arXiv:2601. 02871v3 Announce Type: replace Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion.
arXiv:2608. 19549v1 Announce Type: new Abstract: This paper addresses the issue of the significant labor required to test interview dialogue systems.
arXiv:2607. 00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time.
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv:2609.35812v1 Announce Type: new Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. E...
arXiv:2608. 12062v1 Announce Type: cross Abstract: Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data.
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.
arXiv:2607. 20734v1 Announce Type: new Abstract: As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction.
arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.
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.
The paper introduces a Symbolic-RAG-Generative architecture called GRACE for goal‑oriented conversational systems. GRACE transforms business intent into a fixed objective set and uses a constrained policy to update the conversation state based on visitor‑authored evidence while ensuring visitor utility. Evaluation on real‑estate and professional‑cleaning dialogues shows high accuracy in state transitions, evidence precision/recall, and monotonicity.