Diagnosing Task Insensitivity in Language Agents

Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task insensitivity: when faced with similar but distinct tasks, models might apply patterns learned during training and fail to solve the task at hand. We show that models often continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered. We further

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ethics.ai (25 June 2026), “Diagnosing Task Insensitivity in Language Agents,” evidence record 565, https://ethics.ai/record/565 (originally published by arXiv).

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