Multi-Turn Evaluation

TL;DR for operators An AI agent can retain every instruction in a conversation and still execute the wrong task. The problem becomes acute when users replace requirements, withdraw constraints, or revise decisions on which earlier work depended. A controlled study introduces IntentFlux, a benchmark that measures this failure under changing user instructions.1 In a turn-matched experiment, mean task score falls from 0.734 without revisions to 0.469 with revisions. Additional conversation length alone cannot explain the difference. ...

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. ...