Chubby♨️@kimmonismus · XMIT Buehler团队提出Self-Revising Discovery Systems框架，让AI能自主扩展科学词汇（变量、工具、验证器、模型结构），而非仅搜索固定空间。论文使用typed copresheaf和Kan obstruction数学框架形式化智能体工作流，证明真正发现是可验证的schema扩展：旧证据通过Left Kan extension迁移，新异性由pointwise残差客观量化，区分发现与搜索。三种模态：检索（添加已知对象）、搜索（固定schema）、发现（验证的范式转换）。案例包括Builder/Breaker发现蛋白质模式条件合规性，CategoryScienceClaw发现各向异性纤维网络刚度规则。论文arXiv:2606.01444（2026）。
AI scientists may be moving from search to real discovery.
A new MIT paper proposes a framework for self-revising AI systems that don't just explore a fixed scientific vocabulary， but can expand the vocabulary itself， introducing new variables， tools， verifiers， and model structures when existing ones are no longer enough.
True scientific progress is often not just about finding better answers， but about changing the space in which answers can exist.
If this scales， AI could become far more than a research assistant： it could become an auditable partner in building new scientific world models.
Still early， but conceptually very exciting.