On the opening day of WAIC 2026, Shanghai AI Lab released a new member of the Shusheng series, Intern-S2-Preview-397B. This is not another parameter-stacking exercise, but an architecture-level turn — abandoning the "everything lives inside the Transformer" path, and splitting "knowledge bearing" from "reasoning computation" into two independent but collaborative engines. The new architecture's core is a pair of components: Memory Decoder turns domain knowledge into pluggable external memory modules, mounted on demand onto the base; Mobius is a brand-new reasoning backbone that uses reverse residual connections so deep hidden states can access shallow-layer knowledge, replacing tokens with dynamic latent-space reasoning. The results are tough: on science tasks like molecular design and material structure generation, Intern-S2-Preview-397B matches the lab's previous trillion-parameter model, with end-to-end inference efficiency up roughly 4×. The companion InternBootcamp turns circuit design, financial modeling and other real tasks into "action-feedback" interactive scenarios, letting the model internalize domain logic through trial and error; "Shusheng · Duanyan" has already landed in six fields: life science, key materials, semiconductors, nuclear fusion, quantum, and Earth weather. When parameter expansion hits compute and energy ceilings, "memory-reasoning decoupling + task-level RL" gives scientific agents a sample path that doesn't rely purely on stacking parameters to keep moving forward.