Latent Assembly: Causal Discovery and Cross-Task Linking of Executable Operations in Language Models

Can computations learned by language models be decompiled as executable operations and assembled into new programs? We introduce a causal framework that recovers typed program states, discovers state-dependent operations, and neural interfaces through which one operation’s output becomes another’s input. The framework identifies operations by their causal transformations rather than their physical locations and tests reuse through predicted intermediate states, causal execution, and recompilation. In controlled transformers, our framework discovers ground-truth operation classes and composition laws starting from unlabeled neural activations. In pretrained Qwen, Gemma, and Llama models, we discover SELECT and LOAD operations connected by a reusable record-reference interface and validate their composition and recompilation. Most importantly, we show that three distinct operations discovered and qualified independently from separate tasks using our framework can be linked to execute semantically distinct tasks with no target-task fitting. We discover the frozen neural calling conventions through which the operations communicate. The three-operation chain achieves 84% accuracy, far exceeding baseline controls and Qwen 14B's direct zero-shot performance. Furthermore, interventions on upstream arguments produce the downstream behavior implied by the altered program. These results establish a concrete form of neural programmability: independently discovered computations can exchange arguments and execute together. They open a path toward making computations acquired during training accessible as components that can be inspected, tested, and deliberately recombined.