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</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;"> Next-Embedding Prediction Makes Strong Vision Learners, Let's (not) just put things in Context, Spherical Equivariant Graph Transformers, and moree  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </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> December 23, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EyMhETkN2gXORlUqZk6oqZ09BvwZC1HLWOMwPDNROO5mNt-uXmVajdkLox1rVMfqrrfTWdj_kRXc-TIbL46XuO6Ko86kUsuIMIUcezfp8eUFyTLjWfn_lMecCHOvzg0bOGFlbhwF67i0Ryh0T9VXYJ0k-BHjdIC7fXgWUX630YUxdnYXSw_Zw7qI4X_GDWKm-1BhoAoK9-ZtIeVueZ7dQKRz0rskkZeUIQx9Mge8nyuMZEOAEKdVQ9xemIqJUwBjj-TG3IKmgtGSl7a0G_wylckZN0U3ZdtjztIefMRmhL4k_tTpkpVv5cDBZO2DIdTP_cr6Y0OyjhmPf6mcntlbBb1I2FqXdmgFYzIk8fl82rtAxurtEI3F20yXUR8hs2pqnldObugWdvIaswd6J875OU4maTbgkbV8soG9rogLdIDtgTBJU34TO3h9lQmw9rnF581bmLqNSK4dgI6Qu1Eg_uVi6bixYsvNRzq-oJwYHAO5eupcWBZLrnwZaWuWAbazz45q-D5gn3nDKJD2R6DCkIjbibblyKi3g2IP2XnDLFOuuSgJ45LYCoGXFAumdTLJ_4ulkfs3HkTBtHmkIHIVnhIyXFUU9cYEm9UJzVBUQQbxRalID_IpPeWUeqT1ZR80Ashmaw_W-FwsxnK666dNUGIcK7z2IMzHC2eSLJn658MJVBhbdXQeIXFWsTRtEINCm-g4vI7YhvZ-vNXtu8-tcqti42-7fi9hVbSd3yNh5bk47tGE4HzS_4C4JOqV4-XiEsnC3muW_HdJr7XYIAL4jVSXelIWKl47QdbNE_NjOUEvcDSN3eN_hvdZZpfkVuTt9Q/4mo/LG9ChahRQJWWKwRSAS6A6g/h0/h001.ei93L2Xw0A645avroSf1oe1L9e_sv-7QTCG49c_oVqM"><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;"> Flash Attention Author's New Work: SonicMoE </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;"> Next-Embedding Prediction Makes Strong Vision Learners, Let's (not) just put things in Context, Spherical Equivariant Graph Transformers, and moree </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/4mo/LG9ChahRQJWWKwRSAS6A6g/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:15px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" style="padding: 20px 28px 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:300px;"><p style="opacity: 0.8;"><b>In partnership with</b></p></td></tr><tr><td align="center" valign="top" style="width:300px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2ynbFsRKNFP6p7R1DO_S8w9n3RGYbVnAHE9Pqn3FGnHnCzAQ3OWbfoh7Vuswe25Al1-sD8CjJjo7tL3bZeXsH3RNVArcj_miJHhTH6RbJxXv5r6kufDR7J2dlGjiwiVoJGFse4H6Q3WASFkwmNhcN73wQJol-81sFQHWhNSNKdmCxxOKbtrSsKCm1GysIqUWJ5SWqCt1fTiUk0lkw0Pgs3c6aXI2Ks44j5fpntWFqG1RFOQZIR9dqIm023DdkUKMmDXKCjzDBOZNRfre-ribhH3Xa0SLBmGn5IxeDQTPLs45zERi68lLsotWMFCpp8JFZDyxYLKXUl4J8nd_cpJfHCdCsxzVxeOXNJQFA7-YD4Suenvyvu8Af1-5u5COyDftDA/4mo/LG9ChahRQJWWKwRSAS6A6g/h1/h001.__dmUl53gJruYm-cWn8hQafEHfix5sraYr1kVcCgEJU" target="_blank" rel="noopener noreferrer nofollow" style="text-decoration:none;"><img src="https://beehiiv-images-production.s3.amazonaws.com/uploads/ad_network/advertiser/logo/b39c9321-3657-4b29-83a6-40b2605951c9/AQ_Logo_Horizontal_Light_BG_Oversize.png" height="auto" width="300" style="display:block;" lborder="0"/></a></td></tr></table></td></tr><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>Dec 15th ~ Dec 22nd</i><br><i>#87 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;">♥ 1.5k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j22z91kZt2uOBrE1Zkmkn47N_XuVG6eDQrYlrkVwA99qrhWZQmFWfsld4c0mha5Q8Btr1qY_3ueKu0-d9GTrMTACB_SvOaHX_uUfg43wxyCCdtQOdG0O0EGAY2Ocq_4OEj840xEXTKS9KbyE0UxMOTuuy_GRB5wxHaOTKlHwK9PwxhjmcwgwwhbEZ-tO_x229LwXcpaYLublWpHSQVo-CRXUp0QXREDOl3FEDB6yxfHu31MVs3eHnsvTgXcqO6KkxnQ/4mo/LG9ChahRQJWWKwRSAS6A6g/h2/h001.K_k1nha2KsSTVmOMu6oDcnJi-9rPM9HcJkaFtzDhNQc" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax has launched its M2.1 model</span></a>, which is designed for complex, real-world tasks. In addition to coding, the model introduces powerful capabilities for office automation, enabling it to handle "digital employee" workflows and complex instructions. M2.1 is also more efficient, delivering <b>faster</b>, more concise responses that use <b>fewer resources</b>. You can access the M2.1 model now through the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkO1kHiXB4aukfRrFV1ARoPsNtxOPF_p5PQSc99Wc-85j5ufq-949EXlaG0bAw2rizfSxULhhdK-Ef22_sNAhNkAMLSeQnLMCFjJzB26vETngmEndWrMkwoI8XWbfg52XzdZswJ6jveL5w7VKa9B0k-L3I_5T6AbBeMLxqxtOddfZfTBmDcKI3MEQdS2nkfu-PFLzw6g3LRUH-iMCmgO1Sz5fV0Np-aW9NM7iYRGTgcf0ZRSXvppQpxDzGk49sItpM5INC78kdvMp_uGPJQgC81g/4mo/LG9ChahRQJWWKwRSAS6A6g/h3/h001.