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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 on Self-Play SWE-RL, Step DeepResearch, and Attention Is Not What You Need  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </div><table role="none" width="100%" border="0" cellspacing="0" align="center" cellpadding="0" class="gg"><tr><td align="center" valign="top"><table role="none" width="670" border="0" cellspacing="0" cellpadding="0" class="aa" style="width:670px;table-layout:fixed;"><tr><td class="bodyWrapper" align="center" valign="top" style="padding:7px 7px 7px 7px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" style="border-width:0px 0px 0px 0px;border-style: solid; border-color: #2a2a2a;border-radius:10px 10px 0px 0px;background-color:#ffffff;" class="c"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr id="header"><td style="padding:15px 15px 0px 15px;"><div style="padding-top:0px;padding-right:0px;padding-bottom:20px;padding-left:0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td class="f" align="right" valign="top"><p> December 30, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3Ez6a0PXrhfPu9_gbDwKqNRB1xks_0wgNaT8s7AsOo-2-ywuW6bG_0WNevc2-x5cGecVvGGDx8E6vD6DdFYPrbdkxWNBrIeJAf84N8MHk4zy4Dzq7S65wQZFH2SbONeHzNO14uSAfQIx3gsPX0RQgTntYtSdaK5UVm0RAZhhjCOBXCB6cD1vP_8hNdO5eWtgOtiq5O6KR6DQn_aJAAra-QMt1-R7T3L78-tsaIu9MlaRoQBzilbUSr5o2JXhgf4TKaxoldzonHLSERyRHx7s-gY0arly8JE89dbgAfSqykwiY7d1YrKMV_m6UGGzSgSGSjtliiPRE8ON9nQleIRdDxURFwHVJy1PS30oe7eigbD6zAUAvUrpknIy58LFFyUtA4zpZPOxq3DDXRD9L3axUKAdUIXuEXElyzOCJSztjBy6z2ud1KGmphwHbKjP-aMuyKhrklNQbt28F7M32D3Bxk0uu0TTXNR_dNei4vWrmmqlBL9LpdvtDG1L-Hws_DJXMGMwzzbCtHHaD8k3CoX2r9Jop40uCjR3vLqRDKZXdkQZKROUMIkWGNADyHHG6KQGBV1kyCvz1N0BfAd9jBjvnpSXoeWe7nh3oemAFBmofzMrgt0RcejsQ2Abz4EudGZG9G1ZqXU3psE56o4Y914XjliNDy2tNua80w53MoqcCBzIkfGwPqNIEyI-b8itBRW-0DA5qfgqnk71uZC8MFh41HiuDIOmSL5aoeV5cmOjPECw9hJdGhnz21YBbp98DxsSIxdPQsM0f5ixF-qGilsWVn9x/4mv/aQxUU3d7T_2XlKyRW2rVCA/h0/h001.y5_AgfL0RiGsoNdCsPiW4zTRKxxjux-ENQeHpq5zx0w"><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;"> RoPE Is Inherently Flawed </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 on Self-Play SWE-RL, Step DeepResearch, and Attention Is Not What You Need </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/4mv/aQxUU3d7T_2XlKyRW2rVCA/ho.gif" alt="" width="1" height="1" border="0" style="height:1px !important;width:1px !important;border-width:0 !important;margin-top:0 !important;margin-bottom:0 !important;margin-right:0 !important;margin-left:0 !important;padding-top:0 !important;padding-bottom:0 !important;padding-right:0 !important;padding-left:0 !important;"/> </div></td></tr></table></div></td></tr><tr id="content-blocks"><td class="email-card-body" align="center" valign="top" style="padding-bottom:15px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td id="nov-18-th-nov-24-th-33-latest-ai-re" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h6 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:87.5%;"><i>Dec 23rd ~ Dec 30th</i><br><i>#88 Latest AI Research Explained Simply</i></h6></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="industry-news-in-1-line" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;">🗞️ Industry News in 1 Line</h2></td></tr><tr><td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"><div style="margin-left:0px;" class="edm_outlooklist"><ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 1.2k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.hxhCciyt6_znu6Kpav8XD5mdCw3GQ7S-igDyFh6E09sQoRL197YY77gMZ7YBWkvhzX348-xNmgy7qwSdCc2d6utCPVVmlF9AKeRs6n96lg01c9nMW--jf7BwR8L57Q2fN6HJUxESvdD8UxMnZMmLY2_YqXdpwDzt2ql2ERH3Ullt2MJf7xNVT-Z7oOO_GGlFoEelyWuz19f8cB0tfyWejw/4mv/aQxUU3d7T_2XlKyRW2rVCA/h1/h001.7sUCsYQkHOKNpHCW_lETkvk1sZFpnqNZWrE3S9Nb7Ig" target="_blank" rel="noopener noreferrer nofollow"><span>Z.ai</span></a> is set for its IPO on Jan 8, 2026 on the Hong Kong Stock Exchange and set to raise $560 million at a valuation of 5.6 billion </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;">♥ 2.1k</span></span> Liquid AI has announced the release of LFM2-2.6B-Exp, an experimental checkpoint built on LFM2-2.6B using pure reinforcement learning. The model delivers consistent gains in instruction following, knowledge, and math benchmarks, and outperforms other 3B models across these areas. Liquid AI also reports that its IFBench score surpasses DeepSeek R1-0528, despite being 263× smaller. Now on <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWha0gtp98oQTSMbVNLK-bDl9vTq85e9NfbAls8LcmqVa4FMZamNQUHsrkGz54hRyahTGra1jn8FUrxrP9PgBKwX74NKa4gWG4fU2gkGy_KHDaP5sViI1jhtHjLDL-biEXuRzQuagZCoK2IPLuwuB35htzC4eD90zgrYBh0kGOIa2uUdiaDCxuke8e7cZpC1ehbRtZe_g6g9kx61YRuh76mbiUI4X-oYq6gpOp6TvN-Ek/4mv/aQxUU3d7T_2XlKyRW2rVCA/h2/h001.cbILZyqgrVxv5jzvGsQxqxdRuN9BOiD4DK_iObwHjSY" target="_blank" rel="noopener noreferrer nofollow"><span>Hugging Face</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:469px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/cbb48679-1245-426b-a889-321964d35b79/image.png?t=1767120437" alt="" height="auto" width="469" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 2.4k</span></span> Nvidia recently made a massive $20 billion deal with AI chip startup <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJfY4En7r3p2sm82QXOh1eriZ8AXY4BnxDgQULTXCZQfPR8YQL6HuoJC6y3owq9awVdSk7Uda4yXWUrMONNMWqLokE9mCnBwn6S34vdzSbJ83QBTDDYvOkJugke52Vvl_vJRq0AWvEQrTlPYtmnq8dzQBX5Xipvsk0w8eZ_QLIxtJCZIDp4PRst7WSKRz54wBdqcoi0Enr4ZxzViOauzIw0X_dFyoZZY2x_pmVpbitxjzDKEZfAMSvhcjUAzn7wkVgyHOvfiWWeA5t3htPz9QLZ4YSXO0QtOEJBO3T29xXfsEEMBJ_kj9H0zwBxAM5x3CB82YsTyyOvf5L1XNccbFq0y05ltnShdwlHsRUPJyOvrDwF5CWsZ2d9dAuvqpfiZNKw/4mv/aQxUU3d7T_2XlKyRW2rVCA/h3/h001.Erm7jgmXmNI-YSwyoHtovGx39-C4igGD1vR_NtCQpFw" target="_blank" rel="noopener noreferrer nofollow"><span>Groq</span></a>, acquiring most of Groq's AI chip assets and licensing its inference tech (LPU), while also "acquihiring" Groq's key team, including founder Jonathan Ross, to boost Nvidia's AI inference performance, though Groq remains independent for its cloud services. </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;">♥ 1.4k</span></span> MiniMax has announced the open-source release of <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j22z91kZt2uOBrE1Zkmkn47N_XuVG6eDQrYlrkVwA99qrhWZQmFWfsld4c0mha5Q8Btr1qY_3ueKu0-d9GTrMTACB_SvOaHX_uUfg43wxyCCdvSBDcpwy0kQRAHCWLaOviQBCcsRzM8kIcROoh-KAc70GHjtffVwAvyrvGGafWtoLfxosuOjkw4Xl594TErilevwak96emrnUduegvW7wksUYrTo_2mXpwfvyiEVWbZEF/4mv/aQxUU3d7T_2XlKyRW2rVCA/h4/h001.8_5iKHCE4pTffS7eaZQ3AjbLVDaoPUsAwweUE2T_uBQ" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax M2.1</span></a>, a SoTA model for real-world dev workflows and agentic applications. It uses a MoE with 10B active parameters and 230B total, aiming to be faster to run and easier to deploy than comparable models. Rankings #1 among open-source models and #6 overall for web dev arena. Now on <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWhwd5DtThz7uqdtxVkmmoRP2Q0guC4uCCYI8gncM5A_xfCz6U6D4v5Ii7e2zwM6UOL3JVTfF5ACpnyqK1Wrbgdm8poGixYSTHzFZP1WfJSX7pzH11Fb1ZgBPQwZtTkIoN7SDcatkvXhklHmiBWSLDObngVplFL9bV7BqVC7kkMfiNwHQz1j-Z306IgSRQrppbjcPYGI3DbBos2Fc0LxhmAYHb1Pr2K6qKhD2gnvmcW43/4mv/aQxUU3d7T_2XlKyRW2rVCA/h5/h001.N9I30CnmzlZqUAv58tZhMNz6S3E9HAR-JOopuVMJWMA" target="_blank" rel="noopener noreferrer nofollow"><span>HuggingFace</span></a>. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6cd4a7b8-73d0-4236-901f-0145a897b018/image.png?t=1767120610" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li></ol></div></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="transparent" style="background-color:transparent;border-color:#2C81E5;border-style:solid;border-width:5px;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h2 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:150.0%;"><span style="">Support My Newsletter</span></h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="color:rgb(34, 34, 34);font-family:Georgia, "Times New Roman", serif;font-size:16px;">As I aim to keep this newsletter free forever, your support means a lot. 