Memory-Augmented Reasoning Optimization for Long-Horizon Language Model Tasks
Keywords:
memory augmentation; long-horizon reasoning; language model systems; retrieval-augmented generation; alignment; governance; sustainabilityAbstract
Long-horizon language model tasks require systems that can retain context, revise plans, retrieve evidence, and act coherently across many interdependent steps. Memory-augmented reasoning optimization has therefore become a system-level problem rather than a narrow modeling problem. This paper examines the architectural, infrastructural, and governance conditions under which memory augmentation improves long-horizon reasoning. It argues that durable performance depends less on adding larger context windows than on designing tiered memory, retrieval discipline, reasoning control, alignment mechanisms, and observability as integrated components. The discussion distinguishes working, episodic, semantic, and procedural memory and shows how each supports different aspects of sustained reasoning. It then analyzes architectural patterns such as retrieval-augmented generation, external memory stores, tool-mediated action, and agent orchestration, emphasizing latency, cost, freshness, and failure containment. The paper also considers optimization strategies for long-horizon tasks, including path filtering, reinforcement learning from feedback, self-consistency, and alignment for smaller parameter models. It finds that these methods can improve reliability but often shift complexity into data curation, evaluation, and operational monitoring. Finally, the paper addresses robustness, fairness, privacy, sustainability, and policy implications. It concludes that memory-augmented reasoning should be governed as socio-technical infrastructure, with attention to auditability, energy use, equitable access, and long-term maintainability.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
