cs.AIJul 13, 2026

Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents

Authors: Chenglin YuLi YinYing YuQingxin FanRunyangRay ZhongHongxia YangMing Li

Organizations: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University · Department of Data and Systems Engineering, The University of Hong Kong · College of Economics and Management, Zhejiang Normal University · Shanghai Juepei Flexible Supply Chain Technology Co., Ltd. · Department of Computing, The Hong Kong Polytechnic University · Research Institute for Generative AI, The Hong Kong Polytechnic University

Abstract

Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM performs semantic execution. A three-arm SOPBench study across six models separates representation from runtime: compiled text never significantly hurts and gains up to 16.0 points where official prose underperforms. Runtime guidance is capability-gated. Two strong models independently show positive seven-domain PG contrasts (58:19 and 75:31 discordant pairs), whereas weak models are harmed. A full-program cursor ablation (active frame first, complete program retained) recovers much of the strong-model refusal gain; selective visibility adds a smaller improvement. Paired probe and audit measurements track this divide to spontaneous state discipline rather than reconstruction ability. On Bank the three primary arms rise from 70.4 to 86.4 to 92.8, with 100% refusal correctness. Practical guidance: compile first; enable active-frame paging only after a model-level discipline check.

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