Efficient Long-Context Reasoning through Adaptive Token and Path Selection
Keywords:
long-context reasoning, adaptive token selection, reasoning path selection, retrieval-augmented generation, efficient inference, socio-technical systems, AI governance, sustainable computingAbstract
Long-context reasoning has become a central challenge for artificial intelligence systems that must operate over documents, codebases, logs, legal records, scientific literature, and multi-turn interaction histories. Although transformer architectures and their successors have expanded the practical context window, simply increasing context length does not guarantee efficient or reliable reasoning. This paper examines adaptive token and path selection as a system-level strategy for improving long-context inference under real-world constraints of latency, cost, memory, energy, robustness, and fairness. The argument is that long-context reasoning should be treated less as a single model capability and more as an infrastructure problem involving routing, filtering, caching, retrieval, scheduling, governance, and evaluation. Adaptive token selection determines which portions of an input deserve computation, while adaptive path selection determines which reasoning trajectories, tools, or intermediate steps should be pursued. Together, these mechanisms can reduce waste, improve grounding, and support more sustainable deployment. The paper analyzes architectural trade-offs, deployment patterns, and policy implications, including transparency, contestability, and equitable access. It also considers failure modes such as selective blindness, path collapse, over-reliance on retrieval, and hidden technical debt. Drawing on research in efficient attention, retrieval-augmented generation, reasoning control, and responsible computing, the paper proposes an interdisciplinary agenda for designing long-context systems that are not only capable but also governable, auditable, and economically viable.
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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.
