Fingerprint-Aware Neural Graph Certificates

Integrate this into a PyTorch or JAX computation-graph optimizer, compiler, or neural research agent rather than into the floating-point forward pass itself. First lower the model or pipeline into a DAG: nodes can be layers, tensor transforms, quantizers, pruning passes, robustness analyzers, or benchmark steps; edges record data and certificate dependencies. For every node v, store its output interface and a certificate record containing the certificate type, verifier version, assumptions, and h_v^{certified}. Compute h_v with a canonical serialization of shape, dtype, layout, semantic contract, numerical bounds, and exposed parameters; exclude irrelevant metadata such as object addresses. When a local edit changes v, recompute h_v^{new}, apply the paper's invalidation rule, mark every node in I(v) dirty, and rerun verifiers in topological order. Pseudocode: edit(v); old=h[v]; recompute_interface(v); new=h[v]; if old==new: dirty={v}; else: dirty={v} union descendants(v); for w in topo_sort(dirty): certificate[w]=verify(w, current_inputs, certificate[parent]); h_cert[w]=h[w]. The paper supplies the invalidation logic; empirical code must define certificate types and estimate quantities such as activation ranges or Lipschitz bounds. Start with a 12-layer Transformer on WikiText-2, attach shape, activation-range, and INT8-equivalence certificates, and repeatedly quantize or replace one layer. Compare full re-verification against selective invalidation. Success is at least 2x lower verification time for local edits with zero stale-certificate failures and identical final acceptance decisions.