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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"><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> October 29, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EzqixKv2xChOI0r9msmkRog6HR22UPo9RtfyA-QeHpJuMUcEBhFcGFhAx-XpdZBXN8pY23M0N3VZWyA5qo6x-c2Ve5t0OkL1xaKJv5TMo7_TLDc-exYalAbnzhSCiIAHFGJ8HyAMsY5cPkpE27W5bl8yUyLUUEIFl6Dzx1YTBMVzvMeauoVEXPDV6jvonAMP7IO0H6AmQbC4iQYhV8d9m2NAcku-8nOrDS9bfBan9p_GVh5gM2BsSCzMdcRZc1irSC0IV-5YFwdJjaSlDPVglTwmWdpB9vFF1b9MSThrsYTorOfVi8RZmHB0xSIifEBIV3eUz0wyjIr1W2R21I_F62x1PXWNKYsN5vQpMk_WlhAuCdoomLoQuaBvkLNggxY2Czu1iOlZn8lgbFm8B2KgmsQGHcO_cS3uSnigD-dgfzBLtVSezxkcDpC5lTE_ge2aGhTWPG3tDmvg-G6G6xdwQgw4x2sE9jsq9FwkxwJR8ZDaESxv_l1QJazPS8ccUlEDGq8yNJSwhNesr_jYSibxuWLJc5HZXPq4-Au1kVVXmyBi5bd2LEH1Y4K-RmW46EfAwmWgIe_60-ex_b2wrQpX3FGZcsX9Aajs6IN9FXnMNgPYWgmxjK474eUvJEBkM9qxrKDSyi77HpLXOdxw72yHhuGBYb-BNAazfPqYY-WhXeLAXgO8-fvF6WNMDHKHEPx5Q1ivRD65AsoMyhNYOjRb-n9YFw47sZKybafNAvF1s0RIoz3c4JadgvLF4GLe7M-pZV_bi7elbYl3mjzXyKWT_qaHcxK6H6sxmj9bq9LipHMOugRwtV5Y7OKAqbLhbKRQSaymMTmoMfITaEPkZfvhktFMUR7PDZfA6p7YYfOLtYWQQ/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h0/h001.cPBMsAmjHRB6GQvhGRt8aUGPENN3L-ZEVBDMcHc_0_Y"><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;"> How to Compress Long Text into Images To Reduce LLM Tokens </h1></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/4l5/p8sHzU5SQI6YQTO9bJB4kQ/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>Oct 20th ~ Oct 27th</i><br><i>#79 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;">♥ 7.2k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWhwd5DtThz7uqdtxVkmmoRP2Q0guC4uCCYI8gncM5A_xTtRc0IqKG1we7pDmUbIquWnERLncdj32mfgzJin4jcu4O8ndpvhC6UDuMEBe0LpbaHtQJWHnqG2YQ65FDnK75mw8PEUpyqsz7fJ2GeukhljHNoEA5MQCwC1yXwQ4gwAZ-SJoBnH8sipJWRvLrEAmt3f8e_QkvhyG62x5zftbzL4SQC2cfrDKlz4c6ivmeuxjfIRbfQU4v-vcN0ra2BShk9P-hzAgU18-EQEDlO0pJgU/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h1/h001.fSA28Gbgx8gX81wfS0RE-hObOljYrk72XVJSIsVvzM8" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax</span></a> has open-sourced its new M2 model, which specializes in advanced coding and complex agentic workflows. The model shows impressive performance and runs twice as fast and at <b>only 8% of the cost</b> of competitors like Claude Sonnet. You can explore the code on GitHub or try it out for free for a limited time via the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkO1kHiXB4aukfRrFV1ARoPsNtxOPF_p5PQSc99Wc-85j5ufq-949EXlaG0bAw2rizfSxULhhdK-Ef22_sNAhNkAMLSeQnLMCFjJzB26vETngmEndWrMkwoI8XWbfg52XzTiW6uiXTSxmt3QhHAYsbXGAPMhztO8KvllL2tnKEJ_9EVIOzeJQfv0DfXZnin8JQ3ZGZTgy8CNTMlRIebyXRlQ4CDOTtSnXsxpvJCbN7WV5fCBr-Fd6LMflDd9-xdjiOVr9iUk4UqknYlGhFFVWxmnVin-MMtPg1-uTSY44ENsc/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h2/h001.GYYD3AlBwTZVmgKoxTZQa0OnwrNkRKp41CX8-KUCqeI" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax API</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/c8904c75-a4f6-455d-8116-fd82db7d980a/image.png?t=1761751003" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 1.5k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.gXpuuKq1N_-6FJq3Q1UVsS0d7M80lsWLer4TRcrkikn0ZF6gzb-E8igfSv3OYTDWhAX3KRfiQ8uKqk-6QSMxDxuWfzv5dB_3e9TwL1YY_YmseTnBSCo6_QPmST0Wi6FWCabdeFFcufrgjoYMRjh-jIhp5gvBt7Z2Y-iop90YTeTmD_mhD2BKHAyG-JXdLbC2k75x1BJisHw4Qs0g3nXKUr5wbpJD3DiOM5auaLflzppCmsNkSwjWsY6GwHBBIcGSCzHGsO_hOdnrc4VMni-dYL5DR7YQd-dyy6wfXWnqOJ4/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h3/h001.1yhXp6JgicZ2lFEkHVAQ_c0LUtpxtop8Y7Ge_A2d4_g" target="_blank" rel="noopener noreferrer nofollow"><span>R-HORIZON is a new LLM benchmark</span></a> that addresses a critical weakness in AI: long-horizon reasoning across chained tasks. The research shows that even top models fail as problem chains grow, but that training on these specialized datasets can boost multi-step performance by over 17%. You can now access the full benchmark and training sets on <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWno2StfHgkdXyV9QnRyXhkuMF4OAynO89BMJPt-WnaqnTDQcc6j4vKSdSJU8CDtTSbmgE5a92a4Bv9-DyhzzOaaEWDVvrSlqmfr96GwUspx46VOP1TsS_7ve8oCu-0lCtAo9HoGF3cFe52wnCK0wWmsnO-P4KDZStMJ7GHypJ7aqsWRNpQxs3q4YGdynuhXo2vxeYxxXY0eUQiru-EOGNwYHLhlvMcNySOOHOI0Mec2Je71_U_W46OzzkhlMsNkEBmW01dG_Ra9iPpKJ8jC6_ToIPn3lzQlYDH-AluDcs7GA/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h4/h001.0HgN1ft0VHMtfKm6TS3Hm38IRPC3hs2CdI6Z553Mds4" target="_blank" rel="noopener noreferrer nofollow"><span>Hugging Face to test your own models</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;">♥ 11k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.sa7HIrCkEzyny8lstY55mBhdT_2cUvPms-NaXEzdcIYD57ekqZozDa4AV1qaQRZpSL1h9OlJD88YCO6IV5me8mgpPOkfS3GguZalmppQHtCMQTlafnR2tv6T7KrdV8XkMeSSOSZFCcKvWwxXI7WlcCx17T8isC_RajGl1yt0aZW88OnkNZ5q-tN7fYXJYiDvIEe1OxlBDN4zRZR5hHNTaoylccFWHAcTPTxJCFbXNq4IQsU9IoMFAP2lYmfgP23L_9lK2az9NFVbzsUIDSRqaA/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h5/h001.8NqStZ2ctsnMZpRVGFLyHpwFC4PONFRgCPqGEcVUm5Q" target="_blank" rel="noopener noreferrer nofollow"><span>NotebookLM</span></a> is making document summaries more engaging with two new video styles: an updated modern anime and a brand new <b>kawaii version</b>. The new styles will be rolling out to all users by the end of the week, and the team also confirmed that highly-requested Google Sheets support is coming very soon. Log in to your account to transform your most complex documents into adorable, shareable video summaries.<br></p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:626px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/7e98451e-00c5-4931-bd5f-47bc89fd6d0a/image.png?t=1761751477" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;">♥ 1.4k</span></span> AI robotics company <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j270ADw4GyP3J6j6Ns4qwOkgpcQyo4kjaY0RXAst0UvIVQwHUVc9NC9SAQXvtvl6ripXqw_zKvFXGFXzCqdLrUtjcw1tMOVIkzzDe-UKiDirzmO5XqtDVTdRoS66wCg8ukPJOSoQh1z7_JkfiB2yOPSJSeV_xnRmcPjhM2Z3zdXR-tScrJgcn6tmS_WkRHiiEYlXpQrO_LXWeORZF2qye1Ad3U2bxQhxkm0BDBoF6NXxwsQlcBfG1UmhLxdJiVhBh_w/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h6/h001.hkgV6Ys4cDxO-9WxbrPRXluNTG7AJDqk4-Zlg3uSIeU" target="_blank" rel="noopener noreferrer nofollow"><span>1X has launched NEO</span></a>, a new home robot designed to automate household chores and act as a helpful, conversational companion. It has a soft, safe, and lightweight design, and it is built to learn and expand its capabilities over time, handling everything from cleaning schedules to answering questions. You can <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j25W_MGXkqlUedcdjGZiMS8Lk87M3KdaBmQA-5-o5tFHclX2bEpRTZIL4GQ6wJlrkCwvvc64kWC5u74wUJ_Naebhp9XkjZzSYgJCv2KjcF4P7ThanlUgmAV5hGnzOl70MzsrN74zuRqto0k-TFCuozTMf-UDpl7bm_ByhmdZhSViGjPJHODMLfqp3ayCjK4zw2riIOIZxHo46ib3BYa6PVcj-1NDkyaOr6Ybd9xPOSbvjZGBd-ubZf4Ir20oTk-_7sQ/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h7/h001.sTB2aMvb5315Byc6jakRKOhUdtGEaW69_-5j-2TSGyI" target="_blank" rel="noopener noreferrer nofollow"><span>reserve your own NEO home robot</span></a> for a monthly subscription of $499/month with a $200 deposit. </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/5eddee78-eeef-4b0c-9d43-1e619e95e915/image.png?t=1761751797" 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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[ Stanford 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;"> ♥ 22k </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;"> Image Generation </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’ve shared an open-weight language model, then someone else may be using a derivative of it without permission. How can you prove it? Existing detection methods require inserting hidden markers into the model, which isn’t transparent, while others depend on keeping test data secret, which isn’t always practical. </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 research introduces a clever way to test for model derivation by leveraging how language models memorize training data. The detection method captures how a model’s behavior aligns with the sequence of training examples. </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/ca9435ea-f31f-406d-8c23-1d7773c0e604/image.png?t=1761747557" 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 the query setting, where Alice can prompt Bob’s model, she evaluates the likelihood Bob’s model assigns to her training examples. She then checks for correlation between these likelihoods and the original training order. Since later examples are memorized more strongly, a positive correlation suggests that Bob’s model was created from Alice’s run. </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%;"> In an observational setting, where Alice only sees Bob’s text without model access, the approach adapts by estimating how that text relates to Alice’s training order. This requires counting n-gram overlaps between Bob’s text and Alice’s training data, though this requires large amounts of text to be effective. </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/8ccdaee8-0f3d-4933-9a59-2719f9396898/image.png?t=1761747599" 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>Median p-values and inter quartile ranges over 10 trials</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%;"> Both settings rely on the same underlying principle: if Bob’s model or text is independent of Alice’s randomized training order, no significant correlation should exist. The tests are designed to be transparent, using only the training order and standard model outputs, and noninvasive, requiring no changes to the original training process. </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-f6keVrG1GKuVv1wQKvofJOwldK6EM8XBTalJeudZ7MaCKLzUtsI0Fy26eb8pTis8UqgiMqe8KaWwKz_qxkbjU0ydyQos2q3fqPH7SWa2hddaVITn2jVFoKvhnhAMLIFV7SXYY-BELNdtnfmTt2ZrnPWG56_Wrr0gMyaB7WKdfMEr6nj50eM8Ds6HTfzcdvc-y-9zyswj1fUv6nyRqnlfVQx7DyvAB14G5H1gn8ol_JBdn_r2GnfVgV67mS_MuJooKRDVaH8qeKDO8Y2YX2k/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h12/h001.r4RrVKDTs7xM_ETB7ttbEo_S8m5XTTKUMSEu2efdsFg" 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="deep-seek-ocr-contexts-optical-comp" 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%;">DeepSeek-OCR: Contexts Optical Compression</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>Wei et al. [DeepSeek-AI]</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;"> DeepSeek </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%;"> LLMs often struggle with processing long documents because the computational cost grows quadratically with the sequence length. This makes it expensive and slow to handle extensive texts. DeepSeek-OCR offers a fresh approach by using images as a compression medium for text. The idea is simple: a single image of a document can hold a lot of information while using far fewer tokens than the equivalent digital text. </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/0bf6394b-fb1c-41f9-9481-ef69c1a01e16/show4.jpg?t=1761747945" 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%;"> DeepSeek-OCR is built around two main parts: the DeepEncoder and a decoder based on DeepSeek3B-MoE with 570 million activated parameters. The DeepEncoder acts as the core engine, designed to handle high-resolution inputs while keeping activation memory low and producing a small number of vision tokens. This setup allows the model to process detailed images without overwhelming computational resources, ensuring that the number of vision tokens remains manageable. </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 handle different document sizes and compression needs, DeepSeek-OCR supports multiple resolution modes, such as Tiny, Small, Base, Large, and Gundam. Each mode adjusts the input image size and the resulting vision tokens, enabling flexible compression ratios. For instance, the Tiny mode uses 64 vision tokens for a 512x512 image, while Gundam mode combines tiled local views with a global view for ultra-high-resolution inputs. </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/4499c0cf-34d5-4fca-abac-7c1d37cb8e22/fig1.png?t=1761747905" 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%;"> The decoder then takes these compressed vision tokens and reconstructs the original text. It uses a mixture-of-experts architecture, which activates only a subset of parameters during inference, balancing expressive power with efficiency. </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%;"> On the Fox benchmark, DeepSeek-OCR achieves impressive results, with decoding <b>precision reaching 97%</b> when the compression ratio is under 10 times, meaning the number of text tokens is within 10 times the vision tokens. Even at a 20 times compression ratio, accuracy stays around 60%, showing that the model maintains reasonable performance under high compression. </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-f6keVrG1GKuVv1wQKvpKyGqhgEnua5Qo_5BR-HbIOQpOwbsTq1NKAEFNiLANH3i4lh4AgSMrQQ0YI3UA_o4s7xC--fwBwz7C8zUwZzsdKQrvlKtNgbSTTRWrT_qX2isyMOpL-iLm5xindpag3CBg5xHwngk7SBc68NxpKUQGGOu-gOom7tv6i1RAH3QDpR0CglquzQpJxkgcRlTfVJ1YjVrl91ESvURdxml6X-ISllB3MNQAKiUCsZ1IDQxHGA0KoGy5RVkEYGVdeavwzUs/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h13/h001.nDWXfGclKRptRlHAg_I45TVHgCaODE9kOrGPkLsqagk" 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="glyph-scaling-context-windows-via-v" 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%;">Glyph: Scaling Context Windows via Visual-Text Compression</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>Cheng et al. [Tsinghua University, Zhipu AI]</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;"> ♥ 430 </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 Law </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%;"> LLMs are being asked to handle longer texts, from entire books to complex legal documents. But as context windows stretch to hundreds of thousands of tokens, the computational and memory costs become overwhelming. Traditional methods that expand token limits or modify attention mechanisms still process each token individually, keeping costs high. </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/50c1244d-fdd4-4646-846b-1ccf515c9625/image.png?t=1761749916" 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>Glyph consists of three main stages: continual pre-training on rendered long-text data, LLM-driven genetic search for optimal rendering configurations, and post-training with SFT, 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%;"> Glyph works by converting long text sequences into a series of image pages. Each page contains the visual glyphs of many text tokens, letting a single visual token represent multiple words or characters. The framework involves three key stages to make this process effective. </p></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;"> Continual pre-training teaches the vision-language model to understand text rendered in diverse visual styles. The model learns through tasks like reconstructing text from images, switching between text and image inputs, and generating missing parts of rendered documents. This builds a base model called Glyph-Base that can reason across visually compressed content. </p></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"> Next, an LLM-driven genetic search finds the best way to render text into images. Starting with a population of rendering configurations (varying elements like font size, spacing, and layout) the method evaluates how well each setup balances compression and task accuracy. </p></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"> Finally, post-training fine-tunes the model using the optimal rendering setup. Supervised fine-tuning employs thinking-style responses to encourage step-by-step reasoning over the visual input. </p></li></ol></div></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%;"> Glyph achieves a <b>3–4× compression</b> of long text sequences while matching the accuracy of leading LLMs like Qwen3-8B on standard long-context benchmarks. In tests on LongBench, which includes tasks like multi-document QA and summarization, Glyph scored competitively across the board, even outperforming some models in specific areas like code understanding. This method allows a VLM with a 128K context window to handle tasks requiring up to 1 million tokens effectively. </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/384f8af4-4a6b-44fd-94a5-77695a80da46/image.png?t=1761749990" 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 the Ruler benchmark (%).</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.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKlSkQ08vVCiOtfLgHbiXTqJ55Mapg8m4OwGT4IUSxV4PBP7e2gTvAy7ftHfn4WQP65C9gVpqHkEDzEUSpI7N6ugro0Pj7IUNbQs7-CoxMI_uZbwn5KynsGt0XiDEjioeUUnNzcLmCxtDDSLUiuzaDY7A6PPRgaSSse4k80nFTTXWCe_BZLpDTUWDPeX1sPKPBEreQEe_2YrU41ktvADUclZGg8CAUunaHGYU-Nww3U5F9ickUCcZYeAyApA2JmrKEw/4l5/p8sHzU5SQI6YQTO9bJB4kQ/h14/h001.Stc_g3EdG_rZNxDoTiTJfBWqVSQ9nPo_0RQjaDxY_4M" 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="the-free-transformer" 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%;">The Free Transformer</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>François Fleuret [FAIR at Meta]</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 Transformers </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%;"> Transformers have become the backbone of modern AI systems as they power everything from language models to creative tools. However, they still generate text one token at a time, based only on previous tokens. This autoregressive approach forces the model to make all its decisions implicitly as it goes along. For example, if a Transformer is trained to write both positive and negative movie reviews, it doesn't start by deciding the review's tone. Instead, it gradually infers the sentiment from the words it has already produced, which can lead to inconsistencies or errors if early tokens are ambiguous. </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/a1f909fe-ea1d-4ecf-9ebc-9de23facf6d0/image.png?t=1761750481" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>A standard decoder 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%;"> The Free Transformer paper addresses this limitation by allowing the model to condition its generation on random latent variables. These variables act like hidden decisions made upfront, such as choosing whether a review will be positive or negative before any words are written. By incorporating this capability, the model can avoid the pitfalls of purely implicit decision-making, leading to more stable and efficient 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/67d6f7aa-ade2-48e9-b7c6-3e6b1f4ad63a/image.png?t=1761750514" 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>The Free 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%;"> To understand how the Free Transformer works, it starts with the same foundation as a standard decoder Transformer, processing tokens through a series of layers. The key innovation comes in the middle of this process, where a random latent variable Z is injected. During generation, Z is sampled from a uniform distribution. But, during training, an encoder determines Z based on the full sequence, ensuring it captures meaningful global properties like sentiment or structure. This setup allows the model to use Z to guide the rest of the token generation, making explicit what was previously implicit. </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/1dcf37e9-542b-4b7d-84b2-b6d744d871df/image.png?t=1761750609" 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%;"> Experimental results with 1.5B and 8B parameter models show that the Free Transformer achieves <b>substantial improvements</b> on multiple downstream benchmarks compared to standard decoder Transformers. 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