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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="decoupling-the-what-and-where-with-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;">Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings</h1></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Gopalakrishnan<i> et al. [</i>The Swiss AI Lab (IDSIA), OpenAI, Center for Generative AI, University of Colorado<i>]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 1.3k </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;"> RoPE </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> For modern AI to understand the world, it needs to track two distinct things: <span style=""><i>what</i></span> a piece of information is and <span style=""><i>where</i></span> it sits in a sequence. Rotary Position Embedding (RoPE) accidentally tangles the "what" and the "where" together, which confuses the model when it needs to make precise decisions based on just one of those factors. To solve this, the team developed a new approach called Polar Coordinate Position Embedding, or PoPE, designed to mathematically untangle these signals so the AI can process content and position independently. </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/c1a06335-26f7-436b-b263-18545567fa5e/CleanShot_2025-12-30_at_18.25.15_2x.png?t=1767099330" 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>How RoPE and PoPE encode relative positions via rotations of queries.</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 the polar coordinate system, the magnitude of a signal represents the content, and the angle represents the position. PoPE reduces the confusion found in previous models. When tested against standard baselines, this new method shows superior performance across a diverse range of complex tasks, including the generation of classical music and modeling the human genome. </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/1ae07dc1-b9a8-4bdc-a841-1d0c1151afdc/CleanShot_2025-12-30_at_18.25.55_2x.png?t=1767099369" 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>Zero-shot performance on downstream tasks using Transformer models pretrained on OpenWebText with RoPE or PoPE positional encoding.</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 researchers found that models using PoPE demonstrated a remarkable ability to handle sequences ten times longer than those for which they were trained. Unlike current state-of-the-art methods that require complex fine-tuning to "stretch" a model's attention span, PoPE naturally generalized to these longer contexts immediately, proving to be both more robust and data-efficient. </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/f24bcf6a-34e1-4a1d-ac53-05b68350af30/CleanShot_2025-12-30_at_18.28.48_2x.png?t=1767099544" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92Z4odF7K_Icc6ps5sN2iGLEucBw8OL_Q3gM9r1sHKdZ28TbAz-VhhcUDxIaL55iiT3aszncZ7nS-B6_Pg2-zqon0REn3invarhwbIgwQOEZhsPpPNFACBR9dmf5CdaJqCYjJy-LNnNwGqSg5VGPPeqafY-mIApR4qNeBO63Ma_Co5shPxJYS3K-R2UGrZvrGq8jEMj9eOcs-LNB7ANabuwc/4mv/aQxUU3d7T_2XlKyRW2rVCA/h10/h001.Am28pEeT_zmE2Y77l7y_K6StzRQSaHUBau8aP15U03w" 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="toward-training-superintelligent-so" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Toward Training Superintelligent Software Agents through Self-Play SWE-RL</b></h1></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Wei<i> et al. [</i>Meta FAIR, Meta, TBD Lab, UIUC, CMU<i>]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 1.6k </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 RL </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> bycloud’s pick </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><b>Self-play SWE-RL (SSR)</b> is a new framework designed to train superintelligent software engineering agents without relying on human-curated data. Current agents are limited by their dependence on finite resources, such as GitHub issues and manually written tests, which forces them to imitate human developers rather than discover new solutions. </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/08ea4305-0dce-479d-aa8e-6be3473e4ecc/CleanShot_2025-12-30_at_18.38.58_2x.png?t=1767100155" 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 Self-play SWE-RL.