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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;"> Plus more about Discovery of Unstable Singularities and AToken: A Unified Tokenizer for Vision  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </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 23, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3Ez-SS9V0BJ-zojQhi6PCaXagzT6ztb8LWWESvrUqQAoI55cXmjwVsUcekysgjaFGzpwiZWL-ViR1lwR1fcfeT1WayFfon1xnS05lt3vxrW4c7Zdji0mTdVhH7hivlYAkhlwMBeHg04_eP8dYtWFiQavbhPYD4nTLp19XL2ynOAZdARRwInAbpdO8fVIyUxu5KwmSWtMN1JR507EcEM7MUokQBITo4uNLUZ1-4TMqeyXb8zBnWZHEEo9TCAi9-krMzC_167yTDaoupznLhZQDBg3RZhOMGQyMgJea_MUoHcGL9g-4_ogro49nJmZ6fFvFqEGobkxjBCAGOQgRJGWexpJ0g6HQlXK8bzxvLKcQRA0XsdNo_nrG_-xy6Z5lE4G4HB5ogsN8WsAYDQlh2SZZdPOF-tW-G5fMoU-jZ8KWjybQlAIY8FSRzCDWbzQYQh-ltTgewKVXPtX4K_fVVcwzEc9N_yFEQYmCIW9iNXQoFUMHUdn_AKYPzgn1PKtSUnMK_YMFKGEBV67AlhGr2EamYSXBGY7lL4VO9SfLnCF1hCq0bVEW7M3_GYiCmV-kavwcXbHw6PeKtVxu7nd9IIsGeyFtOLBN3jGO30GH_wavBjVvHsIYzxKYY67B1sqSoX1dsQDXGfpP8Z9aIpaT9giDn7xjo5JyVzN-xFDjeeg-jM8YMMCOCJJTngWXEBcrAa9mMgRNBQ08dXvoHvr03p3jWvFhITP0aTQQcWY95xZy6li7etjSwTiLfN4TUDXALnhep4/4k5/Hv2traIxSCGa_dbdnIssgg/h0/h001.zeZAqr2nOnOUp7bn1dDB-5_ymVn-wM5WL5ZJTzgyCWQ"><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;"> Pre-training under infinite compute </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 Discovery of Unstable Singularities and AToken: A Unified Tokenizer for Vision </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/4k5/Hv2traIxSCGa_dbdnIssgg/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 15th ~ Sep 22nd</i><br><i>#73 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;">♥ 6.5K</span></span> xAI has launched <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJYcw1TXaGKADUp8JWwsbWsNsjIMXNO_uu545SrlW8y3dWnx-eVzmRVZ5jG1nLKnRQDQcdWV_WQJQgvNm2Q3CSJRpM8GaLLbRAeb23F_1S_hB-kZJZHrsx74MdjMdmVBhG0OIQwS7X7qgFJgbHx3FQbSuoa3LiOF9ZQQYVKEMHTV6RL8p0EpMEnNIhxX6u9HLOnA1bGprGGYITy9tLxvvj-w/4k5/Hv2traIxSCGa_dbdnIssgg/h1/h001.VL6jz9dEAzgbDUizsN-3_RqAbpeArZ3tYC9NU4NPBSU" target="_blank" rel="noopener noreferrer nofollow"><span>Grok 4 Fast</span></a>, which is its latest multimodal reasoning model, setting a new standard for cost-efficient intelligence with a massive <b>2 million token context window</b>. The model is now available for free to all users on Grok's web and mobile apps. Grok 4 Fast is also accessible via the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVNYugd1U5NC_psHLt6lFGmTu1qt95Rh5WM7yKPbz8_TQUkEUGFMC4aDo_8jzaXYY0-_VhC12oeeV1PwTqNXIFWqUze10040vbyJzX04tUP2Q1YuqGPVEwTxlJvIlUwUPt4D_UDHM0MYBG1KIiyyjjN-D9m7QAemjJ2HzgxypIN7zKFZZS5xdx6C4ViA_zggE3RY1Hi-qq9GBhD25koJPeQACeC0EAyQ0U0tA4ZW80rrz7P6iv7LCWfCBO-3HEC3w7Q/4k5/Hv2traIxSCGa_dbdnIssgg/h2/h001.208ZaTwH5to2sPOr_q2PCCpm_3betcyi72BjQRrUZNk" target="_blank" rel="noopener noreferrer nofollow"><span>xAI API and is being offered for </span></a><b><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVNYugd1U5NC_psHLt6lFGmTu1qt95Rh5WM7yKPbz8_TQUkEUGFMC4aDo_8jzaXYY0-_VhC12oeeV1PwTqNXIFWqUze10040vbyJzX04tUP2Q1YuqGPVEwTxlJvIlUwUPt4D_UDHM0MYBG1KIiyyjjN-D9m7QAemjJ2HzgxypIN7zR5nkcW4Y_-oD6AY2HksRAMhva9b6znDcnOMvf0izj8mEpTXOIIu9sgPCZVMx7y0frwes46HgWHziRgFpCjtH-A/4k5/Hv2traIxSCGa_dbdnIssgg/h3/h001.NjzLj24faGaCYhpRJDcvQVVUNWryC4xl_kCyEmwuiso" target="_blank" rel="noopener noreferrer nofollow"><span>free</span></a></b><b> </b>for a limited time on platforms like OpenRouter. </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/d5c4342f-d21e-42ea-8f6f-613d88a8a3be/image.png?t=1758650106" 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>Cost comparison of Grok 4 Fast</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;">♥ 1.5k</span></span> ElevenLabs has launched <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.S3-S-66rObX2TUuSZjz2bqPK6zm9Dm3gmRHBE7R0-OJSlCxHqNoMGVTM34LPAPGXoRzzaZad03zo3VB0f0Gl4PucxCfNXCmkH1ueROuYYWY8Y8vPx5iEE6uaZvFdukf868Pu-ZtqJGfu13U3_FsVrfp72wf3ZVK-NidmG5yyJXrUZ8DV8ClDoZdISxh0gcAqu-xcLg4otDlWKmFIYUdYaSwVNmru74Kvm_bJUDv46kCRIgdSDqRtWDCSuWohlSDzcVLIJBi8KtMTCuSt7-NpKw/4k5/Hv2traIxSCGa_dbdnIssgg/h4/h001._UBLIFnWyJXwvUoqP7F8jdazFyJQmSMJQ6cqraejGW4" target="_blank" rel="noopener noreferrer nofollow"><span>Studio 3.0</span></a>, which is its latest AI audio and video editor designed for creators. It integrates text-to-speech with over 10,000 voices, an AI music generator, sound effects, and voice cloning capabilities into a single timeline editor. It also allows creators to enhance video content, clean up noisy audio with a "Voice Isolator," and instantly correct speech by simply editing the text. You can <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.S3-S-66rObX2TUuSZjz2boen8IPIM7V_GEN-xp3iFjm-aiSH1WFn427ziv3e9tIGXg681RT8UoWGKIvRtYmItsQ7GgEUXZ8kMfhvsa_lPMuIZZRf6Fvg8rdcuOM4s7TLRYNjuyDqYiWg-uvefUYdOeFlHipH9klxxIdLceY6S0rM6KupqUblJdkRJ_2U3AdONt1wXJ4x98g93z8N0CBUMI-R2yYfAaufr5fKsat0aQHBd7H3oDm-5hT2naXtyBvL/4k5/Hv2traIxSCGa_dbdnIssgg/h5/h001.vCYZb0zYNoq46_PnKPchSmF4XGyS_J9JaxpCWY9LK3Y" target="_blank" rel="noopener noreferrer nofollow"><span>try Studio 3.0 for free</span></a> right now. </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/f520f1c9-b2fe-44e7-b8e8-7fcbe9883ef0/nlbq01hjb1-Studio_20video.jpeg?t=1758650788" 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.-a_uZGcEKk2OBxkkwsTf3iGWUZbr9d2FbxXT53lRumEyHj-0nEMJRxOWgOSEjcsosAEWIT-pgdsKazSSBjEwhr4_QKy7evsNKyMsJxS1OF9E2mLK5PyygI7FW_Be7jESRb_NtIlZOIbVbodrENAFtCxOKClmZc794Q5EkkEQqCPE9IfPWagNfOt0SHTHVX2phWD4Cb9pEtNiu67i0w-AqtqhseV9pfr48p9IUk-YpOQfs3j6vFkUCjt6TNdTySlF/4k5/Hv2traIxSCGa_dbdnIssgg/h6/h001.jYk3LIVnISR6Ca1cRq1a0AClQTDSgQ2zLGnh_En7OdI" target="_blank" rel="noopener noreferrer nofollow"><span>Luma AI has released Ray3</span></a>, a new AI video model designed for professional-grade storytelling and visual reasoning. It can generate videos in <b>16-bit High Dynamic Range</b> (HDR) color, which makes it suitable for studio production pipelines. Ray3 also introduces a "Draft Mode," allowing creators to iterate on ideas up to five times faster before mastering final shots into high-fidelity 4K footage. </p></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;">♥ 134</span></span> Good news for the merely rich: it turns out you don't need a billionaire's war chest to train a state-of-the-art AI model anymore. A new <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j23lDtJNGVB8PI0GW7NAnXAN9E0BF68GNGQjW_98ig9PLCRPCKqi8GsOYfRSo1l3kkreCjShnntXjl1scSAfgwpRFZGwhX0Q4xNjvk5P_V7okogQ08gzdxUPMkCiL2118GY05rp0OT6aWIUfhia2IuP24Xk6A0E-qcF3ZewFT8KHWNu42Qp0PNREUWc3k2HiEpIdqfWZFtOm-2Fw4HtR4nLlbGKkHK5pLaCB9SWvHkO8pw53RnOpttX4YH2XiztnLgJg7_xV3SvPIuKTP7qbeWu8/4k5/Hv2traIxSCGa_dbdnIssgg/h7/h001.aRX7UwGlIln9jHlK0XaoOgX00EYS7Eg4tWl1LYKAj9c" target="_blank" rel="noopener noreferrer nofollow"><span>report from DeepSeek</span></a> reveals they trained their massive 660B parameter model in just about <b>four days</b> using a <b>cluster of 64*8 H800 GPUs</b>. So if you have ~$250,000 to spare on computing, then you can also build foundational models from scratch. </p></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 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; 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[</i></span><i> Stanford University</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;"> ♥ 218 </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 Pre-training </span></span></p></td></tr><tr><td id="introduction-to-data-efficient-pret" 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 Data-Efficient Pre-training</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%;"> Computational power is growing exponentially, but