“2026-06-22”版本间的差异

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|Wenqiang Du
 
|Wenqiang Du
 
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* music sep
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*AIbabel
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** fixed some bugs on the project
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** Trying some method to optimize the Fewshot model
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* Huohua
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** simulated 3-mic data for mobile, and used the Sound Bubble code to build a baseline(in traing)
 
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第124行： 第129行：
 
|Xiaoxue Luo
 
|Xiaoxue Luo
 
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* Completed the draft of the USS-TDA paper
 
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第135行： 第140行：
 
|Bochao Hu
 
|Bochao Hu
 
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* topic vsr llm: revise paper (VSR-VLLM as main storyline) and continue to supplement exps
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* cncvs3: 4kh SFT is in training
 
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第146行： 第152行：
 
|Hongcheng Zhang
 
|Hongcheng Zhang
 
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*  Try different ratios of real data and synthetic data to see if the synthetic data actually works on Inhome-audio tagging
 
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第168行： 第174行：
 
|Shuailong Li
 
|Shuailong Li
 
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*Clean dataset, keep watching Li Hongyi's course, and do the homework
 
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2026年6月22日 (一) 11:13的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • All middle school and primary school text book done
  • Jiaoyubu project evaluation
Lantian Li
Wenqiang Du
  • music sep
  • AIbabel
    • fixed some bugs on the project
    • Trying some method to optimize the Fewshot model
  • Huohua
    • simulated 3-mic data for mobile, and used the Sound Bubble code to build a baseline(in traing)
Yang Wei
  • Audio separation: train model with larger 3mix ratio.
Yue Gu
  • polish my Phd thesis and begin to prepare my pre-defense
Lily
Pengqi Li
  • Phd Thesis(core content and overall framework)
Junming Yuan
  • Updated the MT-HuBERT project code.
  • Start MT-HuBERT+ work.
Yu Zhang
  • GPU Util: [1]
  • MAS:
    • Across all experiments, the MAS was generally able to identify the underlying problem or bottleneck.
    • When the task mainly required textual consensus, STD added limited value beyond the agreement itself.
    • When solving the problem required coordinated actions across agents, STD was much more useful.
Junhui Chen
  • Continue the LLM-MAS society dilemma experiments:
    • In Public Goods Game (PGG): all topologies converge to zero cooperation.
    • In Common-Pool Resource (CPR): when LLMs are in Non-Thinking mode, all topologies collapse; when in Thinking mode, fully connected causes over-extraction to spike while sparse topologies stay sustainable. The difference tracks degree, not the optimization method.
Xiaoxue Luo
  • Completed the draft of the USS-TDA paper
Bochao Hu
  • topic vsr llm: revise paper (VSR-VLLM as main storyline) and continue to supplement exps
  • cncvs3: 4kh SFT is in training
Hongcheng Zhang
  • Try different ratios of real data and synthetic data to see if the synthetic data actually works on Inhome-audio tagging
Weiman Sun
Shuailong Li
  • Clean dataset, keep watching Li Hongyi's course, and do the homework
取自“http://cslt.org/mediawiki/index.php?title=2026-06-22&oldid=44265”