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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;"> Continuous Autoregressive Language Models and Introducing Nested Learning: A new ML paradigm for continual learning  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </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> November 11, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3Ey6vFJPDB1Fvrj5eFLGOCZXlGY6WFMRaKwY0tyEVNw54VgR30aSmeUU5_LBpsm_HwM5F9RXdYlfc0iqwPNVV-_RI8Ue2IJZDr--cTfIyNVKAyWI_B-eKOdk3nTRhQRWrcp12eTnuqRozFSBlvMfSl2-DSBnC45AUBDCsczCGJpPgftNlPqlLtQrNKB0g9cVxlo6PpK--rK2457wNJAY-frPmpK-RkF8_jW1ljVmqR_ZYXmRoVf_TkEBb-1LogLHcWEMsqghgUKfgF6OJzNyMDphl1zp3PKN1WlTETfGQyHITgqIaTojsXWm8vNYIn1nB_ONJMkDct_rjVwVgalpiuamEDnnqjeN-if3s7g_IZxH1LQN6bVU56lHYU9YRIzsjrS67pQeO-Hg1I-egYf1VzgrSnWN6O2H9Jl3IIoOTGTt-6IrSLu2Z3vmFQcu32fYaBFlpkzaiOg98GxIRLr1OIhYcCvfqmh4dAWf1Ivzz7fjVB_G6LjLODufxb6JMjejhI-tRS620EoQuAVf0saM1EWLqxra3Qik9FvqLUp7nf2PO4K-xUdN1fU9FzvPoXcx5Unq_MM3nt0SM2CYtzRk3K863Vi8EwlJi8H5Guw6lBVLAr81zVdIylDuiY0S_oC4HRT0p7dlveW5HO3ju_-gIx5LY4SSDZi1f8HoWb4f4x_O7gkb9Mair9tKa8g3AV8dCDk53W-ReFYlqReqUv7k82t0IBPUa8nAYXRSHNffoj5UzK41cnWYrpJglLMtNm_jsOFL5sGpNYkcb72MGi_oLSKCys6Pd7-MG8oS85dsAhhr8iPjPxSOmUkPxVEZr7uaW96pCxlcaRm7s1Ur2vryT3Faw0Z6zcuAY3UApud0m3MFp7Cgx1zU9n5-_pufT3Ym-r4/4li/zwOTiP6vQYS_1MW9oHu7zQ/h0/h001.gHhe8B_R4BFtuxgTZsxJYAw1d9xZt4Iry4zFtDwvalg"><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;"> From Memorization to Reasoning in the Spectrum of Loss Curvature </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;"> Continuous Autoregressive Language Models and Introducing Nested Learning: A new ML paradigm for continual learning </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/4li/zwOTiP6vQYS_1MW9oHu7zQ/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>Nov 3rd ~ Nov 10th</i><br><i>#81 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;">♥ 9.6k</span></span> Moonshot AI has released <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSGWTNhdnM83-a93AHZ1JsiWN2PlRHlRNxta6XlYlXj3B__Fb4OuOxmeSZUDOJY3ON-1agPUkKXSAFOZar8PHMFV5ZSaARyVKJpAQncBtbG7bvh3lCNEc2eJqCz7TBwljXtUJAyEAr05RGeNlSslArXpmwM2aDA8b168LzSRlTUifZmA86yv8Litsp0WDgPjec5NtKQqVokstTAcMeL-NfGAEyG65igsmRRQ1ZkzreEQO_ByRaxc5TbzNi3qE8oM3nAIx9clWQu-eCMf51ga4dqPYy1B9YqdtdHWR1an6zWH/4li/zwOTiP6vQYS_1MW9oHu7zQ/h1/h001.48UXWx30_BFy9t3jmdpEXU-3J-7p0cJe4u0sI5jluwk" target="_blank" rel="noopener noreferrer nofollow"><span>Kimi K2 Thinking</span></a>, an open-source model designed to function as a "thinking agent" capable of complex reasoning and problem-solving. The model can execute up to 200–300 sequential tool calls without human intervention and it excels at agentic search, coding, and reasoning tasks, all supported by a 256K context window. K2 Thinking is currently accessible in chat mode and <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkO3UtkflFAGe5v9gWjga7B7z0d_vNRP0chgUiFEX2PXBK45HOA54THjKaLnwEBpjC8mYPI9mGNNedJ3tYcrRql1F_YCK18FAEK8uppYkvOKikXrcEqRZSFGHP7i0fxS7wvyW64BTnN5ScYcjG5SCgPmYkfKJCPnD7G-lL-xbuNIhWkT8bos_hDwoMRA8EMsp3aFLhrXNvfCibzLcuiT6yuhw2ZhTl0HLSmD8fcZ7t-IKp2zkpYmlJxanDLuJhFfu2JaWEyrL2qGuqQXPy13WPZ_wzdjsFI3Ivgypo3Ljjvdx/4li/zwOTiP6vQYS_1MW9oHu7zQ/h2/h001.pd_LSo-n65P-50rWVQhbiVWuRg6yllWsfvGVol49888" target="_blank" rel="noopener noreferrer nofollow"><span>through its API</span></a>, with