arXiv:2606. 31719v1 Announce Type: cross Abstract: In collaborative dialogue, shared perception does not guarantee shared interpretation.
By Nan Li, Albert Gatt, Massimo Poesio
arXiv:2601. 19792v4 Announce Type: replace-cross Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical.
By Peter Zeng, Weiling Li, Amie Paige, Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras, Gregory Zelinsky, Susan Brennan, Owen Rambow
Lexara-RF introduces reference‑free metrics for evaluating conversational visual analytics agents that generate visualizations and natural‑language explanations. The framework uses only the prompt, data, and model response to score outputs, applying 13 metrics derived from visualization design theory and Gricean principles as consistency, intent‑alignment, and design validity checks. In tests against a human‑rated corpus, Lexara‑RF matches reference‑based methods, outperforms surface‑similarity NLG baselines, and accurately identifies structurally grounded failures.
By Srishti Palani, Vidya Setlur
arXiv:2609.00802v1 Announce Type: new
Abstract: Multi-party interaction is a central setting for human communication and a necessary target for human-agent interaction systems that must participate i...
By Taiga Mori, Koji Inoue, Mikey Elmers, Divesh Lala, Tatsuya Kawahara
arXiv:2606. 08081v1 Announce Type: cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history.
By Po-Ya Angela Wang, Chinmaya Mishra, Asl{\i} \"Ozy\"urek, Paula Rubio-Fern\'andez, Esam Ghaleb
The article surveys multi‑turn conversational AI, highlighting its shift from isolated text prompts to sustained, multimodal interactions that involve clarifying goals, revising requests, and switching topics. It reviews literature across text‑only dialogue, AudioLLMs, multimodal and omni‑modal systems, and tool‑augmented agents, organizing findings around datasets, models, training, evaluation, and cross‑cutting challenges. The analysis reveals that while multimodal perception and action have progressed rapidly, systems still struggle with persistent memory, cross‑turn grounding, full‑duplex interaction, robust evaluation, and cultural alignment.
By Syeda Faiza Ahmed, Zien Sheikh Ali, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury