1 citations · 1 across the 7 of their papers we have counts for
9 papers
DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users
Yifei Tao, Xinyu Zhong, Henry Hengyuan Zhao +5
Long-term agents must remember not only what is true about a user, but also how a particular agent should work with that user as their shared history evolves. Existing benchmarks p…
ChartJudgeBench: Evaluating LMM Judges for Chart-to-Code Generation
Lijian Wu, Henry Hengyuan Zhao, Zijian Zhang +3
Building strong chart-to-code systems increasingly relies on reinforcement learning, whose effectiveness depends critically on the quality of the reward signal. Large Multimodal Mo…
TempCloze: Can Video-LLMs Identify the Missing Middle?
Wenqi Pei, Henry Hengyuan Zhao, Yilai Liu +4
Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors. To…
From Charts to Code: A Hierarchical Benchmark for Multimodal Models
Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu +8
We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (LMMs). Chart2Code is explicitly designe…
InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback
Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao +2
Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users, which is vital for developing general-purpose AI assistants. We d…
WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point
Henry Hengyuan Zhao, Kaiming Yang, Wendi Yu +2
Recent progress in GUI agents has substantially improved visual grounding, yet robust planning remains challenging, particularly when the environment deviates from a canonical init…
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao +2
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can mo…
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator
Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou
Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuni…
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao +3
Pre-trained vision transformers have strong representation benefits to various downstream tasks. Recently, many parameter-efficient fine-tuning (PEFT) methods have been proposed, a…