5 papers
Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging
Jinglan Gong, Jiefan Lu, Hewei Guo +5
Evaluating large language models (LLMs) as multi-turn conversational partners requires probing capabilities that single-turn benchmarks miss: persona consistency, evolving intent t…
EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation
Min Lin, Xiwen Liang, Bingqian Lin +13
Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. However, existing VLAs are mostly…
HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Ding Chen, Simin Niu, Kehang Li +6
Memory systems are key components that enable AI systems such as LLMs and AI agents to achieve long-term learning and sustained interaction. However, during memory storage and retr…
InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue
Wenwen Tong, Hewei Guo, Dongchuan Ran +23
We introduce InteractiveOmni, a unified and open-source omni-modal large language model for audio-visual multi-turn interaction, ranging from 4B to 8B parameters, designed to lead…
SegLLM: Multi-round Reasoning Segmentation
XuDong Wang, Shaolun Zhang, Shufan Li +5
We present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual…