SOURCE-LINKED INTELLIGENCE
Disclosure-Gated User Simulation for Companion-Agent Evaluation
Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is excessively cooperative, so a system under test can score by the sheer number of questions it asks rather than by making the user willing to speak. We answer with a disclosure gate conditioning information release on the companion agent's behaviour: its state is a ladder of five ordered gates, merged onto three observable depth layers. We specify, ablate, and audit it, and train a user simulator against that specification. Gating behaviour is learne
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T09:39:41.000Z
First collected: 2026-09-21T06:11:57.537Z. This is not the publication date.