<!DOCTYPE html><html lang="en" xmlns="http://www.w3.org/1999/xhtml" xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" style="font-size:16px;"><head></head><head><meta charset="utf-8"/><!--[if !mso]><!--><meta http-equiv="X-UA-Compatible" content="IE=edge"/><!--<![endif]--><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="x-apple-disable-message-reformatting"/><meta name="format-detection" content="telephone=no,address=no,email=no,date=no,url=no"/><meta name="color-scheme" content="light"/><meta name="supported-color-schemes" content="light"/><title>Why Do MLLMs Struggle with Spatial Understanding? </title><!--[if mso]><xml><o:OfficeDocumentSettings><o:AllowPNG/><o:PixelsPerInch>96</o:PixelsPerInch></o:OfficeDocumentSettings></xml><![endif]--><style>
:root { color-scheme: light; supported-color-schemes: light; }
body { margin: 0; padding: 0; min-width: 100%!important; -ms-text-size-adjust: 100% !important; -webkit-transform: scale(1) !important; -webkit-text-size-adjust: 100% !important; -webkit-font-smoothing: antialiased !important; }
.body { word-wrap: normal; word-spacing:normal; }
table.mso { width: 100%; border-collapse: collapse; padding: 0; table-layout: fixed; }
img { border: 0; outline: none; }
table { mso-table-lspace: 0px; mso-table-rspace: 0px; }
td, a, span { mso-line-height-rule: exactly; }
#root [x-apple-data-detectors=true],
a[x-apple-data-detectors=true],
#MessageViewBody a { color: inherit !important; text-decoration: inherit !important; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit !important; }
span.MsoHyperlink { color: inherit !important; mso-style-priority: 99 !important; }
span.MsoHyperlinkFollowed { color: inherit !important; mso-style-priority: 99 !important; }
.a { background-color:#dedede; }
.b { background-color:#2a2a2a; }
.c { background-color:#ffffff; }
.d { background-color:#fff0c8; }
.d2 { background-color:#FFFFFF; }
.d3 { background-color:#FFFFFF; }
h1 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h2 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h3 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h4 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h5 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h6 a { text-decoration:none;color:#2C81E5;font-style:italic; }
h1, h1 a, h2, h2 a, h3, h3 a, h4, h4 a, h5, h5 a, h6, h6 a, ul, li, ol, p, p a { margin: 0;padding: 0; }
h1 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:700;font-size:28px;color:#2A2A2A;line-height:42px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
h2 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:700;font-size:24px;color:#2A2A2A;line-height:36px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
h3 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:400;font-size:20px;color:#2A2A2A;line-height:30px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
h4 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:400;font-size:18px;color:#2A2A2A;line-height:27px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
h5 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:400;font-size:16px;color:#2A2A2A;line-height:24px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
h6 { font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif;font-weight:400;font-size:14px;color:#2A2A2A;line-height:21px;padding-bottom:4px;padding-top:16px;mso-margin-top-alt:16px;mso-margin-bottom-alt:4px }
p { font-family:'Georgia','Times New Roman',serif;font-weight:400;color:#2D2D2D;font-size:16px;line-height:24px;padding-bottom:8px;padding-top:8px;mso-margin-top-alt:8px;mso-margin-bottom-alt:8px; }
p a, .e a, ul a, li a, .h a, .h2 a, .h3 a { word-break:break-word;color:#2C81E5 !important;text-decoration:none;font-style:italic; }
p a span, .e a span, ul a span, li a span { color: inherit }
p .bold { font-weight:bold;color:#2D2D2D; }
p span[style*="font-size"] { line-height: 1.6; }
.f p { font-size:12px;line-height:15px;color:#2D2D2D;padding:0; }
.f p a { color:#2D2D2D !important; }
.g p { font-family:'Helvetica',Arial,sans-serif;font-size:14px;line-height:20px;font-weight:normal;margin:0; }
.g p a { text-decoration: underline; }
.i p { font-family:'Helvetica',Arial,sans-serif;line-height:23px;font-size:15px;color:#2D2D2D; }
.i p a { color:#2D2D2D !important; }
.i2 p { font-family:'Helvetica',Arial,sans-serif;line-height:23px;font-size:15px;color:#2D2D2D; }
.i2 p a { color:#2D2D2D !important; }
.i3 p { font-family:'Helvetica',Arial,sans-serif;line-height:43px;font-size:24px;color:#2D2D2D; }
.i3 p a { color:#2D2D2D !important; }
.h p a { color:#595959 !important; }
.h2 p a { color:#595959 !important; }
.h3 p a { color:#595959 !important; }
.f p a, .i p a, .i2 p a, .i3 p a, .h p a, .h2 p a, .h3 p a { text-decoration:underline; }
.j { border-top:3px solid #ffeb2d; }
.k p { padding-left:15px;padding-bottom:0px;padding-top:6px;mso-margin-top-alt:6px;mso-margin-bottom-alt:0px;mso-margin-left-alt:15px; }
.o { background-color:#FFFFFF;border:1px solid #F1F1F1;border-radius:5px; }
.o p { font-family:'Helvetica',Arial,sans-serif;padding:0px;margin:0px; }
.l p,
.l p a, .l a { font-size:14px;line-height:20px;font-weight: bold;color:#2D2D2D;padding-bottom:6px;mso-margin-bottom-alt:6px;text-decoration:none; }
.m p,
.m p a { font-size:13px;line-height:18px;font-weight:400;color:#2D2D2D;padding-bottom:6px;mso-margin-bottom-alt:6px;text-decoration:none; }
.n p,
.n p a { font-size:12px;line-height:17px;font-weight:400;color:#2D2D2D;padding-bottom:6px;mso-margin-bottom-alt:6px;text-decoration:none; }
.p { background-color:#FFFFFF;max-width:520px;border:1px solid #E1E8ED;border:1px solid rgba(80, 80, 80, 0.3);border-radius:5px; }
.q { font-size:16px;font-family:Helvetica,Roboto,Calibri,sans-serif !important;border:1px solid #e1e8ed;border:1px solid rgba(80, 80, 80, 0.3);border-radius:10px;background-color:#FFFFFF; }
.q p { font-size:16px;font-family:system-ui,Helvetica,Roboto,Calibri,sans-serif !important;color:#222222;padding:4px 0; }
.r { border:1px solid #E1E8ED !important;border-radius:5px; }
.s p { font-size: 14px; line-height: 17px; font-weight: 400; color: #697882; text-decoration: none; }
.t p { font-family:'Helvetica',Arial,sans-serif;font-size:12px;line-height:18px;font-weight:400;color:#000000;font-style:italic;padding:4px 0px 0px; }
.v { border-radius:10px;border:solid 0px #DFD150;background-color:#2C81E5;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;color:#FFFFFF; }
.v a { text-decoration:none;display:block;color:#FFFFFF; }
.w p { font-size:12px;line-height:15px;font-weight:400;color:#FFFFFF; }
.w p a { text-decoration: underline !important;color:#FFFFFF !important; }
ul { font-family:'Helvetica',Arial,sans-serif;margin:0px 0px 0px 25px !important;padding:0px !important;color:#2D2D2D;line-height:24px;list-style:disc;font-size:16px; }
ul > li { font-family:'Helvetica',Arial,sans-serif;margin:10px 0px 0px 0px !important;padding: 0px 0px 0px 0px !important; color: #2D2D2D; list-style:disc; }
ol { font-family:'Helvetica',Arial,sans-serif;margin: 0px 0px 0px 25px !important;padding:0px !important;color:#2D2D2D;line-height:24px;list-style:decimal;font-size:16px; }
ol > li { font-family:'Helvetica',Arial,sans-serif;margin:10px 0px 0px 0px !important;padding: 0px 0px 0px 0px !important; color: #2D2D2D; }
.e h3,
.e p,
.e span { padding-bottom:0px;padding-top:0px;mso-margin-top-alt:0px;mso-margin-bottom-alt:0px; }
.e span,
.e li { font-family:'Helvetica',Arial,sans-serif;font-size:16px;color:#2D2D2D;line-height:24px; }
.rec { font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji" !important; }
.rec__button:hover { background-color: #f9fafb !important; }
.copyright a {color: inherit !important; text-decoration: none !important; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit !important;}
.txt_social p { padding: 0; word-break: break-all; }
.table, .table-c, .table-h { border: 1px solid #C0C0C0; }
.table-c { padding:5px; background-color:#FFFFFF; }
.table-c p { color: #2D2D2D; font-family:'Helvetica',Arial,sans-serif !important;overflow-wrap: break-word; }
.table-h { padding:5px; background-color:#F1F1F1; }
.table-h p { color: #2A2A2A; font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif !important;overflow-wrap: break-word; }
@media only screen and (max-width:667px) {
.aa, .w100pc { width: 100% !important; }
.bb img { width: 100% !important; height: auto !important; max-width: none !important; }
.cc { padding: 0px 8px !important; }
.ee { padding-top:10px !important;padding-bottom:10px !important; }
.ff ul, .ff ol { margin: 0px 0px 0px 10px !important;padding: 0px !important; }
.ff li { margin:10px 0px 0px 10px !important; }
.r {height:140px !important;}
.s p { font-size:13px !important;line-height:15px !important; }
.mob-hide {display:none !important;}
.mob-show {display: block !important; width: auto !important; overflow: visible !important; float: none !important; max-height: inherit !important; line-height: inherit !important;}
.mob-stack {width:100% !important;display:block !important;}
.mob-w-full {width:100% !important;}
.mob-block {display:block !important;}
.embed-img {padding:0px 0px 12px 0px !important;}
.socialShare {padding-top:15px !important;}
.rec { padding-left:15px!important;padding-right:15px!important; }
.bodyWrapper { padding:7px 4px 7px 4px !important; }
.social-mobile {float:left !important;margin-top:10px !important;}
}
@media screen and (max-width: 480px) {
u + .a .gg { width: 100% !important; width: 100vw !important; }
.tok-heart { padding-top:75% !important; }
.tok-play { padding-top: 250px !important; }
}
@media screen and (max-width: 320px) {
.tok-heart { padding-top:65% !important; }
}
.u { border: 1px solid #CACACA !important; border-radius: 2px !important; background-color: #ffffff !important; padding: 0px 13px 0px 13px !important; font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif !important;font-size: 12px !important; color: #767676 !important; }
.u a { text-decoration: none; display: block !important; color: #767676 !important; margin: 0px !important; }
.u span, .u img { color: #767676 !important;margin:0px !important; max-height:32px !important;background-color:#ffffff !important; }
</style><!--[if mso]><style type="text/css">
h1, h2, h3, h4, h5, h6 {font-family: Arial, sans-serif !important;}
body, table, td, p, a, span {font-family: Arial, sans-serif !important;}
sup { font-size: 100% !important;vertical-align: .5em !important;mso-text-raise: -1.5% !important;line-height: 0 !important; }
ul { margin-left:0px !important; margin-right:10px !important; margin-top:20px !important; margin-bottom:20px !important; }
ul li { margin-left: 0px !important; mso-special-format: decimal; }
ol { margin-left:0px !important; margin-right:10px !important; margin-top:20px !important; margin-bottom:20px !important; }
ol li { margin-left: 0px !important; mso-special-format: decimal; }
li.listItem { margin-left:15px !important; margin-top:0px !important; }
.paddingDesktop { padding: 10px 0 !important; }
.edm_outlooklist { margin-left: -20px !important; }
.embedImage { display:none !important; }
</style><![endif]--><!-- __merge_tags_in_links__ --><style>
@font-face {
font-family: 'Open Sans';
font-style: normal;
font-weight: 700;
font-display: swap;
src: url('https://fonts.gstatic.com/s/opensans/v40/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVIUwaEQbjA.woff2') format('woff2');
}
@font-face {
font-family: 'Open Sans';
font-style: italic;
font-weight: 700;
font-display: swap;
src: url('https://fonts.googleapis.com/css2?family=Open+Sans:ital,wght@1,700&display=swap') format('woff2');
}
</style></head><body class="a" style="margin:0px auto;padding:0px;word-wrap:normal;word-spacing:normal;background-color:#dedede;"><div role="article" aria-roledescription="email" aria-label="email_name" lang="en" style="font-size:1rem"><div style="display:none;max-height:0px;overflow:hidden;"> Plus more about Small Language Models are the Future of Agentic AI and Why Online Reinforcement Learning Forgets Less  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </div><table role="none" width="100%" border="0" cellspacing="0" align="center" cellpadding="0" class="gg"><tr><td align="center" valign="top"><table role="none" width="670" border="0" cellspacing="0" cellpadding="0" class="aa" style="width:670px;table-layout:fixed;"><tr><td class="bodyWrapper" align="center" valign="top" style="padding:7px 7px 7px 7px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><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"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr id="header"><td style="padding:15px 15px 0px 15px;"><div style="padding-top:0px;padding-right:0px;padding-bottom:20px;padding-left:0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td class="f" align="right" valign="top"><p> September 09, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EwPzvDPj1hHaJPd-fylT1yv3TepsKnOIboN-wn6c0twstfYuLnx2NIL1gVfj2pqZuQiK8nszIZJFrTq8iPaRQHTHYDn7WquicuxLrjFFX7IDAJn6tvTdomVbvLV5lU3GiIiw7B__JlD1G0dqO97mIFWx37AppefCZ09wiyJlp8B4L9W0Z_wrfCN7yiVvdIGYWps12_ALvUyS82oogoX4c8rGBSm-2_SBTnR5uh0m5zxvsCMbqCWDwY9YzcFys1x22Ogo_OL3MuPYTpyCZC-SRK95-ok1_34C95ZSD1oTuLrjLoQ18UKmNjF1NLQabeIOJsoBsxDddofAP2V_ka8b50d2KDmST6CRjjvPz9fsNPY9ImwRnzBrM7VH6itYYguBHgHUCkVdcaz8v4nFG6d3GGFAuJTIhPWn2vNzC9i2gIHMlXias8fnPvYUJYJo728tyjiUqw2qOAFaNmMAH2Cuk5ECIsG8p9Iw2jI9pRyrdTTa-GDFqejuDteobLtN-RtYMAHUzTAXkCQJIzdbkUn8LkLKfv0La-AzhrbavG-4jydjBXTsrA97C-IBgJdCwsI4MqpM7fiAYSEQqExCKCOF2GP0uKVGUEixuzyjTVsItbWqGcSQaCZ9C9A4dN1SEPQu4IJEBZmSlN8nJGODR7te6mS/4jr/BUZXRmfkRWiHrtiXrT2f9g/h0/h001.A9OyNt7v2e63x0LCS1OaUxL0UbcSh3CWRztAAfrol5c"><span class="translation_missing" title="translation missing: en.templates.posts.email.header.read_online">Read Online</span></a></p></td></tr><tr><td class="dd" align="center" valign="top" style="padding:15px 0;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top"><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;"> Why Do MLLMs Struggle with Spatial Understanding? </h1><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;"> Plus more about Small Language Models are the Future of Agentic AI and Why Online Reinforcement Learning Forgets Less </p></td></tr></table></td></tr><tr><td style="line-height:0;"><div data-open-tracking="true"> <img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4jr/BUZXRmfkRWiHrtiXrT2f9g/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;"/> </div></td></tr></table></div></td></tr><tr id="content-blocks"><td class="email-card-body" align="center" valign="top" style="padding-bottom:28px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><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;"><h6 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:87.5%;"><i>Sep 2nd ~ Sep 9th</i><br><i>#72 Latest AI Research Explained Simply</i></h6></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><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;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">🗞️ Industry News in 1 Line</h2></td></tr><tr><td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"><div style="margin-left:0px;" class="edm_outlooklist"><ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 1.2k</span></span> After Vibe Coding, we are now seeing a rise in <span style="">voice-first development as tools like </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2511ibB8njdpnM1M2DJZXlOC8HZJFnAyJhaW2MJ8k9ml0B0OqWYjuh_0A1DsfDi2Sf4SLJ_NZmhzybqk69AqscRhlBJcMYpcr0rS4udmjNMTKR7HKCHtab92KKuFO9ZaSQ/4jr/BUZXRmfkRWiHrtiXrT2f9g/h1/h001.0xDhOdV59NKG53d1vfAJwq2eIr37rDIR1AFbHcHdJsM" target="_blank" rel="noopener noreferrer nofollow"><span>Flow</span></a></span><span style=""> and </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.-a_uZGcEKk2OBxkkwsTf3vAsdCJL8j_fR9pbOcVqyuW7uPfZHFTpJEnWQffrHx_rS82WsFQbfwocm19f8LKFClgJZkEg3zkOexy3dT89s0nXvMvqhzIubNI6bHKttHLA/4jr/BUZXRmfkRWiHrtiXrT2f9g/h2/h001.7FBGIoGKspl8oWedHCn01X00ArZHU60jNQ7HKTiuYM4" target="_blank" rel="noopener noreferrer nofollow"><span>Lovable</span></a></span><span style=""> are gaining attention. Flow is enhancing its IDE integration with voice-activated file tagging and syntax-aware variable recognition. At the same time, Lovable has launched a new "Voice Mode" that builds projects entirely from spoken commands. </span></p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/ba15829e-19f4-44d4-97c4-be8384e2cffd/image.png?t=1757444221" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 3.8k</span></span> Alibaba has launched Qwen3-Max-Preview, which is a huge model with over <b>one trillion parameters</b>. Early benchmarks and user feedback show that it delivers stronger performance, broader knowledge, and superior capabilities in agentic tasks and instruction following compared to its predecessors. You can experience Qwen3-Max-Preview for yourself on <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVMd9-lmVPP9PQdUWuwV36ycFKyw_M8n_9ZDHM9y8KGmsCTza__zJa5-lCw3c6FYk_y0A3J6_cNg9CH8kMM09Sb0f8MKTWXrEF8PxLY9e8uIcAq74CzRgIWtVhwlLu0gIQg/4jr/BUZXRmfkRWiHrtiXrT2f9g/h3/h001.YpwyWVW1y_SmUHrS8F2IpgMxriqZdOdC1dA8LOTMRWw" target="_blank" rel="noopener noreferrer nofollow"><span>Qwen Chat</span></a> or via the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxRGJ843ot924cPsXvbMuoA9ktvuajqBJHE2cK8rjY7VarYTJ1mTzFFm0hbZjybqvfuOXSsvDbk5CE-OwDbb7ZhHg72JUDLXUB6eafZOR8MsYaASvSVg2DGg75wVfVN3UYE7UfaBNEq5tT_d0mCCD9zgOx4PtldSWy3hfKA6nw5RB97KOckUhv7XlbPTuJLvVwaatSjyLq97ZAYVIOpCzR7E/4jr/BUZXRmfkRWiHrtiXrT2f9g/h4/h001.e2cyCbRKeJk95vq4daRm2hqxdZigAmWJXxH3Q8P2-PQ" target="_blank" rel="noopener noreferrer nofollow"><span>Alibaba Cloud API</span></a>. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/230bf8e3-0be5-48c2-93c3-917a2463186e/image.png?t=1757444556" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px;"><p>Qwen3-Max-Preview Benchmarks</p></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 4.1k</span></span> Higgsfield AI has launched the Ads 2.0 model, which is designed to create million-dollar quality commercials. The new version can produce a wide range of sophisticated scenes, from dynamic "bullet time" effects and crisp pack shots to full-scale product advertisements. You can <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCVhZCRNOSASj0k2wdMpwLOGfapg6-y10fsgN1dAOA_gTrLuf9Az3cZ-iFKSbOH0C8bjR2ZL73afgOb05_fl4w2x66R2njyTBizIO-UUlaWMSrng8ZdtwKoKToxig3V3I-w/4jr/BUZXRmfkRWiHrtiXrT2f9g/h5/h001.gN-46lC5AnX6zpUC8yIfpG9FsZvk4ZkzfUxfJmruEIw" target="_blank" rel="noopener noreferrer nofollow"><span>view generated samples on Twitter</span></a> or start creating professional-grade ads by trying <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWpseZHGBm_wenLiZgSzb3RWNKOoVZMAV9UtCzdPKsQeSZyP5MA6KEA_uTlWSRh8NFzUuTGEPEarJ_peqeAlpm8pEs6W3kN8SwLbrTBTmTSMJ6LjcsFGPORJSIj0iurVxBg/4jr/BUZXRmfkRWiHrtiXrT2f9g/h6/h001.0jiRGQhOYIsXKMLH_eopHXjgjBo7AKxJdL4u8z_E24I" target="_blank" rel="noopener noreferrer nofollow"><span>Higgsfield Ads 2.0 </span></a>in your browser. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/f365679a-a908-444a-95a2-8249922234cf/image.png?t=1757444903" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li></ol></div></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><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;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"><span style="">Support My Newsletter</span></h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="color:rgb(34, 34, 34);font-family:Georgia, "Times New Roman", serif;font-size:16px;">As I aim to keep this newsletter free forever, your support means a lot. If you like reading The AI Timeline, consider forwarding it to another research enthusiast, It helps us keep this up for free!</span></p></td></tr><tr><td align="center" valign="top"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" style="font-size:0px;line-height:0px;padding:30px 0px 30px;" class="dd"><table class="j" role="none" width="50%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td> </td></tr></table></td></tr><tr><td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">Share The AI Timeline</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> You currently have <strong>0</strong> referrals. </p></td></tr><tr><td align="left" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; display:none;width:0px;max-height:0px;overflow:hidden;mso-hide:all;height:0;font-size:0;max-height:0;line-height:0;margin:0 auto;" class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 0;"><tr><td align="center" valign="top" style="width:313px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxWc4htTObwdorovK0nFHVH-4pUdVE0ELYH5DsNemk732SjNwhPNJ25r0O8B5vYifsGNUqyW5TiZkyMsF1yreu0byy2KW36J1wDdpoLuXg2TU1F1OW8OHoHaU4-ZmrZpPU4RN-crQCEimD190CSn9fPuxpIRojBJyu1VfV5KtQD3QMVdSg2JrjEj5-xm4r4E12Whf08itqPCb9Q5W0X4rt3ubYkqCmWnLeZpmb3_RZcbIk0UE5wZnFLCQJHLFs0qZ0OGpXp89o1HU4mWIBur5Or4tQGm5M_Y8m5PvTEfYfxLRyrcRv7GyVs5oLtFfiySZ2SqtZypLA-h50h61p0uPiA7iA_PiMqlVLtM-87XL33VZi05_O3UTpWE_0nAzFRJ4TW1ayz3_vn4Zlp9IERdbnnAd_1kPLD4lAQcR5PRXgtpCdfTjeg9LZG1qEa98W3Y2TRA_VPhW_RJPFQzBJWsNF1C2asxLp7F7EHWd6M3GN-UTMMo4fIQ1AiFYZlsclhEJC5wITWPMX6jvyvR8Q90iyMeIXGehaewSci-O6BdNsNOybY4aJEoCx8LYnO_DScwSLlHTn2I3b2AfzXDnBw035rA9q6G86szFxhWqqTojI-TT/4jr/BUZXRmfkRWiHrtiXrT2f9g/h7/h001.EYVuuKtyR8C4T7m9fNu3L8QvJxgsQM8LBDlm9A_pxWg" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"><img src="" alt="" height="auto" width="313" style="display:block;width:100%;" border="0"/></a></td></tr></table></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:left;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="left" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxWc4htTObwdorovK0nFHVH-4pUdVE0ELYH5DsNemk732SjNwhPNJ25r0O8B5vYifsGNUqyW5TiZkyMsF1yreu0byy2KW36J1wDdpoLuXg2TU1F1OW8OHoHaU4-ZmrZpPU4RN-crQCEimD190CSn9fPuxpIRojBJyu1VfV5KtQD3QMVdSg2JrjEj5-xm4r4E12Whf08itqPCb9Q5W0X4rt3ubYkqCmWnLeZpmb3_RZcbIk0UE5wZnFLCQJHLFs0qZ0OGpXp89o1HU4mWIBur5Or4tQGm5M_Y8m5PvTEfYfxLRyrcRv7GyVs5oLtFfiySZ2SqtZypLA-h50h61p0uPiA7iA_PiMqlVLtM-87XL33VZi05_O3UTpWE_0nAzFRJ4TW1ayz3_vn4Zlp9IERdbnnAd_1kPLD4lAQcR5PRXgtpCdfTjeg9LZG1qEa98W3Y2TRA_VPhW_RJPFQzBJWsNF1C2asxLp7F7EHWd6M3GN-UTMMo4fIQ1AiFYZlsclhEJC5wITWPMX6jvyvR8Q90iyMeIXGehaewSci-O6BdNsNOybY4aJEoCx8LYnO_DScwSLlHTn2I3b2AfzXDnBw035rA9q6G86szFxhWqqTojI-TT/4jr/BUZXRmfkRWiHrtiXrT2f9g/h8/h001.TFnMkfrXSSB9iLffF_1l9BHk0Ch-jFUYKorsHvJ70PM" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Click to Share </a></td></tr></table></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Or copy and paste this link to others: <a class="link" href="https://mail.bycloud.ai/subscribe?ref=6SqUHb8KiF&_bhlid=bf7a73b936aab597b0df9777ef50b28c5a049d32" target="_blank" rel="noopener noreferrer nofollow" clicktracking="off"><span>https://mail.bycloud.ai/subscribe?ref=6SqUHb8KiF</span></a></p></td></tr><tr><td align="center" valign="top" style="font-size:0px;line-height:0px;padding:30px 0px 30px;" class="dd"><table class="j" role="none" width="50%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td> </td></tr></table></td></tr></table></td></tr><tr class="btn_row"><td 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"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkKSrCVph9-fOYkcjx4VfJRwtQQsKrZC8pi-PiKai2fq4lAto9WepTJo69aQJ1T73b1BYaJHeCrLz1cWpFYfpKjdJ071BkzwRo9IrCS5YAIxy/4jr/BUZXRmfkRWiHrtiXrT2f9g/h9/h001.ww4_nPq1rYVsytDd4yfIvH4d4N9vJGW4RxHzF2yp1G8" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Check Out My Patreon </a></td></tr></table></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoGymQ3NNPtd5dE5MV_8UgjIDFPVXngz8pvQBldSW42yhUe_Qiq6DgEPMEBuPL9yfRpXelTiuu2kS8pLFvsoem_XoZoy_n13sTKUhZIbl0VH6/4jr/BUZXRmfkRWiHrtiXrT2f9g/h10/h001.sGk3tdHnVkbmDQB74z86KeCg0CtqHSwkiI8CELTbl30" target="_blank" rel="noopener noreferrer nofollow"><span>Advertise with The AI Timeline! </span></a></span></p></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="why-do-mll-ms-struggle-with-spatial" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>Zhang</i><span style=""><i> et al. [</i></span><i>Chinese Academy of Sciences</i>, <i>University of Chinese Academy of Sciences</i>, <i>Tsinghua University, Harbin Institute of Technology, Wuhan AI Research</i>, <i>Institute of Microelectronics of the Chinese Academy of Sciences</i><span style=""><i>]</i></span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 485 </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;">Spatial Understanding</span></span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></span></p></td></tr><tr><td id="introduction-to-spatial-understandi" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Introduction to Spatial Understanding in MLLMs</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Spatial understanding is necessary to interpret and reason about the physical world in tasks like navigation or object interaction. However, current models don’t work well with complex spatial scenarios, especially those involving multiple viewpoints or moving images. This paper introduces a new benchmark called MulSeT, which offers a systematic way to evaluate these limitations across three settings: single images, multiple views, and videos. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/eaf717e0-112d-4dc0-815a-c3a78099855e/overview.png?t=1757439783" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>MulSeT benchmark for Spatial Understanding of LLMs</p></td></tr></table></td></tr><tr><td id="inner-workings-of-spatial-reasoning" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Inner Workings of Spatial Reasoning in MLLMs</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> From a data perspective, the performance plateaus as training examples increase. Tasks that require spatial imagination, like imagining an object’s position from a different angle, show especially low ceilings, which suggests that MLLMs lack intuitive spatial reasoning. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Using ablation tests, the authors showed that disrupting positional cues in the vision encoder causes a major drop in spatial performance. At the same time, similar changes in the language module have less effect. This indicates that spatial understanding depends heavily on how the model “sees” and structures visual information, not just how it describes it. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/8095cd34-b105-40ba-b968-7309a45dce4b/attention_visualize.png?t=1757439798" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Interestingly, not all models handle spatial information the same way. Cascaded architectures, where vision and language components are separate, rely more on the vision encoder’s positional data than native end-to-end models. This means improving spatial reasoning may require redesigning how visual position is embedded and used throughout the model. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/1159b4b0-7fc7-4060-ab14-c452cbbe5c62/MulSeT_dataset_overview.png?t=1757439817" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>MulSeT Benchmark Overview</p></td></tr></table></td></tr><tr><td id="evaluation-and-future-directions-fo" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Evaluation and Future Directions for Spatial Understanding</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The tests show that MLLMs perform well on single-view tasks, and they nearly match human ability, but struggle significantly with multi-view and video-based spatial questions. For example, in azimuth transfer tasks, which require imagining directions from different viewpoints, model performance remains low even after fine-tuning. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/4b6bd6b5-39ce-463f-ad61-4b14e9324ab2/image.png?t=1757439954" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>Benchmark Results on MulSeT</p></td></tr></table></td></tr><tr class="btn_row"><td 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"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKnXUE2Y6kiFpnBH6jqEgCVPLJE0n5WPKGkOfxm_T3edsynwpBRwfqcac1sRoILFTpdNWb6E8xB5rSqgR-O78ie0/4jr/BUZXRmfkRWiHrtiXrT2f9g/h11/h001._dPJUe4lakofYQIqmK2KDtLCRT3nhKRlVTz3rFgeNxI" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Read Full Paper </a></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="r-ls-razor-why-online-reinforcement" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">RL's Razor: Why Online Reinforcement Learning Forgets Less</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>Shenfeld</i><span style=""><i> et al. [</i></span><i>Improbable AI Lab at MIT</i><span style=""><i>]</i></span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 22k </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;">Reinforcement Learning</span></span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td id="introduction-to-r-ls-razor-and-cata" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Introduction to RL's Razor and Catastrophic Forgetting</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> When we fine-tune large AI models on new tasks, we often face a frustrating problem: the model gets better at the new skill but forgets some of what it knew before. This phenomenon is known as <b>catastrophic forgetting</b>. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This paper compares two common fine-tuning methods: supervised fine-tuning (SFT) and reinforcement learning (RL). It shows that, even when both methods achieve similar performance on a new task, RL preserves prior knowledge much better than SFT. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/9d52db38-7ada-45c3-9ebc-7f2d3f29ff5c/Screenshot_2025-08-22_at_6.12.34_PM.png?t=1757440213" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="how-rl-minimizes-knowledge-loss" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">How RL Minimizes Knowledge Loss</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The difference between SFT and RL comes down to how they update the model. In SFT, the model is trained on a fixed set of labeled examples, which can push it toward a new distribution that might be very different from its original behavior. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> On the other hand, RL uses an on-policy approach: it samples responses from its current policy and updates based on which lead to good outcomes. This means RL naturally stays closer to its starting point. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/e6acd320-f6eb-4f74-9293-32e3223f0d82/Screenshot_2025-08-22_at_6.22.38_PM.png?t=1757440230" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>Reinforcement Learning Forgets Less than SFT</p></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This behavior is captured by a concept called KL divergence, which measures how much two probability distributions differ. The paper shows that the degree of forgetting is directly related to the KL divergence between the fine-tuned model and the base model when evaluated on the new task. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Because RL samples from its own distribution during training, it is biased toward solutions with low KL divergence from the original model. The authors call this principle "<b>RL’s Razor</b>": <i>out of all ways to solve a new task, RL prefers those closest to the original model</i>. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> To validate this, the researchers constructed an "oracle" SFT distribution that explicitly minimizes KL divergence. When trained on this ideal distribution, SFT actually outperformed RL in preserving prior knowledge. This confirms that it’s not the algorithm itself, but the KL-minimal behavior that reduces forgetting. The advantage of RL is that it implicitly encourages this behavior through its on-policy updates. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6e373366-8f48-4deb-b660-202e83fbded7/Screenshot_2025-08-22_at_6.27.17_PM.png?t=1757440272" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>On-policy methods leads to smaller KL divergence</p></td></tr></table></td></tr><tr><td id="evaluation-and-implications-of-r-ls" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Evaluation and Implications of RL’s Razor</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The researchers performed experiments across multiple domains, including math reasoning, science Q&A, tool use, and robotics. It showed that RL fine-tuning maintains prior task performance much better than SFT for the same level of new-task accuracy. For example, in math tasks, SFT suffered sharp drops in prior capabilities even with small gains on the new task, while RL kept prior performance nearly unchanged. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/d1948070-94cc-48e0-af79-a5e1891d5ea8/image.png?t=1757440334" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> These findings suggest that KL divergence on the new task is a reliable predictor of catastrophic forgetting, independent of the fine-tuning method. This has practical implications: instead of focusing solely on reward or accuracy, training methods should aim to minimize distributional shift from the base model. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/fc98a00b-fba7-43fd-b3ff-6b7fc622a360/image.png?t=1757440357" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr class="btn_row"><td 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"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92Y4vX401ML4vn__DnW3rcoftZWALqqO6Zg6zV8KonXOU4NL1Lvvw0YJUb6Cf4ZU-POzvYZ-sY21C6N-kdDpok-P/4jr/BUZXRmfkRWiHrtiXrT2f9g/h12/h001.snd0dQCTFkR-Q1ZSVclOaudIG22MPqETmfp_769DikM" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Read Full Paper </a></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="small-language-models-are-the-futur" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">Small Language Models are the Future of Agentic AI</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>Belcak et al. [NVIDIA Research, Georgia Institute of Technology]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 424 </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> Small LLMs </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> bycloud’s pick </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/eb4cf42d-52ae-4f3c-996a-ff7fa48a4d5b/diagram.png?t=1757440471" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="introduction-to-the-rise-of-small-l" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Introduction to the Rise of Small Language Models in Agentic AI</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Businesses are increasingly relying on AI agents to handle specialized and repetitive tasks. Although most AI agents used LLMs to power these systems, many people are now turning to small language models (SLMs), which might actually be better suited for many agentic applications. This paper argues that by adopting SLMs, we can make agentic systems more economical, flexible, and scalable. </p></td></tr><tr><td id="inner-workings-and-mechanisms-of-sm" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Inner Workings and Mechanisms of Small Language Models</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> SLMs operate on the same foundational principles as LLMs but are optimized for narrow, well-defined tasks. Their smaller parameter count (below 10 billion) allows them to be fine-tuned more easily and deployed on consumer-grade hardware. This makes them ideal for agentic systems, where tasks like tool calling, code generation, and structured output formatting are repetitive and require strict consistency. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> For example, an SLM can be trained to generate JSON responses for API calls or follow specific dialogue patterns, reducing the risk of formatting errors that could disrupt an agent’s workflow. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The architecture of agentic systems naturally supports the use of multiple specialized models. Instead of relying on a single general-purpose LLM, an agent can invoke different SLMs for different subtasks. This modular approach not only improves performance but also allows for greater customization. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Fine-tuning techniques like LoRA and QLoRA make it cost-effective to adapt SLMs to new tasks quickly, which enables rapid iteration and deployment. Additionally, SLMs' compact size means they can run efficiently on edge devices, offering low-latency responses without depending on cloud infrastructure. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;padding-top:20px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/e3024dea-dc6b-4f2f-8548-5e80d45c48a3/image.png?t=1757440550" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="evaluation-and-benchmark-performanc" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Evaluation and Benchmark Performance of SLMs in Agentic Systems</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Experimental results strongly support the effectiveness of SLMs in agentic applications. Models like Phi-3 (7B), NVIDIA Nemotron (2–9B), and DeepSeek-R1-Distill (1.5–8B) demonstrate performance comparable to much larger LLMs on tasks such as commonsense reasoning, tool calling, and instruction following. For example, Phi-3 matches the capabilities of 70B parameter models while running up to 15 times faster, highlighting the efficiency gains possible with SLMs. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Additionally, deploying a 7B parameter SLM can be <b>10–30 times cheaper</b> than using a 70B–175B LLM, considering factors like latency, energy consumption, and computational overhead. These savings become especially significant at scale, where agentic systems often handle millions of repetitive invocations. </p></td></tr><tr class="btn_row"><td 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"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92bTAdNjLdS1J-gSBCGjuc7XSMwf_PNaxrAsXIHmKZp7uIdPz4D4DAw-S71E2KrLEvSxH77dyWUPJpSAuKoZaezc/4jr/BUZXRmfkRWiHrtiXrT2f9g/h13/h001.g2Q3zt39BGc47CZy3jxtxiel_fgxLgqDIqolrY4ZU0Q" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Read Full Paper </a></td></tr></table></td></tr></table></td></tr><tr><td class="dd" align="center" valign="top" style="padding:20px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmLVa4u3oAHDAeMWAauTyuv2XpLsLxi84ONv807sulCmjSn5o3zR46z-JrnCfQ0zLUxRm8wj-RYzKf5ScdaCIWNf23U96jk3K0afDEqf1tfOpjA1eX_i290wuYacXT3qZog/4jr/BUZXRmfkRWiHrtiXrT2f9g/h14/h001.woNKj7QXKdd1c2jp_a4I8Qoh5k_7kajYXtbd4mADKSg" style="text-decoration:none;"><table align="center" width="100%" cellpadding="0" cellspacing="0" border="0" role="none" style="max-width:520px;margin:0 auto;"><tr><td class="p" width="100%" style="padding:2px;border:none;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td align="center" valign="top" style="width:100%;"><div style="max-height:0;position:relative;opacity:0.999;width:100%;mso-hide:all;"><div style="display:inline-block;width:100%;padding-top:25%;"><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"/></div></div><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmLVa4u3oAHDAeMWAauTyuv2XpLsLxi84ONv807sulCmjPIPZ7GVKV5ziruzk-nXj0Ub5BChYKushuA6cxzEZNTNDoJ32wpz0jxZVbPL3Se0Rd-a37_gmGVYjrhx15VlVMA/4jr/BUZXRmfkRWiHrtiXrT2f9g/h15/h001.jYaCtEBar2ZqaXu3bK82AVPrEFNaqIIjMT_QUWa7oAc" style="text-decoration:none;"><img