CLM 与 Jev：System One 模型推动自定义 Agent Harness 发展

Own your harness, folks. I feel like System One models take custom agent harnesses to a whole different level. It's not a surprise. This comes down to needs that haven't been met with System Two models alone. A need to be in control of your intelligence stack. A need for more reliable agents. Combining System One and System Two models is way better than just relying on one alone. A need for richer agent experiences (more personal and proactive agents). A need to squeeze out more value per token used. You can check my most recent guide on building a custom harness with Pi and Jev to learn more: academy.dair.ai/resources/jev-… But let's not stop here. I expect a new wave from frontier labs pushing decision models further. And even better tools to build even more customized decision models on top. There are more layers to this. This is great news for us builders and an incredible opportunity for those who specifically build custom harnesses. elvis @omarsar0 Pay attention to this new wave of System One models if you are building custom harnesses. First Jev. Now, Contrastive Language Model (CLM). CLM is 9x faster than Jev. CLM seems to be a better verifier than Jev, particularly at long-horizon tasks. How do Jev and CLM differ? CLM is contrastive, and Jev is trained with Reinforcement Learning for Calibrated Decisions (RLCD). Jev receives a situation plus predefined questions, and returns typed decisions with probabilities. CLM embeds the situation and candidate actions, compares their similarity, then ranks or selects the best match. The point is that there are several ways to attack this problem, which is exciting. You can see my recent guide on combining System One and System Two models for building custom harnesses. academy.dair.ai/resources/jev-… 🔗 View Quoted Tweet 💬 22 🔄 9 ❤️ 135 👀 14730 📊 44 ⚡