Skip to content

Author

Mingze Yin

We have 5 of 17 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Sep 2026

SegBanana: Steering Unified Multimodal Models into Medical Segmenters

SegBanana is proposed, to the authors' knowledge, the first agentic visual generation framework for training-free medical image segmentation and builds on a frozen UMM as the core generative model, augmented with Anatomy-Aware Knowledge Retrieval and Comparative Quality Critique to unlock its potential segmentation cap...

Xiao-Ye Liang, Ye Yan, Ming-Ze Yin et al. · 0 citations
Book Open access Aug 2026

Caduceus: MoE Foundation Models for Unifying Biological and Natural Language

This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.

Ming-Ze Yin, Yiheng Zhu, Jialu Wu et al. · 1 citation
#artificial intelligence Preprint Sep 2026

LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios

The unique challenge of jointly integrating perception, understanding, and reasoning for hazard identification poses substantial headroom for further improvement on LogiScope-VQA, and reveals the pervasive security bias issue that impedes LLMs'practical deployment in real-world settings.

Hanjing Zhou, Ming-Ze Yin, Ying Lian et al. · 0 citations
#machine learning Open access Jun 2025

HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model

HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction.

Jialu Wu, Yiheng Zhu, Xiaorui Wang et al. · 0 citations
#natural language process... Book Open access Aug 2026

Caduceus: MoE Foundation Models for Unifying Biological and Natural Language

This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.

Mingze Yin, Yiheng Zhu, Jialu Wu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.