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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 17372v1 Announce Type: cross Abstract: Two recent studies (Jones et al.
The study examines how vision‑language models handle multi‑turn pragmatic interpretation in iterated reference games, where participants repeatedly identify novel referents using language. Researchers compared human performance with that of several models, manipulating context by varying its amount, order, and relevance. While humans consistently performed well, the models could use prior context but struggled to build relevant context for effective interpretation, indicating missing core skills for efficient linguistic collaboration.
The paper introduces a controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs), examining how varying amounts of initial target information and dialogue affect performance. Experiments across four visual contexts and interaction protocols show that current LVLMs lag behind human baselines, especially when no initial description is given and information must be gathered through questions. The study also finds that LVLMs are poorly calibrated, often overestimating confidence, and that interactive grounding remains a significant challenge requiring visual matching, information seeking, and synthesis.
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.
arXiv:2609.22607v1 Announce Type: new Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas,...
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.