Program-Centric Memory: A Representation-for-Function Approach to Experience Reuse

Large language model (LLM) agents can construct memory from past interaction trajectories and reuse it to support future action selection and outcome prediction. However, when these memory functions are carried primarily by language, predictive information may remain implicit, while reusable action patterns require repeated LLM reasoning at runtime. This motivates a memory-form perspective that we call Representation for Function: memory representations should align with the functions they serve at reuse time. By aligning memory form with function, we propose Program-Centric Memory (PCM), which represents explicit probabilistic action-outcome relationships as Predictive Beliefs and reusable action patterns as Executable Programs. PCM achieves consistently strong performance across ALFWorld, WebShop, and TravelPlanner. Controlled experiments further show that, with the underlying information held fixed, using executable rather than textual representations of the same action patterns increases success from 79.9% to 97.8%, while reducing online tokens from 98.4k to 2.5k per task. These results establish memory form as a key dimension of experience reuse.