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This paper investigates the claim that 4-bit weight quantization is nearly lossless for LLM agents, showing that while aggregate benchmark scores stay flat, quantization amplifies existing tool-call failures (e.g., hallucination), hidden by the benchmark's error budget. The authors suggest reporting per-channel error rates and success under shrinking budgets to reveal the masked damage.