the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWpwuXiC8bRxIIctJGlFDhScToSSMS80dRyYhC1JlT6rSCsP3Uzk3VRVWw5Hh6mqf-zugH28bJWLZ-J52SxBgKcOTu7SsaW3VFXXcuWJo3ruLRjmW259tLsdU0P1TnO-xjkFRPaHEKFwBYqb_fGNhtSdR4CyT3IBvjhDcDkXxDC0y9uHqnP2xZEr79PRhoZdsWatxMueD7effaO3QUGijlaelsDTWL9zssgNxWfbfLlmA0ybhoB5pWQu7JoPzNYuJ5DyDayxT_RDe9q-q5ZTS24Q/4li/zwOTiP6vQYS_1MW9oHu7zQ/h3/h001.FPIlT46UEOcM9V-zObUFeX7jxQ8C8vecO-SPpTOXQgY" target="_blank" rel="noopener noreferrer nofollow"><span>weights available on 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/8d1e4588-7d2f-46ba-b60b-126ca7c6b20d/CleanShot_2025-11-11_at_20.03.13_2x.png?t=1762871605" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 3.5k</span></span> Google has added the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.oB7zuO_W-X4Toa45C28ngyEFO5Gj_UpIFzWcxERDRtBJ-eCgpfLld0p0ovem1xfJAhWXbqZm0-1RqYR7aWI82zfn5s0EQXqdu-bUnwr74y8bnMLGGYOi4swgCE5eXhbjS5TNC1E6qyTP_QT82o2KOIzqiuFctwglAtBJap9eoqSv59YpN3IbGEjgKKV4cDXT9xq5OzsJNNiNWVjNqgE20-mA2w4S-OVhIi-MarqVxSM51cOmzyPHIpdm-SuD-XmDt82Yp38Mje41fMAaNYI_xEcZOZrAbn-1dm79N5tqNx5SDNgkTCmtm62IFpd_ZKUG3GNwKrlaoxVyZnR5idctng/4li/zwOTiP6vQYS_1MW9oHu7zQ/h4/h001.pZHBNQaOrTCEgdNXNAbwoyEEG5Tti9XqfykvUhX37zM" target="_blank" rel="noopener noreferrer nofollow"><span>File Search tool in the Gemini API</span></a> to streamline the development of Retrieval-Augmented Generation (RAG) applications. This fully managed, serverless system simplifies the process by allowing developers to ground models like Gemini in their own data from various file formats, including PDFs and DOCX. The File Search tool automates the entire RAG pipeline (managing file storage, text chunking, embedding creation, and context injection). </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/db4b74cd-3d8a-4804-9b91-30347caf004c/CleanShot_2025-11-11_at_20.14.22_2x.png?t=1762872275" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 6.4k</span></span> OpenAI has released a new feature that allows users to interactively guide a model's reasoning process during complex tasks. Instead of passively waiting for a final output, you can now interrupt the model mid-thought, inject additional information or instructions, and then have it resume its work with the new context. </p></li></ol></div></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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nofollow"><span>Advertise with The AI Timeline! </span></a></span></p></td></tr></table></td></tr></table></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="from-memorization-to-reasoning-in-t" 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%;">From Memorization to Reasoning in the Spectrum of Loss Curvature</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>Merullo et al. [Goodfire]</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;"> ♥ 2k </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> LLM Reasoning </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%;"> Have you ever wondered how much of a model's output is genuine reasoning versus just regurgitating memorized training data? Many AI researchers are asking this same question to understand modern AI systems, where verbatim recitation from training sets can raise concerns about privacy and copyright. This research provides a method for identifying and reducing such memorization by analyzing the geometry of a model's internal weights. </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/d078fdbb-bfd2-4cc1-8556-fd4155a34e3a/CleanShot_2025-11-11_at_19.51.27_2x.png?t=1762870903" 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 activations and gradients from a sample of training data</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 method works by examining the "curvature" of the loss landscape across a dataset. It turns out that weights involved in general-purpose reasoning tend to lie in directions of consistently moderate curvature, while those used for verbatim memorization point in many different, sharp directions that average out to appear flatter overall. Using an approximation called K-FAC, the researchers decompose model weights into components ordered from high to low curvature. They found that memorized data interacts much more strongly with components at the low-curvature end of this spectrum. </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/482e32a3-8a8d-4e95-91d8-77fa0c17b242/CleanShot_2025-11-11_at_19.52.54_2x.png?t=1762870986" 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 selectively removing these low-curvature weight components, the method effectively suppresses the recitation of memorized content. In tests, this approach reduced verbatim memorization more effectively than a recent supervised unlearning technique, especially on unseen memorized data, while maintaining similar perplexity. Interestingly, this editing process revealed that certain capabilities, such as arithmetic and closed-book fact retrieval, rely heavily on these removed directions and experienced <b>significant performance drops</b>. These findings suggest that tasks like math may depend on narrow, specialized circuits in the weight space, separate from broader reasoning mechanisms. </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/1d792868-ad10-424b-a3d3-107f1fcea682/CleanShot_2025-11-11_at_19.53.16_2x.png?t=1762871018" 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>Accuracy change across the relations dataset</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-f6keVrG1GKuVv1wQKvrXSXHo3m3UKm4zzZZhnHQWsMULDLbwcL2VDN2-HnSfeCMgck-i55RH914U8tuFGNNpEUe9l7S1zO4LIjOwusDBS4LPEEJY4V-psWgALkk1yK2Y1tY-vqXzBurIOXGMWYoaUCf9kLfkp7VQCoX2IaXWhp85VaLtV17KvsbyBTEOjp-LoqRwLz-yGPq1uy2oLQ1KucOWTEfT1_gu5UDO2ynkSyMrcUx_Id5aJkr-pLQdP1tx6_DvlkF997CF-4fFKic/4li/zwOTiP6vQYS_1MW9oHu7zQ/h9/h001.XpXLfRWF4UQyKhaenuUdQ8ZYJigaW4Jn4fnvxyXwQWI" 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="continuous-autoregressive-language-" 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%;">Continuous Autoregressive 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%;"><i>Shao et al. [WeChat AI, Tsinghua University]</i></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 424 </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> VAE </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%;"> Large language models today are limited by the time it takes to generate text one token at a time. The CALM framework addresses this by grouping multiple tokens into a single continuous vector. This allows the model to predict chunks of text at once instead of individual words. This approach increases the semantic bandwidth of each step, thereby reducing the number of steps required and accelerating generation. </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/d6a10fc6-36b2-4d02-9c1c-1a9a2a74d16e/calm_fig1.png?t=1762871069" 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%;"> To make this work, the model first uses a specialized autoencoder to compress a chunk of tokens into a dense vector, and then reconstruct those tokens with high accuracy. Because