TEXT AS A COMPOSITIONAL INTERFACE: TOWARD DECOMPOSABLE LEARNING WITH RECON- STRUCTED POLICIES

Complex systems should not require every new combination of learned skills to be trained end to end. We study whether a language model can instead externalize a learned component as a local textual policy and use that text as a runtime interface for composition. Models are fine-tuned on short chains of opaque triggers, each denoting a two-branch conditional policy; the definitions are absent from the test prompt, and the model reconstructs the complete policy immediately before each use. We then ask which parts of the reconstructed text actually control the next computation. Exchanging only the two operations inside a written policy, with the upstream trace and state fixed, redirects the next operation on the majority branches of both LLaMA-3.2-3B-Instruct and Qwen2.5-3B-Instruct (82–85% pooled), but far less on rare branches. On a separate customer-service workflow, editing a written threshold on the same record produces a +64.2 percentage-point decision-directed effect beyond an equally sized decision-preserving edit (95% CI 59.4–68.9), and the redirected decision propagates to the final verdict. The interface is not uniformly semantic, however: writing an unfamiliar modular-arithmetic residue into a policy yields no value-specific discrimination (−0.35 points), which bounds what operation following can certify and leaves open whether the bottleneck is reading the predicate or computing it. Finally, a token- and position-matched control shows that informative policy content remains useful at a higher numeric range (+23.3 points, 6/6 seeds), with a six-point attenuation at the held-out depth. Together, the results distinguish policy reconstruction, interpretation, action selection, and continued invocation as requirements on a textual interface, and motivate reconstructed policies as a route toward decomposable learning rather than a demonstration of it.