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1 </head> <body class="fixed-top-nav sticky-bottom-footer"> <link rel="stylesheet" href="/assets/css/site-navigation.css?v=1790064493"> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-lg fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/">Freedom AI</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/"><span data-lang="en">About</span><span data-lang="zh">关于</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/people/"><span data-lang="en">People</span><span data-lang="zh">团队</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/"><span data-lang="en">Publications</span><span data-lang="zh">论文</span></a> </li> <li class="nav-item active"> <a class="nav-link" href="/projects/"><span data-lang="en">Projects</span><span data-lang="zh">项目</span><span class="sr-only">(current)</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/life/"><span data-lang="en">Life</span><span data-lang="zh">生活</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/impact/"><span data-lang="en">Impact</span><span data-lang="zh">影响力</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/startups/"><span data-lang="en">Startups</span><span data-lang="zh">创业</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/blog/"><span data-lang="en">Blog</span><span data-lang="zh">博客</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/join-us/"><span data-lang="en">Join Us</span><span data-lang="zh">加入我们</span></a> </li> </ul> </div> <div class="site-nav-actions"> <button id="light-toggle" type="button" title="Change theme" aria-label="Change theme"> <i class="fas fa-moon" aria-hidden="true"></i> <i class="fas fa-sun" aria-hidden="true"></i> </button> <button id="language-toggle" type="button" title="Switch language" aria-label="Switch language"><span data-language-label>中文</span></button> </div> </div> </nav> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title"> <span data-lang="en">Projects</span><span data-lang="zh">项目</span> </h1> <p class="post-description"></p> </header><article> <link rel="stylesheet" href="/assets/css/project-hub.css?v=1790064493"> <div class="project-hub project-focused"> <div class="project-filter project-filter-toolbar" aria-label="Project filters"> <button type="button" class="is-active" data-project-filter="all"><span data-lang="en">All</span><span data-lang="zh">全部</span></button> <button type="button" data-project-filter="Foundation Models"><span data-lang="en">Foundation Models</span><span data-lang="zh">基础大模型</span></button> <button type="button" data-project-filter="Reasoning &amp; Agents"><span data-lang="en">Reasoning &amp; Agents</span><span data-lang="zh">推理与智能体</span></button> <button type="button" data-project-filter="AI Systems &amp; Infrastructure"><span data-lang="en">AI Systems &amp; Infrastructure</span><span data-lang="zh">AI 系统与基础设施</span></button> <button type="button" data-project-filter="World Models &amp; Simulation"><span data-lang="en">World Models &amp; Simulation</span><span data-lang="zh">世界模型与模拟</span></button> <button type="button" data-project-filter="Healthcare AI"><span data-lang="en">Healthcare AI</span><span data-lang="zh">医疗人工智能</span></button> <button type="button" data-project-filter="Legal AI"><span data-lang="en">Legal AI</span><span data-lang="zh">法律人工智能</span></button> <button type="button" data-project-filter="AI Education"><span data-lang="en">AI Education</span><span data-lang="zh">AI 教育</span></button> <button type="button" data-project-filter="Evaluation &amp; Interpretability"><span data-lang="en">Evaluation &amp; Interpretability</span><span data-lang="zh">评测与可解释性</span></button> </div> <p class="project-empty" hidden=""><span data-lang="en">No projects found for this tag.</span><span data-lang="zh">该标签下暂无项目。</span></p> <section class="project-program-section project-theme-multimodal" id="multimodal-llms" data-project-tags="Foundation Models;Reasoning &amp; Agents;AI Systems &amp;
1 Infrastructure;Healthcare AI" data-project-url="/projects/multimodal-foundation-models/"> <div class="project-program-heading"> <div class="project-eyebrow">Multimodal foundation models and visual reasoning</div> <h2>多模态大模型</h2> <p> 多模态大模型方向把文字、图像、视频、音频和医学视觉放进同一个能力地图:从 LongLLaVA/MileBench 的长上下文视觉理解,到 TRIM 的视觉 token 压缩、ShareGPT-4o-Image/Janus-4o 的开放图像生成,再到 Video-R1、HuatuoGPT-Vision 和 FusionAudio 这类面向推理、医疗和音频场景的模型与数据。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/multimodal-foundation-models/">Open Multimodal LLMs</a> <a class="project-action" href="/projects/long-context-multimodal/">Open LongLLaVA and MileBench</a> <a class="project-action" href="/projects/sharegpt-4o-image-janus-4o/">Open ShareGPT-4o and Janus-4o</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/longllava-architecture.webp" alt="Long-context multimodal LLM architecture"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2409.02889" target="_blank" rel="noopener">LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently</a>:长上下文多图像理解和推理。</li> <li> <a href="https://arxiv.org/abs/2404.18532" target="_blank" rel="noopener">MileBench: Benchmarking MLLMs in Long Context</a>:多图像、视频和长视觉上下文评测。</li> <li> <a href="https://arxiv.org/abs/2409.10994" target="_blank" rel="noopener">TRIM: Less is More for Efficient Multi-modal LLMs</a>:减少冗余视觉 token,降低多模态推理成本。</li> <li> <a href="https://arxiv.org/abs/2506.18095" target="_blank" rel="noopener">ShareGPT-4o-Image and Janus-4o</a>:开放 GPT-4o 风格图像生成/编辑数据和统一多模态模型。</li> <li> <a href="https://arxiv.org/abs/2503.21776" target="_blank" rel="noopener">Video-R1: Reinforcing Video Reasoning in MLLMs</a>:把 R1-style reasoning training 扩展到视频理解。</li> <li> <a href="https://arxiv.org/abs/2406.19280" target="_blank" rel="noopener">HuatuoGPT-Vision</a>:面向医学视觉知识注入的多模态医学大模型。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>模型 / 数据 / 代码入口</span><strong>Multimodal stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/multimodal-foundation-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>多模态大模型</strong><span>总览视觉理解、视觉生成、视频推理、医学视觉和音频多模态。</span></span></a> <a class="project-stack-row" href="/projects/long-context-multimodal/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>LongLLaVA and MileBench</strong><span>长上下文多模态模型和 benchmark。</span></span></a> <a class="project-stack-row" href="/projects/sharegpt-4o-image-janus-4o/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>ShareGPT-4o-Image and Janus-4o</strong><span>开放图像生成/编辑数据和统一多模态模型。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/LongLLaVA</strong><span>长上下文多模态模型资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/TRIM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/TRIM</strong><span>高效多模态 token reduction。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>HuatuoGPT-Vision</strong><span>医学视觉语言模型和数据资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-infra" id="llm-efficiency-ai-infra" data-project-tags="AI Systems &amp;
1 Infrastructure;Reasoning &amp; Agents" data-project-url="/projects/llm-efficiency-ai-infra/"> <div class="project-program-heading"> <div class="project-eyebrow">Efficient training, inference, retrieval, and multimodal context</div> <h2>LLM efficiency and AI Infra</h2> <p> 这条线把“能不能跑得起、训得动、改得快”作为项目主题,覆盖推理路径剪枝、question-free fine-tuning、prefix fine-tuning、editable efficient RAG、长上下文多模态和 token reduction。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/llm-efficiency-ai-infra/">Open LLM Infra</a> <a class="project-action" href="/projects/long-context-multimodal/">Open LongLLaVA and MileBench</a> <a class="project-action" href="/projects/rag-instruction-data/">Open Data Infrastructure</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/longllava-architecture.webp" alt="LLM efficiency and AI infrastructure"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2604.16029" target="_blank" rel="noopener">Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning</a>:提前剪枝低价值推理路径。</li> <li> <a href="https://arxiv.org/abs/2506.12860" target="_blank" rel="noopener">Question-Free Fine-Tuning: Towards Efficient and Adaptive Reasoning in Large Language Models</a>:面向高效自适应推理的 QFFT。</li> <li> <a href="https://arxiv.org/abs/2503.02875" target="_blank" rel="noopener">The First Few Tokens Are All You Need</a>:UPFT,用极少前缀 token 进行高效无监督推理微调。</li> <li> <a href="https://papers.cool/venue/COLM.2025" target="_blank" rel="noopener">E2-RAG: Towards Editable Efficient RAG by Editing Compressed KV Caches</a>:编辑压缩 KV cache,面向快速更新场景。</li> <li> <a href="https://arxiv.org/abs/2409.02889" target="_blank" rel="noopener">LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently</a>:长上下文多图像多模态推理。</li> <li> <a href="https://arxiv.org/abs/2409.10994" target="_blank" rel="noopener">TRIM: Less is More for Efficient Multi-modal LLMs</a>:减少视觉 token,降低多模态推理成本。</li> </ol> </div> </div> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/native-ai-compute-adaptation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong><span data-lang="en">China-Developed AI Computing</span><span data-lang="zh">国产 AI 算力适配与加速</span></strong><span><span data-lang="en">Ascend training, full-stack acceleration, benchmark validation, real-world delivery, and open ecosystem building.</span><span data-lang="zh">昇腾大规模训练、全栈计算加速、Benchmark 验证、真实场景交付与开放生态。</span></span></span></a> </div> <details class="project-resources-panel"> <summary><span>GitHub / 资源入口</span><strong>Infrastructure stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/LongLLaVA</strong><span>长上下文多模态模型。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/TRIM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/TRIM</strong><span>高效多模态 token reduction。</span></span></a> <a class="project-stack-row" href="https://github.com/1KE-JI/UPFT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>1KE-JI/UPFT</strong><span>Unsupervised prefix fine-tuning resources。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/RAG-Instruct" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/RAG-Instruct</strong><span>Retrieval-augmented instruction data。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/LLMZoo" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/LLMZoo</strong><span>开放模型和训练资源集合。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-infra" id="native-ai-compute" data-project-tags="AI Systems &amp;
1 Infrastructure" data-project-url="/projects/native-ai-compute-adaptation/"> <div class="project-program-heading"> <div class="project-eyebrow">China-developed accelerators, inference systems, and open benchmarks</div> <h2> <span data-lang="en">China-Developed AI Computing: Adaptation and Acceleration</span><span data-lang="zh">国产 AI 算力适配与加速</span> </h2> <p><span data-lang="en">End-to-end engineering for large-scale Ascend training, model migration, vLLM/SGLang and KV Cache optimization, operators and kernels, benchmark validation, real-world delivery, and open ecosystem building.</span><span data-lang="zh">覆盖昇腾大规模训练、模型迁移、vLLM/SGLang 与 KV Cache 优化、算子与 Kernel、Benchmark 验证、真实场景交付及开放生态建设的全栈技术链条。</span></p> <div class="project-actions"> <a class="project-action primary" href="/projects/native-ai-compute-adaptation/">Open Compute Adaptation Project</a> <a class="project-action" href="https://github.com/FreedomIntelligence/AccelMark" target="_blank" rel="noopener">Open AccelMark</a> <a class="project-action" href="https://huggingface.co/FreedomIntelligence/openPangu-Embedded-7B" target="_blank" rel="noopener">openPangu on Ascend</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"><img src="/assets/img/native-compute-stack.svg" alt="China-developed AI compute adaptation and acceleration stack"></div> <div> <h3> <span data-lang="en">From accelerator to application</span><span data-lang="zh">从算力到应用</span> </h3> <ol class="project-paper-list"> <li> <strong>Scale:</strong> Arabic LLM training on 2,368 Ascend 910A processors, with results at NAACL 2024, ACL 2025, and NeurIPS 2024.</li> <li> <strong>Systems:</strong> prompt, KV Cache, inference-engine, attention, operator, and kernel optimization.</li> <li> <strong>Evaluation:</strong> CANNBench results and AccelMark's transparent, reproducible heterogeneous-accelerator comparisons.</li> <li> <strong>Delivery:</strong> localized medical agents, HuatuoGPT, and intelligent health-management workflows in a secure hospital environment.</li> <li> <strong>Ecosystem:</strong> 100+ Hugging Face models, million-scale downloads, and 10,000+ GitHub Stars across representative projects.</li> </ol> </div> </div> </section> <section class="project-program-section project-theme-optimization" id="llm-reasoning-agentic-rl" data-project-tags="Reasoning &amp; Agents;AI Systems &amp; Infrastructure" data-project-url="/projects/llm-reasoning-agentic-rl/"> <div class="project-program-heading"> <div class="project-eyebrow">Reasoning, RL, and adaptive test-time compute</div> <h2>LLM Reasoning &amp; Agentic RL</h2> <p> This project organizes the lab's recent work on verifiable reasoning, policy optimization, path pruning, code-integrated thinking, and multimodal R1-style training. The through-line is simple: make LLMs reason with feedback signals that are inspectable, efficient, and useful for downstream agents. </p> <div class="project-actions"> <a class="project-action primary" href="/projects/llm-reasoning-agentic-rl/">Open LLM Reasoning &amp; Agentic RL</a> <a class="project-action" href="/projects/reasoning-math-optimization/">Open Math and Optimization</a> <a class="project-action" href="https://wabyking.github.io/" target="_blank" rel="noopener">Papers from Benyou Wang</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/iclr-financial-ai-award.jpg" alt="LLM reasoning and agentic reinforcement learning project signal"> </div> <div> <h3>Paper organization</h3> <ol class="project-paper-list"> <li> <a href="https://wabyking.github.io/" target="_blank" rel="noopener">OnePO: Direct One-stage Policy Optimization for SFT-free Domain Adaptation</a> - direct policy optimization without a separate SFT stage.</li> <li> <a href="https://arxiv.org/abs/2605.25511" target="_blank" rel="noopener">CRPO: Character-centric Group Relative Policy Optimization for Role-aware Reasoning in Role-playing Agents</a> - RL objectives for role-aware reasoning agents.</li> <li> <a href="https://arxiv.org/abs/2506.12860" target="_blank" rel="noopener">Question-Free Fine-Tuning</a>
1 - efficient and adaptive reasoning fine-tuning.</li> <li> <a href="https://arxiv.org/abs/2604.16029" target="_blank" rel="noopener">Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning</a> - learnable path pruning for large reasoning models.</li> <li> <a href="https://arxiv.org/abs/2503.21776" target="_blank" rel="noopener">Video-R1: Reinforcing Video Reasoning in MLLMs</a> - R1-style reinforcement learning for multimodal video reasoning.</li> <li> <a href="https://arxiv.org/abs/2506.09820" target="_blank" rel="noopener">CoRT: Code-integrated Reasoning within Thinking</a> - executable computation inside the reasoning process.</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>Project stack</span><strong>Reasoning papers, code, and datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://bijiaxihh.github.io/STOP/" target="_blank" rel="noopener"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>STOP / Cut Your Losses</strong><span>Early path pruning for efficient parallel reasoning.</span></span></a> <a class="project-stack-row" href="https://github.com/tulerfeng/Video-R1" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>Video-R1</strong><span>Video-R1-CoT and RL training resources for video reasoning.</span></span></a> <a class="project-stack-row" href="https://github.com/ChengpengLi1003/CoRT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>CoRT</strong><span>Code-integrated reasoning resources.</span></span></a> <a class="project-stack-row" href="https://github.com/1KE-JI/UPFT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>UPFT</strong><span>Unsupervised prefix fine-tuning for efficient reasoning models.</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-human" id="llm-agents-applications" data-project-tags="Reasoning &amp; Agents;World Models &amp; Simulation" data-project-url="/projects/llm-agents-applications/"> <div class="project-program-heading"> <div class="project-eyebrow">Agents, applications, simulators, and human-agent interaction</div> <h2>LLM Agents, Applications &amp; Human-Agent Interaction</h2> <p> This project groups papers where LLMs become agents and interactive systems: tool planners, user simulators, standardized patients, role-playing agents, market participants, speech-to-speech systems, and micro-world actors. The goal is to organize agent papers by what the agent does, what environment it acts in, how it interacts with humans, and how the interaction is evaluated. </p> <div class="project-actions"> <a class="project-action primary" href="/projects/llm-agents-applications/">Open Agent Applications</a> <a class="project-action" href="/projects/human-agent-interaction/">Open Interaction Details</a> <a class="project-action" href="https://wabyking.github.io/" target="_blank" rel="noopener">Papers from Benyou Wang</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/twinmarket-bdi.png" alt="LLM agents and applications project map"> </div> <div> <h3>Paper organization</h3> <ol class="project-paper-list"> <li> <a href="https://aclanthology.org/2025.naacl-long.169/" target="_blank" rel="noopener">Smurfs: Multi-Agent System using Context-Efficient DFSDT for Tool Planning</a> - multi-agent tool planning with context-efficient search.</li> <li> <a href="https://arxiv.org/abs/2308.11534" target="_blank" rel="noopener">Large Language Model as a User Simulator</a> - LLM users for dialogue training and evaluation.</li> <li> <a href="https://openreview.net/forum?id=9nddtu94uX" target="_blank" rel="noopener">PlatoLM: Teaching LLMs via a Socratic Questioning User Simulator</a> - Socratic interaction as a training signal.</li> <li> <a href="https://arxiv.org/abs/2511.14783" target="_blank" rel="noopener">Human or LLM as Standardized Patients?</a> - AI patients for medical education and evaluation.</li> <li> <a href="https://arxiv.org/abs/2602.24080" target="_blank" rel="noopener">Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction</a> - human-likeness evaluation for speech-to-speech interaction.</li> <li> <a href="https://arxiv.org/abs/2502.01506" target="_blank" rel="noopener">TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets</a> - LLM investor agents in market simulation.</li> <li> <a href="https://arxiv.org/abs/2603.00585" target="_blank" rel="noopener">MicroVerse</a> - agentic micro-world simulation for scientific processes.</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>Project stack</span><strong>Agent applications and environments</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://arxiv.org/abs/2405.05955" target="_blank" rel="noopener"><span class="project-stack-type">Paper</span><span class="project-stack-main"><strong>Smurfs</strong><span>
1Multi-agent tool planning through context-efficient DFSDT.</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/EasyMED" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>EasyMED</strong><span>AI standardized patient framework and evaluation resources.</span></span></a> <a class="project-stack-row" href="/projects/twinmarket-financial-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>TwinMarket</strong><span>Financial market simulation with LLM agents.</span></span></a> <a class="project-stack-row" href="/projects/microverse-world-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>MicroVerse</strong><span>Micro-world simulation with hidden mechanisms and evolving states.</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-environment" id="environment-engineering-world-models" data-project-tags="World Models &amp; Simulation;Reasoning &amp; Agents" data-project-url="/projects/environment-engineering-world-models/"> <div class="project-program-heading"> <div class="project-eyebrow">Environment engineering and world models</div> <h2>环境工程和世界模型</h2> <p> 这条线把“模型能力”放进可运行环境中:定义状态、行动、工具、用户、规则、世界动力学和评测对齐,让智能体能在用户模拟、金融市场、科学微世界和可执行优化环境中训练、评测和迭代。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/environment-engineering-world-models/">Open Environment Engineering</a> <a class="project-action" href="https://wabyking.github.io/" target="_blank" rel="noopener">Benyou Wang Papers</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/economic-world-models/ewm-hero.png" alt="Environment engineering connects agents, environments, co-evolution, and real-world alignment"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2603.00585" target="_blank" rel="noopener">MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation</a>:把 world model 扩展到器官、细胞和亚细胞过程,强调隐藏机制、状态演化和科学约束。</li> <li> <a href="https://arxiv.org/abs/2502.01506" target="_blank" rel="noopener">TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets</a>:用 LLM 投资者构造金融市场环境,从个体信念和信息流生成宏观行为。</li> <li> <a href="/assets/pdf/economic-world-models.pdf" target="_blank" rel="noopener">From Economic Agents to Agentic Economies</a>:把智能体、环境、共演化和真实世界对齐组织成经济世界模型的系统蓝图。</li> <li> <a href="https://arxiv.org/abs/2308.11534" target="_blank" rel="noopener">Large Language Model as a User Simulator</a>:用 LLM 构造可控用户环境,为多轮对话训练和评测提供可重复交互对象。</li> <li> <a href="https://arxiv.org/abs/2405.17743" target="_blank" rel="noopener">ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling</a>:把自然语言问题转成优化模型、约束、目标函数和可执行代码。</li> <li> <a href="https://arxiv.org/abs/2510.04204" target="_blank" rel="noopener">CALM Before the STORM</a>:研究优化建模中的原生推理能力,把工程问题组织成可验证建模环境。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>项目 / 论文 / 代码资源</span><strong>Environment stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/environment-engineering-world-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>环境工程和世界模型</strong><span>组织环境定义、智能体接口、世界动力学和评测对齐的总览页。</span></span></a> <a class="project-stack-row" href="/projects/economic-world-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Economic World Models</strong><span>经济世界模型架构、能力阶梯和 agentic economies。</span></span></a> <a class="project-stack-row" href="/projects/twinmarket-financial-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>TwinMarket</strong><span>金融市场中的多智能体社会仿真环境。</span></span></a> <a class="project-stack-row" href="/projects/microverse-world-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>MicroVerse</strong><span>面向生物和科学机制的微世界模拟。</span></span></a> <a class="project-stack-row" href="https://github.com/Cardinal-Operations/ORLM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>Cardinal-Operations/ORLM</strong><span>自动化优化建模训练框架和 benchmark。</span></span></a> <a class="project-stack-row" href="https://github.com/tangzhy/STORM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>tangzhy/STORM</strong><span>CALM/STORM 优化建模推理资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-human" id="mobile-agent" data-project-tags="Reasoning &amp; Agents;AI Systems &amp;
1 Infrastructure" data-project-url="/projects/mobile-agent/"> <div class="project-program-heading"> <div class="project-eyebrow">Phone-use agents and mobile workflow execution</div> <h2>Mobile Agent</h2> <p> 这条线把手机变成通用智能体的真实执行场:从隐私与安全评测,到可控手机环境、GUI / CLI / tool mixed-action harness,再到面向真实 App workflow 的 open model 训练,让 phone-use agents 能可靠、安全、可审计地完成任务。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/mobile-agent/">Open Mobile Agent</a> <a class="project-action" href="https://phoneharness.github.io/" target="_blank" rel="noopener">Open PhoneHarness</a> <a class="project-action" href="https://huggingface.co/datasets/PhoneHarness/phoneharness-bench" target="_blank" rel="noopener">Open Dataset</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/phoneharness-overview.png" alt="PhoneHarness mobile agent overview"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2604.00986" target="_blank" rel="noopener">Do Phone-Use Agents Respect Your Privacy?</a>:用 MyPhoneBench 评测手机智能体的权限、披露和记忆控制风险。</li> <li> <a href="https://arxiv.org/abs/2605.07630" target="_blank" rel="noopener">Safe, or Simply Incapable?</a>:区分真正安全、危险行动和“什么也做不好”的失败。</li> <li> <a href="https://arxiv.org/abs/2605.29486" target="_blank" rel="noopener">PhoneWorld</a>:把真实 GUI 轨迹和截图转成可复用、可执行、可验证的手机环境。</li> <li> <a href="https://arxiv.org/abs/2606.14832" target="_blank" rel="noopener">PhoneHarness</a>:用 GUI、CLI 和 tool action 的混合动作空间评测真实手机工作流完成度。</li> <li> <a href="https://arxiv.org/abs/2606.23049" target="_blank" rel="noopener">PhoneBuddy</a>:结合真实 App 环境和 mock phone environment 训练开放 phone-use agent 模型。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>项目 / 代码 / 数据资源</span><strong>Mobile agent stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/mobile-agent/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Mobile Agent</strong><span>手机智能体的评测、安全、环境、harness 和训练路线总览。</span></span></a> <a class="project-stack-row" href="https://phoneharness.github.io/" target="_blank" rel="noopener"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>PhoneHarness</strong><span>Mixed GUI、CLI 和 tool action 的手机智能体项目主页。</span></span></a> <a class="project-stack-row" href="https://github.com/PhoneHarness/PhoneHarness" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>PhoneHarness/PhoneHarness</strong><span>Phone-use agent harness 和 evaluation workflow 代码。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/PhoneHarness/phoneharness-bench" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>PhoneHarness Bench</strong><span>手机智能体 mixed-action benchmark 数据资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-optimization" id="agentic-creation-executable-artifacts" data-project-tags="Reasoning &amp; Agents;World Models &amp; Simulation" data-project-url="/projects/agentic-creation-executable-artifacts/"> <div class="project-program-heading"> <div class="project-eyebrow">From instructions to runnable games, CAD scripts, solvers, and code</div> <h2>Agentic Creation &amp; Executable Artifacts</h2> <p> 这条线关注的不是“模型说得像不像”,而是智能体能不能把自然语言需求变成真正可运行、可渲染、可验证的产物。代表任务包括生成可玩的 Godot 游戏、可执行的 Blender CAD 脚本、数学建模求解器,以及把推理过程接到代码执行环境中。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/agentic-creation-executable-artifacts/">Open Agentic Creation</a> <a class="project-action" href="https://github.com/FreedomIntelligence/gamecraft-bench" target="_blank" rel="noopener">Open GameCraft-Bench</a> <a class="project-action" href="https://github.com/FreedomIntelligence/BlenderLLM" target="_blank" rel="noopener">Open BlenderLLM</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/github-repos.png" alt="Executable artifacts from agentic creation"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2606.17861" target="_blank" rel="noopener">GameCraft-Bench</a>:评估 coding agents 是否能把游戏规格生成完整、可启动、可 replay 的 Godot 项目。</li> <li> <a href="https://arxiv.org/abs/2412.14203" target="_blank" rel="noopener">BlenderLLM</a>
1:训练 LLM 生成可在 Blender 中执行并渲染 3D 模型的 CAD 脚本。</li> <li> <a href="https://arxiv.org/abs/2405.13144" target="_blank" rel="noopener">Mamo</a>:面向数学建模的 benchmark 和 solver,把自然语言问题转成可求解模型。</li> <li> <a href="https://arxiv.org/abs/2405.17743" target="_blank" rel="noopener">ORLM</a>:训练大模型做自动化优化建模,连接问题描述、约束、目标函数和代码。</li> <li> <a href="https://arxiv.org/abs/2506.09820" target="_blank" rel="noopener">CoRT</a>:把 code execution 放进 reasoning,提升中间计算的可验证性。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>项目 / 代码 / benchmark</span><strong>Executable artifact stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/agentic-creation-executable-artifacts/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Agentic Creation</strong><span>可执行产物生成、验证和交互式评测总览。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/gamecraft-bench" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/gamecraft-bench</strong><span>可玩游戏生成 benchmark、任务、replay 和 hidden rubric。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/BlenderLLM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/BlenderLLM</strong><span>CAD script 生成模型、BlendNet 数据和 CADBench。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Mamo" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Mamo</strong><span>数学建模 benchmark 与 solver 资源。</span></span></a> <a class="project-stack-row" href="https://github.com/Cardinal-Operations/ORLM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>Cardinal-Operations/ORLM</strong><span>自动化优化建模训练框架和 IndustryOR benchmark。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-education" id="ai-education" data-project-tags="AI Education;Reasoning &amp; Agents;Healthcare AI" data-project-url="/projects/ai-education/"> <div class="project-program-heading"> <div class="project-eyebrow">Education agents, tutors, and learning simulation</div> <h2>AI Education</h2> <p> 这条线把 LLM agent 从“答题工具”推进到可教学、可陪练、可评测的教育智能体:包括自我调节学习助手、LLM user simulator、PlatoLM / SocraticChat 的苏格拉底式多轮对话训练,以及 EasyMED / SPBench 中的 AI 标准化病人。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/ai-education/">Open AI Education</a> <a class="project-action" href="https://github.com/FreedomIntelligence/PlatoLM" target="_blank" rel="noopener">Open PlatoLM</a> <a class="project-action" href="https://github.com/FreedomIntelligence/EasyMED" target="_blank" rel="noopener">Open EasyMED</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/socratic-pipeline.png" alt="Socratic dialogue and education agent pipeline"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2506.09968" target="_blank" rel="noopener">SRLAgent</a>:用游戏化机制和 LLM assistance 增强 self-regulated learning。</li> <li> <a href="https://arxiv.org/abs/2308.11534" target="_blank" rel="noopener">Large Language Model as a User Simulator</a>:把 LLM 作为可控用户模拟器,用于多轮对话训练和评测。</li> <li> <a href="https://openreview.net/forum?id=9nddtu94uX" target="_blank" rel="noopener">PlatoLM</a>:通过苏格拉底式追问,把多轮教学对话能力蒸馏进 LLM。</li> <li> <a href="https://arxiv.org/abs/2511.14783" target="_blank" rel="noopener">Human or LLM as Standardized Patients?</a>:用 EasyMED 和 SPBench 评测 AI 标准化病人的医学教育效果。</li> <li> <a href="https://arxiv.org/abs/2602.05856" target="_blank" rel="noopener">It Talks Like a Patient, But Feels Different</a>:从 CHI / co-design 视角研究 AI 标准化病人的学习体验和设计需求ã€
1‚</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>项目 / 数据 / 代码资源</span><strong>Education agent stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/ai-education/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>AI Education</strong><span>教育智能体、学习模拟、Socratic dialogue 和 AI standardized patients 总览。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/PlatoLM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/PlatoLM</strong><span>PlatoLM 模型、Socratic-style dialogue training 和开源资源。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/FreedomIntelligence/SocraticChat" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>SocraticChat</strong><span>苏格拉底式对话数据资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/EasyMED" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/EasyMED</strong><span>AI 标准化病人和医学教育评测资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-economics" id="economic-world-models" data-project-tags="World Models &amp; Simulation;Reasoning &amp; Agents" data-project-url="/projects/economic-world-models/"> <div class="project-program-heading"> <div class="project-eyebrow">Economic world models, financial LLMs, and agent applications</div> <h2>经济世界模型和金融大模型及其 Agent 应用</h2> <p> 这条研究主线把市场、机构、政策和多智能体行为建成可计算、可演化、可对齐的经济世界,同时覆盖金融大模型、金融能力评测、多模态金融模型、中英双语金融模型,以及面向真实市场的 Agent 应用。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/economic-world-models/">Open Economic World Models</a> <a class="project-action" href="https://github.com/FreedomIntelligence/Awesome-Econ-World-Models/" target="_blank" rel="noopener">Awesome EconWM</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/economic-world-models/ewm-hero.png" alt="Economic World Models compare physical-world transitions with agent-generated economic-world transitions"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="/assets/pdf/economic-world-models.pdf" target="_blank" rel="noopener">From Economic Agents to Agentic Economies</a>:提出经济世界模型的系统蓝图,用可计算经济体支持智能体训练、政策沙盒、规划和安全分析。</li> <li> <a href="https://arxiv.org/abs/2502.01506" target="_blank" rel="noopener">TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets</a>:用 LLM 投资者模拟金融市场中的信念、意图、信息流和交易行为。</li> <li> <a href="https://aclanthology.org/2025.findings-naacl.300/" target="_blank" rel="noopener">UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models</a>:从用户需求和动态金融任务出发评测大模型的金融专业能力。</li> <li> <a href="https://arxiv.org/abs/2402.12659" target="_blank" rel="noopener">FinBen: A Holistic Financial Benchmark for Large Language Models</a>:面向多类金融任务的综合大语言模型评测基准。</li> <li> <a href="https://arxiv.org/abs/2408.11878" target="_blank" rel="noopener">Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications</a>:开放面向金融应用的多模态大模型、数据和评测体系。</li> <li> <a href="https://arxiv.org/abs/2403.06249" target="_blank" rel="noopener">No Language Is an Island</a>:统一中英文金融大模型、指令数据与 benchmark,推动双语金融智能。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Project 资源</span><strong>Paper, repositories, project pages</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/economic-world-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Economic World Models</strong><span>总览经济世界模型架构、能力阶梯、代表团队和系统路线图。</span></span></a> <a class="project-stack-row" href="/assets/pdf/economic-world-models.pdf" target="_blank" rel="noopener"><span class="project-stack-type">Paper</span><span class="project-stack-main"><strong>From Economic Agents to Agentic Economies</strong><span>经济世界模型论文 PDF。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Awesome-Econ-World-Models/" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Awesome-Econ-World-Models</strong><span>经济世界模型论文、系统和资源集合。</span></span></a> <a class="project-stack-row" href="/projects/twinmarket-financial-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>TwinMarket Financial AI Simulation</strong><span>经济世界模型中的金融市场原型沙盒。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/TwinMarket" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/TwinMarket</strong><span>金融行为与社会模拟代码资源。</span></span></a> <a class="project-stack-row" href="/projects/microverse-world-simulation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>MicroVerse Micro-World Simulation</strong><span>把世界模型扩展到科学微世界与生物过程。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/MicroVerse" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/MicroVerse</strong><span>MicroVerse 项目资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-optimization" id="optimization-llms" data-project-tags="Reasoning &amp; Agents;AI Systems &amp;
1 Infrastructure" data-project-url="/projects/reasoning-math-optimization/"> <div class="project-program-heading"> <div class="project-eyebrow">Optimization modeling LLMs</div> <h2>运筹优化大模型系列</h2> <p> 运筹优化主线把自然语言问题转成数学优化模型、约束、目标函数和可执行代码。ORLM 提供开放模型与 IndustryOR benchmark,CALM/STORM 进一步研究优化建模中的原生推理能力。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/reasoning-math-optimization/">Open Reasoning and Optimization</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/iclr-financial-ai-award.jpg" alt="Reasoning and optimization project signal"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2405.17743" target="_blank" rel="noopener">ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling</a>:OR-Instruct、ORLM 和 IndustryOR。</li> <li> <a href="https://arxiv.org/abs/2510.04204" target="_blank" rel="noopener">CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling</a>:优化建模中的原生推理。</li> <li> <a href="https://arxiv.org/abs/2403.02884" target="_blank" rel="noopener">MathScale: Scaling Instruction Tuning for Mathematical Reasoning</a>:数学推理 scaling 与 verifier 相关基础。</li> <li> <a href="https://arxiv.org/abs/2506.09820" target="_blank" rel="noopener">CoRT: Code-integrated Reasoning within Thinking</a>:将代码执行接入思维链,也服务于形式化建模和计算。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Hugging Face 资源</span><strong>Code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/Cardinal-Operations/ORLM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>Cardinal-Operations/ORLM</strong><span>自动化优化建模训练框架、数据合成和 benchmark 入口。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/CardinalOperations/IndustryOR" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>CardinalOperations/IndustryOR</strong><span>100 个真实工业运筹问题 benchmark。</span></span></a> <a class="project-stack-row" href="https://github.com/tangzhy/STORM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>tangzhy/STORM</strong><span>CALM/STORM 优化建模推理资源。</span></span></a> <a class="project-stack-row" href="https://github.com/ChengpengLi1003/CoRT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>ChengpengLi1003/CoRT</strong><span>代码集成推理资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-optimization" id="legal-ai" data-project-tags="Legal AI;Reasoning &amp; Agents;Evaluation &amp; Interpretability" data-project-url="/projects/legal-ai-models-agents/"> <div class="project-program-heading"> <div class="project-eyebrow">Foundation models, user-centric evaluation, evidence retrieval, and legal agents</div> <h2> <span data-lang="en">Legal AI Models, Retrieval, and Agents</span><span data-lang="zh">法律大模型、检索与智能体</span> </h2> <p><span data-lang="en">A research path from HanFei-1.0 and UCL-Bench to GenArtID, Legal-R1, and continuously improving Legal AI systems connected to real lawyer workflows.</span><span data-lang="zh">从 HanFei-1.0 与 UCL-Bench,到 GenArtID、Legal-R1 和面向真实律师工作流的法律 AI 闭环。</span></p> <div class="project-actions"> <a class="project-action primary" href="/projects/legal-ai-models-agents/">Open Legal AI Project</a> <a class="project-action" href="https://aclanthology.org/2025.findings-naacl.444/" target="_blank" rel="noopener">UCL-Bench</a> <a class="project-action" href="https://openreview.net/forum?id=2Oe3FRRcVQ" target="_blank" rel="noopener">Legal-R1</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"><img src="/assets/img/legal-ai/joint-lab-signing.png" alt="CUHK-Shenzhen and FaDaFu Legal AI Joint Laboratory signing ceremony"></div> <div> <h3> <span data-lang="en">One connected research program</span><span data-lang="zh">一条连贯的技术路线</span> </h3> <ol class="project-paper-list"> <li> <strong>HanFei-1.0:</strong> Chinese legal foundation-model training.</li> <li> <strong>UCL-Bench:</strong> 5 legal scenarios and 22 user-centric tasks.</li> <li> <strong>GenArtID:</strong> generative statutory article identifier prediction.</li> <li> <strong>Legal-R1:</strong> multi-step agentic retrieval for evidence-based reasoning.</li> <li> <strong>Legal AI within a Loop:</strong> Harness → Benchmark → Training → Data Flywheel.</li> </ol> </div> </div> </section> <section class="project-program-section project-theme-medical" id="huatuogpt" data-project-tags="Healthcare AI;Foundation Models" data-project-url="/projects/medical-ai-ecosystem/"> <div class="project-program-heading"> <div class="project-eyebrow">HuatuoGPT medical model family</div> <h2>AI for Healthcare:华佗GPT系列大模型</h2> <p> 华佗GPT 是 AI for Healthcare 主线:从 Huatuo-26M 中文医学问答数据,到 HuatuoGPT / HuatuoGPT-II 医学适配,再到 HuatuoGPT-o1 的复杂医学推理和 HuatuoGPT-Vision 的医学视觉语言理解。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/medical-ai-ecosystem/">Open HuatuoGPT Ecosystem</a> <a class="project-action" href="/projects/huatuogpt-ii/">Open HuatuoGPT-II</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/huatuogpt-overview.png" alt="HuatuoGPT medical model ecosystem"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2305.01526" target="_blank" rel="noopener">Huatuo-26M, a Large-scale Chinese Medical QA Dataset</a>:中文医学 QA 数据基础。</li> <li> <a href="https://arxiv.org/abs/2305.15075" target="_blank" rel="noopener">HuatuoGPT, towards Taming Language Model to Be a Doctor</a>:早期开放医学对话模型。</li> <li>
