DeepConf: Slash LLM Compute Costs 85% While Boosting Reasoning Accuracy

DeepConf: Enhancing LLM Reasoning Efficiency Through Confidence-Based Filtering Figure 1: DeepConf system overview showing parallel thinking with confidence filtering The Challenge of Efficient LLM Reasoning Large language models (LLMs) have revolutionized complex reasoning tasks, but their computational demands present significant barriers to practical deployment. Traditional methods like majority voting improve accuracy by generating multiple reasoning paths, but suffer from: Diminishing returns: Adding more reasoning paths yields smaller accuracy improvements Linear cost scaling: Each additional path increases compute requirements proportionally Quality blindness: All reasoning paths receive equal consideration regardless of quality This article explores DeepConf, a novel approach that leverages internal …