high-quality web text is limited. Does this make it difficult to train better language models when data is scarce but computing is abundant? </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 introduces a set of practical techniques to overcome these limits. It shows that with careful tuning and clever scaling strategies, we can train models that are significantly more data-efficient without requiring more data. </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/0c81b0fb-fa04-4ef5-a220-a59be92b0036/image.png?t=1758649097" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="how-does-data-efficient-pretraining" 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 Does Data-Efficient Pre-training Work?</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 standard pre-training recipes use regularization techniques, like weight decay, that are too weak for data-constrained settings. The authors of this paper found that the optimal weight decay can be up to 30 times larger than commonly used values. </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%;"> By properly tuning regularization, learning rate, and epoch count, they enabled models to scale predictably: loss decreases smoothly as model size increases. This "regularized recipe" allows training models with parameter counts far beyond what was previously feasible without overfitting. </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/9225e2c8-bf54-474d-8b9c-7e2a855d2ba8/image.png?t=1758649136" 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>Evaluating standard recipe of epoching and parameter scaling for 200M tokens.</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%;"> However, scaling up a single model isn't the only option. The researchers also explored ensembling, i.e., training multiple smaller models independently and combining their predictions. Interestingly, ensembling achieves a lower loss asymptote than simply making a single model larger. </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 paper goes further by combining both approaches: scaling up the size of each ensemble member while also increasing the number of members. This "joint scaling recipe" leverages infinite compute in two dimensions 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/7ddc0e7e-fdc4-4b0c-9290-6e5843aa4407/image.png?t=1758649199" 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>Comparing scaling parameter count vs scaling ensemble member count.</p></td></tr></table></td></tr><tr><td id="evaluation-and-future-implications" 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 Implications</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 proposed methods achieve remarkable improvements in data efficiency. On a 200M token budget, the joint scaling recipe performs as well as a standard recipe would with <b>5.17 times more data</b>. These gains persist across larger token counts, and distillation allows recovering 83% of the ensemble benefit with an <b>8x smaller model</b>. Downstream benchmarks confirm that lower pre-training loss translates to better performance on tasks like PIQA, SciQ, and ARC Easy, with improvements up to 9% over baselines. </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/f6b24b80-a759-49c9-8ca3-5909c1101e93/image.png?t=1758649291" 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>Performance of pre-trained models on downstream tasks.