2 citations · 4 across the 17 of their papers we have counts for
8 papers · 1 filter
Hear, Invoke, and Understand: A Skill-Calling Multimodal Agent for Large Audio Language Models
Yuwen Wang, Tian-Hao Zhang, Minghao Cai +7
Complex acoustic problems may require models to perform acoustic operations, interact with external tools and reason over the resulting textual or processed-audio observations rath…
Demo2Tutorial: From Human Experience to Multimodal Software Tutorials
Zechen Bai, Zhiheng Chen, Yiqi Lin +5
Human experience in digital environments offers a vast, underexplored resource of authentic, untrimmed interactions that contain rich procedural knowledge. We introduce Demo2Tutori…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
SafeGround: Know When to Trust GUI Grounding Models via Uncertainty Calibration
Qingni Wang, Yue Fan, Xin Eric Wang
Graphical User Interface (GUI) grounding aims to translate natural language instructions into executable screen coordinates, enabling automated GUI interaction. Nevertheless, incor…
SAW-Bench: Learning Situated Awareness in the Real World
Chuhan Li, Rilyn Han, Joy Hsu +5
A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over possible actions in context. Howeve…
Cross-Modal Memory Compression for Efficient Multi-Agent Debate
Jing Wu, Yue Sun, Tianpei Xie +7
Multi-agent debate can improve reasoning quality and reduce hallucinations, but it incurs rapidly growing context as debate rounds and agent count increase. Retaining full textual…
ShowUI-Aloha: Human-Taught GUI Agent
Yichun Zhang, Xiangwu Guo, Yauhong Goh +5
Graphical User Interfaces (GUIs) are central to human-computer interaction, yet automating complex GUI tasks remains a major challenge for autonomous agents, largely due to a lack…
PALM-Bench: A Comprehensive Benchmark for Personalized Audio-Language Models
Yuwen Wang, Xinyuan Qian, Tian-Hao Zhang +6
Large Audio-Language Models (LALMs) have demonstrated strong performance in audio understanding and generation. Yet, our extensive benchmarking reveals that their behavior is large…