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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;"> How AI is Learning to Reason, Dress, and Remember: A Look at GLM-4.5, Voost, and Memp  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ </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> August 12, 2025 | <a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EzG0CM1zogkt1-VZJj08OoAluo1AeZmhKAbVNT29WD5X7jVeYfH_8Rcg2IQsUdp93zhZHY39qiQ-I5dYeOEnethn6rG3OKu3wmBgrBpkkaul7TFrzm3NmQHibxEmx-4tncvYmWkRFhhnaKq4rUSKl8t_ny_fTYrkOYyWmQ7bKcHezEVvCcQkGHRfeohRpXWznGRK-oWQYyTFoHUHLE8hK4TjK1BYy_axT2nVMY3eWeOqlQhBUeiQcDwYShK1xyiZEzmxVlc_7-3SrseQr6iTEXrJhM9yuxlmfqwx7Hb7ynjlb-d2wB4gsmllUSxs35irgktWQgVeKK8A0qe8tR4aqzQXBOdzG6lhWkJcf05Jd9BWGtaF9Lw-nDULkKs_qqt-oFls7qBftnKzR3dfdgdT7msXkesK91h7qqE5d5TPQIDr2ushADt0cW79SeIC52vFmzQp7YN-PokFDg4uRb5Por5ctes1ycdcUxgIeCHTlqLyi4ncSzWaC_waOR7dnmtf28LKLtDeQXayuxWo8_zbCONDC1gQ43toiT06W4PsgcXVTewVbIx39cG6R93HFTX2Wd1YQUFPcp4ZbBpwaiAwuv2Y-SCLhyaxHzDoI-gs7Tckbd6DAV33d7S3F3rAjn5RX5lxtNF20zINLwh1N_n-fFtvtDYzQqQIspelLr4Yd2qoeqUM9R_LEXW7zLJCYX77Bg/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h0/h001.-uZC3Lf2FO2typSTImH_UFitfabdrzDSAy52Nwf9Y9Y"><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;"> Smarter AI Agents, Realistic Virtual Try-Ons, and Better Memory </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;"> How AI is Learning to Reason, Dress, and Remember: A Look at GLM-4.5, Voost, and Memp </p></td></tr></table></td></tr><tr><td style="height:0px;width:0px;"><div style="height:1px;" data-open-tracking="true"> <img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4iz/Io1N3kdUSQ-wnmq4AiVqBA/ho.gif" alt="" width="1" height="1" border="0" style="height:1px !important;width:1px !important;border-width:0 !important;margin-top:0 !important;margin-bottom:0 !important;margin-right:0 !important;margin-left:0 !important;padding-top:0 !important;padding-bottom:0 !important;padding-right:0 !important;padding-left:0 !important;"/> </div></td></tr></table></div></td></tr><tr id="content-blocks"><td class="email-card-body" align="center" valign="top" style="padding-bottom:28px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td id="nov-18-th-nov-24-th-33-latest-ai-re" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h6 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:87.5%;"><i>Aug 4th ~ Aug 10th</i><br><i>#68 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;">♥ 1.4k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkCHbOCPJEmNLssk3gUXWqfagvwDbL33VcGn4Zhn-ESQojEmAithyFB4QDxzdr_G0cPNNnva0S162QoZpVKNKNkM/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h1/h001.PdkJkurALlUG19m9IGxakyLbiv7q7N_07-4qhT_lWeg" target="_blank" rel="noopener noreferrer nofollow"><span>Pika Labs</span></a> has announced a new audio-driven performance model designed to generate hyper-real expressions in video in near real-time. The new model can process video of any length or style in under six seconds and is rolling out now in the <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZsA5-5WuwhXMDoKiRgSUttLBnhHkMnm8RYlRWxNkPKCmdY9hYQpS7sMAW1vTVjTVkfx1aSIY55TCom5G-gor9MRNIeuCDaaXBVxq7x3sXXxJpuoqsIBILnJcWwwCN0cIE7qY5hhq0LEcnZ2JYCh3hs/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h2/h001.xzDCLi13zaLa2HqpU1asHNz-DLkL7-o1WVQ98MNlM4k" target="_blank" rel="noopener noreferrer nofollow"><span>Pika Labs iOS app</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:500px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/d2dff4a8-40e2-4ca2-82c9-1368809b9e3e/image.png?t=1755020063" alt="" height="auto" width="500" 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> Microsoft has introduced Copilot 3D, which is a new experimental feature within Copilot Labs that enables users to generate a 3D model from a single 2D image. It lowers the barrier to entry for 3D creation for use in projects like gaming, animation, and 3D printing. The feature, which outputs models in the GLB format, is currently rolling out to a subset of users globally and requires a personal Microsoft Account for access on <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVEqZfvcbqpPTPft6cKIe0QHj8jmE_9UOlT37ceop3akXnb5TtsPzuAGqRfXb7zHJDS6qBTfDUNK8xCYyo1xepw3RbBM60yPBpSf8XjxmBkFRfntVys5akBSAsEJllidVAekoCLGYtSkOn1KK8SKA578/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h3/h001.PIrVR0w9AWrL9lhwNv-_G0WtLl-ChO2DJ7I4embMjso" target="_blank" rel="noopener noreferrer nofollow"><span>copilot.microsoft.com</span></a>. </p><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:500px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/04d6160e-be3e-4221-ad1d-1f325d6261c2/image.png?t=1755020310" alt="" height="auto" width="500" 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;">♥ 11k</span></span> <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxWIO860E9yBAj0r92fM6BBGy36tQbNFiZzlU6JWUI3nRnZBljHjpV1vHZ5zs-0DiL4W8FXw8yD4KR-OAtvk258fIIgRJLPKkRGsh4k83LNfq71XDIqbWeIDinexsww0wvQ/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h4/h001.Y2iJiZ7FMrt_j0RbRIVbpf5he3kj_n2IoCpxEivnSzA" target="_blank" rel="noopener noreferrer nofollow"><span>MiniMax has launched Speech 2.5</span></a>, which is a text-to-speech and <b>voice cloning model</b> that now supports 40 languages. It can produce highly realistic voice clones that preserve the nuanced details of the original speaker, including their accent, age, and emotional tone. It can be used for anything from film dubbing by retaining an actor's original performance across languages to powering hyper-personalized digital assistants and enabling creators to generate audio content in their own voice. You can try it yourself at <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2ytxunouvUN7-KRDtSLESQqszDYzd8OrkLAdrwIbqBpY5w3uhqCLDRTJS48wJuCBZrklVnDyh6Axl04jXAV6Mzd3noq7sJHXOXRXUDxWvouj/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h5/h001.g5z4-0tRte86leZYlGi2oBMxvDaUrlMZ7kltxno-Mi8" target="_blank" rel="noopener noreferrer nofollow"><span>minimax.io</span></a>. </p></li></ol></div></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"></p></td></tr></table></td></tr></table></td></tr><tr><td id="ai-leaders-only-get-100-to-explore-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">AI leaders only: Get $100 to explore high-performance AI training data.</h3></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;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2wXd_Dph6WWBrs09k7dJ8IDoOmLdBSxiR8C4y26rUKCr28CJP4Nj_8QRN85Mrqx9BkZFIoYwwWo6efAspe5i98IohNOeE0QQDqEj5TXwpmS7IHaLHagq3WqrcAtQc6PEiPMKvEoJYwNRvvy98WA0Dgey0xRchM4sqNZS71mmoAvWIbrK7cDt80TcU90wy3EM0SlbzjtVaeYAil7-uzQM9mVqiiNp7GIaXTEREoUeM0RvNuKlKXMbeSQcybjMvEwkJhuD4Wn7oAJKRAN7OL1ufKQdkC8UNIiP3xFjGg7P5gfg4obGhxYyBCEx2W_vRyREmaeAJcVw1nqrMvXK3NBdc_TzlZLiTHCHNH3o1EMW3uqNHTPljJOE1py7g95qSHjwCYeAAcVb-wYo1HTg2tyMrOS4TsUTNgTw86_wS9IKLEn4/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h6/h001.Kqi7LFsK-KT2XaE5hpLIqoziR9mDaETb6Ivv8TjbTGA" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/4f6995c0-8248-49a3-bd9c-f9d5289e3f95/Secondary_Gift_Card_2.png?t=1752259452" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></a></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Train smarter AI with <a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2wXd_Dph6WWBrs09k7dJ8IDoOmLdBSxiR8C4y26rUKCr28CJP4Nj_8QRN85Mrqx9BkZFIoYwwWo6efAspe5i98IohNOeE0QQDqEj5TXwpmS7IHaLHagq3WqrcAtQc6PEiPMKvEoJYwNRvvy98WA0Dgey0xRchM4sqNZS71mmoAvWIbrK7cDt80TcU90wy3EM0SlbzjtVaeYAil7-uzQM9mVqiiNp7GIaXTEREoUeM0RvNuKlKXMbeSQcybjMvEwkJhuD4Wn7oAJKRAN7OL1ufKQdkC8UNIiP3xFjGg7P5gfg4obGhxYyBCEx2W_vRyREmaeAJcVw1nqrMvXK3NBdc_Rtvz44okNmd_ZuyogXWECUTz_skjc591fD-sVJ5Okbp4BkU_vdnBpBORT2HER6-lK349sj6m13mW7ilaUI_cal/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h7/h001.abpek45b9dyOYqUfWAiZqOBFb2Z-dE0dxKtCVHrzU44" target="_blank" rel="noopener noreferrer nofollow"><span>Shutterstock’s</span></a> rights-cleared, enterprise-grade data across images, video, 3D, audio, and more—enriched by 20+ years of metadata. 600M+ assets and scalable licensing, We help AI teams improve performance and simplify data procurement. 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However, it is still pretty hard to build a single model with agentic, reasoning, and coding capabilities in one system. Proprietary models like Claude and GPT-4 excel in specific areas, but no open-source solution has matched their performance. </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%;"> GLM-4.5, a new Mixture-of-Experts (MoE) model, bridges this gap. Developed with 355 billion total parameters (32 billion activated per query), it introduces hybrid reasoning that switches between reflective "thinking" for complex tasks and direct responses for simpler queries. Trained on 23 trillion tokens, it targets real-world productivity by integrating tool interaction, logical deduction, and code generation into a single open framework. </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/fe2bc49c-8e9f-4267-9287-5e591369839f/image.png?t=1755015197" 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 the Slime RL infrastructure.</p></td></tr></table></td></tr><tr><td id="inner-workings-of-glm-45" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Inner Workings of GLM-4.5</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> GLM-4.5 uses a MoE architecture to balance computational efficiency and performance. Unlike dense models, it activates only 32 billion parameters per query via specialized "experts," reducing inference costs. </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 design pays more attention to depth over width, more layers improve reasoning, and incorporates innovations like Grouped-Query Attention for faster processing and QK-Norm to stabilize training. During pre-training, data is carefully filtered: web content is ranked by quality, code repositories undergo rule-based and model-based screening, and math/science materials are up-sampled to boost 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/544a334e-54e7-4863-b86b-a38d5b19ed2b/image.png?t=1755015131" 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>Pre-training and mid-training stages for GLM-4.5. We adapt a multi-stage training recipe and extend the sequence length from 4K to 128K.</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 used repo-level code training links files within GitHub projects to teach cross-file dependencies and synthetic reasoning data to enhance problem-solving. The context window expands from 4K to 128K tokens, accommodating long-form tasks like browsing agent trajectories. </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 fine-tuning, they used a hybrid approach that combines supervised learning with reinforcement learning. They also used an XML-like function-calling template to minimize character escaping in code parameters, streamlining tool use. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> After training, they combined specialized "experts" (reasoning, agent, chat) through self-distillation. They used reinforcement learning to optimize each domain: curriculum learning escalates task difficulty dynamically, while single-stage training at full context length avoids capability loss. </p></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_FMc4kJW4GJGfv-KjJwA5S2Xl3XLRROg4FivloutNRaE2Q5dTDCua9sMt0nrApNR3RnAbUnbcwnzpWmPdX7JtwGlb2LauGIx6dfbHXufJlgwqMEepD/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h9/h001.ie__RKuWGeZ7c5pA6BybmYbXmdXprDsnwy0jfl6p4-c" target="_blank"><img src="https://opengraph.githubassets.com/8b1496c0821a43da960d66fcd579b9899118a16e7e623ae9a12a72f91e1a9676/zai-org/GLM-4.5" 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_FMc4kJW4GJGfv-KjJwA5S2Xl3XLRROg4FivloutNRaE2Q5dTDM7N_dLWf6fB5RClQUfX0bn676RQcPxO3GWLmAxR-u2PteKAdm2eNk20itBCYbsXr/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h10/h001.4qfgT42W5O7ZZTkZwDteJSMvQTSPKkDMSEKvSQe05JA" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"> GitHub - zai-org/GLM-4.5: GLM-4.5: An open-source large language model designed for intelligent agents by Z.ai <tr><td align="left" valign="top" class="m"><p style="font-size:13px;line-height:19px;color:#2D2D2D;"> GLM-4.5: An open-source large language model designed for intelligent agents by Z.ai - zai-org/GLM-4.5 </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/zai-org/GLM-4.5</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_FMc4kJW4GJGfv-KjJwA5S2Xl3XLRROg4FivloutNRaE2Q5dTDZh-MsTTDbxQlXMN9T7TNeJzITeDZeY4UE2G0khjiQlOh4NHgOg4-MqZYdkej58lm/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h11/h001.ySD0O2QjnBHV4-JV8pAy6wLx99Uf67TI1FqR8Jmi5_w" target="_blank"><img src="https://opengraph.githubassets.com/8b1496c0821a43da960d66fcd579b9899118a16e7e623ae9a12a72f91e1a9676/zai-org/GLM-4.5" width="230" style="height:auto;display:block;"/></a></td></tr></table></td></tr></table></td></tr><tr><td id="evaluation-and-impact-of-glm-45" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Evaluation and Impact of GLM-4.5</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> GLM-4.5 was able to achieve top-tier results across 12 benchmarks. On agentic tasks, it scores <b>70.1% on TAU-Bench</b> (e.g., retail/airline simulations) and 26.4% on BrowseComp, which outperforms Claude Opus 4 in web interactions. </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 reasoning, it hits <b>91.0% on AIME 24</b> (math competitions) and 79.1% on GPQA (science questions), which is quite similar to Claude Sonnet 4. In coding, it reaches 64.2% on SWE-bench Verified (real GitHub fixes) and 37.5% on Terminal-Bench, which surpasses GPT-4.1. Despite having fewer parameters than rivals like Kimi K2, GLM-4.5 ranks 3rd overall among leading models and 2nd in agentic tasks. </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/121484f8-292c-4c50-8625-95faf01b13c6/bench.png?t=1755015061" 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 compact GLM-4.5-Air (106B parameters) also excels, matching larger models in efficiency. Both versions advance open-source