1 <a href="https://arxiv.org/abs/2311.09774" target="_blank" rel="noopener">HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs</a>:统一一阶段医学适配训练。</li> <li> <a href="https://arxiv.org/abs/2412.18925" target="_blank" rel="noopener">Towards Medical Complex Reasoning with LLMs through Medical Verifiable Problems</a>:HuatuoGPT-o1 复杂医学推理。</li> <li> <a href="https://arxiv.org/abs/2406.19280" target="_blank" rel="noopener">HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale</a>:医学多模态视觉知识注入。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Hugging Face 资源</span><strong>Code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Huatuo-26M" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Huatuo-26M</strong><span>中文医学 QA 数据集与构建资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/HuatuoGPT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/HuatuoGPT</strong><span>华佗GPT 基础医学对话模型代码。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/HuatuoGPT-II" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/HuatuoGPT-II</strong><span>HuatuoGPT-II 训练和评测材料。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/HuatuoGPT-o1</strong><span>医学复杂推理模型和数据。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/HuatuoGPT-Vision</strong><span>医学视觉语言模型训练代码。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/FreedomIntelligence/PubMedVision" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>FreedomIntelligence/PubMedVision</strong><span>医学图文视觉指令数据。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-eval" id="medical-evaluation" data-project-tags="Healthcare AI;Evaluation &amp; Interpretability" data-project-url="/projects/medical-evaluation-benchmarks/"> <div class="project-program-heading"> <div class="project-eyebrow">Healthcare AI evaluation</div> <h2>医疗 AI 评测系列</h2> <p> 医疗 AI 不能只看聊天流畅度。这条线把评测拆成中文医学知识、多模态医学理解、可解释诊断、实时临床、医生工作流和医学教育几层,让模型在更接近临床的任务上暴露短板。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/medical-evaluation-benchmarks/">Open Medical Evaluation Benchmarks</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/cmb-overview.png" alt="CMB medical benchmark overview"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2308.08833" target="_blank" rel="noopener">CMB: A Comprehensive Medical Benchmark in Chinese</a>:中文医学综合 benchmark。</li> <li> <a href="https://arxiv.org/abs/2408.03361" target="_blank" rel="noopener">GMAI-MMBench</a>:面向通用医疗 AI 的多模态评测。</li> <li> <a href="https://arxiv.org/abs/2407.13301" target="_blank" rel="noopener">Chain-of-Diagnosis / DxBench</a>:可解释诊断模型和真实医患对话诊断评测。</li> <li> <a href="https://arxiv.org/abs/2602.16747" target="_blank" rel="noopener">LiveClin: A Live Clinical Benchmark without Leakage</a>:持续更新、抗泄漏的真实临床 benchmark。</li> <li> <a href="https://arxiv.org/abs/2510.11040" target="_blank" rel="noopener">Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks</a>:DoctorFLAN / DotaBench,面向医生工作流。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Hugging Face 资源</span><strong>Code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/CMB" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/CMB</strong><span>
1中文医学 benchmark 代码。</span></span></a> <a class="project-stack-row" href="https://github.com/uni-medical/GMAI-MMBench" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>uni-medical/GMAI-MMBench</strong><span>GMAI-MMBench 多模态评测代码。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Chain-of-Diagnosis" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Chain-of-Diagnosis</strong><span>CoD、DxBench、DiagnosisGPT 统一入口。</span></span></a> <a class="project-stack-row" href="https://github.com/AQ-MedAI/LiveClin" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>AQ-MedAI/LiveClin</strong><span>LiveClin 代码和数据入口。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/DotaGPT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/DotaGPT</strong><span>DoctorFLAN / DotaBench 训练与评测代码。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-education" id="medical-education-sp" data-project-tags="Healthcare AI;AI Education;Reasoning &amp; Agents" data-project-url="/projects/medical-education-standardized-patients/"> <div class="project-program-heading"> <div class="project-eyebrow">Medical education and AI standardized patients</div> <h2>医学教育与 AI 标准化病人</h2> <p> 这条线面向医学教育中的 SP(standardized patients)训练,把 AI 病人做成可控、可复用、可评测的练习对象。它同时连接 ACL 医学教育评测与 CHI/人机交互里的共创设计问题:AI 病人不只要“像病人说话”,还要真正帮助学生练习病史采集、沟通和临床推理。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/medical-education-standardized-patients/">Open AI Standardized Patients</a> <a class="project-action" href="https://github.com/FreedomIntelligence/EasyMED" target="_blank" rel="noopener">Open EasyMED</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/huatuogpt-auto-eval.jpeg" alt="Medical education and standardized patient evaluation"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2511.14783" target="_blank" rel="noopener">Human or LLM as Standardized Patients? A Comparative Study in Medical Education</a>:提出 EasyMED 与 SPBench,对比 AI SP 与人类 SP 的教学效果。</li> <li> <a href="https://arxiv.org/abs/2602.05856" target="_blank" rel="noopener">"It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners</a>:从 CHI 视角总结医学学习者对 AI SP 的体验和设计需求。</li> <li> <a href="https://arxiv.org/abs/2510.11040" target="_blank" rel="noopener">Doctor-centric workflow-aligned tasks and benchmarks</a>:把医学 AI 评测放进医生真实工作流。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / 论文资源</span><strong>Code and papers</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/EasyMED" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/EasyMED</strong><span>AI 标准化病人框架和医学教育评测入口。</span></span></a> <a class="project-stack-row" href="https://arxiv.org/abs/2511.14783" target="_blank" rel="noopener"><span class="project-stack-type">Paper</span><span class="project-stack-main"><strong>Human or LLM as Standardized Patients?</strong><span>EasyMED、SPBench 和医学教育对比实验。</span></span></a> <a class="project-stack-row" href="https://dl.acm.org/doi/10.1145/3772363.3798336" target="_blank" rel="noopener"><span class="project-stack-type">CHI</span><span class="project-stack-main"><strong>Co-designing AI standardized patients</strong><span>
1医学学习者共创设计与交互需求。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-multilingual" id="multilingual-llms" data-project-tags="Foundation Models;Healthcare AI" data-project-url="/projects/multilingual-localized-llms/"> <div class="project-program-heading"> <div class="project-eyebrow">Multilingual and localized LLMs</div> <h2>多语言大语言模型</h2> <p> 多语言主线从 Phoenix / LLMZoo 的开放多语言聊天开始,到 AceGPT 的阿拉伯语本地化,再到 Apollo / ApolloMoE 的多语言医学模型、语料和评测。核心目标是让医学和通用 AI 不只服务少数高资源语言。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/multilingual-localized-llms/">Open Multilingual Project</a> <a class="project-action" href="/projects/rag-instruction-data/">Open Data Infrastructure</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/apollo-map.jpg" alt="Apollo multilingual medical model map"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2304.10453" target="_blank" rel="noopener">Phoenix: Democratizing ChatGPT across Languages</a>:早期开放多语言聊天模型。</li> <li> <a href="https://aclanthology.org/2024.naacl-long.450/" target="_blank" rel="noopener">AceGPT, Localizing Large Language Models in Arabic</a>:阿拉伯语大模型本地化与对齐。</li> <li> <a href="https://arxiv.org/abs/2403.03640" target="_blank" rel="noopener">Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People</a>:多语言医学模型、数据和评测。</li> <li> <a href="https://arxiv.org/abs/2409.02615" target="_blank" rel="noopener">ApolloMoE</a>:面向 50 种语言的医学 MoE 模型。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Hugging Face 资源</span><strong>Code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/LLMZoo" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/LLMZoo</strong><span>Phoenix / LLMZoo 多语言模型资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/AceGPT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/AceGPT</strong><span>阿拉伯语 LLM 本地化代码和资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Apollo" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Apollo</strong><span>Apollo 多语言医学模型、数据、评测代码。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/ApolloMoE</strong><span>面向 50 种语言的医学 MoE 模型代码。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-speech" id="speech-llms" data-project-tags="Foundation Models;Reasoning &amp; Agents" data-project-url="/projects/speech-audio-talking-head-ai/"> <div class="project-program-heading"> <div class="project-eyebrow">Speech LLMs and speech-to-speech agents</div> <h2>Speech LLMs</h2> <p> Speech LLMs 方向把大模型从文本扩展到语音输入、语音输出、语气、情绪和副语言信息。相关项目包括 Soundwave、S2S-Arena、EchoMind、FusionAudio、UNSPOKEN,以及 speech-to-speech Turing Test。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/speech-audio-talking-head-ai/">Open Speech and Audio Project</a> <a class="project-action" href="https://github.com/FreedomIntelligence/Soundwave" target="_blank" rel="noopener">Open Soundwave</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/socratic-pipeline.png" alt="Speech LLM and interactive AI pipeline"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2502.12900" target="_blank" rel="noopener">Soundwave: Less is More for Speech-Text Alignment in LLMs</a>:用更少数据完成 speech-text alignment。