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cs.LG2026
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
Ruihan Xu, Yuting Gao, Lan Wang +5
Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference…
cs.LG2025
M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance
Qingpei Guo, Kaiyou Song, Zipeng Feng +9
We present M2-omni, a cutting-edge, open-source omni-MLLM that achieves competitive performance to GPT-4o. M2-omni employs a unified multimodal sequence modeling framework, which e…