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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 Parallel Scaling Law for Language Models and faster matrix multiplications  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </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:28px 28px 0px 28px;"><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> May 21, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EzDqcfESyqLZ14uWqxqHllXV1IBzu98RPEiTIkilWG6yexIq7eayEs2YWqAb6WyJKW_kIJTDkC-IKnP18YqxvyxLVIFkkjKvVOR37-lyM-S1vn8EWAppViJSZ3hLgs4UT-As3tynUusS9mWnK8ewb_l3BQNjzNQggIxTdEnmZhj5ud1scaxtM57Zs3lHNfSa_kc2_20nwMRoatndwZF8UGgmchPEVp4qL3pC6yiSqoyYxgPxIktTudbsua5vloxp-X7LfC_Lmp0oXrW20dxsskG5DdNf6b-Jrg4p_nAcIyfkG3FI8BbfRyrYyVnGSrBrfwsJjUeNeqDwSAKK1UWM5uCg6s3WYF1et87-jFUn7BQsorj3zjRdERtLLd6ogJ1zSW84D0iFMUB3npEySOJFX3OyyQs5iCfIJlt9m32E4vxAZhl5bGt-u5akkUUuo_15zc-Ajrz_m9E4ctzvRXSF7uxGmrOCwnsCl_6Fd9eEb7fv5sLv1rdbKP1IFFDUTcp08USl4N6qc4I6gpOGJOI-N5VKAw8clzmghl98_Qgr9gZ8zH2pQabamzngsuN39NqfTly4nhA3OzNbbxyTKHnjG1DpwB_6pb_zMuLq7ZZBeZWzA/4go/t3BkbGR-SEqKLuX4HtUfpA/h0/h001.Lh21dN7j1uCWKPTEKg53ypFMeJLXG8SpJNdwUseECkU"><span class="translation_missing" title="translation missing: en.templates.posts.email.v3.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;"> AlphaEvolve: A coding agent for scientific and algorithmic discovery </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 Parallel Scaling Law for Language Models and faster matrix multiplications </p></td></tr></table></td></tr><tr><td style="height:0px;width:0px;"><div style="height:1px;" data-open-tracking="true"> <img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4go/t3BkbGR-SEqKLuX4HtUfpA/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>May 12th ~ May 19th</i><br><i>#56 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;">♥ 5.4k</span></span> You can now use <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DIqyAo9xTeoWriogq2VlWeUmi9WmFR4pnC4wMSHAHOGT3HrVVbsE9_VunaYh8wdHYFZkx786iewCSD4Bhtei6-EyiMckDNcnyaBFLjMaNKxlJAeAbHyhrrRf2BsaPtbjwhrjzwlTHe939uQkRnbLKw/4go/t3BkbGR-SEqKLuX4HtUfpA/h1/h001.aoEgYL4pwnqIw7VMcuaABgidog3K8TyL1d1ib6dVRaw" target="_blank" rel="noopener noreferrer nofollow"><span>GPT-4.1 in ChatGPT</span></a> if you are a Plus, Pro, or Team user. It's a faster model with 1 million context window that's good at coding and following instructions. All users will also get the new GPT-4.1 mini (replacing GPT-4o mini), and Enterprise/Edu users will get GPT-4.1 access in the coming weeks. OpenAI has also released a test version of <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVLMc57LSiFhZ_NnE878yIfc_ZOwVwo0RzyHD9MKHNhIDkGUxCQVV_i2Jm6f01M03IkGmE60N7rn-CdwMy4UkowdBvCUkLmmA2suTld0FyjerB7ucuHmHvHjuzj7tRZSzLA/4go/t3BkbGR-SEqKLuX4HtUfpA/h2/h001.argSz5wuhwM0wrVVUyafg95auUrFgvDsSkwkmjnMgQI" target="_blank" rel="noopener noreferrer nofollow"><span>Codex</span></a>, a new agentic tool that can help with many coding tasks at the same time, like finding issues or suggesting code changes. Pro, Enterprise, and Team users can start using Codex in ChatGPT today. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:480px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/62be154b-950b-4844-959d-6fbd4315a823/landing-e8737b0.jpg?t=1747804468" alt="" height="auto" width="480" 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;">♥ 1.5k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.sa7HIrCkEzyny8lstY55mAXsWWiqRLG5rGk9g8WJDhEOxPBWcAVRiOq2e3WpZOhPsAhI3azQqlY3vea9CH026qxJC9YVl51qBEtNCAv2KnAcrNOKhTIEOT_oTnJLBoQxkJx4OW9fFWzafSD5zA69ZnDQR8MzgSn_IH-WpGjKoeo/4go/t3BkbGR-SEqKLuX4HtUfpA/h3/h001.Ri_sSrW20G2gWiZ7jSdCSjJZOrYlJctxwoHlfBlnDdo" target="_blank" rel="noopener noreferrer nofollow"><span>Nous Research has launched Psyche</span></a>, a decentralized network that allows many different computers to work together to train large AI models. They are currently testing the network by training a 40b LLM called <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkPLwxhfjLByHqejJo-tgXyZz1_bnT7Vs4pGeWsUPsg7H0sHBs0ExiZ8NWt5Nd2Ut2X_ikNVQT8ZGuaJph9n5yVpCdSgwTghTPWRDt1nq6lzG-Nl7kiNzsRYHDp2R243f1Q/4go/t3BkbGR-SEqKLuX4HtUfpA/h4/h001.dmuObRZ4LMHm6oPQPb_EWKtEznYaAwUOWZ0FjC9YYy4" target="_blank" rel="noopener noreferrer nofollow"><span>consilience-40b</span></a>, the biggest of its kind to be trained over the internet so far. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6a6bfc43-511e-4366-b779-04df7bc48000/image.png?t=1747844527" alt="" height="auto" width="600" 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;">♥ 12k</span></span> During Google I/O, their annual developer conference, they had some really impressive announcements. We will highlight a few important ones below. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:480px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/7714cf42-ee43-4b95-a1f4-3a3a366246c3/Gram3zUbAAIJt7w.jpg?t=1747841218" alt="" height="auto" width="480" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:480px;"><p>all Google I/O announcements</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;">♥ 11k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.bB1n55dCqTOIgwzU2HCGlVMy_Tqq-s98GlOf7Kxb3enqvtQDJrnjn-wpVphqWL2sDoGlh5cpXrZDM-uFIBfpuApEuh7Ml7o0B_887hM0d4Nxj4i5BYBGYIdLZjsS_CYNpC9E23B-SX6fuBlJ8hGhnw/4go/t3BkbGR-SEqKLuX4HtUfpA/h5/h001.G3vW6kGKjaaNdTHsoC5ab4crg4ofXpZq68W1HE_kzeY" target="_blank" rel="noopener noreferrer nofollow"><span>Google has launched Jules</span></a>, a experimental coding agent that helps you fix bugs, add documentation, and build new features by connecting to your GitHub. It works on tasks in the background while you do other things, and you can start using it by visiting <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.bB1n55dCqTOIgwzU2HCGlagJmjJvcIsZ0u3BittPL-7HBKI5DQ1umN99L78ihWU8r5wlX9xs02FQUsZPmX2Frerq3QhZ6bBX9QxwYP4Dywe4WbC4pUHFvUaVBRg6nKy7IgjmdZyRpuwuyngI9eUW_g/4go/t3BkbGR-SEqKLuX4HtUfpA/h6/h001.ObXUSn2Z0TVX-W3TAx7ChgXJM7XB-fT18ZCoOW6Oboc" target="_blank" rel="noopener noreferrer nofollow"><span>jules.google.com</span></a>. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:480px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/a90585a4-6ec3-49de-97e7-7e3df9dfa917/image.png?t=1747804862" alt="" height="auto" width="480" 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;">♥ 13k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVEk4KSgcgKpezdth2Z1cAXgGKgc5G6HYm_S5R9LifiIuZmz0hSzv-TzYwVIoQV-V2aqg2AKqTZIfCHzxgUcLBEMCbZn8lbNFDy6x7YpETWhxmGNEjTUMD8C_Uo1hR4DUXvrhiH5aFkyZL8xVU7HgbMHCegWKDeDEi1yn54MV_n5XnUuBUe2Pu7zq0T4oeqdwBlA46mHEtnM7FCEcYINhX_u1d7E_vu76hhhXVSv9qshZ/4go/t3BkbGR-SEqKLuX4HtUfpA/h7/h001.i9dzthbm61oLrcq7IE9aCznjcGC2pZM9LnC4bGJe_oo" target="_blank" rel="noopener noreferrer nofollow"><span>Google has launched Veo 3</span></a>, a state-of-the-art video generation model that now includes built-in sound generation, allowing you to add dialogue, sound effects, and background noise. This updated version, which also features extremely realistic generations, is <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJVJebGQSxsAOZVK7PvOBHPVa8_uL3hfPic3tPZszdU57xTWLFwgn4hhwg6Cst69T5KZdwCyd6bmidnJ_jEU4nCyVnAbHlprfhpM9KmUyskwUeEtj8_4G_rl5gTvE-kPr2A/4go/t3BkbGR-SEqKLuX4HtUfpA/h8/h001.ybDyNzXCNPfAQPyem_ZGQOhOAAB_FrdLXf5Ro-kYNY4" target="_blank" rel="noopener noreferrer nofollow"><span>available now in the Gemini App</span></a> for Google AI Ultra subscribers in the U.S. <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJRyvSZBDiGJnl_AIHqY-J1JGWQGJGG4tKIphIiNQoBkrAXEY1jckfEFEV8nkkoDYt5cK7lYJUshqzI_ru5KWJpdbuJtafj-fcZQ1eb2wW7JZBW_32jCNDFvTkwkwWgBBkQ/4go/t3BkbGR-SEqKLuX4HtUfpA/h9/h001.nYrkEA2U_gzPFzVeGOYZFRDZ5QabW89bpuEaKyEad6A" target="_blank" rel="noopener noreferrer nofollow"><span>Check out Veo 3 demos</span></a>. </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;">♥ 3.8k</span></span> Google DeepMind has released <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpVBTPNlWC-j3uokAFpEXiu0No65Tpfoq3_VY-jkOMtxOuE8IV5snK90MfaDeoUj3bFnQ1EK2tKtWdBDPiGw6FEPGLCr9xx7S7NPRAkZQutl1mxRemHQb3_kk8MFdznoVug/4go/t3BkbGR-SEqKLuX4HtUfpA/h10/h001.LaKc770GrUhojBACbeuyT6PnlD6vHpE2_7wYa5lFWZs" target="_blank" rel="noopener noreferrer nofollow"><span>Gemini Diffusion</span></a>, a state-of-the-art text diffusion model. I tried it, and it can generate at a staggering 1000 token/second. You can sign up to its waitlist <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.QhNdRDI8q4ViyLFF_wOv2Lg88NvjCd-h9R-SBxHqAQaXRUwRbtxfAeI6k_tGX1PaUfqv9Bf_2mvTa8vhR0cmAsMhEqJ9myqkWBanHdC6MXw/4go/t3BkbGR-SEqKLuX4HtUfpA/h11/h001.hkNKEPqf1tL8uTnd5wZO14LCqlDKmV9hahn0Efhf-1c" target="_blank" rel="noopener noreferrer nofollow"><span>here</span></a>. </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><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="">we have just revamped </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Y7-_bQjp1fXYJa9abKZXRUgyOs1NPSA74uaogeIHbgopbVpZ_VynKBPt72DltmApNNJhGKptQzw6LEjgr_5wTg/4go/t3BkbGR-SEqKLuX4HtUfpA/h12/h001.WBrzNe5uEOgMxyZlfOUq4SXsMKTj9YGKWaxFzbhZDF4" target="_blank" rel="noopener noreferrer nofollow"><span>findmypapers.ai</span></a></span><span style=""> !</span></h2></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/9f784584-53f3-48dc-afcc-d94c93ccaf87/image.png?t=1747842897" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"><p><span style="">If you stare closely… Something is different</span></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%;"><span style="">Remember </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j29rweNtSHTMPdCBNsuu15hTaXQnuJ6et9mPIu5rp0DhN7QgdaRP1oBtJimyMNOx3hLOay5qWvLMbx3oTwPXpLtf6511DC_pnAxxgX4i529ugubAP0vgeile6043E6_QvIw/4go/t3BkbGR-SEqKLuX4HtUfpA/h13/h001.sY6PAiL5y8UriXDUloz2HryFw4HKdsEdbgCxi5fm2-E" target="_blank" rel="noopener noreferrer nofollow"><span>findmypapers.ai</span></a></span><span style="">, our project designed to revolutionize how you survey for AI research papers? Well, we've got some exciting news! Since our initial launch, we've been all ears, soaking up your amazing feedback, and have been hard at work making improvements to create an even better experience for you.</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="">This time, we have </span><span style=""><b>improved everything UI related, and made the UI a lot faster and responsive</b></span><span style="">, so your experience is a lot better. Check out the new UI below!