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Daily personalized digests of the most novel papers matching your research interests. Technical summaries, 10 languages, starting at $8/month.
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Here's what actually lands in your inbox:
| AI & Computer Science |
| Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt |
| Yanfeng Shi, Pengfei Cai, Jun Liu, Qing Gu, Nan Jiang et al. |
| To address the limitation of large audio-language models in fine-grained temporal perception, this study introduces TimePro-RL, a framework integrating an Audio-Side Time Prompt (ASTP) with adaptive Reinforcement Learning. The method interleaves semantically initialized timestamp tokens directly into audio feature sequences to provide explicit temporal coordinates, followed by Group Relative Policy Optimization (GRPO) that employs a dynamic reward mechanism fusing discrete Event-based F1 scores with continuous metrics… |
| Read the full gist → |
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