Adaptive Reasoning Path Selection for Resource-Constrained Large Language Models
Keywords:
adaptive reasoning, large language models, resource-constrained computing, system architecture, governance, sustainability, robustness, fairnessAbstract
Large language models have become general-purpose computational artifacts, yet their most capable reasoning behaviors are often entangled with substantial memory, latency, energy, and governance costs. Resource-constrained settings, including edge devices, regulated enterprise enclaves, classrooms, clinics, public-sector terminals, and intermittently connected field environments, cannot assume unlimited accelerator capacity or persistent cloud connectivity. This paper examines adaptive reasoning path selection as a system-level strategy for reconciling reasoning quality with constrained computation. Rather than treating reasoning as a monolithic capability, the paper conceptualizes it as a portfolio of selectable trajectories whose depth, branching, tool use, retrieval intensity, and verification overhead can be adjusted at runtime. The analysis connects model architecture, inference serving, memory hierarchy, adaptation methods, and orchestration policy to the practical question of which reasoning path should be chosen under a given resource envelope. It also considers structural trade-offs among accuracy, robustness, fairness, latency, maintainability, and sustainability. The paper argues that adaptive path selection cannot be reduced to a model-side optimization because deployment context, infrastructure ownership, monitoring, fallback behavior, and accountability determine whether such systems remain trustworthy. Governance implications are examined through transparency, contestability, procurement, and environmental accounting. The conclusion is that resource-constrained reasoning systems require adaptive, auditable, and institutionally embedded selection mechanisms, not merely smaller models.
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