src="https://i.ytimg.com/vi/LUQkWzjv2RM/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%;"/></a></td></tr><tr><td><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;"> New AI Paradigm?! Energy-Based Transformers Explained </p></td></tr></table></td></tr></table></a></td></tr></table></td></tr></table></td></tr><tr><td align="center" valign="top"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td><tr><td class="b" align="center" valign="top" bgcolor="#2a2a2a" style="padding:0px 0px 0px 0px;border-style:solid;border-width: 0px 0px 0px 0px;border-color: #2a2a2a;border-bottom-left-radius:10px;border-bottom-right-radius:10px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" bgcolor="#73ddff" style="padding:12px"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td><span style="padding-left:1px;"></span></td><td align="center" valign="middle" width="75" style="width:75px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCQqcWoV4NNHHr5SkP9THApWuHAAlWLQxI3Q_IqFmt_DcyAxeC8jDApCnHmMSBGpBb5sgtimvBYgxRX-Rp7s0F3LjCHoSwdhr83OBqRFhJ1y_/4jr/BUZXRmfkRWiHrtiXrT2f9g/h16/h001.pA_Ix2o52dHkFA4fFaWv4zYCypym9dmerwEGBLx_Kvk" style="text-decoration:none;"><img width="22" height="22" alt="tw" border="0" style="display:block;max-width:22px;color:Dark" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/x_dark.png"/></a></td><td align="center" valign="middle" width="75" style="width:75px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmBoQnQ9VXnB2zTxBG4HeHBgjMqVxpoXRdj01cjwyoVlHgiebEOgBvwHtevoVpsSvpn3Q1di2ml6sb3cBM-X6IStQbj_zQSVGWJ8AAmPw2en2/4jr/BUZXRmfkRWiHrtiXrT2f9g/h17/h001.5Yy-8OWvPEiW-9wPADyHrB6up8mJ_DLy3H4wCKphhBo" style="text-decoration:none;"><img width="22" height="16" alt="yt" border="0" style="display:block;max-width:22px;color:Dark" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/youtube_dark.png"/></a></td><td><span style="padding-left:1px;"></span></td></tr></table></td></tr><tr><td height="10" style="line-height:1px;font-size:1px;height:10px;"> </td></tr><tr><td class="w" align="center" valign="top" style="padding:15px 15px 15px 15px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top"><p style="font-family:'Verdana',Geneva,sans-serif;color:#FFFFFF!important;"> Update your email preferences or unsubscribe <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxWc4htTObwdorovK0nFHVH-4pUdVE0ELYH5DsNemk732SjNwhPNJ25r0O8B5vYifsBhEpz-DJgyVFmavJPa0OyKRRnvw4o7XGyvIv7PRofnmrwNsgSyDZm_uaWdKvptG_SHPEqpFaO_JeDQ0y3WG23JdqMktIr0SZ-S8-j5bqajz1r1Ns7cnmLWxescFxH-47nuTJp_29knA0wX2jci0IVQAaH4FtUp_9XmjmSDDWPMuQuPiETE-l0L2Dn3Mork8lw-LRvrdF8Z8nQzTk_zfb3zsq46v3NP9VBkx4fUNnRDyyDjoARXmomxbLnisiSImaKp17bXT7NQ0I-ZN3YmQtnzCRTAB3RC80P9RWDjaZLa9-rthxqJf8QJIrN2rZQH4WKDq6VjAvjrVbx8eY8UfBcHm7R1dfDnwyMmyxMsqmN5UxqsK5iGvLDdkT8qfx7N11xzYYcMhd_craHJzQVpcQoN4nwotltcmWs9kLlpVcphbkZqnVRbzJMKgU99wHwQlYdM-cuL-7n1762kP9mpjzMpxK53NpvayI7vPzNQpKifsfGHmJYm2yb8KXaUgK1myTN_0AeqFalEhJ_TKK_FXBubBEq2YBhzv-_A4_z-fiFyfWS-EGOtHRyw3xahTcc4gzpAOtqORW6oFYtNLKpcFkDDh1bOZws-vRlxDwlIQPsX5OpQs4mTf-BGej6hDD2UstYaGbecfZNBOxd3aaNQuBiGqisL5KGxMOCFiNEM-1E7UOYnUsl3A93QrctJUBoI9kCXzdicjTdY-Cn5RlNS_S7JCxjlKd4qBoje4shEoBQdatgxI7trZuZ991Duvhcg4bA/4jr/BUZXRmfkRWiHrtiXrT2f9g/h18/h001.1ucPkVewxxkdUU7IqNAwc5ijosv4t4V80Anu1O7cSk8" style="text-decoration:underline;text-decoration-color:#FFFFFF!important;color:#FFFFFF!important;"> here</a></p><p class="copyright" style="font-family:'Verdana',Geneva,sans-serif;color:#FFFFFF!important;"> © 2025 bycloudai </p><p style="font-family:'Verdana',Geneva,sans-serif;color:#FFFFFF!important;"> 228 Park Ave S, #29976, New York, New York 10003, United States </p></td></tr><tr style="display: table-row !important;"><td align="center" valign="top" style="padding-top:20px;" style="display:table-cell !important;"><table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="display:table !important;"><tr style="display:table-row !important;"><td class="u" align="center" valign="middle" height="32" style="height:32px;display:table-cell !important; max-height: 32px !important;margin:0px !important; background-color: #ffffff !important;"><a style="line-height:32px !important;text-decoration:none;display:block !important;" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28olDWFpV5DDKfdk_OdOKOhKqX5tVKGxJ_rMnY03xOMeOOSMxjTqVGfxVYXIu9Xhn2-if61z4-R1Sz_huNpYnW5PLSkQH00nk_rGPhyEyuEG8Dnc7HIc86bvBi3rbPASpx7e9QgMCHgGwqTMDj6JfMTpF5OwMiEIl4y_4I0Noqub0IGrby_CgKgcYbu5kXMTMIA1jKc-L8UMmz7yB00lS1LxOFMt5Pn8xrMs_7k9y9O0/4jr/BUZXRmfkRWiHrtiXrT2f9g/h19/h001.SXVXgGjH40YzSsrAIa5ScqJg6LtyYU7vpb9_kp6h5qw"><img src="https://media.beehiiv.com/output-onlinepngtools.png" width="16" alt="beehiiv logo" style="display:inline-block !important;max-width:16px !important; vertical-align:-3px !important;width: 16px !important;" border="0"/><span style="padding-left:11px !important;display: inline-block !important;">Powered by beehiiv</span></a></td></tr></table></td></tr><tr><td align="left" valign="top" height="2" style="height:2px;"><a href='https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWsHIaP4XNp0WgUYqLvHcKk_3uqk_KIkz4ddLinhFbud6JuxLFdSUhYnR7b1NSsmbtzXNGNblnEEMKUtkCAjkn8Y/4jr/BUZXRmfkRWiHrtiXrT2f9g/h20/h001.K53bsXL5-CGSVyJ_8MukPUEF7nbeG3Su5WODoqDQzwQ' style="color: #2a2a2a !important; cursor: default; font-size: 1px; text-decoration: none;"> Terms of Service </a></td></tr></table></td></tr></table></td></tr></td></tr></table></td></tr></table></td></tr></table></td></tr></table></div></body></html>