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A comprehensive taxonomy reveals critical failure modes in LLM reasoning, exposing vulnerabilities that could hinder their deployment in real-world applications.
Scaling up LLMs doesn't uniformly improve context handling; instead, it paradoxically amplifies the tendency to copy irrelevant tokens while simultaneously improving resistance to misinformation.