AI by offering state-of-the-art reasoning and tool use. </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/d74bd7c5-63d2-40fa-91f4-c2726fc79130/image.png?t=1755015241" 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 8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92ZEPWHIefFBYpgw8DT6SYzBUBp9D-2Aetv7GuHfcrdwkk_7-cKx3qj5qHLWGjpAa_pIqb99Fe4Gs29tNkpBowZN/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h12/h001.vjAevcN4uFkoNzcSDSE8tIsCbk2JN_4L9UOPs-Srw74" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px 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="voost-a-unified-and-scalable-diffus" 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%;">Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off</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>Lee and Kwak</i><span style=""><i> [</i></span><i>NXN Labs</i><span style=""><i>]</i></span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 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 id="introduction-to-virtual-try-on-and-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Introduction to Virtual Try-On and Try-Off with Voost</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> We buy pretty much everything online, from clothes to books, so it should be no surprise that virtual try-on technology can revolutionize online shopping. However, accurately placing digital garments onto diverse body shapes is a stubborn challenge. Existing methods often struggle with pose variations, fabric details, or occlusions, leading to unrealistic results. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> This paper introduces Voost, which tackles this by introducing a streamlined framework that jointly learns two complementary tasks: virtual try-on (placing garments onto people) and virtual try-off (reconstructing garments from dressed images). This bidirectional approach strengthens garment-body reasoning without extra networks or labels. </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/0df0bf2c-034c-4e5e-a743-259597c50d85/tryon_grid.jpg?t=1755015397" 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>Example of Virtual Try-On outputs produced by Voost</p></td></tr></table></td></tr><tr><td id="inner-working-of-voost" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Inner Working of Voost</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Voost uses a single diffusion transformer to handle both try-on and try-off. The model combines a garment image and a person image into a horizontally concatenated layout. A task token specifies the operation direction, try-on or try-off, and the garment category. </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 try-on, the person’s clothing region is masked, prompting the model to synthesize the dressed result. For the try-off, the garment area is masked instead, asking the model to reconstruct the original garment. This shared setup exposes the model to diverse spatial relationships, improving its grasp of how fabrics interact with bodies. </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/8aa06e22-b95e-4d03-98c6-3e218c196b95/method.jpg?t=1755015452" 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%;"> During training, Voost adopts a flow matching strategy that simplifies optimization by predicting displacement vectors between noisy and clean latents. Only the transformer’s attention layers are fine-tuned, preserving the model’s generative capabilities while adapting to garment-body dynamics. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The researchers use two techniques to improve inference accuracy: attention temperature scaling adjusts for resolution mismatches by modulating attention sharpness based on token counts, while self-corrective sampling alternates between try-on and try-off predictions to refine outputs using their mutual consistency. </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/f8847960-2cc7-4287-a856-44522c0aff9a/image.png?t=1755015551" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td id="evaluation-and-results-of-voost" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Evaluation and Results of Voost</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> The researchers tested this model against existing state-of-the-art models, and Voost outperformed specialized baselines across VITON-HD and DressCode benchmarks. It achieved state-of-the-art scores in Fréchet Inception Distance (5.27 vs. 6.34 in prior work) and structural metrics like SSIM (0.898 vs. 0.881). User studies also favored Voost for <b>photorealism</b> and detail preservation. </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/ce21b1d7-072b-459a-8c73-a6003153263a/image.png?t=1755015611" 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>Quantitative results on VITON-HD and DressCode for the try-on task.