</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 overcome this barrier, SSR allows a Large Language Model (LLM) to self-improve by interacting with raw, sandboxed code repositories. The system requires only the source code and dependencies, which eliminates the need for pre-existing test suites or natural language issue descriptions. </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/e37cd236-d494-42e8-af0d-d1b08eb24a96/CleanShot_2025-12-30_at_18.34.37_2x.png?t=1767099888" 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>Bug-injection patches generated by code hunk removal (left) and historical change reversion (right). </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 training process uses a single LLM alternating between two roles: a <b>bug-injection agent</b> and a <b>bug-solving agent</b>. The injection agent explores the repository to generate a "bug artifact," which consists of a bug-inducing patch, a custom test script, and a patch that weakens existing tests to hide the bug. </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%;"> These valid bug artifacts are then passed to the solver agent, which attempts to fix the codebase using the strict test specifications defined by the injector. Failed attempts by the solver are converted into "higher-order bugs," creating an evolving curriculum that becomes increasingly complex as the agent improves. </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/ee769c62-240a-4ba5-a35d-ddfeef8780cf/CleanShot_2025-12-30_at_18.38.06_2x.png?t=1767100099" 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>Key consistency checks applied to validate bug artifacts, the full set described in the text.</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%;"> SSR was tested on the <b>SWE-bench Verified</b> and <b>SWE-Bench Pro</b> benchmarks using the Code World Model (CWM) as a base. The results show that SSR achieves significant self-improvement (+10.4 and +7.8 points, respectively) and consistently outperforms baselines trained on human-curated 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.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvraL8giWnJMWB1LDG3-gRpe-HvZFSWjyTRSoaJ5iXIGVranch8KkeKWvRNX4JwTBf3mWEaAvQPNt4ahy8sXJwNNjn_UAzNge5ejmGKwTyy_ctpFlpHpeXxe7jYMi1ZCpHHEuKW12aODWS95Sa8ffo-rUmi1a9TwM8hdNcretW2plh8Tm0s-9IySX3ZPqjw-PGE/4mv/aQxUU3d7T_2XlKyRW2rVCA/h11/h001.V-xvffWPNTFzm27in9F0jEbNdIC3IsoNFyzxJY_rEQ8" 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="meta-rl-induces-exploration-in-lang" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Meta-RL Induces Exploration in Language Agents</b></h1></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Jiang<i> et al. [</i>EPFL, ETH Zurich, Idiap Research Institute<i>]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 877 </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 RL </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> AI can handle some complex tasks, but it struggles when we ask it to explore. While current LLMs can be trained via RL to solve specific problems, they often become rigid, and end up memorizing a single successful path rather than understanding how to adapt. </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/1ae95bda-a27a-41d3-b674-7e76c7878a9b/CleanShot_2025-12-30_at_18.46.17_2x.png?t=1767100589" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>Comparison of RL and Meta-RL training on the MineSweeper environment.</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%;"> When faced with a new or slightly changed environment, these agents frequently fail because they haven't learned how to learn from their mistakes. The research team sought to bridge this gap by designing a system that treats failure not as a dead end, but as a strategic investment. Their goal was to create agents that actively experiment with their surroundings and use that feedback to improve, mimicking the way a human might play a few practice rounds of a new game to understand the rules before trying to win. </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/bc9461ce-844f-4896-930a-f51bc69f6256/CleanShot_2025-12-30_at_18.46.45_2x.png?t=1767100614" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>Comparison between the training processes of RL (top) and Meta-RL used in LAMER (bottom).