arXiv:2511.12309v2 Announce Type: replace Abstract: Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning. It consists of generating multiple responses, or ``samples", from a large language model (LLM) and selecting the most frequent answer. This procedure can naturally be viewed as a majority vote or empirical mode estimation. Despite its effectiveness, self-consistency is prohibitively expensive at scale when naively applied to datasets, and it lacks a unified theoretical understanding of sample efficiency and scaling beh
The paper addresses a significant challenge in scaling LLM inference, aligning with the current push for more efficient and cost-effective AI operations.
Improving the efficiency of self-consistency, a key technique for LLM reasoning, directly impacts the economic viability and widespread adoption of advanced AI applications.
The development of 'optimal self-consistency' suggests a potential reduction in the computational cost of achieving high-quality LLM outputs, making powerful reasoning techniques more accessible.
- · Large Language Model developers
- · AI-powered application providers
- · Cloud computing providers
- · Researchers in AI efficiency
- · Inefficient AI inference architectures
More cost-effective deployment of advanced LLM reasoning capabilities.
Accelerated development and adoption of complex AI agentic systems due to lower operational costs.
Enhanced competition in the AI services market as advanced reasoning becomes more commoditized and accessible.
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Read at arXiv cs.LG