Twin Prompt: An End-to-End Framework Inspired by Human Cognition for Navigating Language Model Reasoning

Abstract

Large language models (LLMs) provide essential capabilities for smart systems, yet navigating complex reasoning frontiers reliably remains challenging, hindering deployment in dynamic environments. Existing prompting methods often lack robustness or demand costly multi-step interaction. We introduce Twin Prompt, a novel automated framework inspired by human cognition, operationalizing structured problem reformulation and answer refinement within a single, end-to-end interaction requiring no manual examples. This cognitively grounded structure guides the LLM’s internal reasoning, enhancing analysis and leveraging latent self-correction capabilities to improve accuracy and reliability. Evaluations on challenging mathematical and general reasoning benchmarks (GSM8K, MATH, MMLU, BBH) demonstrate Twin Prompt significantly boosts performance over standard baselines across diverse LLMs. These findings highlight the potential of structured, single-pass prompting to advance LLM reasoning, enabling more capable and dependable AI components for navigating a dynamic world.