arXiv:2606. 00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person.
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The paper introduces RAVEL, a retrieval‑aware online reinforcement learning framework designed to improve interactive retrieval under partial evidence. RAVEL begins with supervised question generation, directly observes the top‑4 retrieval candidates, and refines its question policy using rank feedback from the full question‑answer‑retrieval loop. Experiments on the Interactive‑PEDES dataset demonstrate that RAVEL progressively enhances retrieval performance over five interaction rounds, reallocating questioning toward localized open‑ended attributes that yield the greatest gains on challenging queries.
arXiv:2607. 00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user.
The paper introduces PersonaLink, a training‑free method that distills a user’s interaction history into a bounded three‑field persona and iteratively refines it by self‑evaluating a frozen 7B language model on held‑out labeled data. Each refinement rewrites the persona only if it does not regress on that slice, ensuring the persona remains bounded and query‑independent. On a 200‑user news categorization task (LaMP‑2), PersonaLink achieves 0.745–0.755 accuracy, statistically indistinguishable from BM25 retrieval’s 0.760–0.765 accuracy, demonstrating that distilled personas can match retrieval for classification but not for regression tasks.
arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
arXiv:2603.27476v3 Announce Type: replace Abstract: AI-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized be...