1 citations · 1 across the 8 of their papers we have counts for
9 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
DesignAgent3D: Interactive 3D Scene Editing via Designer-like Multimodal Reasoning
Xiujin Liu, Tianyu Yang, Yilun Zhao +1
Text guided 3D scene editing provides an intuitive interface for modifying reconstructed environments, but remains difficult because natural language design requests are often sema…
Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
Tianyu Yang, Shir Simon, Zhenzhen Li +2
Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal in…
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning
Tianyu Yang, Sihong Wu, Yilun Zhao +6
Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities.…
JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks
Lanbo Lin, Jiayao Liu, Tianyuan Yang +5
Evaluating agentic AI on open-ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail…
Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving
Kaiyue Feng, Yilun Zhao, Yixin Liu +4
We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving. It contains 1034 annotated, open-ended problems from PhD-qualifying exams ac…
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis
Yicheng Lang, Kehan Guo, Yue Huang +5
Due to the widespread use of LLMs and the rising critical ethical and safety concerns, LLM unlearning methods have been developed to remove harmful knowledge and undesirable capabi…
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Tianyu Yang, Lisen Dai, Xiangqi Wang +3
Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process. While progress has been…
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark
Zhenwen Liang, Kehan Guo, Gang Liu +7
The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. It addresses a critical educational phase often overlooke…