Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs

• URL: http://arxiv.org/abs/2506.17630v1
• Date: Sat, 21 Jun 2025 08:15:45 GMT
• Title: Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
• Authors: Yang Wu, Yifan Zhang, Yiwei Wang, Yujun Cai, Yurong Wu, Yuran Wang, Ning Xu, Jian Cheng,
• Abstract summary: Large Language Models (LLMs) demonstrate impressive reasoning capabilities.<n>Evidence suggests much of their success stems from memorized answer-reasoning patterns rather than genuine inference.<n>We propose a five-level answer-visibility prompt framework that systematically manipulates answer cues and probes model behavior through indirect, behavioral analysis.
• Score: 28.556628696390767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, growing evidence suggests much of their success stems from memorized answer-reasoning patterns rather than genuine inference. In this work, we investigate a central question: are LLMs primarily anchored to final answers or to the textual pattern of reasoning chains? We propose a five-level answer-visibility prompt framework that systematically manipulates answer cues and probes model behavior through indirect, behavioral analysis. Experiments across state-of-the-art LLMs reveal a strong and consistent reliance on explicit answers. The performance drops by 26.90\% when answer cues are masked, even with complete reasoning chains. These findings suggest that much of the reasoning exhibited by LLMs may reflect post-hoc rationalization rather than true inference, calling into question their inferential depth. Our study uncovers the answer-anchoring phenomenon with rigorous empirical validation and underscores the need for a more nuanced understanding of what constitutes reasoning in LLMs.

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