the model now operates in a continuous space, it can't rely on standard next-token prediction methods. Instead, it uses an energy-based generative head that samples the next vector directly. This component refines random noise into a meaningful vector using the model’s current hidden state, all in a single efficient step. </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/27cbf1a1-c7fa-4987-be15-cba769ef47f8/calm_fig2.png?t=1762871078" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> In testing, CALM achieved a significantly better performance-compute trade-off while using substantially less computation. Although the method currently requires an autoencoder and doesn’t yet efficiently support very low temperatures, it opens a promising new direction for ultra-fast, high-capacity AI systems. </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/e7510a59-068f-4885-831a-a3d14654b692/calm_fig5.png?t=1762871093" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr class="embed-gen-img-r"><td align="center" valign="top" style="padding:12px 27px 12px 27px;" class="dd"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" class="o" style="padding:12px 12px 12px 12px;;background-color:#FFFFFF;border-color:#F1F1F1;border-radius:5px 5px 5px 5px;border-width:1px 1px 1px 1px;"><!--[if !mso]><!--><div style="display:none; float:left; overflow:hidden; width:0; max-height:0; line-height:0;" class="mob-show"><table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"><tr><td align="center" valign="top"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJf-gMT4KNtrf7nWSiaWC-R2Y5TqrCpeMlnc_cFRIKpIC4zfp7cnNHxpnhBXuEo0B7LjkQiJrQmrvqmSV3LqYkXO764GM83SXdP3R4CeGWVglm9R49rChtEBO_l1Ce65UHzA_ZS0EU_OJ2FdqFZlaLm7Dw0tyvJIACUftkSSrPvQ4rVlaZcwJO0QFUDjTFCNxZeHG5kP0_hP1gxMeWGjX_se-nOgonwRGl4i9Hk8CEtXw7SlZOoOjHYAmolo828rXuRHnOAatP_50XGMGywSE1Yk/4li/zwOTiP6vQYS_1MW9oHu7zQ/h10/h001.334Rmr1YftL1NNC5xpGW-Cp8Iq4epPhPw2K-ngPMSmY" target="_blank"><img src="https://opengraph.githubassets.com/795e3037353d8f2ae206a72c8e7324ac904f622281b14dbab1fd76236796ccc0/shaochenze/calm" width="100%" style="height:auto;display:block;"/></a></td></tr><tr><td height="16" style="font-size:16px;line-height:16px;"> </td></tr></table></div><!--<![endif]--><table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"><tr><td width="57%" align="center" valign="middle" class="mob-stack"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="left" valign="middle" class="l"><p><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJf-gMT4KNtrf7nWSiaWC-R2Y5TqrCpeMlnc_cFRIKpIC4zfp7cnNHxpnhBXuEo0B7LjkQiJrQmrvqmSV3LqYkXO764GM83SXdP3R4CeGWVglm9R49rChtEBO_l1Ce65UHzA_ZS0EU_OJ2FdqFZlaLm7Dw0tyvJIACUftkSSrPvQ4rVlaZcwJO0QFUDjTFCNxZeHG5kP0_hP1gxMeWGjX_se9HCsXYpKfzxCTN7oEl_CjSs4oiQUzOaRPJ9p2g79kWE6DDKuAjSqeK-tHy-PgL_0/4li/zwOTiP6vQYS_1MW9oHu7zQ/h11/h001.raZ_kVPOeKq_lOvghT2p4oMm7shRZ_aJaxARu4kb2o8" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"> GitHub - shaochenze/calm: Official implementation of "Continuous Autoregressive Language Models" <tr><td align="left" valign="top" class="m"><p style="font-size:13px;line-height:19px;color:#2D2D2D;"> Official implementation of "Continuous Autoregressive Language Models" - shaochenze/calm </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/shaochenze/calm</p></td></tr></a></p></td></tr></table></td><td width="3%" style="font-size:16px;line-height:16px;" class="mob-hide"> </td><td width="40%" align="left" valign="top" class="mob-hide"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJf-gMT4KNtrf7nWSiaWC-R2Y5TqrCpeMlnc_cFRIKpIC4zfp7cnNHxpnhBXuEo0B7LjkQiJrQmrvqmSV3LqYkXO764GM83SXdP3R4CeGWVglm9R49rChtEBO_l1Ce65UHzA_ZS0EU_OJ2FdqFZlaLm7Dw0tyvJIACUftkSSrPvQ4rVlaZcwJO0QFUDjTFCNxZeHG5kP0_hP1gxMeWGjX_sf0AyzAdKunujTfUrdQBGHbglHp8fzcnuOUY2j2cZ_nZMuPW91F2CiNNPqRE0CrcWQ/4li/zwOTiP6vQYS_1MW9oHu7zQ/h12/h001.sn3m3Ck1nfYx4mPTv9qt_na30CU5WMSuOSR2t2W5OFo" target="_blank"><img src="https://opengraph.githubassets.com/795e3037353d8f2ae206a72c8e7324ac904f622281b14dbab1fd76236796ccc0/shaochenze/calm" width="230" style="height:auto;display:block;"/></a></td></tr></table></td></tr></table></td></tr><tr class="btn_row"><td valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"><table width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"><tr><td align="center" valign="middle"><table role="none" border="0" cellspacing="0" cellpadding="0"><tr><td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvoFv-6ueHgJGy0HyzMoH62jh7a-XIBOn16jE3k5hVV3JmEmxIT3ZQAnZX5o_4HmfzEDYTdDTD5kmjhh5WbcQJ7qlFTC6jyQUyJWIwkmL0bVOCU4Q-mBHcY6jgsgDC9HJaCAD0BcvvGJ8NC2EhHLitl7f6ljuoUeVCEpPvHefyupG5bwvZhVohQChgMK85CxOvbtISqyBLIgjYs5l2hCP8tBdTJ_cDVstJ30RBXAPglV2apiF8YhxTGmSDvtOYHIMSg/4li/zwOTiP6vQYS_1MW9oHu7zQ/h13/h001.FNjvSynNNwTxKG0vTQfcxZruzWhqoE3_-HFgfJ5IVnk" 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="not-all-bits-are-equal-scale-depend" 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%;">Introducing Nested Learning: A new ML paradigm for continual learning</h2></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><i>Behrouz et al. [Google Research]</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;"> ♥ 855 </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 Sampling </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%;"> Have you ever noticed how large language models seem to forget new information as soon as it leaves their context window? This static nature limits their ability to learn continuously, much like a person with anterograde amnesia who can't form new long-term memories. The Nested Learning (NL) approach addresses this by reimagining models as interconnected systems of nested optimization problems, each operating at its own update frequency. </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/67699d18-c00b-4b75-9dfa-b2296f2aed97/NestedLearning-1a-Inspiration.width-1250.png?t=1762871159" 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 performance on language modeling (perplexity; left) and common-sense reasoning (accuracy; right) tasks between different architectures: Hope, Titans, Samba and a baseline Transformer.</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%;"> NL reveals that familiar components, such as gradient-based optimizers, are actually associative memory modules that compress their input context. For instance, momentum in optimizers acts as a memory that stores past gradients and allows us to design deeper, more expressive versions. By structuring models into multiple levels (where inner loops handle fast updates, such as attention and memory, and outer loops manage slower parameter adjustments), NL enables richer in-context learning and continual adaptation without requiring retraining. </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/09a4ab15-1080-4b69-86bd-8139985e888b/NestedLearning-1-Performance.width-1250.png?t=1762871175" 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 performance on language modeling (perplexity; left) and common-sense reasoning (accuracy; right) tasks between different architectures: Hope, Titans, Samba and a baseline Transformer.