6 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…
LVMH: A Meta-Optimization Harness for Long-Video Agent Memory
Liwei Che, Benlin Liu, Dai Li +7
Long-video agents must map hours or days of multimodal evidence into a bounded context while respecting token and latency budgets, making memory design a context-management challen…
Why MLLMs Struggle to Count: Overcoming Individuation and Aggregation Bottlenecks with ConvStack
Liwei Che, Yihao Quan, Sen Fang +4
Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. In this work, we present a mec…
MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Zeru Shi +14
Long-term agent memory is increasingly multimodal, yet existing evaluations rarely test whether agents preserve the visual evidence needed for later reasoning. In prior work, many…
Counting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language Models
Liwei Che, Zhiyu Xue, Yihao Quan +7
Counting serves as a simple but powerful test of a Large Vision-Language Model's (LVLM's) reasoning; it forces the model to identify each individual object and then add them all up…
Hallucinatory Image Tokens: A Training-free EAZY Approach on Detecting and Mitigating Object Hallucinations in LVLMs
Liwei Che, Tony Qingze Liu, Jing Jia +3
Despite their remarkable potential, Large Vision-Language Models (LVLMs) still face challenges with object hallucination, a problem where their generated outputs mistakenly incorpo…