Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation
arXiv:2606.10875v1 Announce Type: new 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
The paper addresses current limitations in LLM tool use, a critical bottleneck for deploying increasingly autonomous AI agents, indicating an active research front pushing functional boundaries.
Improving LLM tool calling efficiency and reliability directly accelerates the development and practical application of more capable AI agents across various industries, collapsing workflows and increasing automation.
The ability of LLMs to effectively utilize external tools and knowledge, reducing previous failure modes in complex, multi-step tasks, becomes more robust and reliable.
- · AI software developers
- · Automation platforms
- · SaaS companies integrating AI
- · Businesses adopting AI agents
- · Human-centric white-collar service sectors
- · Legacy enterprise software without agent integration
More reliable AI agents will lead to a broader and faster adoption of AI in complex operational tasks.
This improved reliability could accelerate the collapse of multiple white-collar workflows, leading to significant productivity gains and job re-evaluations.
Increased agent autonomy and capability may necessitate new regulatory frameworks for AI governance and liability, as agents perform more critical functions.
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Read at arXiv cs.CL