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Evidence record 4234 · automatically gathered

To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents

LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high call accuracy but much lower no-call accuracy, leaving overall accuracy in the 55%-70% range. We trace this to an Intrinsic Bias Hypothesis (IBH): the call/no-call decision mapping carries an activation-independent call offset, so the model favors call even at activation parity. Using Sparse Autoencoders (SAEs), we rec

Record details

Published: 16 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (16 May 2026), “To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents,” evidence record 4234, https://ethics.ai/record/4234 (originally published by arXiv).

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