Let Minstrel Tell Your Story: Reconstructing Character Profiles from Descriptions for Role Playing

Role-playing agents increasingly stand in for a specific person or character, in digital twins, social simulation and expert decision support. The characters the field builds for are canonical figures from widely circulated fiction, and each arrives with a gold-standard profile. Users also want niche characters, original creations and people they know, none of which come with a profile or with an identity the backbone can draw on. Hiding the name of a known character does not close this gap, since removing the identity also removes most of the value the profile carried. We propose Minstrel, which rebuilds a profile from free description and delivers it identity-free. It poses three profiling questions for each of eight dimensions drawn from personality psychology, converts the material each question retrieves into personality content, and assembles one conditioning text. Across four open-source actors and two cross-family judges, every Minstrel configuration beats a no-information control. Built from public comments alone, the profile reaches win shares of 0.570 and 0.610 while the actor identifies the character in 1.6% of trials. It also keeps the sixteen characters as distinct as their official profiles do. The code are available at https://anonymous.4open.science/r/Minstrel.