2606.02762 Toward a Minimal Handcrafted RAM-Editing Neural Network for WebAssembly: Reducing a Constructed WASM Transformer, and a RAM-State Machine on a Tensor Substrate
A transformer with **analytically computed (untrained) weights** can execute arbitrary WebAssembly programs — Percepta's `transformer-vm`. We study this artifact as a **handcrafted, constructed-weight neural network that edits RAM to process WebAssembly**: attention is used as exact, content/location-addressed memory access, the feed-forward layers are the per-step compute, and the append-only token sequence together with a memory region is the machine's state.