</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%;"> In continued pre-training experiments, these techniques enabled a model trained on just 4B tokens to outperform a baseline trained on 73B tokens, which is a <b>17.5x data efficiency improvement</b>. </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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92ahyTJiNjtd7N3poCyE_kl76BCthfyZcYviuBrbmXEIApa40cclQob5RS9jBcA3Icqi04gIstHvf7rcrn-2iw6hQe__BnqFKWHMrjmHHpybhGhSL2FqgD3PD8qxNSfP5YwFi9QYhXbIJyE7LER9exnUBQnbS0BJ8PpwKPGNmA9Pffs48vEacFU0cwbohUAeJwclODhCxX0XMWfH9PQJk-R3/4k5/Hv2traIxSCGa_dbdnIssgg/h10/h001.EcaePaWvzHbnRoGJ3RpPJq3N_cxaDc1NgSEpCm2ga4I" 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="discovery-of-unstable-singularities" 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%;">Discovery of Unstable Singularities</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=""><i>Wang et al. [</i></span><i>New York University, Stanford University, Google DeepMind, Ecole Polytechnique Federale de Lausanne, Brown University</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;"> ♥ 4.2k </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;"> Fluid Dynamics </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td id="introduction-to-unstable-singularit" 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 Unstable Singularities in Fluids</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 question of whether fluids can develop infinitely sharp features from smooth initial conditions has puzzled mathematicians for centuries. This phenomenon, known as singularity formation, occurs when solutions to fundamental equations like the 3D Euler equations blow up in finite time. </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%;"> While some singularities are stable (they form reliably even with small changes to the initial setup), many important open problems, including the famous <b>Navier-Stokes Millennium Prize problem</b>, are thought to involve unstable singularities. These are much harder to find: they require infinitely precise initial conditions, and even a tiny perturbation can steer the solution away from blowing up. </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/7c511003-2b5a-432b-8869-fe928b2ca29d/image.png?t=1758649365" 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 this paper, researchers present the first systematic discovery of new families of unstable singularities. They use a computational approach that blends tailored machine learning with high-precision optimization. The study uncovers multiple unstable self-similar solutions for key fluid equations. </p></td></tr><tr><td id="how-to-find-unstable-singularities-" 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 to Find Unstable Singularities in Fluids</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%;"> To find these unstable singularities, the team reformulated the fluid equations using self-similar coordinates, which rescale space and time around the point of blow-up. This turns the dynamic problem of tracking a singularity into a static one: finding a smooth, stationary profile. </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%;"> After this, they modeled these profiles using physics-informed neural networks (PINNs), but with important enhancements. Instead of treating PINNs as general-purpose equation solvers, the researchers carefully embedded known mathematical properties (like symmetries, boundary conditions, and expected behavior near the origin) directly into the network architecture. This provided strong inductive biases that guided the search toward physically meaningful solutions. </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, it used a high-precision Gauss-Newton optimizer, which leverages second-order curvature information for more accurate and stable convergence. This was combined with a multi-stage training process, where a second network corrects errors left by the first; this approach achieved unprecedented accuracy. </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 some solutions, residuals reached levels near double-float machine precision, limited only by GPU hardware rounding errors. This precision is necessary because it meets the bar for stringent validation via computer-assisted proofs. </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/fb6a26cd-d193-4fbe-9837-e0c6e4446b3a/image.png?t=1758649409" 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>Self-similar singularities to IPM and Boussinesq.