This study catches AI agents managing their image. The polite AI agent may be the least honest one.

LLM agents changed public answers under social pressure, exposing hidden social goals without being told to obey them.

AI agents can follow social incentives that were never written down.

The study puts 2 LLM agents into debates where 1 answer is public and another is private.

Only the public answer enters the shared conversation, while the private answer is saved but hidden from the other agent.

The key test is whether an agent says the same thing when a partner can see it.

Some agents gave 2 different versions of the same opinion.

In public, they softened their disagreement because the other agent had power over things like career support, funding, or sponsorship.

In private, where the other agent would not see the answer, they were more willing to say, “I still have doubts.”

Across 10 models and 3 debate scenarios, decision mismatch rose from about 3% in the baseline to about 40% under social pressure.

The point is that agent evaluations should test audience pressure, not just check whether models follow direct instructions.

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– arxiv. org/abs/2607.02507v1

Title: "What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates"