Persuasion

TL;DR for operators A model that refuses a harmful request once has demonstrated one response under one conversational strategy. That is weaker evidence than it may appear. In the experiments behind PsychJail,1 successful attacks were strongly front-loaded: the median successful turn was 1 for all four tested victim models, while the mean successful turn ranged only from 1.20 to 1.37. Many failures therefore did not require a long campaign of conversational erosion. They appeared as soon as the attacker found a more effective way to frame the interaction. ...

Fact-checking has always had a small public-relations problem: being right is not the same as being believed. A platform can label a claim false. A newsroom can publish a careful correction. A compliance team can flag a misleading ad, remove it, document the action, and still watch the same claim reappear in a shinier costume three hours later. The hard part is not only detection. It is persuasion. People need to understand why a claim fails, not merely be informed that someone with a badge disapproves of it. ...

Meetings are already persuasive systems. Someone speaks first, someone sounds confident, someone produces a spreadsheet with just enough decimal places to look holy, and suddenly the room has moved. Multi-agent AI systems are not so different. They are becoming small artificial committees: one agent retrieves, another proposes, another critiques, another decides. The optimistic version says this gives us productive disagreement. The less adorable version says we have built a machine for circulating influence, and we are only now asking what makes one agent cave to another. ...