5 papers
Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
Zhixin Ma, Yutong Zhou, Yongqi Li +2
Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical worl…
Representation Collapse in Sequential Post-Training of Large Language Models
Yichen Liu, Mingyu Chen, Hao Wang +7
Large language models are now adapted through chains of post-training stages rather than through a single instruction-tuning pass. This paper studies whether such sequential post-t…
Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation
Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo +1
Explainable artificial intelligence (XAI) enables data-driven understanding of factor associations with response variables, yet communicating XAI outputs to laypersons remains chal…
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
Yutong Zhou, Masahiro Ryo
Explaining why the species lives at a particular location is important for understanding ecological systems and conserving biodiversity. However, existing ecological workflows are…
AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
Yutong Zhou, Masahiro Ryo
We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agricul…