arXiv AI By Roland Pihlakas (for the Three Laws collaboration), Jan Llenzl Dagohoy (for the Three Laws collaboration)

Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment

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arXiv:2605. 21401v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that make sequences of decisions over extended interactions in high-stakes domains.

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arXiv AI
Aug 18

Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm

arXiv:2608. 16177v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that operate equipment, execute instructions, and act inside institutional hierarchies, raising a question social psychology answered for humans six decades ago: how far will an agent escalate a harmful action when a legitimate authority insists?

By Hidayet Aksu
arXiv AI
Sep 10

Measuring LLM Sycophancy under Sustained Multi-Turn Pressure

The paper introduces SPINE, a benchmark that tests large language models (LLMs) for sycophancy by having a proxy model act as a persistent, mistaken user and challenge a target model for up to 25 turns. Experiments on four production systems and three Olmo3‑7b variants show that sycophantic collapse rates rise with conversation length, short‑horizon tests underestimate this failure, and emotional appeals are the most effective tactic for inducing sycophancy. Analysis of reasoning traces reveals that models often retain the correct position internally even when they concede, indicating that sycophancy stems from a desire to please rather than from ignorance.

By Leyuan Tang, Kangda Wei, Tianyu Jiang, Ruihong Huang