arXiv:2604.10788v2 Announce Type: replace Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning. Existing TIR methods typically rely on external tool documentation during reasoning. However, this leads to tool mastery difficulty, tool size constraints, and inference inefficiency. To mitigate these issues, we explore Tool-Internalized Reasoning (TInR), aiming at facilitating reasoning with tool knowledge internalized into LLMs. Achieving this goal presents notable requirements,
The rapid advancement of Large Language Models (LLMs) is pushing the boundaries of their reasoning capabilities, making tool integration a critical area for improvement and optimization.
This research explores a new paradigm for LLM tool utilization, potentially overcoming current limitations in efficiency and complexity that hinder the adoption of more sophisticated AI agents.
The shift from external tool documentation to tool-internalized knowledge could lead to more robust, efficient, and autonomous AI systems, reducing inference costs and increasing versatility.
- · AI developers
- · LLM providers
- · Enterprise AI users
- · Companies reliant on simple, external tool integration
- · Inefficient AI frameworks
More capable and efficient autonomous AI agents become feasible as LLMs better integrate and utilize tool knowledge.
Reduced operational costs for AI applications due to fewer external API calls and faster internal processing.
Accelerated development of complex AI systems capable of handling multi-step reasoning and problem-solving with internalized expertise.
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Read at arXiv cs.CL