Implementation Opinions on the “AI + Manufacturing” Special Initiative - CSET | Center for Security and Emerging Technology
<a href="https://news.google.com/rss/articles/CBMikgFBVV95cUxPRkFCU0V1U1N1QVdhZmZoV19fYk9SNVMyb2xfMWNfd3BCMVhsMXpVSXhkOVU3Zy1rUTlRck9oRnUzLThCUmRHSXdoOWdYRFhIeW9rTjNBNjBlWW5NbUR4aTJRbFFIXzhRamhSeW5xQjZlaXdaZ3hjNXJnNkFDSUd4TDV0elpHRGl3emt6LXh6VWZFdw?oc=5" target="_blank">Implementation Opinions on the “AI + Manufacturing” Special Initiative</a> <font color="#6f6f6f">CSET | Center for Security and Emerging Technology</font>
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Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
arXiv:2604.02668v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents' sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer's tendency toward sycophancy. These rankings are based on scores calculated using various static (pre-discussion) and dynamic (online) strategies. We find that providing sycophancy priors reduces the influence of sycophancy-prone peers, mitigates e
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