</li> <li> <a href="https://arxiv.org/abs/2503.05085" target="_blank" rel="noopener">S2S-Arena</a>:评测 speech-to-speech 模型的语义理解和副语言指令跟随。</li> <li> <a href="https://arxiv.org/abs/2510.22758" target="_blank" rel="noopener">EchoMind</a>:面向 empathetic Speech Language Models 的多层级 benchmark。</li> <li> <a href="https://arxiv.org/abs/2602.24080" target="_blank" rel="noopener">Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction</a>:评测 S2S 系统是否真正像人类对话。</li> <li> <a href="https://arxiv.org/abs/2506.01111" target="_blank" rel="noopener">FusionAudio-1.2M</a>:细粒度音频 caption 与 multimodal contextual fusion。</li> <li> <a href="https://github.com/Hongru0306/UNSPOKEN" target="_blank" rel="noopener">UNSPOKEN</a>:用未说出口的声学线索评测 audio language models 的隐喻推理。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>GitHub / Hugging Face 资源</span><strong>Code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Soundwave" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Soundwave</strong><span>Speech-text alignment for LLMs。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/FreedomIntelligence/Soundwave" target="_blank" rel="noopener"><span class="project-stack-type">HF Model</span><span class="project-stack-main"><strong>FreedomIntelligence/Soundwave</strong><span>Soundwave 模型卡。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/S2S-Arena" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/S2S-Arena</strong><span>Speech-to-speech paralinguistic benchmark。</span></span></a> <a class="project-stack-row" href="https://hlt-cuhksz.github.io/EchoMind/" target="_blank" rel="noopener"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>EchoMind</strong><span>Empathetic speech language model benchmark。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/FusionAudio" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/FusionAudio</strong><span>Fine-grained audio captioning data and models。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-explain" id="interpretable-llms" data-project-tags="Evaluation &amp; Interpretability;Healthcare AI" data-project-url="/projects/llm-interpretability/"> <div class="project-program-heading"> <div class="project-eyebrow">Explainable and verifiable LLMs</div> <h2>LLM Interpretability</h2> <p> 可解释性是横向能力:它关心模型为什么这样回答、哪些内部特征真正有用、以及如何把机制理解转成可控编辑。这个方向现在以 sparse autoencoder、model unlearning 和 circuit analysis 为主线,同时连接医学诊断链、verifier 和代码集成推理。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/llm-interpretability/">Open LLM Interpretability</a> <a class="project-action" href="https://arxiv.org/abs/2510.03659" target="_blank" rel="noopener">Open ICLR 2026 paper</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/llm-interpretability.svg" alt="LLM interpretability and sparse autoencoder analysis"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2510.03659" target="_blank" rel="noopener">Does Higher Interpretability Imply Better Utility? A Pairwise Analysis on Sparse Autoencoders</a>:ICLR 2026,系统比较 SAE 的可解释性指标和实际 steering utility;获 NeurIPS 2025 ResponsibleFM Workshop Outstanding Paper Awardã€
1‚</li> <li> <a href="https://arxiv.org/abs/2505.24428" target="_blank" rel="noopener">Model Unlearning via Sparse Autoencoder Subspace Guided Projections</a>:EMNLP 2025,用 SAE subspace guided projections 做更可控的模型遗忘。</li> <li> <a href="https://arxiv.org/abs/2502.11812" target="_blank" rel="noopener">Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis</a>:ICML 2025,用 circuit analysis 理解微调如何改变模型内部计算。</li> <li> <a href="https://arxiv.org/abs/2407.13301" target="_blank" rel="noopener">CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis</a>:用五步诊断链和置信度分布提升医学诊断可解释性。</li> <li> <a href="https://arxiv.org/abs/2311.09724" target="_blank" rel="noopener">Outcome-supervised Verifiers for Planning in Mathematical Reasoning</a>:用结果监督 verifier 辅助数学规划推理。</li> <li> <a href="https://arxiv.org/abs/2506.09820" target="_blank" rel="noopener">CoRT: Code-integrated Reasoning within Thinking</a>:把代码执行放入思维过程,让中间计算更透明。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>论文 / GitHub / Hugging Face 资源</span><strong>Papers, code, models, datasets</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/llm-interpretability/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>LLM Interpretability and Sparse Autoencoders</strong><span>SAE utility、SAE-guided unlearning 和 circuit analysis 的详情页。</span></span></a> <a class="project-stack-row" href="https://arxiv.org/abs/2510.03659" target="_blank" rel="noopener"><span class="project-stack-type">ICLR 2026</span><span class="project-stack-main"><strong>Does Higher Interpretability Imply Better Utility?</strong><span>Pairwise analysis on sparse autoencoders。</span></span></a> <a class="project-stack-row" href="https://arxiv.org/abs/2505.24428" target="_blank" rel="noopener"><span class="project-stack-type">EMNLP 2025</span><span class="project-stack-main"><strong>Model Unlearning via SAE Subspace Guided Projections</strong><span>可解释子空间引导的模型遗忘。</span></span></a> <a class="project-stack-row" href="https://arxiv.org/abs/2502.11812" target="_blank" rel="noopener"><span class="project-stack-type">ICML 2025</span><span class="project-stack-main"><strong>Fine-Tuning Mechanisms via Circuit Analysis</strong><span>用 circuit analysis 理解 LLM 微调机制。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Chain-of-Diagnosis" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Chain-of-Diagnosis</strong><span>CoD 代码、诊断链数据、疾病库、DxBench 和 DiagnosisGPT 入口。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/FreedomIntelligence/DiagnosisGPT-6B" target="_blank" rel="noopener"><span class="project-stack-type">HF Model</span><span class="project-stack-main"><strong>FreedomIntelligence/DiagnosisGPT-6B</strong><span>基于 CoD 的可解释诊断模型。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/FreedomIntelligence/DxBench" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>FreedomIntelligence/DxBench</strong><span>真实医患对话构建的疾病诊断 benchmark。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/OVM" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/OVM</strong><span>Outcome-supervised verifier 相关代码。</span></span></a> <a class="project-stack-row" href="https://github.com/ChengpengLi1003/CoRT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>ChengpengLi1003/CoRT</strong><span>代码集成推理项目。</span></span></a> <a class="project-stack-row" href="https://github.com/tulerfeng/Video-R1" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>tulerfeng/Video-R1</strong><span>视频推理强化学习代码。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-eval" id="general-llm-evaluation" data-project-tags="Evaluation &amp; Interpretability;Foundation Models;Healthcare AI" data-project-url="/projects/general-llm-evaluation/"> <div class="project-program-heading"> <div class="project-eyebrow">Rubrics, judges, freshness, bias, ethics, and rewards</div> <h2>通用大模型评估</h2> <p> 这个方向把 Rubric-based Evaluation &amp; Reward Design 和 Trustworthy / Responsible AI Evaluation 合并成一条更大的“通用评估”主线:从 per-sample criteria、LLM-as-a-judge 偏差、时间泛化、文化偏差,到医疗伦理和 rubric-to-reward,把开放式能力评测变成可审计、可复现、可训练的反馈系统。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/general-llm-evaluation/">Open General Evaluation</a> <a class="project-action" href="https://github.com/FreedomIntelligence/Awesome-Rubrics" target="_blank" rel="noopener">Open Awesome-Rubrics</a> <a class="project-action" href="https://github.com/FreedomIntelligence/MLLM-Bench" target="_blank" rel="noopener">Open MLLM-Bench</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/freedomai-2026/impact/github-impact.jpg" alt="General LLM evaluation project signal"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://aclanthology.org/2025.naacl-long.256/" target="_blank" rel="noopener">MLLM-Bench</a>:用 per-sample criteria 评估多模态大模型,让样本级标准成为 judge 的核心输入。</li> <li> <a href="https://arxiv.org/abs/2402.10669" target="_blank" rel="noopener">Humans or LLMs as the Judge?</a>:系统分析人类和 LLM judge 的判断偏差,为自动评测校准提供依据。</li> <li> <a href="https://github.com/FreedomIntelligence/FreshBench" target="_blank" rel="noopener">FreshBench</a>:评估大模型的 temporal generalization,判断模型是否“过时”。</li> <li> <a href="https://arxiv.org/abs/2505.18562" target="_blank" rel="noopener">From Word to World</a>:用 word association test 评估和缓解 LLM 的文化偏差。