</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 team introduced a framework called LAMER that fundamentally shifts the training objective from winning a single episode to maximizing success over a series of attempts. By analyzing how agents behave across multiple tries, the researchers found that their model learned to sacrifice immediate rewards in favor of gathering information. </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 agent effectively "reflects" on its previous performance, using the context of past failures to adjust its strategy in real-time without needing complex mathematical updates to its core programming. In testing across diverse environments (ranging from logic puzzles like Minesweeper to web navigation tasks), this approach created agents that were significantly more successful and creative. Instead of collapsing into repetitive behaviors, these agents maintained a diverse set of strategies and proved capable of solving problems that standard reinforcement learning models simply could not handle. </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/6a409df5-36a1-4958-ad37-66fbce7dfc20/CleanShot_2025-12-30_at_18.47.46_2x.png?t=1767100677" 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 on Sokoban, MineSweeper and Webshop environments.</p></td></tr></table></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvp8ud9-WkEkRW5e5eXzh-rU3WKugjPhFqH750lJZ71URb7jlHqAlo1Eh5CDZowPYxJa1CFjSjMpBeIHRvyzpmrrSFcVBnXO6BCyPQDEVAc8yTcj0pz4L8ILeRu6-lMacvh4zrnAuZF6BaEVyYzqZ8wehjqVJsrOFKjg5YmbIe6wAyPheKSirj8tjV5_4CQ7rcU/4mv/aQxUU3d7T_2XlKyRW2rVCA/h12/h001.qNQFVeETAhF91FKTIcZO-MS0kKLzPbUpi0i1hqL8Dgk" 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="step-deep-research-technical-report" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Step-DeepResearch Technical Report</b></h1></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>StepFun</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 757 </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 Deep Research </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Researchers have identified an important distinction between simple search and true research. While current AI is excellent at answering specific, closed questions, it often stumbles when faced with open-ended projects that require long-term planning and logical structuring. It is challenging to create an agent that doesn't just retrieve links but understands the intent behind a request and can navigate the ambiguity of the real world. </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/b40ae042-a431-40c5-9a03-6be739715fde/CleanShot_2025-12-30_at_18.54.31_2x.png?t=1767101082" 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>Comprehensive Evaluation of Step-DeepResearch.</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 team developed Step-DeepResearch, a framework that achieves expert-level performance without relying on massive, expensive computational resources. Instead of just feeding the model more data, they focused on training "atomic capabilities", fundamental skills like decomposing a complex problem, verifying information across multiple sources, and reflecting on mistakes in real-time. </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/d90440bb-144a-42d9-83bd-bfb495843efd/CleanShot_2025-12-30_at_18.55.09_2x.png?t=1767101124" 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%;"> By teaching the model to internalize this cognitive loop of planning, executing, and self-correcting, they created a medium-sized system that rivals the performance of the industry's largest proprietary models. It shows that a refined training strategy, which prioritizes decision-making and synthesis over raw size, can produce an agent that effectively navigates complex workflows to produce comprehensive reports. </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/f5ca0e30-e320-422f-88fd-157f94cd6282/CleanShot_2025-12-30_at_18.56.17_2x.png?t=1767101187" 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>Step-DeepResearch System Architecture.</p></td></tr></table></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/288c1cbb-4416-4b2e-8d01-151e5dde5655/CleanShot_2025-12-30_at_18.57.05_2x.png?t=1767101249" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvr_4MZV36CgEc2TLLsM3_R90V_jBzMeOBoD0V_WFaRSfF4Nqo5ktQjwt2tvUoIMT2dIN-w26FkolICTYwfUUwHBMST0pTT8pTS6PbvmALzlDxtsimWfiNk-dHWImavLgWmENAo89e3iANV1NKwXE9pNX92vx9DeuUzPT4wIuV_-HBHQef8PQDFXC5uq5ifInRs/4mv/aQxUU3d7T_2XlKyRW2rVCA/h13/h001._OPIOkuO5ZfBlaH5Sg1WFriBIdKcBcEHxLQUl5EudXg" 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="attention-is-not-what-you-need" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:Bold;mso-line-height-alt:175.0%;"><b>Attention Is Not What You Need</b></h1></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>CHONG [Meta, UT Austin, UCL, UC Berkeley, Harvard University, Periodic Labs]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 1.1k </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;"> Attention Alternative </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> If you know anything about how LLMs work, then you would have heard about Transformers. Transformers use "self-attention", which is a mechanism that requires every word in a sequence to continuously check its relationship with every other word. </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%;"> Although it is incredibly effective, this process creates a massive, opaque web of calculations that becomes computationally expensive and notoriously difficult for humans to interpret. Researchers recently posed a provocative question: Is this expensive "attention" mechanism actually necessary for AI to reason, or is it just one inefficient way to achieve a goal? </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> To solve this, the team developed a new architecture called the Causal Grassmann Transformer, which completely removes the standard attention mechanism. Instead of building a massive grid of connections between all words, the model treats language processing as a flow through a specific mathematical landscape known as a Grassmann manifold. The system condenses information into lower dimensions and interprets the relationships between words as geometric subspaces. </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/af56be0b-f42a-446c-9db8-c9de4895a0bf/image.png?t=1767120832" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This geometry-first approach proved that high performance doesn't require the traditional heavy machinery of self-attention. When tested on standard language modeling tasks, the simplified Grassmann model performed competitively with standard Transformers, achieving accuracy levels within a close margin of the established 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/1e64508b-deb8-4454-ac43-f8cb9e64bd6c/image.png?t=1767120809" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This paper suggests that the future of language models may not rely solely on scaling up existing architectures, but rather on redesigning their mathematical foundations. By proving that "attention" can be replaced by "geometric evolution", this work opens the door to AI systems that are drastically more efficient and easier to audit. </p></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvoXrwmKB8mIMyj72prq2V4aX4USb99-XwDYHK7KqnfNk_LvnjU8DafX9w1Y94go9vHfKW4UtHf-mmDsibF8O7HGLpzvp-Eyd6mMBNYpWoiFdNwLD-x3LSSKsz7QhOWbZaYLUFmRYrOiPPVR9wfAe78Pi9uLnLioEAtgY06CpOp3viAR5QL22RGJkQOODynKnY8/4mv/aQxUU3d7T_2XlKyRW2rVCA/h14/h001.66IxuBiCjggEfQ0ZwpaZ8tHx9RatfTAjilJ1AqxAvpY" 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 class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Message from bycloud: </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 will be the last issue of 2025! 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