This paper completely changed how I think about agentic AI architecture

I just read "Self-Revising Discovery Systems for Science" (arxiv: 2606.01444) and wanted to share the key ideas and hear from people building agents in practice. **The problem with current agents** Most agentic systems today are doing one of two things --> retrieval or search. They're either fetching known artifacts or finding new combinations within a fixed vocabulary of tools and concepts. The paper argues this is fundamentally different from discovery, and that current architectures have no mechanism to recognize when their world model is simply wrong rather than incomplete. **The proposed architecture** * Everything stored as a strongly-typed DAG. Every hypothesis, action, and failure as an immutable, typed artifact * When new evidence can't be represented in the current schema, the system performs a schema migration using Left Kan extension, which carries old artifacts forward and guarantees nothing is silently lost * The content that can't be explained by transporting old artifacts into the new schema is precisely where discovery happens which they call as residual * An MDL gate acts as referee and a revision only gets committed if it compresses the full accumulated evidence better than the incumbent model, after both are refit on the same data **The part that stuck with me** Rejected alternatives are preserved as first-class typed artifacts, not deleted. The audit trail includes not just what the agent accepted but what it considered and why it rejected it. That's a very different model from how most agent memory systems work today. **Questions for people building agents** 1. How are you currently handling the case where an agent's existing tools and schemas genuinely can't represent a new problem (not just a hard problem), but a categorically different one? 2. Has anyone implemented a complexity penalty on agent-generated artifacts analogous to MD? Something that penalizes bloat rather than just rewarding task completion?