22 citations · 102 across the 15 of their papers we have counts for
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cs.AI2026
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
cs.AI2026
DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
Jiaqi Xiong, Yunjia Qi, Qi Cao +6
Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process a…
cs.AI2024★ 19 cited
A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks
Jiaqi Wang, Hanqi Jiang, Yiheng Liu +21
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (…