The latest in Machine Learning

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</div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/txagent-an-ai-agent-for-therapeutic-reasoning">TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/mims-harvard/TxAgent" onclick="captureOutboundLink('https://github.com/mims-harvard/TxAgent'); return true;" style="font-size:13px"> mims-harvard/TxAgent </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">14 Mar 2025</span> </p> <p class="item-strip-abstract">It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation.</p> <div class="sota"> </div> <p> <a href="/task/ai-agent"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>AI Agent</span> </span> </a> <a href="/task/decision-making"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decision Making</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 225</span> </div> <div class="stars-accumulated text-center"> 2.73 stars / hour </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/txagent-an-ai-agent-for-therapeutic-reasoning" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/txagent-an-ai-agent-for-therapeutic-reasoning#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/neural-fields-with-thermal-activations-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/61e9a567-75bd-4ae6-a905-a1398911d002.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/neural-fields-with-thermal-activations-for">Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/prs-eth/thera" onclick="captureOutboundLink('https://github.com/prs-eth/thera'); return true;" style="font-size:13px"> prs-eth/thera </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/jax-6ee30fa5.png" /> </span> • <span class="author-name-text item-date-pub">29 Nov 2023</span> </p> <p class="item-strip-abstract">We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.</p> <div class="sota"> </div> <p> <a href="/task/image-super-resolution"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/5071982a-dbf1-4ad0-a76e-e4aa34c7c310.jpg"> <span>Image Super-Resolution</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 504</span> </div> <div class="stars-accumulated text-center"> 2.64 stars / hour </div> <div class="entity" style="margin-bottom: 20px;"> <a 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0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/reinforcement-learning-outperforms-supervised"> <div class="item-image" style="background-image: 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14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/xiaomi-research/r1-aqa" onclick="captureOutboundLink('https://github.com/xiaomi-research/r1-aqa'); return true;" style="font-size:13px"> xiaomi-research/r1-aqa </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">14 Mar 2025</span> </p> <p class="item-strip-abstract">Recently, reinforcement learning (RL) has been shown to greatly enhance the reasoning capabilities of large language models (LLMs), and RL-based approaches have been progressively applied to visual multimodal tasks.</p> <div class="sota"> </div> <p> <a href="/task/audio-question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Audio Question Answering</span> </span> </a> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/reinforcement-learning-outperforms-supervised#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" 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href="/paper/reinforcement-learning-outperforms-supervised#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/agent-s-an-open-agentic-framework-that-uses"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/51d046a3-07eb-43a9-9acb-8440ddf6499e.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/agent-s-an-open-agentic-framework-that-uses">Agent S: An Open Agentic Framework that Uses Computers Like a Human</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/simular-ai/agent-s" onclick="captureOutboundLink('https://github.com/simular-ai/agent-s'); return true;" style="font-size:13px"> simular-ai/agent-s </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">10 Oct 2024</span> </p> <p class="item-strip-abstract">We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks.</p> <div class="sota"> </div> <p> <a href="/task/ai-agent"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>AI Agent</span> </span> </a> <a href="/task/task-planning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Task Planning</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" 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Paper </a> <br/> <a href="/paper/agent-s-an-open-agentic-framework-that-uses#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/2503-01710"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/9cc95510-ccfe-4314-880d-8eaf90186e3c.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/2503-01710">Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/sparkaudio/spark-tts" onclick="captureOutboundLink('https://github.com/sparkaudio/spark-tts'); return true;" style="font-size:13px"> sparkaudio/spark-tts </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">3 Mar 2025</span> </p> <p class="item-strip-abstract">Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis.</p> <div class="sota"> </div> <p> <a href="/task/attribute"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Attribute</span> </span> </a> <a href="/task/text-to-speech"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Text to Speech</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/2503-01710#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" 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src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">12 Mar 2025</span> </p> <p class="item-strip-abstract">Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation.</p> <div class="sota"> </div> <p> <a href="/task/denoising"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/6c4d53f8-9c6d-47c8-80c7-1b8e1c0a7d42.jpg"> <span>Denoising</span> </span> </a> <a href="/task/language-modeling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Language Modeling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/block-diffusion-interpolating-between#tasks"> <span class="badge 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</div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/yoloe-real-time-seeing-anything">YOLOE: Real-Time Seeing Anything</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/THU-MIG/yoloe" onclick="captureOutboundLink('https://github.com/THU-MIG/yoloe'); return true;" style="font-size:13px"> THU-MIG/yoloe </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">10 Mar 2025</span> </p> <p class="item-strip-abstract">Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.</p> <div class="sota"> </div> <p> <a href="/task/10-shot-image-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>10-shot image generation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 750</span> </div> <div class="stars-accumulated text-center"> 1.50 stars / hour </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/yoloe-real-time-seeing-anything" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg 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8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/foundationstereo-zero-shot-stereo-matching"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/c68b98d1-156e-4115-b6ee-86aed2286513.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/foundationstereo-zero-shot-stereo-matching">FoundationStereo: Zero-Shot Stereo Matching</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/NVlabs/FoundationStereo" onclick="captureOutboundLink('https://github.com/NVlabs/FoundationStereo'); return true;" style="font-size:13px"> NVlabs/FoundationStereo </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">17 Jan 2025</span> </p> <p class="item-strip-abstract">However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching.</p> <div class="sota"> </div> <p> <a href="/task/diversity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Diversity</span> </span> </a> <a href="/task/stereo-depth-estimation"> <span class="badge badge-primary"> <img 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23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/ZongqianLi/ReasonGraph" onclick="captureOutboundLink('https://github.com/ZongqianLi/ReasonGraph'); return true;" style="font-size:13px"> ZongqianLi/ReasonGraph </a> </span> • <span class="author-name-text item-date-pub">6 Mar 2025</span> </p> <p class="item-strip-abstract">Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span 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42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/open-sora-2-0-training-a-commercial-level"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/c43963e9-fce6-40f7-9ea7-58cf7d0dc073.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/open-sora-2-0-training-a-commercial-level">Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 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