Empowering Sequential Decision Intelligence via Long Term Memory Augmentation and Recurrent Reinforcement Learning within Large Language Model Architectures
DOI:
https://doi.org/10.66280/ijair.v1i2.150Keywords:
Sequential Decision Intelligence, Long-Term Memory Augmentation, Recurrent Reinforcement Learning, Large Language Model Architectures, Socio-Technical Infrastructure, Algorithmic Governance, Decision Support Systems.Abstract
The integration of generative pre-trained transformers into complex decision-making pipelines has revealed significant limitations regarding temporal consistency and the retention of stateful information over extended operational horizons. While large language models demonstrate remarkable zero-shot reasoning capabilities, their inherent reliance on static context windows often results in catastrophic forgetting or context-drift when applied to sequential decision tasks. This paper explores the architectural convergence of long-term memory augmentation and recurrent reinforcement learning as a means to empower sequential decision intelligence. By moving beyond the limitations of the attention-only paradigm, we propose a systemic framework that incorporates externalized memory structures and recursive feedback loops to stabilize policy generation. We analyze the structural trade-offs between computational overhead and cognitive fidelity, emphasizing the necessity of robust socio-technical infrastructures to support these high-stakes deployments. Furthermore, the discussion extends to the governance of autonomous systems, addressing critical concerns of fairness, algorithmic bias, and the long-term sustainability of large-scale intelligence infrastructures. Through a detailed conceptual analysis, we argue that the future of decision intelligence lies not in increasing parameter counts alone, but in the sophisticated management of state and experience across multi-modal and multi-temporal environments.
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