[FEEDBACK] Daily Papers
Note that this is not a post about adding new papers, it's about feedback on the Daily Papers community update feature.
How to submit a paper to the Daily Papers, like @akhaliq (AK)?
- Submitting is available to paper authors
- Only recent papers (less than 7d) can be featured on the Daily
Then drop the arxiv id in the form at https://huggingface.co/papers/submit
- Add medias to the paper (images, videos) when relevant
- You can start the discussion to engage with the community
Please check out the documentation
We are excited to share our recent work on MLLM architecture design titled "Ovis: Structural Embedding Alignment for Multimodal Large Language Model".
Paper: https://arxiv.org/abs/2405.20797
Github: https://github.com/AIDC-AI/Ovis
Model: https://huggingface.co/AIDC-AI/Ovis-Clip-Llama3-8B
Data: https://huggingface.co/datasets/AIDC-AI/Ovis-dataset
we are excited to share our work titled "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models" : https://arxiv.org/abs/2406.12644
Hi @AdinaY , could you help me remove the media from my paper 2605.06356? I'd like to re-upload the correct version. Thank you!
Hi @LazySheeep - I can remove the paper so you can upload it again. Let me know if this works for you.
Hi @AdinaY @kramp , could you help with denied authorship claim?
arXiv: https://arxiv.org/abs/2605.06169
HF page: https://huggingface.co/papers/2605.06169
I'm the author of this paper. My HF account email matches
the author email on arxiv. I claimed authorship via the
standard flow but it was denied with "no match found", and the
system now blocks me from re-submitting the claim.
Thanks!
Hi @LazySheeep - I can remove the paper so you can upload it again. Let me know if this works for you.
Hi @AdinaY . I noticed that you said this wouldn't change anything about the upvote, right? If that's the case, please remove it. I'll re-upload it.
Hi, everyone!
We propose MatryoshkaLoRA, a general, Matryoshka-inspired training framework for LoRA that learns accurate hierarchical low-rank representations by inserting a fixed, carefully crafted diagonal matrix P between the existing LoRA adapters to scale their sub-ranks accordingly.
By introducing this simple modification, our general framework recovers LoRA and DyLoRA only by changing P and ensures all sub-ranks embed the available gradient information efficiently.
Our MatryoshkaLoRA supports dynamic rank selection with minimal degradation in accuracy. We further propose Area Under the Rank Accuracy Curve (AURAC), a metric that consistently evaluates the performance of hierarchical low-rank adapters.
Our results show that that MatryoshkaLoRA learns more accurate hierarchical low-rank representations than prior rank-adaptive approaches and achieves superior accuracy-performance trade-offs across ranks on the evaluated datasets.
Hi, @akhaliq , @Kramp , @AdinaY ,
Could you please remove https://huggingface.co/papers/2605.08703 from the daily papers? There's a problem. I'll upload again. Thanks!
Hi @AdinaY , could you please remove https://huggingface.co/papers/2605.18396 from the daily papers? I'd like to re-upload with a video cover. Thank you.
Hi @AdinaY , We noticed an issue with https://huggingface.co/papers/2605.18565: it appears to be linking to the v1 version of the arXiv paper rather than v2. Could you help identify what might be causing this? Thank you.