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When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning
arXiv – CS AI|Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Aokai Hao, Haishu Zhao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Qiaozhi He, Tong Xiao, Jingbo Zhu||1 views
🤖AI Summary
Researchers identified a critical problem in Large Audio-Language Models (LALMs) where audio perception deteriorates during extended reasoning processes. They developed MPAR² framework using reinforcement learning, which improved perception performance from 31.74% to 63.51% and achieved 74.59% accuracy on MMAU benchmark.
Key Takeaways
- →Large Audio-Language Models experience audio perception decay as reasoning chains become longer, creating a fundamental bottleneck.
- →Traditional test-time scaling approaches show marginal or negative gains in LALMs compared to direct answering methods.
- →CAFE evaluation framework was introduced to precisely quantify and measure audio reasoning errors in language models.
- →MPAR² paradigm uses reinforcement learning to decompose complex questions into perception-rich sub-problems.
- →The new approach nearly doubled perception performance and helps models dynamically adapt reasoning budget to task complexity.
#large-language-models#audio-processing#reinforcement-learning#model-reasoning#ai-research#perception-models#benchmark-performance
Read Original →via arXiv – CS AI
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