New paradigm for deep research models! Apodex-1.0-H is a new model that introduces a completely new way of working. Apodex comes in several flavors, including an open-weight Apodex-1.0-mini and 0.8B, 2B, and 4B Smol series of models. This is what's new with Apodex: 1. The model works like a team 2. It dynamically improves its own work 3. It verifies every answer First, the model works natively like a team of subagents to solve a task: You have the main agent decomposing the query and spawning specialist sub-agents on demand. All of these subagents work asynchronously. You have agents for research, verification, fact-checking, and auditing, all working together. I call this "natively" because the ability to decompose a task into multiple subagents was trained into the model. Second, the model can dynamically improve its own answers. As it works to produce an answer, the model grades it, notes where it's weak, and rewrites it based on that feedback. loop: generate → verify → revise Each pass of this loop learns from the last and improves on the answer. The grader never sees a correct answer. It's judging whether its own work holds up, with nothing to compare it to. Third, the model uses different agents to reason and verify. The obvious hallucinations are easy to catch, but those where the answers look and feel right are much harder and more dangerous. Models that check their own work have huge blind spots. The verification process in Apodex uses a separate team of subagents to score answers on several categories to determine which answer actually solves the problem. You can find the open-weight variants of Apodex on HuggingFace: https://huggingface.co/apodex To see Apodex-1.0-H in action, go here: https://apodex.ai Thanks to the Apodex team for partnering with me on this post.