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This research introduces BOOKMARKS, a search-based memory framework designed to enhance long-horizon consistency in role-playing agents by retaining full storyline access rather than relying on lossy incremental summarization. By implementing passive updating to reuse semantically equivalent checkpoints, the method significantly reduces redundant computation while improving next-action prediction and reasoning accuracy in complex narrative tasks.
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BOOKMARKS: Efficient Active Storyline Memory for Role-playing

arXiv3 min read

arXiv:2605.14169v2 Announce Type: replace Abstract: 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 discards details which become inaccessible to subsequent grounding. To address this issue, we propose a search-based memory framework called \textbf{\underline{BOOKMARKS}} for \textbf{active grounding}, which retains access to the full preceding storyline and collects task-relevant information on demand. Since summarizing the preceding storyline anew for each grounding request incurs substantial redundant computation, BOOKMARKS introduces \textbf{passive updating} to reuse earlier search results as checkpoints. Each \textbf{bookmark} represents the \textbf{content} about a particular aspect (\textbf{section}) of story information at a specific synchronization \textbf{point}. For current task, BOOKMARKS searches for only useful contents, reuses existing semantically equivalent bookmarks or initializes new ones, and synchronizes the selected ones from their stored checkpoints to the current scene. A reused bookmark thus only needs to process the newly observed storyline suffix, avoiding repeated synchronization. We evaluate BOOKMARKS across six narrative artifacts involving 47 characters and 9,537 test cases against non-active grounding baselines, covering next-action prediction and challenging, human-authored reasoning questions from a mystery game. BOOKMARKS improves next-action fidelity in a five-dimensional evaluation (emotion, intent, causality, position, and content) and raises mystery-game reasoning accuracy from 39.44\% to 45.42\% over the strongest baseline.

Abstract: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 discards details which become inaccessible to subsequent grounding. To address this issue, we propose a search-based memory framework called \textbf{\underline{BOOKMARKS}} for \textbf{active grounding}, which retains access to the full preceding storyline and collects task-relevant information on demand. Since summarizing the preceding storyline anew for each grounding request incurs substantial redundant computation, BOOKMARKS introduces \textbf{passive updating} to reuse earlier search results as checkpoints. Each \textbf{bookmark} represents the \textbf{content} about a particular aspect (\textbf{section}) of story information at a specific synchronization \textbf{point}. For current task, BOOKMARKS searches for only useful contents, reuses existing semantically equivalent bookmarks or initializes new ones, and synchronizes the selected ones from their stored checkpoints to the current scene. A reused bookmark thus only needs to process the newly observed storyline suffix, avoiding repeated synchronization. We evaluate BOOKMARKS across six narrative artifacts involving 47 characters and 9,537 test cases against non-active grounding baselines, covering next-action prediction and challenging, human-authored reasoning questions from a mystery game. BOOKMARKS improves next-action fidelity in a five-dimensional evaluation (emotion, intent, causality, position, and content) and raises mystery-game reasoning accuracy from 39.44\% to 45.42\% over the strongest baseline.

Computation and Language (cs.CL)

arXiv:2605.14169 [cs.CL]

(or arXiv:2605.14169v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.14169 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 303 words

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