-ucEmNVGkTt1iSO99g77K_4FXyK76m5IiV3c7wEGzSE" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax API</span></a> and as an open-source download on HuggingFace. </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/5c0bbd40-f3da-419d-833f-39b506954754/5c13032c-d33a-4e6f-be03-bac1606294a2.PNG?t=1766514080" 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;">♥ 2.8k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.E0zXMShwGen8LmcPPNsEqGfua4HXmRuZb5m-LrnoBjTjrPrq_vsbdN1C3uilUI71YKIpHy4YGuDl3B903watF_ycgbl1Gky-KLt3ZURmU1MiTRWmgWZsy0UUWg7dBaZ3-X_nTXJLH9jToRmOi5iE5HN87FrIUio3e1G5dS8eAXR72X8ZCCyhBJNzdq5p5-3_1XY6Ac-EpgMnsvSWIRKsu4SWad6l4HYJ1ErOZglOZy-lGYG4VhClyyRXbyrUPAis/4mo/LG9ChahRQJWWKwRSAS6A6g/h4/h001.luadEDyu9SruwR5ojo0lh3t2S-dPgoOhL3lHp_mZTOw" target="_blank" rel="noopener noreferrer nofollow"><span>Z.ai has launched GLM-4.7</span></a>, a new AI model that delivers significant improvements in coding and reasoning capabilities. This update introduces advanced features like "Preserved Thinking," which allows the model to retain its reasoning process across multiple steps, making it more stable and effective for complex, long-term tasks. Developers can access the model now through the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpSrFFzDetpQb3JmVcgzZn5azlpYTeXkoE_Dvgizen334h0etRt6L58K8AaeO7vYSTjZKq_WeIj5p5uFN11z0a6Q2uHtY0JbBYXWFOLtw5vo-j_SGJADBSa3PKca8ZU55wXbG8GRJW0DYpWOGRHI6imdnCZuKU9v9obPeiklYxQsV3pJ_KaZNXBHZBwd05ftlzLAvT-n6vYBonF92YqRr3-sqa21gKF0wVSK-ffhbo3Gh0fuk7Xwx-W3nrOiajOv8rw/4mo/LG9ChahRQJWWKwRSAS6A6g/h5/h001.5X1Hv3isVtGzTMTLh2L21YKZWYp25p-ighXvWr7M5L0" target="_blank" rel="noopener noreferrer nofollow"><span>Z.ai platform</span></a> and OpenRouter, or download it for local use via <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWr6LdnAmKyWSp5j0x8wnkgFcSF3Cl07QyO0_nPgjIvBvyIhglkC8eGzGYJmhWZXtwo3H9cuNl_Qd2tLREBXYfhi-g05r8hnzpnmqUEDzO6351lmjbKIBHE5xUuBzn86b0FDEDEjVpcbrUilE9fo9VjbBA2ZjC3zsnb2DUhjj7dlracWbMMLgPvvsZDgQeXkOxyIv9vFE9rV_qqJ381eExYaNveCxMWhrAjNWuPHhgbyLYxwA3ITOy2RE1DgW3tkMrA/4mo/LG9ChahRQJWWKwRSAS6A6g/h6/h001.fp8K2jihEFyxZM7Xm-n80_PhzHXk-rQuorAwOhq0Kpw" target="_blank" rel="noopener noreferrer nofollow"><span>HuggingFace</span></a>.<br></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/c1d64e32-fa32-4641-a59c-62c58b6423ff/upload_058e166eb117f1c394d0505429b6248c.png?t=1766514356" 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;">♥ 1.1k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.qZVn6KJQuMivjuNasJr7IFevxeZNgDI0XQfNKRNrxYaeWOdyzGPBbW1i4a7FCWyuFueliKNCtLt2GJRY1OnmuqVMTE9lA0WAd_R0KTxs64UWdx8bVDiXbWUkroJJw9WJujIzOxZ7F6PLbfN6BVeelA_sUfYLwkElKE2iLDQXEqiN5egmmOYzDboVm2L3PGH0J_bf40NGOwiXBY8VZPFvJYgwpQ2I4gOALquLBjIAOY7rybNPaPXInxGObCLqOVVd6uykv41JtztMTC6bE_5bxg/4mo/LG9ChahRQJWWKwRSAS6A6g/h7/h001.nd7AjKaq712fJy_jbSaNIPUe3b6kGArF3KSWfuKBnO4" target="_blank" rel="noopener noreferrer nofollow"><span>Bytedance has introduced Seed-1.8</span></a>, a generalized agentic model designed to efficiently handle complex, real-world tasks. This new release excels in multimodal processing and it supports both text and image inputs with strong performance in <b>Graphical User Interface (GUI) interaction</b>, coding, and information retrieval. The model can perceive <b>video streams in real-time</b>, perform non-blocking interactions, and utilize specific video tools to analyze details or extract highlights from long-form content.<br></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/b77e0243-95a0-4b89-97be-dcf461043b9c/4og2ymj9w2ywr.png?t=1766514622" 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;">♥ 1.8k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxVL1bgX23MWnUPDCBV_G0qzexIL8LIf14pNq09PqBNvri7GMy2z0ljXfskRINSO8GiZFhzwaMRQNT29RPaDE5JaymJX62ps5qdRoHMqlZh-O3sQ_xq_sfDvSY7Hg9DxrYGOaE9auOF8HHu7maPOed7JHCsE2LHzflKhU5-cDUnOD2guL-vHEFcRMOMY4Evvc6Q12blSZpEfjG_BWXdZ4bCi01k1Lk-pyKbHNyAXRhsPbeNX5MYNNDZnlqmY2JTHeJg/4mo/LG9ChahRQJWWKwRSAS6A6g/h8/h001.qLYtObgYLPKYvMu_9ne2edzzlHfDvFfmk53dWCJTHXE" target="_blank" rel="noopener noreferrer nofollow"><span>Xiaomi has released MiMo-V2-Flash</span></a>, a new open-source AI model built for high-speed reasoning and coding tasks. This model uses a specialized "Mixture-of-Experts" architecture that delivers exceptionally fast responses (up to 150 tokens/s) while remaining highly cost-effective. With a massive context window capable of handling long documents and seamless integration with coding tools like Cursor and Claude Code, MiMo-V2-Flash is designed to act as an efficient digital