Discovery Model
A discovery model is the source’s name for an AI system that can propose new hypotheses and verify whether they hold. In E242｜最快半年AI跑通自进化？与陈天桥首席科学家聊聊硅谷模型必争之地, Apodex uses this frame to distinguish itself from generative chat, image, or video products: the goal is not only to answer questions, but to solve hard scientific and technical problems.
The episode argues that the hard part is not producing a plausible hypothesis. A useful Discovery Model has to find questions humans have not already written down, search and reason across evidence, use code or simulation when possible, and pass AI Verification. For open scientific domains, it also needs Research Taste: the ability to prefer fundamental problems over shallow or merely publishable ones.
Key Claims
- Discovery requires novelty, but novelty without verification is not enough.
- Out-of-distribution hypotheses are especially hard because the model cannot simply retrieve the training-set answer.
- Deep Research is a stepping stone because search, planning, and synthesis are needed before a model can make scientific proposals.
- Code, simulation, formal math, and agent-team critique are all possible verification surfaces.
- Top-scientist feedback is treated as a scarce post-training signal for teaching problem choice and research taste.
Connections
- Apodex, Chen Tianqiao, Du Shaolei, and Li Beibin — company and people attached to the concept in the source.
- AI For Science, AI Materials Discovery, and Causal AI — adjacent scientific AI themes in the wiki.
- Research Taste and Problem Definition In Research — judgment about which scientific questions matter.
- Recursive Self-Improvement, Deep Research, and AI Verification — technical loop needed for discovery rather than answer generation.