Cognitive Modeling for Long-Horizon Agent Learning via Integrated Long-Term Memory and Reasoning

This study focuses on the tendency of agents in long-horizon sequential tasks to rely on short-term states and to underutilize historical information, and proposes a cognitive modeling and learning framework with long-term memory and reasoning capabilities. The framework provides a unified cognitive description of the agent's decision process. It introduces a structured long-term memory mechanism to support continuous storage and selective updating of cross-temporal key information. On this basis, a memory retrieval-driven reasoning module is constructed so that experience can explicitly participate in the formation of current decision logic. To address the separation between memory and decision making in conventional policy models, the framework tightly couples perception representation, memory management, reasoning processes, and policy generation into an end-to-end cognitive loop. This design strengthens goal consistency and behavioral stability in long-horizon interactive environments. Comparative evaluations in open source interactive task settings demonstrate consistent advantages in task completion quality, decision efficiency, and long-term information utilization. The results indicate that the proposed cognitive modeling framework effectively mitigates decision difficulties caused by long-range dependencies and partial observability. Overall, the study shows that integrating long-term memory and reasoning within a unified learning framework is an important approach for improving sustained decision-making capability in complex environments.