assistant. You can access the model now through <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWob05iaJ-FnIbuqVdGK4YUlNGvRXPWs-3igt9f4XxPII9r001sfoVoSC3cHqxKU5eZiOmgH2g9D3bQP1-FtT9t-R9c9Y8yMNkgBDcc8GlS52uZ6KXXYaGK0eog9nmOJgkViXFQOjr4HcIwaS_npn0yxobh9FF8ocxA83VMNtRqOLnPJPIQ4KilxXwubk0m-tKd-UF8urutGgBUwUxiSw1By6GiFf8a_1FupFReZRzcUYpe3rYbhbmbNtlFndK7qkgQ/4mo/LG9ChahRQJWWKwRSAS6A6g/h9/h001.UGkwZKzfalDJY85o0EBqvSJyJhL9wLWxvCRz76Rqs1M" target="_blank" rel="noopener noreferrer nofollow"><span>HuggingFace</span></a> or <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkLSfYo98Wcv0ohidzP4N-2m2P1BR0wRJIY-BXSySSARoJTEA6VAzs3t3FExYu-BmSUkDObDz6WKo_vekvaXmzWz22xJzhuSCMdn8zGYEADIMfFdcYEU64yro4wW1rfE3KxPICMENHsGLqkhw6V5-WL1mW_VVug8qgDkwzlNX5UlT-t19KQPIZtNo_zx6MMCrO0CkNTo3AI9K44fSggvN--t3JnmcESSeMrLvip0YiRgAe_-Cb6iOcdJ3T0hy5uwFLA/4mo/LG9ChahRQJWWKwRSAS6A6g/h10/h001.lt0SdJonYwJk5dbP_a0JHMBMPfFV8nrPAGYqD_NE4Xc" target="_blank" rel="noopener noreferrer nofollow"><span>Xiaomi's API platform</span></a>.<br></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/d5f34d25-0ce4-488b-b0d1-41c787ac3c4f/image.png?t=1766514799" 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 id="modernize-your-marketing-with-ad-qu" 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%;">Modernize your marketing with AdQuick</h3></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;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2ynbFsRKNFP6p7R1DO_S8w9n3RGYbVnAHE9Pqn3FGnHnCzAQ3OWbfoh7Vuswe25Al1-sD8CjJjo7tL3bZeXsH3RNVArcj_miJHhTH6RbJxXv5r6kufDR7J2dlGjiwiVoJGFse4H6Q3WASFkwmNhcN73wQJol-81sFQHWhNSNKdmCxxOKbtrSsKCm1GysIqUWJ5SWqCt1fTiUk0lkw0Pgs3c6aXI2Ks44j5fpntWFqG1RFOQZIR9dqIm023DdkUKMmDXKCjzDBOZNRfre-ribhH3Xa0SLBmGn5IxeDQTPLs45zERi68lLsotWMFCpp8JFZHyYz-L1CSJ4-PClhOQJZEfNujsRyI7n1cVN_QrVWUVRT55WPa9ueG5pAq1DpObhzw/4mo/LG9ChahRQJWWKwRSAS6A6g/h11/h001.A9lBL2nacOJg9AahXRI3vP3F1aHyx_ZlFIL-RaD4UUA" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/dcbc0411-8010-4366-a920-d2f1da8f4081/AdQuick_Newsletter_Hero_2025__1_.png?t=1738376775" alt="" height="auto" width="626" style="display:block;width:100%;border-radius:0px 0px 0px 0px;border-style:solid;border-width:0px 0px 0px 0px;box-sizing:border-box;border-color:#E5E7EB;" border="0"/></a></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%;"><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2ynbFsRKNFP6p7R1DO_S8w9n3RGYbVnAHE9Pqn3FGnHnCzAQ3OWbfoh7Vuswe25Al1-sD8CjJjo7tL3bZeXsH3RNVArcj_miJHhTH6RbJxXv5r6kufDR7J2dlGjiwiVoJGFse4H6Q3WASFkwmNhcN73wQJol-81sFQHWhNSNKdmCxxOKbtrSsKCm1GysIqUWJ5SWqCt1fTiUk0lkw0Pgs3c6aXI2Ks44j5fpntWFqG1RFOQZIR9dqIm023DdkUKMmDXKCjzDBOZNRfre-ribhH3Xa0SLBmGn5IxeDQTPLs45zERi68lLsotWMFCpp8JFZHuyJeVHlFgnUzOQx9lVn7r87Qr2-McowrAoTxVBwjtNrF9j1WVETWl_4WPD4ALLiw/4mo/LG9ChahRQJWWKwRSAS6A6g/h12/h001.XB5_aTejyuK2CtcqzM7_D5aa7G8hdAKnKL7yN0ILIL8" target="_blank" rel="noopener noreferrer nofollow"><span>AdQuick</span></a> unlocks the benefits of Out Of Home (OOH) advertising in a way no one else has. Approaching the problem with eyes to performance, created for marketers with the engineering excellence you’ve come to expect for the internet. </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%;"> Marketers agree OOH is one of the best ways for building brand awareness, reaching new customers, and reinforcing your brand message. It’s just been difficult to scale. But with AdQuick, you can easily plan, deploy and measure campaigns just as easily as digital ads, making them a no-brainer to add to your team’s toolbox. </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%;"><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2ynbFsRKNFP6p7R1DO_S8w9n3RGYbVnAHE9Pqn3FGnHnCzAQ3OWbfoh7Vuswe25Al1-sD8CjJjo7tL3bZeXsH3RNVArcj_miJHhTH6RbJxXv5r6kufDR7J2dlGjiwiVoJGFse4H6Q3WASFkwmNhcN73wQJol-81sFQHWhNSNKdmCxxOKbtrSsKCm1GysIqUWJ5SWqCt1fTiUk0lkw0Pgs3c6aXI2Ks44j5fpntWFqG1RFOQZIR9dqIm023DdkUKMmDXKCjzDBOZNRfre-ribhH3Xa0SLBmGn5IxeDQTPLs45zERi68lLsotWMFCpp8JFZJYIcLk60TimcgMq40VjBbowKs09eu4xvgB-vLtvcUx2hn20uylL4UyBiQfeZtwjFA/4mo/LG9ChahRQJWWKwRSAS6A6g/h13/h001.EjX2UZG7J2jt_vCjJC7wibFYjKZfjMmZ5UFaS9LQwbs" target="_blank" rel="noopener noreferrer nofollow"><span>Learn more now.</span></a></p></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="lets-not-just-put-things-in-context" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Let's (not) just put things in Context: Test-Time Training for Long-Context LLMs</b></h1></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%;"> Bansal et al.<i> [</i>Meta, Harvard University, Kempner Institute at Harvard, OpenAI, UC Berkeley, UT Austin<i>]</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;"> ♥ 444 </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;"> LLM Context </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </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%;"> AI models can theoretically process millions of words at once. However, there is a gap between what these models can read and what they can actually use. When you ask a model to find a specific "needle" in a massive "haystack" of text, it often fails, getting distracted by the sheer volume of information. </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/5d08c4ee-47df-4ffb-8f4f-ef05be5e01dd/CleanShot_2025-12-23_at_22.19.33_2x.png?t=1766508581" 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%;"> Until now, the industry standard solution has been to let the model "think" longer by generating more text before answering. But researchers have discovered that for truly long documents, simply generating more words doesn't help as the model’s internal attention mechanism gets overwhelmed. </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/a04979af-2750-4efe-94e1-387356f58737/CleanShot_2025-12-23_at_22.19.46_2x.png?t=1766508595" 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%;"> This paper has identified a mathematical bottleneck called "<b>score dilution</b>". As a document gets longer, the "signal" of the correct answer gets drowned out by the "noise" of unrelated text. To fix this, they developed a technique called query-only test-time training (qTTT). Instead of asking the model to generate more text to solve a problem, this method allows the model to pause and perform a tiny, temporary update to its internal settings based specifically on the document it is reading. </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/641ca0b4-08f6-4a17-b105-5862baf42a46/CleanShot_2025-12-23_at_22.19.58_2x.png?t=1766508608" 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>A visual representation of how qTTT improves the logit margin. </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%;"> It effectively lets the model "study" the context for a moment before answering. This approach proved far more effective than standard methods, and provided massive double-digit improvements in accuracy on difficult tasks like finding bugs in code or details in long records. </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.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvoURVwn8vpFpFcK7mJcJWflXAWLjjCnvO7RRRY81Z_wY99sxpGzEF2Uc2meDa3AnXE41nLtidCQ1PIEZZLhZnvBx-UqSOa9O-wwKNC2TCrXEStJb2szptUmlOdxs-_7yvPEAFK6qIAW7xrW4cls_R8gq1IE-MvFpFev8wY8eJnL71XPBtuRbV2WAcU6T8bR7jqA3BU1BJO-2xdh71cI3-tBz47hwQ21ovx3Ah6jvza6yA/4mo/LG9ChahRQJWWKwRSAS6A6g/h14/h001.bgiT27pyLzvm2GtzrkxDU_oP9bBrchZFi3xj4E6NXAE" 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="a-complete-guide-to-spherical-equiv" 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%;">A Complete Guide to Spherical Equivariant Graph Transformers</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%;"> Sophia Tang<i> [</i>University of Pennsylvania<i>]</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;"> ♥ 876 </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;"> Protein Generation </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 class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> For a long time, teaching AI to navigate the 3D world of atoms and molecules was a challenge. The laws of physics remain constant regardless of which way a molecule is facing, but the standard ML models often struggle to recognize a structure simply because it has been rotated. To solve this, researchers have developed a<b> Spherical Equivariant Graph Neural Network</b>. </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/8c8db974-596f-49f9-a2b8-129663677a5d/CleanShot_2025-12-23_at_22.36.27_2x.png?t=1766509601" 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%;"> Rather than treating molecular features as static lists of numbers, this framework uses "spherical tensors", which is a mathematical concept taken from <b>quantum mechanics</b>. The researchers proved that by representing data this way, the model can perform "equivariant" message-passing. This means that if a molecule rotates in virtual space, the model's internal calculations transform in perfect synchronization, and it preserves the correct physical relationships between atoms. </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/116451f3-5ecb-4dda-ab23-85afc45a858a/CleanShot_2025-12-23_at_22.36.55_2x.png?t=1766509625" 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>A feature vector f is split into its type-0, type-1, and type-2 components and arranged into a feature tensor with a tensor axis, a channel axis, and a tensor-component axis.