Weight-of-Thought Reasoning: Exploring Neural Network Weights for Enhanced LLM Reasoning (2025)

Abstract: Large language models (LLMs) have demonstrated remarkable reasoning capabilities when prompted with strategies such as Chain-of-Thought (CoT). However, these approaches focus on token-level output without considering internal weight dynamics. We introduce Weight-of-Thought (WoT) reasoning, a novel approach that examines neural network weights before inference to identify reasoning pathways. Unlike existing methods, WoT explores the weight space through graph-based message passing, multi-step reasoning processes, and attention mechanisms. Our implementation creates an interconnected graph of reasoning nodes. Experiments on diverse reasoning tasks (syllogistic, mathematical, algebraic, combinatorial, and geometric) demonstrate that WoT achieves superior performance compared to traditional methods, particularly for complex problems. This approach leads to both improved performance and greater interpretability of the reasoning process, offering a promising direction for enhancing LLM reasoning capabilities.

## introduction large language models (llms) have demonstrated remarkable proficiency in natural language understanding and generation, significantly advancing diverse applications @xcite . however, mastering complex reasoning tasks requiring logical deduction, multistep mathematical problem-solving, and structured thought processes remains a significant challenge @xcite . to bridge this gap, techniques like chain-of-thought (cot) prompting @xcite and its variants @xcite have emerged. by eliciting intermediate reasoning steps as textual output, cot has substantially improved * lead author llm performance on reasoning-intensive benchmarks. despite these advances, a fundamental limitation persists: current reasoning enhancement methods primarily operate at the level of model output, treating the reasoning process as an observable sequence of tokens. they largely overlook the internal neural mechanisms and weight configurations that fundamentally enable these reasoning capabilities within the model itself. this paper introduces weight-of-thought (wot) reasoning, a novel paradigm that shifts the focus inward. we propose analyzing the neural network's weights and activations prior to or during inference to identify, structure, and leverage latent reasoning pathways encoded within the model's parameters, thereby enhancing the agent's reasoning performance. our core insight is that complex reasoning abilities are not merely an emergent property reflected in output sequences but are intrinsically linked to the structured knowledge and computational patterns embedded within the network's weight space. wot operationalizes this insight by explicitly exploring this weight space and transforming it into an interconnected graph of specialized "reasoning nodes." this creates a dynamic "network of weighted thoughts," enabling more sophisticated, potentially non-linear reasoning patterns that go beyond simple sequential chains. as illustrated conceptually in figure 1 , wot aims to harness the internal computational fabric of the model to facilitate more robust and adaptive reasoning. the weight-of-thought approach integrates several key components designed to work synergistically. a graph-based framework models reasoning as information exchange between nodes via dynamic message passing @xcite . crucially, this information flow is weight-directed, guided by learned edge weights and attention mechanisms derived from analyzing relevant weight patterns, allowing the model to prioritize salient connections. multi-step refinement layers enable iterative improvement of the reasoning process, mimicking deliberative thought. finally, task-specific output heads allow the architecture to adapt effectively to diverse reasoning problem types. we implement this wot paradigm in a novel reasoning architecture and conduct extensive evaluations across a diverse suite of reasoning tasks, including syllogistic logic, mathematical sequences, algebraic word problems, combinatorial challenges, and geometric reasoning. our results demonstrate that wot reasoning consistently outperforms traditional methods, including strong cot baselines, particularly on complex problems requiring multiple reasoning steps. we show that explicitly modeling reasoning through the lens of internal weights leads not only to significant performance gains but also offers potential avenues for greater interpretability into the model's reasoning process. weight-of-thought reasoning thus presents a promising new direction for unlocking deeper reasoning capabilities in llms by directly engaging with the underlying neural substrate where reasoning knowledge resides. ## related work the development of weight-of-thought (wot) reasoning builds upon and extends several key research areas, including methods for enhancing reasoning in large language models (llms), graphbased neural networks, and the analysis of internal neural network mechanisms. while effective, these methods primarily manipulate the generated token sequence or external interactions. in contrast, wot analyzes the internal weight structure to identify and guide reasoning pathways from within the model itself. ## weight-of-thought reasoning weight-of-thought (wot) reasoning fundamentally shifts the focus from analyzing generated output sequences (e.g., chain-of-thought @xcite ) towards leveraging the intrinsic computational structure encoded within a neural network's weight space. the core idea is to analyze the model's weights (w) to identify and utilize latent "reasoning pathways" (p)-patterns within the weights that correspond to specific reasoning operations or information flows. as conceptually shown in figure 2 , wot aims to transform the reasoning process from a chain into a graph of specialized nodes. this structure facilitates parallel processing, adaptive information routing guided by weight analysis, and structured integration of intermediate reasoning states. ## discussion our evaluation highlights weight-of-thought (wot) reasoning's potential to advance neural network reasoning by shifting focus from output sequences to internal weight structures @xcite . structuring insights from the weight space into a dynamic reasoning graph changes the approach to complex tasks compared to traditional sequential methods @xcite . ## conclusion this paper introduces weight-of-thought reasoning, a novel paradigm that fundamentally reconceptualizes how neural networks approach complex reasoning tasks. by examining and structuring neural network weights before inference, our approach reveals and enhances the reasoning pathways embedded within the weight space itself @xcite . the wot architecture we developed implements this concept through an interconnected graph of specialized reasoning nodes communicating via dynamic message passing @xcite , creating a sophisticated reasoning system that transcends the limitations of traditional sequential approaches @xcite . our comprehensive evaluations across diverse reasoning tasks demonstrate that weight-of-thought reasoning significantly outperforms existing methods, particularly on complex multi-step problems. the wot approach achieves this superior performance with remarkable parameter efficiency ( the visualizations we developed (e.g., figure 4 , appendix figures 7 8 9 ) provide unprecedented insights into the reasoning process, revealing how different nodes specialize in particular aspects of reasoning and how information flows through the reasoning network. these visualizations not only enhance interpretability @xcite but also offer valuable diagnostic tools for understanding and improving reasoning capabilities in neural networks. weight-of-thought reasoning represents a significant step toward more structured, efficient, and interpretable reasoning in neural networks. by focusing on the weight space rather than just output tokens, our approach opens new avenues for enhancing the reasoning capabilities of ai systems across diverse domains. the parameter efficiency and interpretability of our method make it particularly promising for applications where computational resources are limited or where understanding the reasoning process is critical @xcite . as neural networks continue to play an increasingly central role in complex decision-making processes @xcite , approaches like weight-of-thought reasoning that enhance both performance and interpretability will be essential for building ai systems that can be trusted with increasingly sophisticated reasoning tasks. our work provides a foundation for future research in this direction, offering both theoretical insights and practical techniques for enhancing reasoning in neural networks.