</p></td></tr></table></td></tr><tr><td id="evaluation-and-implications-for-flu" 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 for Fluid Dynamics</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 method successfully discovered unstable singularities for three canonical fluid systems: the Córdoba-Córdoba-Fontelos (CCF) model, the incompressible porous media (IPM) equation, and the Boussinesq equations. </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 each, the team identified not only stable blow-up profiles but also several unstable ones, with each higher-order unstable solution exhibiting more instability modes. In the case of the CCF equation, the discovery of a second unstable solution suggests that singularities can persist under stronger dissipation than previously thought. </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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92YYxWl5TzfYmkBek9m6sMlWTDEkD7Zg13j7YABavrwEO_tsQnCI3iaqNVjCEETF-NuOdSN-w6e_G62aqBr4rQGhh-7isLdrsGDyCRlVt7TMifuZDUcDoxKrjMNib8GhPlWu2otbp5y0Ib8_pan4lmu-AIjBW0Un5YmckguPFBwLBVSUh0X1S_kH9TwaGLSN2H1AAZbsd3LoRE3C0RjRrYHI/4k5/Hv2traIxSCGa_dbdnIssgg/h11/h001.scUe16JdB--nXUnpXwvk-bVodz5gHfBSHAJDQVU3y04" 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-token-a-unified-tokenizer-for-vis" 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%;">AToken: A Unified Tokenizer for Vision</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=""><i>Lu et al. [</i></span><i>Apple</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;"> ♥ 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;"> Tokenization </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 id="introduction-to-atoken-unified-visu" 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 ATOKEN: Unified Visual Tokenization</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%;"> Visual AI has long been stuck in a siloed approach: some models excel at reconstructing pixels but miss the semantics, while others grasp concepts but can’t reproduce visual details. Imagine if we could process images, videos, and 3D objects the same way we handle text, just with a single model that understands both the fine details and the broader meaning. </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/1fc0e754-a7f7-48f4-938b-649118129c10/image.png?t=1758649467" 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>Illustration of our method on different visual modalities.</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%;"> In addition, handling different data types such as 2D images, time-based videos, and spatial 3D assets is challenging, which is why there is no standard solution. ATOKEN breaks these barriers by introducing a shared 4D representation that <b>works across modalities</b> and tasks, all within one transformer-based framework. </p></td></tr><tr><td id="inner-workings-of-atoken" 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 ATOKEN</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%;"> ATOKEN’s biggest innovation is in its 4D latent space, where images, videos, and 3D data are mapped as sparse sets of feature-position pairs. Instead of treating each modality separately, the model uses space-time patch embeddings to convert inputs, such as video frames or multi-view 3D renders, into a consistent format. </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 pure transformer encoder then processes these patches, enhanced with 4D rotary position embeddings to handle variations in resolution and duration natively. This design