Zhang, Jiageng

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Improving Multi-Agent Debate with Sparse Communication Topology

Li, Y., Du, Y., Zhang, J., Hou, L., Grabowski, P., Li, Y., & Ie, E. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingMulti-Agent Systems and Negotiation

Cited by 20