</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%;"> By adding specific geometric tools like spherical harmonics and Clebsch-Gordan coefficients, the architecture guarantees that the AI respects the rotational symmetries of the physical world without needing to be retrained on every possible orientation of a protein or chemical compound. </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/3a446adc-e095-4743-bc47-e22a32bfa33e/CleanShot_2025-12-23_at_22.37.49_2x.png?t=1766509685" 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.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvoeWycFU8i0uzJvzWZarGh63ivLrTpl_ftWkGNE-UHHNl8oihhiPCC-iXh2gMq_IPUHbClKwsRcVK1nmayxUN4QQiR8rZsvNsWBEDpGYHIQrpwuZGACnEE7sYC0kalbHBnUlYjrIiYZdtSIpXhRlPCVwrqtOt_N8mBlKGnMZrCAGYTdWM1aBlnDYqft23Vew4uD6wuqySPAjfaFg3g1oIGERvfbUcZu9xvrtVAZFgucRg/4mo/LG9ChahRQJWWKwRSAS6A6g/h15/h001.EHTpoILcr97NIYIWruyvLBtXpf1pygdZk29-hKFqM20" 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="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="">New Premium Insights release</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="">For context, Premium Insights is where I write down longer form content that I think is interesting but not long enough to be make into YouTube videos. </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="">Last week, I published the following blog:</span></p></td></tr><tr class="embed-gen-img-r"><td align="center" valign="top" style="padding:12px 27px 12px 27px;" class="dd"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><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;"><!--[if !mso]><!--><div style="display:none; float:left; overflow:hidden; width:0; max-height:0; line-height:0;" class="mob-show"><table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"><tr><td align="center" valign="top"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExvEJjT2Stk8Shs_WP0OMsML830bjr633m6HwK1LNQtyvf2iBr7Shhj6Tv349xuuwuKfT8r1BI6DEyUBU61UAXgVA7PVg7mtwU4sfuiGVUe9mbPymKyBF3WgAf8AiRzqlvijWs0mFxzuuqOujNnYXoql1AEfYu-M_W_w1iTzQ8M_mhqrZInYmNHO3O07I2h4d4UI73WZK2Onz_yb661S0DB5k4Y15w4NR0wqZC6bFSf34CG-i0LEyXnD2d_LRE1GopMqmCOScKHhUZSE7oZsjghiMbqkfoegqnjUCZPDAiMTgyX0Rqy1O6gl7HMryxclfI-_QwWAKcxCcZHWkwwWsCWpjrwjdgJL8ea2QSkCJ5iw9App7VjVkYeHViRuFRFqQ_DGY3-QK3dS4L8uwwhqF8BiMym5Nn9XcdWfVzYz6A6pcGW4J1Dj9jabnNoRLmSGNz8BxGQiw6iSKMn52BA3kN_JvxSeKYmkWu0YLEhN02EeDGjkp-0CeiKWHmQ-Hl9FvkYNNtPLj04TnrnJegEcE4SUiNoCzkMQhbGqJRH1ZlyU92ZBPtPg6IuBmrTmvFAaQFcgqLj95Qpt70aGEpWUs1T95b3veOeAIeQXqaL6RRw4kiO4xtV2K7MaspbBISFMTOS7b6BJEJSrP-a7pz7yLNja4Z3t-EkX4Toy-5XkhBkddqLpLBpnfOZtBDWgWSQLnSehddI1W68QB_yg226J5Q1yQ91rGK8FAxmEjuavZ67Wi0HKaWM67YPayL4xbptgFFlFYRvrDACXQS-yPJOczKPvzOPrsUyNj2Pug4UOo6E6DNk1v7mcaa6EOQEUnODGM4/4mo/LG9ChahRQJWWKwRSAS6A6g/h16/h001.HcdgB04wmeBo4lpu9S8ptbSrMeTydRW5bmA01dnZOk4" target="_blank"><img src="" width="100%" style="height:auto;display:block;"/></a></td></tr><tr><td height="16" style="font-size:16px;line-height:16px;"> </td></tr></table></div><!--<![endif]--><table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"><tr><td width="57%" align="center" valign="middle" class="mob-stack"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="left" valign="middle" class="l"><p><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExvEJjT2Stk8Shs_WP0OMsML830bjr633m6HwK1LNQtyvf2iBr7Shhj6Tv349xuuwuKfT8r1BI6DEyUBU61UAXgVA7PVg7mtwU4sfuiGVUe9mbPymKyBF3WgAf8AiRzqlvijWs0mFxzuuqOujNnYXoql1AEfYu-M_W_w1iTzQ8M_mhqrZInYmNHO3O07I2h4d6lDFzsQjGqV8GIHkwjwEeKZCCG5dDjISi0z18M0dpg8360z0x4SN8PG9-oHKBf1329C6u7vbkUFwdyVkFGDNZsdjG7SWiwMBP5Uy6DD0ckp_dlrkduefkYfsVL2lXjcYTqptLC92JhNyY5YflhEcg1yiMIQKQvs842XuinE2DdBgEaJcWKqgYQ3dIx8MyVI-WjtHuMMW-gr7SYWQeE0CD-LCVbWZHwv36nryEIdZJAzTyJb1yCO8Q2wnz_tjFjKtd-HeHhvlM28FUnbHIW3MsVf0prkQhRLYIGMd0cS1D8ZuIlEVyVJ9lliD4DvCGjCNwXsi-RW7xZSOP_n71kgfchBBhbxtJUXaHLKlnJWkAFwlcXV-m8FdmriMDCdqr1GYjHpA0aSEF3d0fWvUfahxx5S9YJpSYfvyBTbZ436TiwvhZZ_2D0jPrw_aM9iGICRPqt9GekcFatvRJfDhdbY1dUggDrF9gWpenAVILUkLBk0NJCxL_9zBUgLbQwYNo0t21nVTAzg10jL_t5u8MvNeKXP1TY4Qv-fv-QiBcDukVdiwxAHPzekkc1-YCe_XJwPF_4jFDJhAZNfdqEBpzXZe6xoeXXTJb3fIdVgJGWYxK_BWCaXbg71y6BaIXcLq7RNHo/4mo/LG9ChahRQJWWKwRSAS6A6g/h17/h001.wvhfp1nNACqLH45AQSdpvYQo-go1mI5xJV3PH0Gh2gY" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"> Aug~Nov