COGENT: A Modular Cognitive Architecture for Agentic User Simulation

LLM-based user simulation can support interface design, accessibility research, and interactive-agent evaluation, but standard computer-use agents are optimized for task completion rather than reproducing how people perceive, reason, and act. We introduce COGENT (COGnitive agENT), a modular cognitive architecture for simulating user interaction with desktop interfaces. COGENT draws on established cognitive theories, such as Norman’s action cycle and ACT-R, to enable explain- able, intervenable, and modular user simulation by decomposing the simulation process into cognitive modules with explicit representations of perception, memory, user state, intentions, and action selection. We begin by presenting six interven- ability demonstrations, each tracing how a single edit to memory, a user attribute, or a processing module propagates through the cognitive pipeline to produce a behavioral change. We evaluate COGENT’s behavioral and attributional fidelity across three benchmarks – AgentNetBench, A11y-CUA, and SENSE-42 – finding that it achieves statistically significant fidelity gains over computer-use baselines in reproducing recorded human behavior and recovers the relational structure among human self-reported psychological states Together, these demos and evaluations demonstrate COGENT’s utility toward more faithful, explainable, intervenable, and modular LLM-based cognitive simulations.