4 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…
PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning
Hang Yu, James Staley, Cheng Xi Tsou +8
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rath…
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…
NumerosityVLM: A Cognitively Inspired Benchmark for Interpreting Numerosity Representations in Vision-Language Models
Yiming Fu, Fangjun Li, Xiujin Liu +7
Vision-language models (VLMs) achieve strong performance on high-level multimodal tasks, yet numerosity perception, a cognitive ability that emerges in human infants before languag…