AI Research Trend ReportAuBasically recapping what I missed in the last 4 months g~mail.bycloud.ai/p/aug-nov-ai-research-trend-reportNov AI Research Trend Report <tr><td align="left" valign="top" class="m"><p style="font-size:13px;line-height:19px;color:#2D2D2D;"> Basically recapping what I missed in the last 4 months </p></td></tr><tr><td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"><p style="word-break:break-word;">mail.bycloud.ai/p/aug-nov-ai-research-trend-report</p></td></tr></a></p></td></tr></table></td><td width="3%" style="font-size:16px;line-height:16px;" class="mob-hide"> </td><td width="40%" align="left" valign="top" class="mob-hide"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExvEJjT2Stk8Shs_WP0OMsML830bjr633m6HwK1LNQtyvf2iBr7Shhj6Tv349xuuwuKfT8r1BI6DEyUBU61UAXgVA7PVg7mtwU4sfuiGVUe9mbPymKyBF3WgAf8AiRzqlvijWs0mFxzuuqOujNnYXoql1AEfYu-M_W_w1iTzQ8M_mhqrZInYmNHO3O07I2h4d7inMWzmxj7LcCnk_z0iWiMkbLJd3SPgU75DTS7wAtZzL03IMtFxc7MEQC5OYprbMNCmLibXbAqbN-xMrln00_VgyQd7sfYvcRcsUwh6cZ-YGhQDV311ipILZ4J08Ard9rg7FGWiPf_jPCp3Pc8iqOPmvQkxE3R3MQPb_QLlJCaCAdkHW_uyshte436yAPVatFhF5ez1bQ3enXMuGIRpZK6NnHUKvIazhMKSql5JhhPaLV57pTsv5j7rTqn8ZcUqGcL0LX1Oelb-lA4if9yvGOK1OfRhJ7tiH-eCN6lc-aqJBHkPnerH6JJcsKiqUqs6ia6NR2h1qfAMNHScFnNuSCHDcVZoiu-SCFbCqx1lyY8pZoyqAd8jQ-_cOcQk21wTrRPr4eABZeV94eVIJID3sZyeceFuLGYos0veMIcqSb3QYSZZVI0Qq47v3twjP81JjzG4u8PkKMZwZ53LZDBNHLF1XAK9SBBmAPVMShNPYc1HY2KcRtU65C0h0W9estDT1xnCHXB18uL6XUF4oAFz6TnE6FmvguMcLM-E6z5I7UKp1qVJEfXeKhpD6Xmigl8N1Aph6NUD5cL1g6aF31jLz-JTVg-AMszxQI456XHj8jIhJJ3zYOW82GfvTfDJc3AZrw/4mo/LG9ChahRQJWWKwRSAS6A6g/h18/h001.XDE-oExqlQ2_VcaYQAbIXi3JX2F-wuK8i2NDM884bxw" target="_blank"><img src="" width="230" style="height:auto;display:block;"/></a></td></tr></table></td></tr></table></td></tr><tr><td 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%;"><span style="">Aug~Nov AI Research Trend Report</span></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%;"><span style="">Basically recapping what I missed in the last 4 months </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="">I spent a lot of time on this quarterly(?) AI research trend report (~4000 words), so don’t miss out! </span></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.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EzPyKM-qWYD2kOl7BDZ-_rrxEwCEAfAc9jvNkesapA9vx3ePpFnC2QfvH5kYdUIkV8Hj0_OBOScuDLoVQQE-iuvSDWMfPaLTb4gZ_g2XGxO9Ej8aMP6sgWJsmusTTSo3CBSpBP50FSJPYHGUSXAyxDSBT21v1yHhRVpWxU7afnOuqhllsA4JL9D8GwXYSyzBhCpTPpn2As6w80ZWNeasWTfbyyvzWCbzyIEIJmW3KsTFb0om8TpVIBaECZ8ksyFyYnnt9rv-drH6gxiEbtXml-cyflbz-6nq-HJicHUFGIhS3eYiim7NWDP0xnJYzZd0g6HyBzjRltWQK-loGJc7I52WVXmLkyemIVRdPsn-shJ55g0PSBX1fe7y5hzHx7pEQl5Qo82b0P007mLXh_hHqzkuJtQ7OO3bnFi0Xel5adSudzTv17Eg5E5eb6BOQ0Vg4FGu3KilMkjLYP27ikxqCY4DUhMYE3kQ7aAP1x5iX5ZMDr46wWp1MkEF8CExVL-m_Prqc52nuKhmh_3Dx8ZdFAIHRmHaK0I6f46GJjy4ztvHGcdUh90zGQ4SwcBbtOpNDo9l6dlyRxaDHX7byleEDYcmePeUnSVSV01cj93ZH0iLqceP9NKSBvxf3xGPR0blTZ-CFr62hOiTL_yNYfwj3Pi71qGeZ33r4QARKLUcdYwgZOAiFwLEi4LPyY_Q939fuEDzqcgBoY9D2d9pJtPppBkOPhzHoRoq_fw5lOoFC4-JcbTybA3LxDfuiWUPsKSJ90cmQm-w4YxVO5rarXdyuhBYhENJuvfD-yZvJkaNqKlJGzcgu_RszO_jihmmtWU7U97IFJyW17ADkkupzdAOMMn/4mo/LG9ChahRQJWWKwRSAS6A6g/h19/h001.dOFrCkIdb5y4aQsWJj3kzXy-aOq_JJPd_yxiDfN6SMs" 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 It Out Now </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_8UgjLbPKYFbBPtV6oAT4VYSncNiXOMe0ETHKViEemkGKRuti97gDsqlNJXOC9cMEoZt4vqGEMzd3CYIoAvubE-GTMMv0R4cvjjFP5GSo0u9oVNLbCfjuz2euc3Qmm3WzwrFcNADXkTfCp8htikHagV6VYnI6pN7OAyguFo6iXHRMYvv3Vq452Ixm2U_u_C9usofBC_mVDfj3-xpZB7kKiDZWn2/4mo/LG9ChahRQJWWKwRSAS6A6g/h20/h001.3Rbpl7cGKp4JYkiwFfSnZF9NWHq9ggst1LGZzTP230k" 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 id="t-5-gemma-2-seeing-reading-and-unde" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>T5Gemma 2: Seeing, Reading, and Understanding Longer</b></h1></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 et al. [</i>Google DeepMind<i>]</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;"> ♥ 1.4k </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;"> LLM </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </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%;"> AI can generate text, but it can’t see images, process multiple languages, and remember long conversations without losing the thread. Recent models have focused on "decoder-only" models, but we still don’t have a lightweight model that can handle all these tasks simultaneously. </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/cda70a65-7f81-42db-9bd5-941b28297b16/CleanShot_2025-12-23_at_22.45.30_2x.png?t=1766510141" 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>Summary of pretraining (top) and post-training (bottom) performance for Gemma 3 and T5Gemma 2 at 270M, 1B and 4B over five capabilities.</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 paper introduces T5Gemma 2, which is a new family of models that successfully adapts the powerful Gemma 3 foundation into this specialized encoder-decoder structure. They utilized a clever "recipe" that initializes the new system using pre-existing technology. This teaches a text-generator to become a better reader and observer. </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/391ca6fa-43ff-4afa-9788-e6a7e3f9fe23/CleanShot_2025-12-23_at_22.46.05_2x.png?t=1766510176" 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>Overview of T5Gemma 2. Encoder/decoder parameters are initialized from the pretrained decoder-only model, and then pretrained with UL2.</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%;"> To make these models highly efficient, the researchers streamlined the internal machinery by merging different attention mechanisms into a single, unified module and sharing vocabulary tools across the system. </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/fa2dd73c-7611-4e07-9564-c8531f8b4cb7/CleanShot_2025-12-23_at_22.47.19_2x.png?t=1766510249" 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>Detailed post-training results for Gemma 3, T5Gemma, and T5Gemma 2.</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%;"> The results were impressive: despite being trained on shorter sequences of data, the models demonstrated a surprising ability to <b>handle extremely long contexts</b>, extrapolating well beyond their training wheels. Furthermore, even the smallest versions of the model proved capable of understanding images alongside text, matching or even outperforming their predecessors. </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.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvr1HSD0eGACb_cHqzUZcuVfolp0Dph6I_1zG-NM6pJtSxdy7z9Rm15GCz-d6ARrD0SuNdd6foZ_Scj6QjC81BOUDlNgrAgPjuQ43hEorvN9DRz7N8ymOtLp3pAaxkVRKUYoPX7LihqnEf0QEl6jsQrQ_t5ia2GIo6smahYkIl7UnLOnNY1nMStvS85Xk3TPnTNwEWVj3h5G3fK-Ac0lM5BhtLcqmIruZaPyZ0-wmien7A/4mo/LG9ChahRQJWWKwRSAS6A6g/h21/h001.F0kwyQ7UMcfmdjI9EYhcHw4ym9oPzzMBN37tK04usUU" 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="sonic-mo-e-accelerating-mo-e-with-i" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations</b></h1></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>Karan and Du [Harvard University]</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;"> ♥ 1.4k </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;"> LLM MoE Optimization </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </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%;"> We want better AI, but don’t want to pay millions of dollars to train it. Right now, one popular solution is to use the "Mixture of Experts" approach, which works by dividing a large neural network into many specialized sub-networks, activating only the few necessary ones for any given 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%;"> This approach theoretically promises smarter and more efficient models, but as these experts become smaller and more numerous to improve precision, the computer chips running them struggle to keep up. The <b>hardware begins spending more time simply moving data around than actually processing it</b>. </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/9735b758-d055-4bea-9e52-c209b4e1e93c/CleanShot_2025-12-23_at_22.58.27_2x.png?t=1766510918" 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>Computational workflow of SonicMoE’s 8 launched kernels, grouped by yellow boxes. </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 paper has introduced a new system called SonicMoE that changes