</p></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_FMc4kJYImdURJQEA0trewM8vVTkOOzTe9Esf8fsTF0NBvGfJhazeksBpg-EhFKo2RZOurvoq8LTFoVOLcD9jL465NBZrd2igXgrU_Oq-ChgUJDZ-t/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h13/h001.gnpcR26e2D0SRC8_eoKs-Jd-8AurlZ5MbEwHOYoyQEY" target="_blank"><img src="https://opengraph.githubassets.com/37ca587bd2a3bd7f0a24b768edeee71b58fdd5cc0110b41f6ffafbfca8dbd556/nxnai/Voost" 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_FMc4kJYImdURJQEA0trewM8vVTkPCpZnzanpoTLO5rEbL6TZHoZ5V74Jby1ardF32fij-G3KOXYYV_PZbdQVa2iAAu6IF6zzBnJP_1ZQP1MbjWXzx/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h14/h001.rlPVoC68pnJCzBZgkz9DI6HtO5IOimQYWOoRWSP_a_8" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"> GitHub - nxnai/Voost: [Official] Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off <tr><td align="left" valign="top" class="m"><p style="font-size:13px;line-height:19px;color:#2D2D2D;"> [Official] Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off - nxnai/Voost </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/nxnai/Voost</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_FMc4kJYImdURJQEA0trewM8vVTkPIgRw6v33HiiAmbteXTgheqMH4PvkXPpxBouSCUQiBq1PJm13Hhk_U2kaADI1y8x3cQILhPmjQKTqwg4f8OVk7/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h15/h001.4lBzJWKgp5ktLcW0_DPofiWUhGxrYpehKgZyJDm0AdU" target="_blank"><img src="https://opengraph.githubassets.com/37ca587bd2a3bd7f0a24b768edeee71b58fdd5cc0110b41f6ffafbfca8dbd556/nxnai/Voost" 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 8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92bz1XfjsqIocRP2mzUs9BxiaepQNPoekh2Xab2OJSyJtWQX7LUdfxd9xCNhdT9tbLPxzlWp2RLxcdg4jVGJCWCA/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h16/h001.NWOJoAoOFcPLHLPSIw21wZ5K3dPo3BmHxaQRgd6XB9s" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px 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="memp-exploring-agent-procedural-mem" 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%;">Memp: Exploring Agent Procedural Memory</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>Fang</i><span style=""><i> et al. [</i></span><i>Zhejiang University, Alibaba Group</i><span style=""><i>]</i></span></p></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><span style="background-color:#e0e0e0;"><span style="color:rgb(255, 58, 58);font-size:0.6rem;"> ♥ 424 </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span><span style="background-color:#e0e0e0;"><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> LLM Agents </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;"> byclo’s pick </span></span><span style="color:rgb(44, 129, 229);font-size:0.6rem;"> </span></p></td></tr><tr><td id="introduction-to-procedural-memory-i" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Introduction to Procedural Memory in LLM Agents</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> LLM-based agents are getting pretty good at complex tasks like data analysis and web research. Still, they have a big limitation: their procedural memory is either rigidly hand-crafted or frozen within static parameters. </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%;"> When environments shift, say, a website layout changes or a tool fails, agents can't adapt quickly, which forces them to restart tasks from scratch. This brittleness wastes time and computational resources. </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 Memp framework tackles this by giving agents a dynamic, learnable procedural memory that evolves with experience. By converting past successes into reusable knowledge, Memp helps agents handle new challenges faster and more reliably. </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/7e933203-8854-48aa-bc6c-a7f7a664b44e/image.png?t=1755015905" 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>With procedural memory, agents can improve their success rate (accuracy ↑) and execution efficiency (steps ↓) when solving similar tasks.</p></td></tr></table></td></tr><tr><td id="how-memp-builds-and-uses-adaptive-m" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">How Memp Builds and Uses Adaptive Memory</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Memp transforms raw task trajectories into procedural memory through three key phases: Build, Retrieve, and Update. During the <b>Build phase</b>, it distills successful task completions into two formats: fine-grained step-by-step instructions (e.g., "grab the egg, microwave it for 30 seconds") and higher-level script abstractions (e.g., "heat perishable items before disposal"). This dual approach captures both concrete actions and general principles. </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 the <b>Retrieve phase</b>, Memp uses vector similarity matching to fetch relevant memories when a new task arrives. Instead of random recall, it compares the new task’s description or keywords to stored memories, prioritizing the most relevant ones. For example, asking "How do I reheat leftovers?" might retrieve memories tagged with "microwave" or "food preparation." </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/78a0e147-1e39-4579-bca2-e7818520b6ba/image.png?t=1755015813" 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 procedural memory framework.</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 <b>Update phase</b> ensures memory stays current. After each task, Memp refines its repository by adding new successes, correcting flawed memories (e.g., if a step caused failure), and discarding outdated entries. This continuous tuning prevents memory bloat and keeps knowledge aligned with real-world dynamics. Together, these phases let agents bypass repetitive trial-and-error, directly applying proven strategies to similar problems. </p></td></tr><tr><td id="performance-gains-and-future-direct" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"><h3 style="color:#2A2A2A;font-weight:normal;mso-line-height-alt:125.0%;">Performance Gains and Future Directions</h3></td></tr><tr><td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> On benchmarks like TravelPlanner and ALFWorld, Memp improved the task success rates by up to 38% while cutting execution steps by 30–40%. For instance, in ALFWorld household tasks, agents using Memp completed "heat an egg and discard it" in <b>14 steps instead of 23</b>, saving 685 tokens. </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 framework also showed strong transferability: procedural memories built by powerful models like GPT-4o lifted the performance of smaller models like Qwen, raising their accuracy by 5% despite lower base capabilities. While this research sounds promising, it has one big limitation. It relies on clear success/failure signals, which aren’t always available in messy real-world scenarios. </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/69b9e988-247a-4a3c-8e9e-9cccfe382a37/image.png?t=1755015868" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/></td></tr><tr><td align="center" valign="top" class="t" style="width:626px; padding: 4px 0px 4px 0px;"><p>Comparing trajectories with and without procedural memory shortens the process by nine steps and saves 685 tokens.</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 8px;mso-padding-alt:14px 20px;" class="btn"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92bEeFK9Fn4M-wBqXmINN_BKoji0cg50Z8Qe55Q7a4oSorGMuPjjYuXtJN6FUhkMbPSkos3pRGybBK2hPBG0AgU1/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h17/h001.DPwsYYPd5ux-_dY25OaI-izGED31N4cv28J_K3_iN8o" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px 8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;text-decoration:none;"> Read Full Paper </a></td></tr></table></td></tr></table></td></tr><tr><td class="dd" align="center" valign="top" style="padding:20px;"><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j25aF_udDsq8EAwNLhMGYMECYh72NgoRkV0X3OxD56hmr4Dct8UlMC_kMchSiUB1d_4pMGYq0BkTWIhxajNiyabgCQHM1eWXOJ5jbb4-mydYDkB5PDXEldtNXzrJiaNqGkw/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h18/h001.Bw89HONwVFEXCHQdyYZdqEwilGDuxHule5GCDh--Zew" style="text-decoration:none;"><table align="center" width="100%" cellpadding="0" cellspacing="0" border="0" role="none" style="max-width:520px;margin:0 auto;"><tr><td class="p" width="100%" style="padding:2px;border:none;"><table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"><tr><td align="center" valign="top" style="width:100%;"><div style="max-height:0;position:relative;opacity:0.999;width:100%;mso-hide:all;"><div style="display:inline-block;width:100%;padding-top:25%;"><img width="20%" height="auto" loading="lazy" alt="" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/youtube_play_icon.png"/></div></div><a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j25aF_udDsq8EAwNLhMGYMECYh72NgoRkV0X3OxD56hmrEgZFoTD4Js2spRqpMgcHv396RIrJJ7wVCNeXagOdIkKh_vWBSBLcI0eamUdnxW-YLl3EEDpR6ppYC4HEtvzuwA/4iz/Io1N3kdUSQ-wnmq4AiVqBA/h19/h001.LDiF1JcKFTSU8SH9QdfchvULJPj-6sEfYrOBBn9LkgY" style="text-decoration:none;"><img src="https://i.ytimg.com/vi/SWFjE1W6ENA/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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