</span></p></td></tr><tr><td class="dd" style="padding: 20px;"><table width="100%" cellpadding="0" cellspacing="0" role="none" style="max-width:520px;margin:0 auto;"><tr><td class="q" style="padding:16px 16px 6px 16px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoIUr1uTXAvJq8Slu0a4aZuSl1Ct-sKyKnN6bb8fafueln5PWSEgk-m8Zi67qD6GlJ9fvbEodCnsuxhlugUbR13p1EIUBMH8zwyICiCv_i0dUmd2_OGR5m5c7g2lQTQq36PBHRMXzTeZU-t1Kb7UaaaU/4go/t3BkbGR-SEqKLuX4HtUfpA/h14/h001.RrRzNb9MeAJcnrtdaC0wBW0jG-SUzNqDCHvIgtb5bbs" style="text-decoration:none !important;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td width="100%" style="padding: 0 0 14px 0;text-decoration:none;width:100%;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td width="36" style="width:36px;"><img src="https://pbs.twimg.com/profile_images/1906476449512472576/Gz5ViADI_normal.jpg" alt="tw profile: find my papers 🖊" style="display:block;width:36px;height:36px;border-radius:50%;border:0;"/></td><td width="400" style="padding:0 0 0 8px;text-decoration:none;"><span style="display:block;font-size:14px;color:#1c2022;font-weight:700;"> find my papers 🖊 </span><span style="display:block;color:#697882;font-size:14px;"> @findmypapersAI </span></td><td width="24" align="right" style="vertical-align:text-top;"><img width="24" height="24" loading="lazy" alt="tw" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/x_logo.png"/></td></tr></table></td></tr><tr></tr><tr><td style="word-break:break-word;"><p><span>findmypapers.ai</span> just got an upgrade 🎉</p><p>We have revamped the interface <br>The UI now should feel much faster and reactive</p><p>try it out now, we have also reset the credit for everyone!</p></td></tr><tr><td style="padding:12px 0 0 0;"></td></tr><tr><td align="center" style="padding:8px 0 0 0;width: 480px;"><img src="https://pbs.twimg.com/amplify_video_thumb/1924821671459893248/img/9ER0RdjCG-WJdOIr.jpg" width="480" height="auto" style="display:block;border:1px solid #E1E8ED;border-radius:5px;width:100%;max-width:480px;height:auto;"/></td></tr><tr><td height="8" style="line-height:1px;font-size:1px;height:8px;"> </td></tr><tr><td align="left" valign="top" class="s"><p>1:39 PM • May 20, 2025</p></td></tr><tr><td height="10" style="line-height: 1px; font-size: 1px; height: 10px;"> </td></tr><tr><td height="1" bgcolor="#e1e8ed" style="line-height:0px;font-size:0px;height:1px;"></td></tr><tr><td height="10" style="line-height:1px;font-size:1px;height:10px;"> </td></tr><tr><td align="left" valign="top" class="s"><p><b style="color:#1C2022">4</b> Likes <b style="color:#1C2022">0</b> Retweets </p></td></tr><tr><td align="left" valign="top" class="s"><div align="center" style="text-align:center;margin-top:4px;margin-bottom:4px;padding:8px;border:1px solid #ccd6dd;border-radius:9999px;color:#1B95E0"><b>0 Replies</b></div></td></tr></table></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%;"><span style="">For a bit of context, </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Wpe7ldTezUu5UQjr3YxkjDr64dknbQx1-38CKAMc8wQVG5Tk8oQAyiI7e9mg3sBDvoe5v43TFe36B13zOTMDEQ/4go/t3BkbGR-SEqKLuX4HtUfpA/h15/h001.9fgJ2_nxSU2mHBjttWSTEWr_S96mQBK1ybbkHdRAJLU" target="_blank" rel="noopener noreferrer nofollow"><span>findmypapers.ai</span></a></span><span style=""> is a semantic search engine</span><span style=""><b> </b></span><span style="">for 340k+ AI research papers, </span><span style=""><b>outcompeting Deep Research apps</b></span><span style=""> like Grok, OpenAI, Perplexity, and Gemini at finding relevant AI research papers. </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="">To celebrate how far we've come with your help, you can still grab </span><span style=""><b>50% off </b></span><span style="">with code </span><span style=""><b>BETAN50</b></span><span style=""> if you want to unlock more usage (only a handful of redeems left!). Head over to </span><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Y7-_bQjp1fXYJa9abKZXRU8O0Ee_APbcpBoULhq-8D4x_nvdwok3H4TEu427lMGNq4BxOHL4PmfgkClRhlySFY/4go/t3BkbGR-SEqKLuX4HtUfpA/h16/h001.VEbEzoJ6hKTe7lECTVS2HHOQbC-J-OfRzfhUOV6aFSc" target="_blank" rel="noopener noreferrer nofollow"><span>findmypapers.ai</span></a></span><span style="">, give it a spin, and keep the amazing feedback coming on our X account or Discord!</span></p></td></tr><tr><td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j29rweNtSHTMPdCBNsuu15hSzQcOxkOpREY7CmsUqazs5MMa1uyTWQcHA44VJKvv5LOrj73ya7S6uuAAMt4LiYwQ-PzqQ4QspN0c-91wcDG-u/4go/t3BkbGR-SEqKLuX4HtUfpA/h17/h001.06GkP_khsmCDLmDO7-owgysYtADwS20QdVNdDeBA0_8" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"> Check Out findmypapers.ai </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%;"><span style=""><a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoGymQ3NNPtd5dE5MV_8UgjIDFPVXngz8pvQBldSW42yhUe_Qiq6DgEPMEBuPL9yfRpXelTiuu2kS8pLFvsoem_XoZoy_n13sTKUhZIbl0VH6/4go/t3BkbGR-SEqKLuX4HtUfpA/h18/h001.9eqw7ocOmVQZJP5vt3wP1DK6eCVHjNbuS1KWhxvNxIE" target="_blank" rel="noopener noreferrer nofollow"><span>Advertise with us! </span></a></span></p></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="alpha-evolve-a-coding-agent-for-sci" 