Semantic Interface Distillation: Turning Native Runtimes into Reusable Latent Interfaces

Large language models form rich latent runtimes while reading a source, but these states are not necessarily reusable after the original execution environment is removed. We study source-free latent reuse: a model reads a source once, exports a latent interface before future requests are known, and must later answer new queries, actions, or evidence edits without the source, its KV cache, or its original positions. We show that latent information can remain readable while hard-reset execution fails, because useful computation is distributed across the native runtime and lacks a reusable reading protocol. We propose Semantic Interface Distillation (SID), which distills the full runtime into a query-independent slot bank, uses counterfactual routing to select slots relevant to a later request, and rehydrates them into a temporary working state for hard-reset decoding. We evaluate SID on controlled symbolic reasoning, clinical evidence inference, and contract NLI, testing later actions, evidence edits, and document queries from one encoded source. Across these settings, SID consistently improves source-free reuse over contract-adapted latent interfaces, achieving the strongest performance on compositional generalization, evidence-branch reuse, and document-level causal transfer. These results show that one-time latent computation can be converted into a reusable source-free interface.