Intent, a critical cognitive notion and mental state, is ubiquitous in human communication and problem-solving. Accurately understanding the underlying intent behind questions is imperative to reasoning towards correct answers. However, this significant concept has been largely disregarded in the rapid development of language models (LMs). To unleash the potential of intent and instill it into LMs, this paper introduces Intentional Analysis (IA), which explicitly invokes intent-aware analysis and reasoning during the problem-solving process. Comprehensive experiments across diverse benchmarks, model types, and configurations demonstrate the effectiveness, robustness, and generalizability of IA. Notably, IA consistently improves task performance even on SOTA proprietary models like GPT-5 and Claude-Opus-4.6. Moreover, IA not only outperforms Chain-of-Thought (CoT) across various experimental settings, but it can also synergistically work with CoT reasoning. Further qualitative analysis and case studies reveal that the benefits of IA stem from addressing several weaknesses in baseline methods, such as intent misunderstanding, hasty generalization, and mental laziness. Case studies also provide insights into the mechanisms underlying IA and clarify how it differs from CoT in mitigating these weaknesses. This study sheds light on a promising direction for the development of future LLMs with intentional analysis.

翻译：意图，作为一种关键的认知概念和心理状态，在人类交流与问题解决中无处不在。准确理解问题背后的潜在意图对于推理出正确答案至关重要。然而，在语言模型（LMs）的快速发展中，这一重要概念在很大程度上被忽视了。为释放意图的潜力并将其注入语言模型，本文提出意图分析（Intentional Analysis，IA），该方法在问题解决过程中显式地调用基于意图感知的分析与推理。跨多种基准测试、模型类型及配置的全面实验证明了IA的有效性、鲁棒性与泛化能力。值得注意的是，即使对于GPT-5和Claude-Opus-4.6等最先进（SOTA）的专有模型，IA也能持续提升任务性能。此外，IA不仅在各类实验设置中优于思维链（Chain-of-Thought，CoT），还能与CoT推理协同工作。进一步的定性与案例分析表明，IA的优势源于其对基线方法若干弱点的克服，例如意图误解、草率泛化与思维惰性。案例研究还揭示IA的运作机理，并阐明其与CoT在缓解这些弱点上的区别。本研究为未来具备意图分析能力的大语言模型（LLMs）的发展指明了一条有前景的方向。