AGENTIC-SYSTEMSApril 23, 20261 min read9 views

The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?

Researchers have identified a pervasive 'tool overuse' phenomenon in LLMs, where models rely on external tools unnecessarily. By addressing epistemic illusions and reward structures, they reduced redundant tool calls by up to 82.8% without sacrificing performance.

Computer Science > Artificial Intelligence

Title:The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?

Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecessary tool-use during reasoning. In this paper, we first reveal this phenomenon is pervasive across diverse LLMs. We then experimentally elucidate its underlying mechanisms through two key lenses:

(1) First, by analyzing tool-use behavior across different internal knowledge availability regions, we identify a knowledge epistemic illusion: models misjudge internal knowledge boundaries and fail to accurately perceive their actual knowledge availability. To mitigate this, we propose a knowledge-aware epistemic boundary alignment strategy based on direct preference optimization, which reduces tool usage in by 82.8% while yielding an accuracy improvement.

(2) Second, we establish a causal link between reward structures and tool-use behavior by visualizing the tool-augmented training process. It reveals that outcome-only rewards inadvertently encourage tool overuse by rewarding only final correctness, regardless of tool efficiency. To verify this, we balance reward signals during training rather than relying on outcome-only rewards, cutting unnecessary tool calls by 66.7% (7B) and 60.7% (32B) without sacrificing accuracy.

Finally, we provide theoretical justification in this two lenses to understand tool overuse.

Source: arXiv cs.AI Recent

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