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%;">AlphaEvolve: A coding agent for scientific and algorithmic discovery</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>Novikov et al. [Google DeepMind]</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;"> ♥ 7k </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 Ensemble </span></span></p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/f73d33ea-46be-4be1-a4ea-5e719910e078/image.png?t=1747803026" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="introduction-to-alpha-evolve" 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="color:rgb(67, 67, 67);">Introduction to AlphaEvolve</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%;"> This week Google launched AlphaEvolve, a new evolutionary coding agent that combines LLMs with evolutionary computation to tackle problems ranging from matrix multiplication optimizations to open mathematical conjectures. </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%;"> Until now, LLMs have shown promise in accelerating scientific discovery, but their ability to generate <span style=""><i>entirely novel, high-impact algorithms</i></span> has been limited. This is mainly because they rely on static training data and can not refine their solutions. AlphaEvolve fixes this by creating a dynamic feedback loop where code is written, tested, criticized, and improved autonomously. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/eb9d670c-0f2e-44ee-b936-4f0046466ac2/image.png?t=1747802975" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="inner-workings-of-alpha-evolve" 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="color:rgb(67, 67, 67);">Inner Workings of AlphaEvolve</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%;"> The AlphaEvolve architecture uses a distributed evolutionary pipeline. It has a number of code variants, each of which represent a potential solution to a user-defined task (e.g., “optimize this matrix multiplication kernel”). The system begins with an initial codebase which is annotated by developers. The developers mark specific components for evolution. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/0cc95a40-562b-4400-8647-183ee42796c0/image.png?t=1747803145" alt="" height="auto" width="600" 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%;"> After this, a controller orchestrates LLMs (like Gemini 2.0 Flash and Pro) to propose code modifications in the form of <span style=""><i>diffs</i></span>. If you have worked with git, then you would know that a diff is a targeted edit that replaces existing code blocks with new versions. These diffs are generated using rich contextual prompts that include past successful solutions, evaluation metrics, and problem-specific instructions. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/dc6c042a-4db8-42eb-a63e-670f52ee1883/image.png?t=1747803079" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"><p>Example diff output generated by the LLM</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 evolutionary process relies on two key mechanisms. First, an automated evaluation function scores each proposed variant on task-specific metrics (e.g., computational speed, mathematical correctness). Second, a program database acts as a collective memory, storing high-performing solutions and feeding them back into LLM prompts to inspire future generations. </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 creates a survival-of-the-fittest dynamic: code that improves performance persists, while less effective variants are pruned. However, unlike traditional genetic programming, the “mutation” operator here isn’t random. The mutations are guided by LLMs capable of reasoning about algorithmic improvements. </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%;"> One of the strongest suits of AlphaEvolve is its ability to handle <span style=""><i>multi-component optimization</i></span>. For example, when improving a matrix multiplication algorithm, it might simultaneously evolve the numerical initialization scheme, gradient update rules, and noise injection strategies. The system supports cascading evaluations, where promising candidates undergo increasingly rigorous testing. This balances exploration (trying radically new ideas) with exploitation (refining known good solutions), allowing AlphaEvolve to escape local optima that trap conventional approaches. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/77ddd3c1-f94f-4c34-8396-8063d8423fbe/image.png?t=1747803195" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"><p>Examples of SOTA-breaking mathematical constructions</p></td></tr></table></td></tr><tr><td id="benchmark-results-of-alpha-evolve" 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="color:rgb(67, 67, 67);">Benchmark results of AlphaEvolve</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%;"> AlphaEvolve’s has made several computational breakthroughs. In matrix multiplication, which is arguably the most important mathematical operation for linear algebra (and AI), it discovered a 48-scalar-multiplication algorithm for 4×4 complex matrices, <span style="font-weight:700;"><b>breaking a 56-year record</b></span> held by Strassen’s algorithm. This single improvement could ripple through countless applications, from machine learning training to scientific simulations. In addition to solving math problems, the system <span style="font-weight:700;"><b>optimized critical infrastructure at Google</b></span>, including data center scheduling algorithms and TPU arithmetic circuits. </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%;"> AlphaEvolve solved or improved upon 20% of a curated set of 50+ open math problems. This includes better constructions for Erdős’ Minimum Overlap Problem and 11-dimensional kissing numbers. These successes highlight its versatility: the same architecture that tunes low-level code also navigates abstract combinatorial spaces. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/e321d5b6-9102-4f56-aa60-7e9a4d509731/image.png?t=1747803292" alt="" height="auto" width="600" 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%;"> However, AlphaEvolve isn’t without constraints. Its effectiveness depends on having automated evaluation metrics, which limits its applicability to domains requiring manual experimentation. Additionally, while sample-efficient compared to brute-force search, the system still requires substantial compute for complex tasks (hours of parallel accelerator time). </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpSbxj5I3gsY0nXQWe4O2XZcrGyADrD3PO45JHsKYLT1FXsZnvEnxfk0ezXbhC6JV28I3yaQtdL8_XwQR7oR7CU2yvu6Wc4I0s31FJV3_A2MeD_ZRcynKsT0D7mRrt0BGz5Uot6UdnfC18Vn28L-g_eURvqTRamv3qWhNJWOPxdx2hpotTqfZicFCAUif5_57V4dSgdh69yppT47RlKWz3LWTGkPP9amKCrV0_FXMTaYy/4go/t3BkbGR-SEqKLuX4HtUfpA/h19/h001.qOdJiOIGwnxve3bCiAs_D4yWDkw2mTXHSIwE9YGS9yk" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"> Read Full Paper </a></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 class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> We've just published the <span style=""><i>very first post</i></span> in our brand new "Premium Insights" series! We're diving deep into <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExcsBUgAwWgXosV3JvU2U4ccfv2_iZAhfyaY-LUNmY0abroQzjZ02uUXtV3AGexpmKafSCLddktutztevWq0nTorSNeKd-9NiHH16n22a8guQ_1LA3kNxD_9fkfBIgYXgmTRolJbeDQVuGsptLajxbnFsQwVS-yyAHhhOMnovo8xE8QQxrEDXLHJIcmvi7oxgigfPIVjn4K06sav_Nh2kWhYHWSd6YK9IVglWzrRIyX3PQ-QVQiMzVYiHGK_2KxPjZbSL8Mp27sfLMx0xBBpz3fOlR65OAJZNBbIewZX8p0LSBj8hT1uT5D-4-Jhp8UyWuIDrsAF__VvQfKfDnkOkQDoAXNBvdelwoPCh_HeuIp9smdScOLxRDxFbzlRn5hKWwXReYqllW2itb6cI9GpADQcugbp-J9oHqTza8Aw-Pq-nN1kQlkMzko1BSTBIyoZAYFsGcnLeAhE76NjZEGqXM_YH5USKUKujzoa3HHKiyOV_uJi3EmS__wlsBFz5xrlqbXQQFxdKn-chBh47-zk9XDAQeJ29c8035vZiQcqNctMSBueKZa9s8ApUn6XlZmJR-FazDQsZqDWYcrxaB56-sDkxXzZlJqlCCGFTkf7rbvP8rC5PMkSsgHJM1yRUYyxqWxqTRu46LInAFxtRWSkifF/4go/t3BkbGR-SEqKLuX4HtUfpA/h20/h001.VakQvcsO0S1qitkeD2s8YOe7gDlJjTqCl4FI05c4Mgo" target="_blank" rel="noopener noreferrer nofollow"><span>how DeepSeek absolutely CRUSHED formal math</span></a>, creating the current best math prover. In this in-depth blog post, we will explain how DeepSeek got a <b>+500% improvement</b> over the previous best, it's insane! </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExcsBUgAwWgXosV3JvU2U4ccfv2_iZAhfyaY-LUNmY0abroQzjZ02uUXtV3AGexpmIxfUsUcsRGviET-JJwDr9Td9nUhCNJohdEp3v_i95-msYaKXiz1cyXq-EmpqHdTpTA7LRPiZZvCPrb0wQGdeLSTaMop_MngGnSnW80cRNRz7l9q7pn5aID0bzsDgpTTSv6GpCJ8gCGktGfXRylaI-L_yRjASPbbIMDktOA_KFCNVLRxmgiwdB2RN5PEf1lReNC1sEiyWW_BmXzOoMZMfgamBpzn96b7sAWvWEmy313ybHoFEGu0CJZ8Yv0aGbwsM4hiqN9jhHeDJIvrES__h0GUlgVETu0L_kBB3o5hnEJeJGwAPsab5AbMhN-jCRZwJds1Q_zKwxrSq9Y2qvb5cL5WdONadhlAaiRVK1717fGGcxzyZWhVhaK8wpVwFx3ySXaQaxE1LFFmE6HyCWROkUzdC9jhgYbIqgjkvGC863QjhHtWXn7w0v9x9W9agCPydoToJvOgMaTrPFxN63nDCeLQOO2Cjs1MKqKYeJwbOpMJPrhgQla9WpMMxHch7XZ48iI-EqWWO9Evnp0EXEZd7C5OFhgBSzlI-0zpqWe0Av-iiI5ykjMQZ1UGtd5AiOOoy77D-ybAdnNagdW2jliKINl/4go/t3BkbGR-SEqKLuX4HtUfpA/h21/h001.9lyoGj1KeoV96g46MHqQ6ri-yZgLQetDYymKN-fs0X4" 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/e9264270-6be3-4c50-ab49-5ca7314b6018/image__3_.png?t=1747627013" alt="" height="auto" width="600" style="display:block;width:100%;" 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%;"> We'd love for you to <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExcsBUgAwWgXosV3JvU2U4ccfv2_iZAhfyaY-LUNmY0abroQzjZ02uUXtV3AGexpmIxoic1SNJ0TphXA-UCzI4Qjq1nUdJesxSJRA7XMv21sNNDBE_bb-ZMaXPWaWgHlfECCTFAUjeNHeyRtqUH7Z6xOHbDi0aVX6j22jR5T8pYlDdQ0j-JdVKtx6AqQIt0IkPFTbrzSduLqe7GsmW40HVPZFdBXjsy9xdBD-wAGK_ixB6zzoRav5qMms4Vj7rSIRjc96js5Roo5YTNfAs1DlrmkmZoDh8Eh0RL6p1V07rFrqptmyhMWeuZ9JK7isDPyeGgVMznRJ539uEKcrFVGTmG7l8uDX2WZKwjydkysyxk4Py__T6BpTJ_IEq6VPOEwVcofxumbXUN05rBYRFwKQB8SccghXmn1RGR2lcbCDsmdEO7Z4foaAyPBbOlkiKoCb8KABTCACQA6y81t9dyEO0isnwTeMPzP8vfQ8ZyeBO-IAIrnQqDdOAAqxMPvyfFz6nFlHB_cx7-oJc29aUqcj0aaFEak6pGB79kmLTrEBT4kXaODEFUjeC_FV1j1jfbQemAGJ5O9MouIi0PYA1VEGixmCMIN2__nAfaKRvTJgm1F1XeCIqIxE2dJcnLLmglRCDJgbKHHp9qeJQD7KGHTdjx/4go/t3BkbGR-SEqKLuX4HtUfpA/h22/h001.7kg5sbFW8ooRh6M1RXA-CqkfAsp77f33K-lWq0IZDb4" target="_blank" rel="noopener noreferrer nofollow"><span>check it out</span></a> and then let us know what you think. We're looking forward to hearing your feedback on this first