【文章标题】：Thomson Reuters Launches Its Own Frontier Model 汤森路透推出自有前沿模型
【文章正文】： August 24, 2026 2026年8月24日
Thomson Reuters Leverages its World-Class Data Assets to Launch Its Own Frontier Model 汤森路透利用其世界级数据资产推出自有前沿模型
Thomson, the company’s proprietary LLM, was trained and is run at a fraction of the cost of comparable frontier models and remains fully owned and controlled by Thomson Reuters. Thomson 是该公司专有的大语言模型，其训练和运行成本仅为同类前沿模型的一小部分，并且完全由汤森路透拥有和控制。
TORONTO, August 24, 2026 – Thomson Reuters (Nasdaq/TSX: TRI), a global content and technology company, today announced the launch of Thomson, the company’s first proprietary large language model, developed in-house. Frontier labs have typically spent billions of dollars on compute and years of infrastructure investment to reach the frontier. Thomson Reuters took a different path: starting from a strong open-source foundation and investing $40 million to train Thomson into the right intelligence for the jobs that matter most, covering talent and compute. The result is a model Thomson Reuters fully controls, without the heavy inference costs of typical frontier models. 多伦多，2026年8月24日——汤森路透（纳斯达克/多伦多证券交易所代码：TRI），一家全球内容和技术公司，今天宣布推出 Thomson，这是该公司首个内部开发的专有大语言模型。前沿实验室通常需要花费数十亿美元的计算资源和多年的基础设施投资才能达到前沿水平。汤森路透走了一条不同的道路：从强大的开源基础出发，投资4000万美元将 Thomson 训练成最适合关键工作的智能，涵盖人才和算力。其结果是汤森路透完全控制的模型，且没有典型前沿模型的高昂推理成本。
As one of the world’s leading providers of trusted content and expertise for professionals, Thomson Reuters built Thomson on decades of proprietary content, technology, and domain expertise no other company can match. Training on that foundation is what made Thomson possible: a model built to Fiduciary-Grade™ standards, at a fraction of the typical cost. 作为全球领先的专业人士可信内容和专业知识提供商之一，汤森路透基于数十年的专有内容、技术和领域专业知识构建了 Thomson，这是其他公司无法比拟的。在这一基础上进行训练使 Thomson 成为可能：一个按照 Fiduciary-Grade™ 标准构建的模型，成本仅为典型成本的一小部分。
“For years, the AI industry has treated scale as the answer: bigger models, more compute, more money. Thomson shows there is another path,” said Joel Hron, Chief Technology Officer, Thomson Reuters. “Start with a strong foundation, specialize it deeply for the work that matters, and you can build intelligence that is highly capable, far more efficient and entirely under your control. We think that changes the economics of professional AI.” “多年来，人工智能行业一直将规模视为答案：更大的模型、更多的算力、更多的资金。Thomson 表明还有另一条道路，”汤森路透首席技术官 Joel Hron 表示。“从强大的基础开始，针对重要工作进行深度专业化，你就能构建出能力强大、效率高得多且完全由你控制的智能。我们认为这将改变专业人工智能的经济学。”
What Makes Thomson Different Thomson 有何不同
Thomson starts from a strong open-source foundation. What makes it different is what happens next: state-of-the-art mid-training and post-training techniques, drawing on decades of authoritative content from Westlaw, Practical Law, Checkpoint, and Reuters, with hundreds of subject matter experts integrated from the design of training objectives through to the final evaluations. Thomson 从强大的开源基础出发。其不同之处在于后续步骤：采用最先进的中期训练和后期训练技术，利用来自 Westlaw、Practical Law、Checkpoint 和路透社数十年的权威内容，并有数百名主题专家从训练目标设计到最终评估全程参与。
“Thomson proves what’s possible when you build AI on decades of proprietary content and editorial expertise,” said Steve Hasker, CEO of Thomson Reuters. “That’s an advantage only Thomson Reuters has, and it shows in the results: our early evaluations put Thomson on par with the latest frontier models across a range of tasks. We’re putting it to work in CoCounsel Legal, with more capabilities and sovereign AI options to come. This is the bar we intend to keep raising.” “Thomson 证明了当你基于数十年的专有内容和编辑专业知识构建人工智能时，什么是可能的，”汤森路透首席执行官 Steve Hasker 表示。“这是只有汤森路透才拥有的优势，并且体现在结果中：我们的早期评估显示，Thomson 在一系列任务上与最新的前沿模型不相上下。我们正在将其应用于 CoCounsel Legal，未来还将提供更多功能和主权人工智能选项。这是我们打算不断提高的标准。”
The model has been trained on less than 10% of Thomson Reuters content so far, and what comes next is not simply feeding it more data. It is continued discovery of new kinds of specialization and understanding, made possible only by building on decades of proprietary content and editorial expertise. 到目前为止，该模型仅使用了汤森路透不到 10% 的内容进行训练，接下来的工作不仅仅是向其提供更多数据。而是持续探索新的专业化和理解类型，这只有通过基于数十年的专有内容和编辑专业知识才能实现。
AI Sovereignty, and Why It Matters Now 人工智能主权，以及为何现在至关重要
Professionals are paying closer attention to questions of AI sovereignty: how a model is trained, what behaviors and biases live inside it, where it runs, and how the privacy of their information is protected. Thomson marks a shift for Thomson Reuters into a world where those questions are answered directly, not left to third parties alone. 专业人士越来越关注人工智能主权问题：模型如何训练、其内部存在哪些行为和偏见、在哪里运行，以及其信息隐私如何受到保护。Thomson 标志着汤森路透进入一个直接回答这些问题、而非仅交由第三方处理的世界。
Thomson shows a meaningful uplift from its base model in instruction following, the ability to execute complex, multi-part professional instructions precisely. It demonstrates an even greater uplift in navigating dense, domain-specific content, the kind of nuanced reasoning the hardest professional tasks require. It is also able to be trained alongside Thomson Reuters proprietary tools like Westlaw and Practical Law, which makes it more sophisticated and nuanced in its work. Thomson 在指令遵循方面相比其基础模型有显著提升，即精确执行复杂的多部分专业指令的能力。在驾驭密集的领域特定内容方面，它展现出更大的提升，这正是最困难的专业任务所需的细致推理能力。它还能够与汤森路透的专有工具（如 Westlaw 和 Practical Law）一起训练，这使其在工作中更加精密和细致。
The domain-specific gain challenges a common assumption, that the most capable general-purpose models only need access to the right content to perform at an expert level. Thomson Reuters’ early results suggest otherwise. Proprietary training and human subject matter expertise, applied to a strong foundation, produces gains that content access alone does not. 领域特定的提升挑战了一个普遍假设，即最强大的通用模型只需要访问正确的内容就能达到专家水平。汤森路透的早期结果表明并非如此。专有训练和人类主题专业知识应用于强大的基础，所产生的提升是仅靠内容访问无法实现的。
Evaluations of Thomson’s underlying foundation model are available in the technical report about the model’s development. 关于 Thomson 底层基础模型的评估可在该模型开发的技术报告中查阅。
Put To the Test 接受检验
Ahead of today’s launch, Thomson Reuters began opening the model to a group of legal and AI academics for direct evaluation. We will continue to make the model available to external parties to aid in the further validation and development of Thomson over the coming weeks and months. Thomson Reuters is also making a “small” version of Thomson available as an open-weight model on Hugging Face for academic and non-commercial use to further aid in this validation. 在今天发布之前，汤森路透已开始向一组法律和人工智能学者开放该模型进行直接评估。我们将在未来数周和数月内继续向外部各方提供该模型，以帮助进一步验证和开发 Thomson。汤森路透还在 Hugging Face 上提供 Thomson 的“小型”版本作为开放权重模型，供学术和非商业用途使用，以进一步帮助验证。
“I tested Thomson against ChatGPT and Claude using some of the more challenging questions students have asked in my Corporate Tax class. All three models answered the q “我使用学生在我的公司税课程中提出的一些更具挑战性的问题，将 Thomson 与 ChatGPT 和 Claude 进行了对比测试。所有三个模型都回答了 q