Hidden APIs in Language Models: Discovering Reusable Causal Interfaces from Forked Futures

Identical language-model answers can arise from hidden states that support different future computations, so present-answer decodability leaves the organization of reusable state unresolved. We introduce forked futures: future operations are sampled only after a prefix state has formed, and the resulting response distributions define a future causal signature over that state. Shared, Local, Mixture, and Distributed interfaces then compete under prequential causal description length with matched capacity and future-signature fidelity. Shared achieves the lowest held-out description length on Qwen2.5-1.5B and Llama-3-8B, with Sharedness Gains of 0.216 and 0.294 nats, while preserving comparable or lower mean, worst-family, and tail distortion; the preference remains positive across a five-backbone sweep. Role-aligned transplantation gives Shared the strongest joint target-correctness, locality, and copy-preservation profile, and API-aligned paths mediate 0.749 of the target effect versus 0.150 for matched null paths. In blind model organisms with known routing, the procedure recovers 14/16 architectures. Together, these results identify a compact reusable causal interface through convergent evidence from description length, held-out generalization, transplantation, mediation, and ground-truth architecture recovery.