Compliance vs. Rationality: A Deep Exploration of Reasoning Controllability in Large Language Models

This article focuses on the research of reasoning controllability in large language models, exploring the inherent tension between the model's compliance with instructions (compliance) and maintenance of logical rationality, and presents an implementation of a reasoning guidance module based on EasyEdit. The study aims to address the dilemmas of over-compliance (e.g., catering to wrong instructions) and under-compliance (e.g., refusing reasonable instructions), providing technical support for scenarios such as AI alignment and personalized assistants.

While the reasoning ability of large language models has improved, there are two major issues: over-compliance (abandoning reasonable judgment to cater to user instructions, e.g., proving the Earth is flat) and under-compliance (being stubborn and refusing reasonable instructions, e.g., repeating original opinions). The tension between these two is the core focus of this study.

• Compliance: The degree to which the model follows user instructions, preferences, or constraints; high compliance may lead to loss of independent judgment.
• Rationality: The logical consistency, factual accuracy, and alignment with common sense of reasoning; high rationality may lead to rigidity.
• Tension: The ideal state is a balance between the two, but trade-offs are needed in reality, and the key lies in a controllable balance point.

The project uses the EasyEdit framework to build a reasoning guidance module. The core idea is: identify reasoning paths → directional intervention on activation values → dynamically balance the two. The technical route includes activation patching (modifying intermediate layer activation values), representation editing (learning the direction of concept representation), and contrastive guidance (constructing guidance vectors based on activation differences between positive and negative samples).

The research has guiding significance for multiple scenarios:

• AI safety alignment: Prevent malicious use and avoid excessive rejection of legitimate requests;
• Personalized assistants: Meet different users' expectations for compliance;
• Educational tutoring: Control the degree of reasoning revelation to guide students' thinking;
• Creative writing: Balance between constraints and originality.

Significance: Reflects the deep-seated contradictions between usefulness and safety, autonomy and controllability in AI design, and provides a perspective for technical adjustment. Future directions: Fine-grained control, dynamic adaptation to context, interpretability, and multimodal expansion.

Although the code volume of this project is not large, it raises key questions: Should AI be an unconditionally obedient tool, a principle-adhering advisor, or a flexibly switching assistant? The ability to control is itself a sign of technological progress.