Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911018583588864 |
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| author | Wu, Yang Zhang, Yifan Wang, Yiwei Cai, Yujun Wu, Yurong Wang, Yuran Xu, Ning Cheng, Jian |
| author_facet | Wu, Yang Zhang, Yifan Wang, Yiwei Cai, Yujun Wu, Yurong Wang, Yuran Xu, Ning Cheng, Jian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs Wu, Yang Zhang, Yifan Wang, Yiwei Cai, Yujun Wu, Yurong Wang, Yuran Xu, Ning Cheng, Jian Computation and Language 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. |
| title | Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.17630 |