7j8lBYmt7wxtN3w7Ziy8_OwouckCZS1MNe96qvbNNoGYDjdejBW9H4aeo7rhc/4im/UYmj5S4nTfKie84sa8X2EA/h21/h001.B0ihH26HBzgvY_xLDMG9sLOgzg5_q6rmqb8Xsa8In8Y" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Read Full Paper &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" style="padding: 20px;"&gt;&lt;table width="100%" cellpadding="0" cellspacing="0" role="none" style="max-width:520px;margin:0 auto;"&gt;&lt;tr&gt;&lt;td class="q" style="padding:16px 16px 6px 16px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoDDFT6eh5Nsg0xYVQj-h6I3o9m2k79_qw4izMYhmcI360Gb7UwawdQNSpE6RVTF3uZIsSgLLaAONlVEEDU6SQg3KbBP-DtB6HYvYZntu6U0Bdo8zTFC2B9g9ZwgPkNh85uaCWc13WxB4trJgNBRSw4E/4im/UYmj5S4nTfKie84sa8X2EA/h22/h001.n4IFwr-XOEW7Bb98jcStHCY1Rc9ig8Pz1qCGP5dejHM" style="text-decoration:none !important;"&gt;&lt;table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"&gt;&lt;tr&gt;&lt;td width="100%" style="padding: 0 0 14px 0;text-decoration:none;width:100%;"&gt;&lt;table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"&gt;&lt;tr&gt;&lt;td width="36" style="width:36px;"&gt;&lt;img src="https://pbs.twimg.com/profile_images/1698572487909400576/BvncwnrP_normal.jpg" alt="tw profile: The AI Timeline" style="display:block;width:36px;height:36px;border-radius:50%;border:0;"/&gt;&lt;/td&gt;&lt;td width="400" style="padding:0 0 0 8px;text-decoration:none;"&gt;&lt;span style="display:block;font-size:14px;color:#1c2022;font-weight:700;"&gt; The AI Timeline &lt;/span&gt;&lt;span style="display:block;color:#697882;font-size:14px;"&gt; @TheAITimeline &lt;/span&gt;&lt;/td&gt;&lt;td width="24" align="right" style="vertical-align:text-top;"&gt;&lt;img width="24" height="24" loading="lazy" alt="tw" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/x_logo.png"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="word-break:break-word;"&gt;&lt;p&gt;🚨This week's top AI/ML research papers:&lt;/p&gt;&lt;p&gt;- GSPO &lt;br&gt;- Diffusion Beats Autoregressive in Data-Constrained Settings &lt;br&gt;- Gemini 2.5 Pro Capable of Winning Gold at IMO 2025 &lt;br&gt;- Rubrics as Rewards &lt;br&gt;- Deep Researcher with Test-Time Diffusion &lt;br&gt;- Learning without training &lt;br&gt;- Stabilizing Knowledge,&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding:12px 0 0 0;"&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" style="padding:8px 0 0 0;width:480px;"&gt;&lt;img src="https://pbs.twimg.com/media/Gw3cPKlacAAM_RI.jpg" width="480" height="auto" style="display:block;border:1px solid #E1E8ED;border-radius:5px;width:100%;max-width:480px;height:auto;"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="8" style="line-height:1px;font-size:1px;height:8px;"&gt;&nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="s"&gt;&lt;p&gt;1:16 PM • Jul 27, 2025&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="10" style="line-height: 1px; font-size: 1px; height: 10px;"&gt;&nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="1" bgcolor="#e1e8ed" style="line-height:0px;font-size:0px;height:1px;"&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="10" style="line-height:1px;font-size:1px;height:10px;"&gt;&nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="s"&gt;&lt;p&gt;&lt;b style="color:#1C2022"&gt;544&lt;/b&gt; Likes &nbsp; &lt;b style="color:#1C2022"&gt;70&lt;/b&gt; Retweets &nbsp; &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="s"&gt;&lt;div align="center" style="text-align:center;margin-top:4px;margin-bottom:4px;padding:8px;border:1px solid #ccd6dd;border-radius:9999px;color:#1B95E0"&gt;&lt;b&gt;5 Replies&lt;/b&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" valign="top" style="padding:20px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmKtR3-564kV4XtoF1Y52vGvkDzSCFTU6aDgl1lYPSXn5IY0Z7W5NOwMe4WOsasiPC8_coO395fmfOQhvjrSPCTlw72bZwUUrles-mf3_xUcv/4im/UYmj5S4nTfKie84sa8X2EA/h23/h001.q6I_reBFkOsFAoSXgXHO5UytBSnrX17f_lUVxmOX1Mg" style="text-decoration:none;"&gt;&lt;table align="center" width="100%" cellpadding="0" cellspacing="0" border="0" role="none" style="max-width:520px;margin:0 auto;"&gt;&lt;tr&gt;&lt;td class="p" width="100%" style="padding:2px;border:none;"&gt;&lt;table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:100%;"&gt;&lt;div style="max-height:0;position:relative;opacity:0.999;width:100%;mso-hide:all;"&gt;&lt;div style="display:inline-block;width:100%;padding-top:25%;"&gt;&lt;img width="20%" height="auto" loading="lazy" alt="" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/youtube_play_icon.png"/&gt;&lt;/div&gt;&lt;/div&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmKtR3-564kV4XtoF1Y52vGvwuQiLL76F8MT4auSGRpumtjnxwI-xrG5TEY8aDONSm76IpAUHEa_STfL2LdHiI-RbBuB7qcw_8u9qra8pAMYQ/4im/UYmj5S4nTfKie84sa8X2EA/h24/h001.b0prAae0iPWjeUoypZddXWu71qHwwZZVBltRWU2ithI" style="text-decoration:none;"&gt;&lt;img src="https://i.ytimg.com/vi/TVlFf_Po1bs/maxresdefault.jpg" width="480" height="auto" loading="lazy" alt="YouTube video by bycloud" style="display:block;height:auto;border:0;outline:none;text-decoration:none;background-color:#000000;width:100%;"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;p style="font-size:12px;font-weight:500;font-style:italic;font-family:Helvetica, Calibri, sans-serif;color: #686a6d; padding-top:0 !important;padding-bottom:6px !important; padding-left:4px !important;"&gt; 1,000 tok/s?! 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