Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
Journal:
arXiv
Published Date:
Jun 16, 2025
Abstract
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.
