arXiv AI By Rafael Ferreira, Diogo Tavares, Diogo Gl\'oria-Silva, David Semedo, Jo\~ao Magalh\~aes

Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution

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arXiv AI
Aug 19

Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges

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
arXiv AI
4d ago

Multimodal Duplex Interaction Agent

arXiv:2609.08977v3 Announce Type: replace-cross Abstract: In this work, we present Gander, a native multimodal duplex interaction model that builds on MiniCPM-o 4.5 and is further adapted for realtim...

By Orantqing, Shengpeng Ji, Junlong Tong, Jialong Zuo, Dongjie Fu, Di Cao, Yangzhuo Li, Shangda Wu, Franz, Evan, Theron Veyra, Changhao Pan, Jingyu Lu, Dongchao Yang, Zhifei Xie, Yang Tan, Xiaoyu Shen, Xiaoda Yang, Wenfu Wang, Teddy Sun, Steve Yves, Zhou Zhao
arXiv Computation and Language
Aug 27

EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports

EgoArgus is a new, human‑annotated dataset that tests visual‑language models (VLMs) as situational assistants in five everyday dialogue‑video scenarios. It evaluates how well VLMs understand and decide when to intervene, especially when visual and textual cues are helpful, irrelevant, or conflicting. The study finds that current VLMs still struggle to reliably act as egocentric assistants and that existing modality‑bias mitigation methods offer limited improvement.

By Yu-Chien Tang, Yu-Hsiang Liu, An-Zi Yen
arXiv AI
2d ago

MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents

MIRAGE is a controlled study that examines how multimodal personal agents use historical evidence when conversation state changes. The study keeps evidence, questions, and scoring constant while varying only the conversation state, then checks if agents can determine answerability, recover the correct source, and answer from it. Results across seven multimodal backbones show distinct failure regimes before and after compaction, heavy reliance on context continuity by open-weight models, and mixed effects of retrieval pressure on source attribution.

By Yu Liu, Wenxiao Zhang, Cheng Hu, Cong Cao, Fangfang Yuan, Xinyu Wang, Jin B. Hong, Yanbing Liu