installment! </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="parallel-scaling-law-for-language-m" 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%;">Parallel Scaling Law for Language Models </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>Chen et al. [Zhejiang University, Qwen Team, Alibaba Group]</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;"> ♥ 642 </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 Scaling </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </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="how-to-scale-language-models" 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="color:rgb(67, 67, 67);">How to Scale Language Models </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%;"> LLMs are getting bigger and bigger and deploying them is becoming a logistical nightmare. On top of that, a lot of the traditional scaling methods are just not accurate enough, especially when it comes to the latest advanced LLM methods. </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 from Alibaba’s Qwen Team proposes a new perspective: <span style=""><i>parallel scaling</i></span>. This architecture rethinks how we use the compute which doesn’t require bigger and more powerful hardware to run bigger models. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/65e86702-c97d-4adb-b3f1-4e0655622304/image.png?t=1747803442" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="the-mechanics-of-parallel-scaling-i" 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="color:rgb(67, 67, 67);">The Mechanics of Parallel Scaling in LLMs</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%;"> The idea behind parallel scaling (PARSCALE) is straightforward. Instead of stacking layers or widening networks, the method runs multiple parallel "streams" of computation through the same model and each stream processes a slightly modified version of the input. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> For example, a stream could differ by adding unique learnable prefixes; after processing the streams the results are dynamically aggregated. You can think of it as brainstorming with multiple copies of the same model, each offering a different perspective before combining the best ideas. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/77cb918a-5a82-42ac-b117-249bb8f9d8a9/image.png?t=1747803466" alt="" height="auto" width="600" 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%;"> For a given input, the model appends <span style="font-weight:700;"><i><b>P</b></i></span><span style="font-weight:700;"><b> distinct prefixes</b></span> (e.g., <span style=""><i>P=8</i></span>), processes them in parallel, and merges the outputs using a lightweight neural network that learns optimal weights for combining predictions. This approach recycles the model’s core parameters and adds only a tiny fraction of new parameters (0.2% per stream). Even minor variations in input prefixes lead to divergent predictions, and aggregating them amplifies the model’s reasoning power. </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 researchers draw inspiration from classifier-free guidance in diffusion models, where perturbing inputs during inference improves output quality. But PARSCALE takes this further by making both input perturbations and aggregation <span style=""><i>learnable</i></span> during training. This turns a heuristic trick into a systematic scaling strategy. </p></td></tr><tr><td id="performance-and-efficiency-of-parsc" 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="color:rgb(67, 67, 67);">Performance and Efficiency of PARSCALE Architecture </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%;"> The Qwen team tested the effectiveness of PARSCALE through extensive experiments. The biggest finding is that <span style="font-weight:700;"><b>scaling parallel computation (P streams) matches the benefits of scaling parameters by a factor of </b></span><span style="font-weight:700;"><i><b>O(log P)</b></i></span>. For instance, an 8-stream PARSCALE model behaves like a model with <span style="font-weight:700;"><b>22× fewer parameters but similar performance</b></span>. This holds across tasks but shines brightest in reasoning-heavy domains like math and coding. On GSM8K, scaling to <span style=""><i>P=8</i></span> boosted accuracy by 34% using the same training data. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/26f6374d-1959-49b3-9426-525458a86fcf/image.png?t=1747803535" alt="" height="auto" width="600" 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%;"> Efficiency metrics are equally compelling. Compared to parameter scaling, <span style="font-weight:700;"><b>PARSCALE reduces memory overhead by 22×</b></span> and latency by 6× for batch size 1. Even at larger batch sizes, the computational overhead remains manageable, thanks to optimized GPU utilization. The method also supports dynamic adjustment: models can switch <span style=""><i>P</i></span> during deployment, scaling capabilities up or down without retraining. </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%;"> However, there are still a few limitations with the PARSCALE Architecture. First of all, training multi-stream models still demands careful resource allocation, and the interplay between data diversity and parallel scaling needs deeper exploration. </p></td></tr><tr><td align="center" valign="top" style="padding:14px 32px 14px 32px;" class="dd"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJarbTL27sB4efNdgx1w2ZOJ1p7UkVNfarwokz0M-KES94HpZg7RuGJ_XK9UAEvg0n5dYkVkoIyoZB3wpPJqq92fgEGCbJIXlYeDB78za9pUW/4go/t3BkbGR-SEqKLuX4HtUfpA/h23/h001.fWFEnY98rfi0A0E3IF-MhtBd7Z6IMkMLIr3u_EHJc9k" style="text-decoration:none;" target="_blank"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center" style="margin-bottom:12px;margin-top:12px;padding-left:12px;padding-right:12px;"><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;"><table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"><tr><!--[if mso]><td width="0"><table cellpadding="0" cellspacing="0" border="0" role="presentation" style="display:none;"><tr><![endif]--><td class="embed-img" align="center" valign="top" style="width:100%;min-height:100px;vertical-align:middle;padding:0px 0px 12px 0px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJarbTL27sB4efNdgx1w2ZOJ1p7UkVNfarwokz0M-KES9Ad4iUKlcP-yM6Oqvb8QhY3yFj27JImcn4o-SfZgIUGK1OJBFiiFkSX9j5y6f9gNv/4go/t3BkbGR-SEqKLuX4HtUfpA/h24/h001.0I1rINnpQMT9FgQzmipeWyTTH_VAVjdCKFTwd8rTVxE" style="text-decoration:none;" target="_blank"><img src="https://opengraph.githubassets.com/18ee2acc108a121ce58e8840d777622a9c25fe6f43e42a3e373285f33acab598/QwenLM/ParScale" width="100%" style="display:block;"/></a></td><!--[if mso]></tr></table></td><![endif]--></tr><tr><td align="center" valign="top" class="cc"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="left" valign="top" class="l"><p>GitHub - QwenLM/ParScale: Parallel Scaling Law for Language Model — Beyond Parameter and Inference Time Scaling</p></td></tr><tr><td align="left" valign="top" class="m"><p>Parallel Scaling Law for Language Model — Beyond Parameter and Inference Time Scaling - QwenLM/ParScale</p></td></tr><tr><td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"><p style="word-break:break-word;">github.com/QwenLM/ParScale</p></td></tr></table></td></tr></table></td></tr></table></a></td></tr><tr><td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92ahp-uEQo1k3mhjqe2CZ6Oi38l04Z9iq_6987graiT9Yi-IEb--v9lAbrusJQ1x__nM61XcM47VbacstBMh9Mo8aQvZsKopQ0L_D56s8yGQ6A/4go/t3BkbGR-SEqKLuX4HtUfpA/h25/h001.Q3e-j6gT3mz8gRFtBA6syIWnARh1Tqjqxejm5qzx9gE" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"> Read Full Paper </a></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="x-xt-can-be-faster" 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%;">XX<sup>t</sup> Can Be Faster </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>Rybin et al. [The Chinese University of Hong Kong, Shenzhen Research Institute of Big Data]</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;"> ♥ 4.3k </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;"> Matrix Computation </span></span></p></td></tr><tr><td id="accelerating-matrix-computations-wi" 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="color:rgb(67, 67, 67);">Accelerating Matrix Computations with Machine Learning-Guided Algorithms</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%;"> Matrix multiplication is one of the most important mathematical operations required for processing data. Matrix multiplication is used for everything from neural networks to scientific simulations. Even if you just display a picture on your computer, the processor performs hundreds of thousands of matrix multiplications just to display that picture. Since it is so widely used, it would be great if we can optimize this operation. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/2eb466bf-af73-4d31-bc4e-c6973ca5c419/image.png?t=1747803582" alt="" height="auto" width="600" 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 introduces a new approach that rethinks how we compute a fundamental operation: the product of a matrix with its own transpose, <span style=""><i>XXᵗ</i></span>. These operations are ubiquitous in machine learning (e.g., covariance estimation, optimizer steps like Shampoo) but are often treated as generic matrix multiplications, ignoring their inherent symmetry. </p></td></tr><tr><td id="how-rxtx-multiplies-matrices" 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="color:rgb(67, 67, 67);">How RXTX Multiplies Matrices</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%;"> The RXTX approach uses the recursive block matrix multiplication. It partitions the input matrix into 4×4 blocks and processes them using a combination of recursive calls and carefully orchestrated intermediate products. Unlike previous approaches that adapt general-purpose algorithms like Strassen’s, RXTX introduces a novel decomposition strategy. By reorganizing arithmetic operations and reusing intermediate terms, it sidesteps redundant calculations inherent in symmetric products. </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 RXTX algorithm’s design is derived using a hybrid search methodology. A reinforcement learning agent proposed candidate computation steps, while combinatorial optimization techniques pruned these candidates to find minimal-operation configurations. This two-stage process effectively navigated the vast space of potential algorithms, balancing exploration (via RL) with rigorous validation (via MILP). </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;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:600px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/02cc7bde-6ed7-4517-8b44-d474645b90e9/image.png?t=1747803677" alt="" height="auto" width="600" 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 process was further improved by exploiting shared sub-expressions. For example, terms like <span style=""><i>X₆ + X₇</i></span> appear in multiple steps of the computation. By identifying and reusing these overlaps, RXTX reduces the number of additions required, further trimming operational overhead. The result is a recursive blueprint that scales efficiently: for an <span style=""><i>n×n</i></span> matrix, the algorithm requires 26/41 the multiplications of Strassen’s method asymptotically, with gains visible even at small <span style=""><i>n</i></span>. </p></td></tr><tr><td id="benchmarks-results-of-rxtx-matrix-m" 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="color:rgb(67, 67, 67);">Benchmarks results of RXTX Matrix Multiplication</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%;"> The researchers tested the RXTX algorithm against existing methods and found that RXTX <span style="font-weight:700;"><b>consistently outperforms</b></span> recursive Strassen-based approaches. For large matrices, it achieves a <span style="font-weight:700;"><b>5% reduction in both multiplications and total operations</b></span> (additions + multiplications). For smaller cases like <span style=""><i>n=4</i></span>, the improvement jumps to 10%. These gains can be pretty useful for building LLMs as several model structures use the <span style=""><i>XXᵗ</i></span> computations thousands of times during training. </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%;"> However, the algorithm’s recursive nature introduces implementation complexity. Managing block subdivisions and intermediate terms requires careful engineering, and the benefits depend on using optimized low-level kernels for base-case computations. Additionally, while RXTX is great at solving the <span style=""><i>XXᵗ </i></span>operation, using the same method for other similar operations like <span style=""><i>XᵗX</i></span> is still not possible. </p></td></tr><tr><td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92Y_tjiGNAc7SBvRn7m4FA1orHWgmad2dH1p18WZdEiJ8_C34bPFBApjYEyAd03Pyw97v1wrfowm5byZElUllPd7RuZtTVgM-YDD4YoVoC-wWw/4go/t3BkbGR-SEqKLuX4HtUfpA/h26/h001.ppIfII1c6Q-XIuKULS_HxDqwNwTeWbXLVGvIdVJPVak" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"> Read Full Paper </a></td></tr></table></td></tr><tr><td class="dd" style="padding: 20px;"><table width="100%" cellpadding="0" cellspacing="0" role="none" style="max-width:520px;margin:0 auto;"><tr><td class="q" style="padding:16px 16px 6px 16px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoDDFT6eh5Nsg0xYVQj-h6I3o9m2k79_qw4izMYhmcI36wZoMc-wcSPSofD5453laMnEsM8hYwwhOtAEJqflT3jl39n2Vz2kAFVRN_sIDhK3DdzX0-h9k2fUkSVI08fWaafwhnveqjXH0cGARt2r_1Aw/4go/t3BkbGR-SEqKLuX4HtUfpA/h27/h001.EPGxA9orA2HWe2kzHm-2vn98XF22rcxS2nodMhOT354" style="text-decoration:none !important;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td width="100%" style="padding: 0 0 14px 0;text-decoration:none;width:100%;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td width="36" style="width:36px;"><img src="https://pbs.twimg.com/profile_images/1698572487909400576/BvncwnrP_normal.jpg" alt="tw profile: The AI Timeline" style="display:block;width:36px;height:36px;border-radius:50%;border:0;"/></td><td width="400" style="padding:0 0 0 8px;text-decoration:none;"><span style="display:block;font-size:14px;color:#1c2022;font-weight:700;"> The AI Timeline </span><span style="display:block;color:#697882;font-size:14px;"> @TheAITimeline </span></td><td width="24" align="right" style="vertical-align:text-top;"><img width="24" height="24" loading="lazy" alt="tw" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/x_logo.png"/></td></tr></table></td></tr><tr></tr><tr><td style="word-break:break-word;"><p>🚨This week's top AI/ML research papers:</p><p>- AlphaEvolve <br>- Qwen3 Technical Report <br>- Insights into DeepSeek-V3 <br>- Seed1.5-VL Technical Report <br>- BLIP3-o <br>- Parallel Scaling Law for LMs <br>- HealthBench <br>- Learning Dynamics in Continual Pre-Training for LLMs <br>- Learning to Think <br>- Beyond</p></td></tr><tr><td style="padding:12px 0 0 0;"></td></tr><tr><td align="center" style="padding:8px 0 0 0;width:480px;"><img src="https://pbs.twimg.com/media/GrQ94b2WEAATR-f.jpg" width="480" height="auto" style="display:block;border:1px solid #E1E8ED;border-radius:5px;width:100%;max-width:480px;height:auto;"/></td></tr><tr><td height="8" style="line-height:1px;font-size:1px;height:8px;"> </td></tr><tr><td align="left" valign="top" class="s"><p>10:34 PM • May 18, 2025</p></td></tr><tr><td height="10" style="line-height: 1px; 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