BOOKMARKS: Checkpoint-Accelerated Active Grounding for Role-playing

Memory systems are critical for role-playing agents (RPAs) to maintain long-horizon consistency. However, existing RPA memory methods (e.g., profiling) mainly rely on incremental summarization, whose compression inevitably discards important details. To address this issue, we propose a search-based memory framework called BOOKMARKS, which actively initializes, maintains, and updates task-relevant bookmarks for the current task (e.g., character acting). A bookmark is structured as the answer to a question at a specific point in the storyline, serving as knowledge checkpoints. For each current task, BOOKMARKS proposes search questions and goes through storyline for answers. If a proposed question matches an existing bookmark, the search will start from that checkpoint instead of the storyline beginning, which accelerates searching. The synchronized bookmark will also represent a closer checkpoint for faster future search. Compared with incremental summarization, BOOKMARKS offers (1) active grounding for capturing task-specific details and (2) passive updating to avoid unnecessary computation. Our implementation supports concept, behavior, and state searches, each powered by an efficient synchronization method. BOOKMARKS significantly outperforms RPA memory baselines on 85 characters from 16 artifacts, demonstrating the effectiveness of search-based memory for RPAs.The code and data will be released for reproduction.