Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913895645446144 |
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| author | Tang, Xintong Zhang, Meiru Xiao, Shang Jin, Junzhao Zhao, Zihan Li, Liwei Zheng, Yang Wu, Bangyi |
| author_facet | Tang, Xintong Zhang, Meiru Xiao, Shang Jin, Junzhao Zhao, Zihan Li, Liwei Zheng, Yang Wu, Bangyi |
| contents | Large language models (LLMs) are often constrained by rigid reasoning processes, limiting their ability to generate creative and diverse responses. To address this, a novel framework called LADDER is proposed, combining Chain-of-Thought (CoT) reasoning, Mixture of Experts (MoE) models, and multi-dimensional up/down-sampling strategies which breaks the limitations of traditional LLMs. First, CoT reasoning guides the model through multi-step logical reasoning, expanding the semantic space and breaking the rigidity of thought. Next, MoE distributes the reasoning tasks across multiple expert modules, each focusing on specific sub-tasks. Finally, dimensionality reduction maps the reasoning outputs back to a lower-dimensional semantic space, yielding more precise and creative responses. Extensive experiments across multiple tasks demonstrate that LADDER significantly improves task completion, creativity, and fluency, generating innovative and coherent responses that outperform traditional models. Ablation studies reveal the critical roles of CoT and MoE in enhancing reasoning abilities and creative output. This work contributes to the development of more flexible and creative LLMs, capable of addressing complex and novel tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs Tang, Xintong Zhang, Meiru Xiao, Shang Jin, Junzhao Zhao, Zihan Li, Liwei Zheng, Yang Wu, Bangyi Computation and Language Artificial Intelligence Large language models (LLMs) are often constrained by rigid reasoning processes, limiting their ability to generate creative and diverse responses. To address this, a novel framework called LADDER is proposed, combining Chain-of-Thought (CoT) reasoning, Mixture of Experts (MoE) models, and multi-dimensional up/down-sampling strategies which breaks the limitations of traditional LLMs. First, CoT reasoning guides the model through multi-step logical reasoning, expanding the semantic space and breaking the rigidity of thought. Next, MoE distributes the reasoning tasks across multiple expert modules, each focusing on specific sub-tasks. Finally, dimensionality reduction maps the reasoning outputs back to a lower-dimensional semantic space, yielding more precise and creative responses. Extensive experiments across multiple tasks demonstrate that LADDER significantly improves task completion, creativity, and fluency, generating innovative and coherent responses that outperform traditional models. Ablation studies reveal the critical roles of CoT and MoE in enhancing reasoning abilities and creative output. This work contributes to the development of more flexible and creative LLMs, capable of addressing complex and novel tasks. |
| title | Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13192 |