allows the same architecture to work on a still image, a video clip, or a 3D shape without modification. </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/bbfcc32b-bf29-4d16-a939-3042f6bd3ee9/image.png?t=1758649530" 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 AToken Architecture</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 ensure stable training without adversarial networks, ATOKEN uses a combination of perceptual loss and Gram matrix loss. The Gram loss, in particular, focuses on capturing textures and styles by comparing feature covariances, which accounts for most of the reconstruction error. </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 model is trained progressively: it starts with images, adds video understanding and reconstruction, and then incorporates 3D data. At each stage, the model retains previous capabilities, and surprisingly, multimodal training even improves single-modality performance (image reconstruction gets better as video and 3D are added). </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/3a5c69ef-961f-40fc-a67c-eecd5e38b634/image.png?t=1758649577" 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>Progressive training curriculum of AToken.</p></td></tr></table></td></tr><tr><td id="evaluation-and-performance-of-atoke" 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 Performance of ATOKEN</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%;"> On ImageNet, ATOKEN achieves 0.21 rFID for reconstruction and <b>82.2% accuracy</b> in zero-shot classification, which outperforms earlier unified tokenizers like UniTok. In video, it scores 3.01 rFVD on TokenBench and <b>40.2% retrieval accuracy</b> on MSRVTT, competitive with specialized video models. For 3D reconstruction on Toys4K, it reaches 28.28 PSNR, close to the dedicated Trellis-SLAT model. The discrete token variant (ATOKEN-So/D) maintains this performance, enabling generative applications across all modalities. </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/f5fbc2a6-2066-440b-a48b-2a7cb6c4804b/image.png?t=1758649648" 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>Video understanding performance on multimodal LLMs.</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%;"> One limitation is that video-text retrieval, while reasonable, lags behind pure understanding models, suggesting room for better pooling strategies or more video-text data. </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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92aXQ0zfAUSxX0HMITGWGuUkQ7Jw3PSCUvZ_3xi0nCdX9offOrEDNJCeGu3H4nto6VTeyq6vJuvYD5qxuI0w3qm2n4WjHm63GL0Y6ipSrzC-LZVe3mjSCC7rQ2Nw6aeBgOG6ExIXdAAvOnzSmCNRKRGRVC9AMl_hXqdfwFdbWnAzTi9WzLLz4cahX8bvJlRESv5JhFR9xqMNKkGshnqD7E0O/4k5/Hv2traIxSCGa_dbdnIssgg/h12/h001.IBMJjNdOTwLoziJnbMo2SfbATiJRuUqnxQTQvlha8Wg" 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.DUiN96-Eq7pUHzwEhy5j25aF_udDsq8EAwNLhMGYMEDf2J49SeczvIGjRMdUrBKDvRJbuxt8PxhyREP_BXKIybPZRRaBagbASV3KCSiOKeCWOHyzhHslkQ-9kuALHuN_rrPrJGFtjMj7qoJLgFOB6-3wMIH4VfM8VqtzNcSf10MHqMe97YOPOqlAjt2uCP0zL5cnp5ncO2evCMfWqsYwChCzqOUNB-uL_q-8Xt3LNvtQu5t9zOpshYjwthi5wea6JC8OW0J0ucL2oyG_gzJUVg/4k5/Hv2traIxSCGa_dbdnIssgg/h13/h001.OiPWEBkfXwuppR8Vtb3iqpAXch8CEBBC7Uyxe9c4oMY" 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.DUiN96-Eq7pUHzwEhy5j25aF_udDsq8EAwNLhMGYMEDf2J49SeczvIGjRMdUrBKDvRJbuxt8PxhyREP_BXKIybPZRRaBagbASV3KCSiOKeCWOHyzhHslkQ-9kuALHuN_rrPrJGFtjMj7qoJLgFOB6-3wMIH4VfM8VqtzNcSf10MHqMe97YOPOqlAjt2uCP0zIlPIDBnjiJjqUKydDuX0wB6p9GF17BxFoWxhLO-4SHbYdW3-v58rsSRRsNVGOcRq6h09p2i-qxtWAxu9O2ri3w/4k5/Hv2traIxSCGa_dbdnIssgg/h14/h001.PB88sspK56msC2WbrRbdVPeyXnwjpsTuP-GhymDVFfo" style="text-decoration:none;"><img src="https://i.ytimg.com/vi/Nie5L7Z6-qk/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; 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