how these complex networks use computer memory. They discovered that by intelligently reorganizing the mathematical operations required for training, they could drastically reduce the amount of temporary data the system needs to remember, which cuts the memory footprint nearly in half without losing any information. </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/7f43334c-adeb-4834-a777-440f51adbad8/CleanShot_2025-12-23_at_22.58.52_2x.png?t=1766510941" 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%;"> Additionally, they introduced a clever "token rounding" strategy that ensures data is assigned to experts in perfectly sized chunks. Previously, the hardware would often waste energy processing empty filler space just to satisfy rigid computational requirements. </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/fbd07b2e-39ef-4073-a210-0c9ef1ff0840/CleanShot_2025-12-23_at_22.57.52_2x.png?t=1766510883" 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>MoE Scaling Trends</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%;"> By aligning the data flow with the physical design of the chips and performing data transfer and calculation simultaneously, the researchers were able to process information nearly <b>twice as fast</b> as previous methods on modern graphics processors. </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.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvqtLFWFole_m8T7VJyqlw9nIS3uc8UgPH70bX0OQCbukvN2E6vNrPuhOkdwXRX1Z12EmV48VcRCxPkKM_RArQU_P-H-D0bM-ibaOsBanGa-LQQPEVFJ3yEY8PgvLPqkfJWQN9aw7-9djruni8BQiSpEY3NguXlfIY6IZNAgwgy634Vxe2_oV-M2F5OI2UWfymJ9iWZGoJD9APKxchuI2uKYC4fhR1pzaLiQY1IZfmyhww/4mo/LG9ChahRQJWWKwRSAS6A6g/h22/h001.OCpQFKZ0m9SIGyqyKfd0nWSaHfW4ebDHwhQkFRO0vDM" 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="next-embedding-prediction-makes-str" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Next-Embedding Prediction Makes Strong Vision Learners</b></h1></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%;"> Xu<i> et al. [</i>University of Michigan, New York University, Princeton University, University of Virginia<i>]</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;"> ♥ 624 </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;"> Vision </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </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%;"> Teaching computers to "see" has required a different playbook than teaching them to read. While LLMs grew powerful by simply <b>guessing the next word in a sentence</b>, vision models rely on complex engineering tricks, such as reconstructing missing pixels like a puzzle or comparing thousands of image pairs to learn differences. </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%;"> A team of researchers recently asked could the simple "predict what comes next" strategy that worked for NLP, work just as well for images? The team introduced a method called Next-Embedding Predictive Autoregression, or NEPA. Instead of asking the model to paint back missing parts of a picture pixel-by-pixel, they trained it to predict the abstract features (or "embeddings") of the next patch of an image based on what it has already seen. </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/21cd41c2-961a-4b03-9b50-366e33375f87/CleanShot_2025-12-23_at_23.08.33_2x.png?t=1766511525" 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%;"> In simple words, this is like asking a person to guess the meaning of the next puzzle piece before picking it up, rather than trying to draw the picture from memory. Remarkably, this simplified approach allowed a standard Transformer model to achieve top-tier accuracy on major classification benchmarks without needing complex decoders, specific visual vocabularies, or heavy data augmentation. </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/50a61796-6b59-4cc2-9769-b548765e256c/CleanShot_2025-12-23_at_23.08.58_2x.png?t=1766511549" 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>Comparison of different self-supervised learning frameworks on ImageNet-1K classification. </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%;"> By focusing on predicting high-level information rather than raw details, the model naturally learned rich, transferable visual concepts that performed exceptionally well even on dense tasks like segmentation. 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