</li> <li> <a href="https://github.com/FreedomIntelligence/PrinciplismQA" target="_blank" rel="noopener">PrinciplismQA</a>:把四原则医学伦理转成 MCQA、临床病例和 rubric keypoints。</li> <li> <a href="https://github.com/FreedomIntelligence/Awesome-Rubrics" target="_blank" rel="noopener">Awesome-Rubrics</a>:整理 rubric-based evaluation、reward modeling 和 post-training 资源。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>论文 / GitHub / 资源入口</span><strong>Evaluation and reward stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/general-llm-evaluation/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>General LLM Evaluation</strong><span>通用大模型评估、rubric judge、可靠性和 reward design 总览。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Awesome-Rubrics" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Awesome-Rubrics</strong><span>Rubric-based evaluation、alignment 和 agentic AI 文献地图。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/MLLM-Bench" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/MLLM-Bench</strong><span>Per-sample criteria 多模态评测代码与数据。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/FreshBench" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/FreshBench</strong><span>
1时间泛化和模型新鲜度评测资源。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/PrinciplismQA" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/PrinciplismQA</strong><span>医学伦理知识、病例和 rubric scoring 资源。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-medical" id="clinical-agents-hospital-world-models" data-project-tags="Healthcare AI;World Models &amp; Simulation;Reasoning &amp; Agents" data-project-url="/projects/clinical-agents-hospital-world-models/"> <div class="project-program-heading"> <div class="project-eyebrow">Clinical agents, patient dynamics, HIS environments, and hospital twins</div> <h2>Clinical &amp; Hospital World Models</h2> <p> 这个方向把医疗 AI 从静态问答推进到临床环境中的 agent:它关心 ICU 患者动态、医院信息系统、医生工作流、多角色医院仿真、工具调用和安全治理。牙科 benchmark 和 DentalGPT 已单独放入牙科大模型方向,这里只保留临床与医院世界模型。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/clinical-agents-hospital-world-models/">Open Clinical &amp; Hospital Models</a> <a class="project-action" href="https://github.com/FreedomIntelligence/SepsisAgent" target="_blank" rel="noopener">Open SepsisAgent</a> <a class="project-action" href="https://github.com/FreedomIntelligence/TwinHospital" target="_blank" rel="noopener">Open TwinHospital</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/huatuogpt-auto-eval.jpeg" alt="Clinical agents and hospital world models"> </div> <div> <h3>相关论文整理</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2605.14723" target="_blank" rel="noopener">SepsisAgent</a>:通过 Clinical World Model 进行 propose-simulate-refine,并用 agentic RL 学习 ICU 治疗策略。</li> <li> <a href="https://github.com/FreedomIntelligence/Agentic-Hospital" target="_blank" rel="noopener">Agentic-Hospital</a>:把 HIS/EMR、角色仿真、工具接口、治理和 rubric 评测组织成医院级 agent 环境。</li> <li> <a href="https://github.com/FreedomIntelligence/TwinHospital" target="_blank" rel="noopener">TwinHospital</a>:结合真实 HIS data flow 与多角色仿真,复现挂号、问诊、治疗和管理流程。</li> <li> <a href="https://arxiv.org/abs/2510.11040" target="_blank" rel="noopener">Doctor-Centric Medical AI</a>:把任务和 benchmark 对齐到医生真实工作流。</li> <li> <a href="https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills" target="_blank" rel="noopener">OpenClaw-Medical-Skills</a>:开放医疗 AI skills library,为临床 agent 提供可调用技能入口。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>临床 agent / 医院环境 / 技能库</span><strong>Hospital world-model stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/clinical-agents-hospital-world-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Clinical &amp; Hospital World Models</strong><span>临床与医院数字环境、患者动态世界模型总览。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/SepsisAgent" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/SepsisAgent</strong><span>ICU sepsis treatment recommendation with Clinical World Model。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/Agentic-Hospital" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/Agentic-Hospital</strong><span>Hospital-scale actionable environment for autonomous agents。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/TwinHospital" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/TwinHospital</strong><span>HIS data-flow-driven hospital process simulation。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/OpenClaw-Medical-Skills</strong><span>面向 medical agents 的开放技能库。</span></span></a> </div> </details> </section> <section class="project-program-section project-theme-medical" id="dental-foundation-models" data-project-tags="Healthcare AI;Foundation Models;Evaluation &amp; Interpretability" data-project-url="/projects/dental-foundation-models/"> <div class="project-program-heading"> <div class="project-eyebrow">Dental LLMs, multimodal diagnosis, clinical reasoning, and benchmarks</div> <h2>牙科大模型</h2> <p> 牙科是可以独立成方向的垂直医疗 AI:既有 DentalGPT 这样的多模态牙科大模型,也有 GlobalDentBench、OmniDentBench 等面向临床推理、安全风险和专家校准的 benchmark。它强调口腔影像、病例推理、专科 taxonomy、真实临床风险和专家评分。 </p> <div class="project-actions"> <a class="project-action primary" href="/projects/dental-foundation-models/">Open Dental LLMs</a> <a class="project-action" href="https://freedomdle.cn/" target="_blank" rel="noopener">Open OmniDentBench</a> <a class="project-action" href="https://github.com/FreedomIntelligence/DentalGPT" target="_blank" rel="noopener">Open DentalGPT</a> </div> </div> <div class="project-program-layout"> <div class="project-program-media"> <img src="/assets/img/media-roundup-2026/cmb-overview.png" alt="Dental foundation models and benchmarks"> </div> <div> <h3>相关论文与 benchmark</h3> <ol class="project-paper-list"> <li> <a href="https://arxiv.org/abs/2512.11558" target="_blank" rel="noopener">DentalGPT</a>
1:用 120K+ 牙科图像、domain knowledge injection 和 GRPO 强化多模态复杂推理。</li> <li> <a href="https://arxiv.org/abs/2605.24636" target="_blank" rel="noopener">GlobalDentBench</a>:覆盖 88 个国家/地区、14 个牙科专科、8,978 道专家校准题的 multinational dental benchmark。</li> <li> <a href="https://freedomdle.cn/" target="_blank" rel="noopener">OmniDentBench</a>:面向复杂临床决策和生物医学研究的全球牙科基准评测平台。</li> <li> <a href="https://arxiv.org/abs/2508.20416" target="_blank" rel="noopener">DentalBench</a>:双语牙科 QA benchmark 和大规模 DentalCorpus,用于评估和增强牙科理解。</li> <li> <a href="https://arxiv.org/abs/2511.22055" target="_blank" rel="noopener">OralGPT-Omni</a>:牙科多模态大模型与 MMOral-Uni benchmark,覆盖多种口腔影像任务。</li> </ol> </div> </div> <details class="project-resources-panel"> <summary><span>模型 / benchmark / 平台</span><strong>Dental AI stack</strong></summary> <div class="project-stack project-resource-stack"> <a class="project-stack-row" href="/projects/dental-foundation-models/"><span class="project-stack-type">Project</span><span class="project-stack-main"><strong>Dental Foundation Models</strong><span>牙科大模型、牙科 benchmark 和 OmniDentBench 总览。</span></span></a> <a class="project-stack-row" href="https://freedomdle.cn/" target="_blank" rel="noopener"><span class="project-stack-type">Platform</span><span class="project-stack-main"><strong>OmniDentBench</strong><span>全球牙科基准评测平台和 ODB leaderboard。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/DentalGPT" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/DentalGPT</strong><span>牙科多模态大模型、训练 pipeline 和评测设置。</span></span></a> <a class="project-stack-row" href="https://github.com/FreedomIntelligence/GlobalDentBench" target="_blank" rel="noopener"><span class="project-stack-type">GitHub</span><span class="project-stack-main"><strong>FreedomIntelligence/GlobalDentBench</strong><span>专家校准的跨国牙科临床推理 benchmark。</span></span></a> <a class="project-stack-row" href="https://huggingface.co/datasets/FreedomIntelligence/GlobalDentBench-OA" target="_blank" rel="noopener"><span class="project-stack-type">HF Dataset</span><span class="project-stack-main"><strong>GlobalDentBench-OA</strong><span>GlobalDentBench 开放评测子集。</span></span></a> </div> </details> </section> </div> 
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1<script defer src="/assets/js/language-toggle.js"></script>
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.