Harvey发布了法律专用模型Tenet，在法律任务上表现优异，成本还很低，展示了AI原生公司如何构建自己的智能栈。
Harvey发布了其首个法律专用模型Tenet。该模型基于Kimi K3，在法律数据集上进行了微调。Tenet在LAB Contracts基准上达到SOTA性能，在LAB基准上排名第二。其所有通过率比Kimi K3基座模型提升了82%。该模型还针对代币效率进行了优化，成本仅为领先基础模型的四分之一。
This is just the beginning of what's coming. Not paid to say this, but I think Harvey provides a gli...
This is just the beginning of what's coming. Not paid to say this, but I think Harvey provides a glimpse into the future of what successful AI-native companies will look like and how they operate. Successful companies will need to think of how to build and own their entire intelligence stack. They own the models, agents, and everything in between. Harvey @harvey Introducing Tenet, our first model post-trained for legal. Tenet is a Kimi K3 base that we post-trained with @FireworksAI_HQ on a corpus of publicly available legal data, synthetic data, and human expert data simulating long-horizon legal work. Training increases Tenet's all-pass rate by 82% on LAB and 22% on LAB Contracts relative to the Kimi K3 base model. It achieves state-of-the-art performance on LAB Contracts and places second on LAB. These gains generalize to other leading agentic benchmarks including @mercor 's Apex Agents - Corporate Law, @crosbylegal 's Redline Bench, and @scale_AI 's Professional Reasoning Bench. Tenet is also optimized for token efficiency, operating at less than a fourth the cost of leading foundation models. We additionally post-trained three specialist models for Tenet to use as subagents: 1) M&A Diligence: post-trained with @baseten on our LAB Diligence environment in an RLM harness, this model is optimized for high-scale, long-horizon tasks. 2) Review Tables: trained with @appliedcompute on our Review Table environment, this model is state-of-the-art and cost-effective at high-volume document review and structured data extraction. 3) Firm Knowledge: trained with @EngramLab on our synthetic law firm environment, this model is optimized to learn and search over a firm's knowledge via memory and structured notes. More details on model training, environment design, benchmarking, results, and more in the article by @gabepereyra below. What's next for Harvey’s research? - Scaling LAB to more jurisdictions, practice areas and workflows - Scaling compute to bring new generalist models and capabilities to Harvey More to come soon. 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 7 👀 1597 📊 1 ⚡