Uncertainty-Aware Reasoning Path Filtering for Reliable Large Language Model Inference
Keywords:
uncertainty-aware inference; reasoning path filtering; large language models; reliable inference; system architecture; calibration; AI governance; deployment sustainabilityAbstract
Large language models increasingly operate as general-purpose reasoning engines in high-stakes environments, yet their outputs remain difficult to audit, calibrate, and govern. This paper examines uncertainty-aware reasoning path filtering as a system-level strategy for improving the reliability of large language model inference. Rather than treating uncertainty as a post hoc confidence score attached to a final answer, the discussion positions uncertainty as a structural signal distributed across multiple candidate reasoning paths. The paper develops an architectural account of reasoning path generation, scoring, filtering, and escalation, with attention to the trade-offs that emerge among accuracy, latency, computational cost, fairness, and operational sustainability. It argues that reliable inference depends less on a single model improvement than on the surrounding socio-technical infrastructure that decides when to trust, revise, defer, or reject generated reasoning. The analysis connects calibration, out-of-distribution detection, ensemble disagreement, and verbalized uncertainty to practical deployment concerns such as auditability, model documentation, human oversight, and environmental cost. It also considers how filtering mechanisms may amplify or mitigate inequities when confidence estimates are unevenly distributed across languages, domains, and populations. The paper concludes that uncertainty-aware reasoning path filtering offers a promising but incomplete foundation for reliable inference, and that its benefits depend on transparent governance, cross-domain validation, and infrastructure capable of supporting adaptive computation without creating new forms of exclusion or hidden technical debt.
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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.