</p></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This approach led to innovations such as deep optimizers and a self-modifying sequence model, combined with a continuum memory system, known as HOPE. Early tests show HOPE delivers promising results in language modeling, continual learning, and long-context reasoning. </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/fdcbebeb-a923-4028-ac22-4f1b51e32273/NestedLearning-2-LongContext.width-1250.png?t=1762871185" 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 comparison on long-context tasks with different levels of difficulty between different architectures: Hope, Titans, TTT, and Mamba2. NIAH-PK, NIAH-H, and NIAH-W are needle-in-a-haystack tasks with pass-key, number, and word, respectively.</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.gXpuuKq1N_-6FJq3Q1UVsYoH4jrmyKS_63j1cggnNZUtHRkc5N40ts_SJnEN1bVUvRYwU8U_yP2OHIkaJbJWtMfIlYQtUn1shwAKFeOHZca4XDXiYGQgOtjf-qBNlLj1aIfM8-3wscI4MSih0mglVZPxJL4tpDtKd7v37yp5LGPw4GZzfZW15j3YYNbb0LoRjjGZuQ6t1SKjUKFdpPc4iLUmvCDD7hGmWFWzck3iZrvId5LxmGqQLVGmao7sLbFMcp3kJTsxT2bHnFchLehMtNHHP1hidYEYTbMDn_NnT4YZFBzzgVJAh5BQmW-NHYRr0ES3uymfQ_5Qc9WYmBawArvD6KIjnDU3-JXKfjetdYqvr2XHKYHaK44Zyle7-jNL/4li/zwOTiP6vQYS_1MW9oHu7zQ/h14/h001.O0jZ5np2WZu-nnto-xV80T3nOpoO5z3Q-_ViL7y622Q" 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="center" valign="top" style="padding:20px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmF6U5Gfz7Ypkzogc36GdU_2VXQPGj30ebhTJVAmo_RjhOynma2IcjFsNcW5QhRB6jKRPgNeseYvVSYUo6Ey8jly3VnjTP49PUGX57vITGfk9pXtnY0HWHe7AaUCVt2y5RudcQ2moTlEiwAE1a_lb2gLVwUzgtuJoXu_m2xFC-g2h5sBTtX6g2BQFAbuNeX3aGob9_-9I137PKy-QT6p4BYWGboXytyUwo9SAUUZ0XYpUKNmgeooegvw_IG7CGbAVJ3NvkA5H__sLp9LkUkAhzXfqthsJ8FVxxrYgpCqTUj_s/4li/zwOTiP6vQYS_1MW9oHu7zQ/h15/h001.OCLtc861Js1iwLFvZ4n-aq2tEDSxdgZ5CcOxKyCT6wE" style="text-decoration:none;"><table align="center" width="100%" cellpadding="0" cellspacing="0" border="0" role="none" style="max-width:520px;margin:0 auto;"><tr><td class="p" width="100%" style="padding:2px;border:none;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td align="center" valign="top" style="width:100%;"><div style="max-height:0;position:relative;opacity:0.999;width:100%;mso-hide:all;"><div style="display:inline-block;width:100%;padding-top:25%;"><img width="20%" height="auto" loading="lazy" alt="" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/youtube_play_icon.png"/></div></div><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmF6U5Gfz7Ypkzogc36GdU_2VXQPGj30ebhTJVAmo_RjhOynma2IcjFsNcW5QhRB6jKRPgNeseYvVSYUo6Ey8jly3VnjTP49PUGX57vITGfk9pXtnY0HWHe7AaUCVt2y5RudcQ2moTlEiwAE1a_lb2gLVwUzgtuJoXu_m2xFC-g2h5sBTtX6g2BQFAbuNeX3aGob9_-9I137PKy-QT6p4BYU06zW9myXrOmvXO1tv4Cnl8_V0IXAEsMrMj39yRl6F2mQjtPq7l5pP-3-ra_Kp3unsbbrF9F3HZlZd5vAjUg5G/4li/zwOTiP6vQYS_1MW9oHu7zQ/h16/h001.p7pGIZJUFGcsZu0eoE5ntlNVKm9OU9c_73SLbXhHxXE" style="text-decoration:none;"><img src="https://i.ytimg.com/vi/XFhUI1fphKU/maxresdefault.jpg" width="480" height="auto" loading="lazy" alt="YouTube video by bycloud" style="display:block;height:auto;border:0;outline:none;text-decoration:none;background-color:#000000;width:100%;"/></a></td></tr><tr><td><p style="font-size:12px;font-weight:500;font-style:italic;font-family:Helvetica, Calibri, sans-serif;color: #686a6d; 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