Self-Consistency Guided Path Selection for Complex Reasoning in Large Language Models
Keywords:
large language models; self-consistency; reasoning path selection; socio-technical systems; governance; robustness; sustainability; fairnessAbstract
Large language models have demonstrated substantial capacity for complex reasoning when prompted to produce intermediate rationales, yet their outputs remain sensitive to sampling, prompt formulation, and opaque internal state. Self-consistency methods improve reliability by generating multiple reasoning paths and selecting or aggregating among them, but naive deployment becomes computationally expensive and operationally fragile as task complexity and model scale increase. This paper examines self-consistency guided path selection as a socio-technical systems problem rather than a narrow decoding heuristic. It argues that path selection must be understood through layered architecture, resource governance, robustness, fairness, and sustainability. The paper first reviews foundational work on prompting, reasoning, and alignment, then develops a conceptual architecture that separates path generation, scoring, consensus, retrieval, and observability. It analyzes trade-offs involving accuracy, latency, cost, interpretability, and environmental burden. It further considers how path selection interacts with adversarial inputs, biased training distributions, human feedback, auditability, and policy frameworks. A central claim is that guided selection can improve complex reasoning only when paired with transparent evaluation, calibrated uncertainty, and infrastructure-aware deployment. The discussion draws on research in chain-of-thought reasoning, self-consistency, retrieval augmentation, reinforcement learning from human feedback, model reporting, and green computing. The paper concludes that robust path selection is not merely a model-level optimization but a governance and infrastructure commitment that shapes the legitimacy, equity, and long-term viability of reasoning systems in high-stakes settings.
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