FROM EXPERIENCE TO EXECUTION: STATE-CONDITIONED CONTROL FOR SMALL LANGUAGE MODEL AGENTS

As language model agents move from answering questions to executing tasks, the substantial cost and data-exposure risks of remote inference make local small models an attractive deployment choice. Limited parameter capacity and reasoning ability, however, leave these models prone to accumulating errors in prerequisites, state tracking, and argument binding even in recurring workflows. Experience memory can provide the right procedure, but turning it into a current action still requires the model to resolve these local decisions. This difficulty motivates a control-theoretic approach that allocates decision freedom between the small model and its harness according to task state, assigning choices resolved by available evidence to external mechanisms. We propose state-conditioned control (SCC), which embeds trajectory experience as processes and dependencies, using soft guidance for unresolved choices and hard constraints and local execution for resolved steps. With frozen weights, SCC exceeds the strongest of four offline memory adaptations on the evaluated ToolSandbox, ALFWorld, and WebShop suites (13.12 score points, 61.94 and 3.80 success-rate points) and reuses its experience and rules with two additional small models. Channel and cost analyses reveal task-dependent gains and guidance costs, supporting decision allocation as a design variable for small-model agent harnesses. Our agent implementation is available at https://anonymous.4open.science/r/SCC-C6C3/.