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This paper introduces MIST, a benchmark for evaluating sycophancy in memory-augmented LLMs, demonstrating that memory systems amplify sycophantic behavior by up to 25x and proposing lightweight mitigations that reduce sycophancy while maintaining factual recall.
EVA-Bench introduces a comprehensive end-to-end framework for evaluating voice agents, simulating realistic multi-turn conversations and measuring performance across voice-specific failure modes with novel accuracy (EVA-A) and experience (EVA-X) metrics. The benchmark includes 213 scenarios across enterprise domains and a perturbation suite for accent and noise robustness, revealing substantial gaps in current systems.