arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.
arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.
arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
arXiv:2607. 27056v1 Announce Type: new Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks.
arXiv:2606. 02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited.