From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025)

Abstract: As large language models (LLMs) are increasingly applied to complex scientific problem-solving, their effectiveness is often limited by unconscious or failed tool usage. To address this issue, we introduce the Tool-Awareness Training (TAT) method, designed to enhance scientific reasoning. This approach leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks. Our method unfolds in three stages: (1) developing tool-knowledge through backward tooluse data generation (2) enhancing tool-awareness in multi-step reasoning by utilizing forward reasoning data, and (3) improving domain adaptability through large-scale domain-specific data for multi-task learning. These three stages progressively establish the foundation for tool learning and scientific reasoning, effectively integrating both, enabling the model to tackle multi-domain scientific tasks while optimizing tool usage. Our experimental results demonstrate that TAT significantly enhances LLM performance in mathematical and scientific reasoning tasks, particularly by improving the model’s tool utilization capabilities, including proactivity and execution success rates.

## introduction recent advancements in large language models (llms) have significantly expanded ai's capabilities, excelling in complex reasoning tasks and showing promising potential for scientific research and discovery @xcite @xcite . however, current studies consistently report that llms' capabilities in scientific domains are largely confined to a high school level @xcite , thereby falling short in tackling more complex, specialized * corresponding author. figure 1 : illustration of two error types: (1) tool usage failure, reflecting challenges in accurately invoking unfamiliar or complex functions, and (2) lack of tool usage,stemming from the failure to proactively leverage python libraries for computational optimization. existing approaches enhance reasoning through chain-of-thought @xcite , program-of-thought @xcite , and tool-integration @xcite , primarily relying on python interpreters for computation. however, they overlook active tool awareness and the use of advanced tools like python libraries, as shown in figure 11 . llms struggle with proactive library usage and invoking complex functions, reducing problem-solving efficiency @xcite . addressing these gaps is crucial for improving llms' effectiveness in advanced scientific reasoning. inspired by human learning behavior as detailed in section 3, we propose the tool-awareness training (tat) framework to enhance the ability of llms to effectively utilize tools in multi-step reasoning tasks. tat combines forward and backward data generation strategies to guide the model's tool usage. initially, we build tool knowledge through backward data, designing problems around tools to establish a foundational understanding. next, we enhance multi-step reasoning with forward data that helps the model decide when to use tools. finally, large-scale domain-specific data is used to fine-tune the model through multi-task learning, optimizing performance across various scientific fields. the core idea of tat is to teach the model when and how to apply external tools, especially specialized python libraries, empowering the model to solve complex problems by applying the right tools at the right time. the effectiveness of our approach is validated on scientific, mathematical, and tabular reasoning tasks. we evaluate the model's performance across several aspects, including accuracy, code executability, and tool usage proficiency. our results show that tat substantially enhances the model's accuracy and tool utilization in solving complex problems requiring tool integration. tool-augmented language models due to their reliance on static, parametric knowledge, llms often struggle with complex computations. enhancing llms with tools can significantly alle-viate these limitations and improve their reasoning and generation performance @xcite @xcite . many current studies explore the potential of llms in tool usage: for example, toolllama @xcite generates data that stimulates the model's ability to use real-world apis, while toolalpaca @xcite creates tool usage data through a model-based toolkit and multi-agent interactions. while existing approaches offer some enhancement, they struggle to actively leverage advanced libraries and fail to improve the model's domain knowledge and reasoning. our work aims to build on python toolkits, fostering the model's tool awareness, usage and reasoning ability to use python tools for scientific problem solving. ## motivations this section investigates the dual role of python tool in scientific reasoning: its potential to enhance complex problems while risking unnecessary reliance in simpler scenarios. we analyze leveldependent tool necessity, evaluate library optimization strategies, and provide insights into strategic tool utilization based on task complexity. ## tat framework overview we introduce the tat framework, a three-stage paradigm to enhance an llm's autonomous tool usage in multi-step reasoning tasks: utilization (learning tool usage through examples), awareness (developing decision-making for tool application), and generalization (improving adaptability across domains). together, these stages enable effective tool application, enhancing performance in complex scientific tasks. ## discussion and conclusion our work highlights the importance of selective tool use and optimized python libraries in enhancing llms' scientific reasoning. the tat framework fosters tool awareness and refines reasoning process, enabling llms to tackle complex scientific problems effectively. our experiments reveal tool usage patterns, demonstrating tat's impact on tool application. future research will focus on expanding data scale and domain coverage, improving data quality, bridging distribution gaps, and refining sampling and filtering strategies to enhance the generalization and diversity of tool-assisted reasoning.