Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation
KATE framework enhances LLM tool use by integrating experiential knowledge and expanding reasoning width, achieving a 15% improvement on BFCL-V3.
Key Findings
Methodology
The study introduces the KATE framework, enhancing LLM tool use through the acquisition, activation, and internalization of experiential knowledge. It categorizes knowledge into instance-level and intent-level, employing reasoning width expansion strategies.
Key Results
- On the BFCL-V3 dataset, the KATE framework improves average performance by 15% on the Qwen3-8B model and 4.5% on the Qwen3-32B model.
- Experiments show instance-level knowledge significantly boosts tool use performance, especially in multi-step tasks.
- Reinforcement learning outperforms supervised fine-tuning in internalizing experiential knowledge, further enhancing model performance.
Significance
This research significantly improves LLM performance in complex tool use tasks by systematically integrating and activating experiential knowledge. It addresses multi-step execution failures, offering new insights for academia and industry.
Technical Contribution
The KATE framework introduces reasoning width expansion and knowledge-enhanced training in tool use, significantly improving model accuracy. This breakthrough opens new possibilities for LLM applications.
Novelty
The KATE framework is the first to systematically integrate experiential knowledge and activate latent knowledge through reasoning width expansion, offering a more efficient tool use solution compared to existing methods.
Limitations
- In extremely complex tasks, reasoning width expansion may introduce noise, affecting decision accuracy.
- Errors in intent inference may impact knowledge activation effectiveness.
Future Work
Future research could explore more efficient knowledge integration strategies and applications on larger-scale models. It could also extend to more diverse tool use scenarios.
AI Executive Summary
Large language models often fail in multi-step tool use due to insufficient knowledge. The KATE framework integrates instance-level and intent-level experiential knowledge to significantly enhance tool use performance. Experiments show that reasoning width expansion strategies effectively activate latent knowledge, while reinforcement learning further internalizes experiential knowledge, improving model performance. KATE performs well on both BFCL-V3 and AppWorld datasets, demonstrating its potential in complex tasks. Although it may introduce noise in extremely complex tasks, KATE offers new solutions and research directions for LLM tool use.
Deep Analysis
Background
Large language models have made significant progress in NLP but still face multi-step execution failures in tool use. Existing methods focus on prompt design and API documentation, lacking experiential knowledge utilization.
Core Problem
LLMs often fail in multi-step tool use due to insufficient knowledge, especially in parameter constraints and error recovery strategies. This limits their ability as autonomous agents.
Innovation
The KATE framework integrates instance-level and intent-level experiential knowledge, proposing reasoning width expansion strategies to effectively activate latent knowledge. Compared to existing methods, KATE offers a more efficient tool use solution.
Methodology
- �� Knowledge Acquisition: Categorize instance-level and intent-level knowledge, build a unified retrieval mechanism.
- �� Knowledge Activation: Use reasoning width expansion strategies, activate latent knowledge through parallel sampling and aggregation.
- �� Knowledge Internalization: Post-training with knowledge-augmented data, reinforcement learning outperforms supervised fine-tuning.
Experiments
Experiments conducted on BFCL-V3 and AppWorld datasets, comparing different knowledge types and integration settings. Qwen3-8B and Qwen3-32B models used, evaluating the impact of reasoning width expansion and knowledge-enhanced training.
Results
KATE improves by 15% on BFCL-V3 and outperforms ReAct baseline on AppWorld. Instance-level knowledge boosts tool use performance, reinforcement learning further enhances model performance.
Applications
KATE framework applicable to tasks requiring complex tool use, such as multi-step interactions and programming agents. Its knowledge-enhanced strategies significantly improve model autonomy and accuracy.
Limitations & Outlook
In extremely complex tasks, reasoning width expansion may introduce noise, affecting decision accuracy. Future research should explore more efficient knowledge integration strategies to enhance model performance in diverse scenarios.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and KATE is like a smart assistant that knows the detailed steps for each dish. It not only remembers past cooking experiences but also offers help when needed. Even if you forget a step, it can find the best solution through various methods.
ELI14 Explained like you're 14
Imagine you're playing a complex game, and KATE is like a super guide assistant. It knows the way to clear each level and gives you the best advice based on your actions. Even if you face difficulties, it helps you find solutions through different methods!
Glossary
Large Language Model (LLM)
A large-scale neural network model capable of understanding and generating natural language.
Used for tool use and natural language processing tasks.
Tool Use
The process by which LLMs execute specific tasks by calling external tools.
Enhances model autonomy and functionality.
Experiential Knowledge
Knowledge gained through practical operation and experience accumulation.
Used to improve tool use accuracy and efficiency.
Reasoning Width
Considering multiple possible solutions simultaneously during reasoning.
Used to activate latent knowledge and improve decision accuracy.
Reinforcement Learning
A machine learning method that optimizes decision-making strategies through trial and error and reward mechanisms.
Used to internalize experiential knowledge and enhance model performance.
Open Questions Unanswered questions from this research
- 1 How to effectively integrate and activate experiential knowledge in extremely complex tasks remains to be explored.
- 2 Errors in intent inference may impact knowledge activation effectiveness.
Applications
Immediate Applications
Multi-step Tasks
KATE can be used for multi-step tasks requiring complex tool use, enhancing model autonomy and accuracy.
Long-term Vision
Intelligent Agents
KATE is expected to become an important component of intelligent agents, improving their performance in diverse scenarios.
Abstract
Large language models (LLMs) rely on tool use to act as autonomous agents, yet often fail in multi-step execution due to insufficient tool-related knowledge and ineffective knowledge activation. Therefore, we present a systematic study on how knowledge influences tool-use performance, covering the stages of knowledge acquisition, activation, and internalization. In the knowledge acquisition stage, we acquire and evaluate various forms of experiential knowledge, and our analysis shows that simple instance-level knowledge can already provide strong and reliable gains, while abstract intent-level knowledge offers limited benefits. At inference time, to activate knowledge, we find that prompting LLM to expand the depth of reasoning yields diminishing returns, whereas expanding the width of reasoning by parallel sampling with aggregation more effectively activates latent experiential knowledge. At training time, for knowledge internalization, post-training with knowledge-augmented data further improves performance, with reinforcement learning outperforming supervised fine-tuning. Based on these insights, we propose the Knowledge-Augmented Tool Execution (KATE), a knowledge-augmented tool execution framework that integrates experiential knowledge with reasoning-width-expanded inference and knowledge-aware training. Experiments on BFCL-V3 and AppWorld demonstrate consistent and substantial improvements over strong baselines across model scales. Our Code is available at https://github.com/hypasd-art/KATE.