This page shows the most relevant public items for llm, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jul 10, 2025 • arxiv.org • Jean-Francois Ton, Muhammad Faaiz Taufiq, Yang Liu
Large Language Models (LLMs) have shown impressive performance in complex reasoning tasks through the use of Chain-of-Thought (CoT) reasoning, allowing models to break down problems into manageable...
Paper • Nov 13, 2024 • arxiv.org • Xiaonan Nie, Qibin Liu, Fangcheng Fu, Shenhan Zhu, Xupeng Miao, Xiaoyang Li, Yang Zhang, Shouda Liu, Bin Cui
Larger transformer models always perform better on various tasks but require more costs to scale up the model size. To efficiently enlarge models, the mixture-of-experts (MoE) architecture is widel...
Paper • Nov 11, 2024 • arxiv.org • Yichun Shi, Peng Wang, Weilin Huang
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining th...
Paper • Mar 20, 2025 • arxiv.org • Zhijian Zhuo, Ya Wang, Yutao Zeng, Xiaoqing Li, Xun Zhou, Jinwen Ma
Transformers have found extensive applications across various domains due to the powerful fitting capabilities. This success can be partially attributed to their inherent nonlinearity. Thus, in add...
Paper • Mar 20, 2025 • arxiv.org • Xin Gu, Ming Li, Libo Zhang, Fan Chen, Longyin Wen, Tiejian Luo, Sijie Zhu
High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text...
Paper • Nov 6, 2024 • arxiv.org • Zilong Huang, Qinghao Ye, Bingyi Kang, Jiashi Feng, Haoqi Fan
We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text encoder, SuperCla...
Paper • Jun 22, 2025 • arxiv.org • Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao, Kaixin Wang, Gao Huang, Jiashi Feng
OpenAI's Sora highlights the potential of video generation for developing world models that adhere to fundamental physical laws. However, the ability of video generation models to discover such law...
Paper • Nov 5, 2024 • arxiv.org • Ziming Li, Qianbo Zang, David Ma, Jiawei Guo, Tuney Zheng, Minghao Liu, Xinyao Niu, Yue Wang, Jian Yang, Jiaheng Liu, Wanjun Zhong, Wangchunshu Zhou, Wenhao Huang, Ge Zhang
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists...
Paper • Oct 24, 2024 • arxiv.org • Chenxin An, Jun Zhang, Ming Zhong, Lei Li, Shansan Gong, Yao Luo, Jingjing Xu, Lingpeng Kong
Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work reveals that the e...
Paper • May 11, 2025 • arxiv.org • Shenao Zhang, Zhihan Liu, Boyi Liu, Yufeng Zhang, Yingxiang Yang, Yongfei Liu, Liyu Chen, Tao Sun, Zhaoran Wang
Preference alignment in Large Language Models (LLMs) has significantly improved their ability to adhere to human instructions and intentions. However, existing direct alignment algorithms primarily...
Paper • Mar 1, 2025 • arxiv.org • Kaijing Ma, Xinrun Du, Yunran Wang, Haoran Zhang, Zhoufutu Wen, Xingwei Qu, Jian Yang, Jiaheng Liu, Minghao Liu, Xiang Yue, Wenhao Huang, Ge Zhang
In this paper, we introduce Knowledge-Orthogonal Reasoning (KOR), a concept aimed at minimizing reliance on domain-specific knowledge, enabling more accurate evaluation of models' reasoning abiliti...
Paper • Oct 26, 2025 • arxiv.org • Yihong Dong, Ge Li, Yongding Tao, Xue Jiang, Kechi Zhang, Jia Li, Jinliang Deng, Jing Su, Jun Zhang, Jingjing Xu
Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about periodic phenomena...
Paper • Apr 2, 2025 • arxiv.org • Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang, Jiashi Feng, Xihui Liu
It is desirable but challenging to generate content-rich long videos in the scale of minutes. Autoregressive large language models (LLMs) have achieved great success in generating coherent and long...
Paper • Mar 18, 2025 • arxiv.org • Defa Zhu, Hongzhi Huang, Zihao Huang, Yutao Zeng, Yunyao Mao, Banggu Wu, Qiyang Min, Xun Zhou
We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual conn...
Paper • Dec 8, 2024 • arxiv.org • Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen
Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model for transitioning...
Paper • Oct 22, 2024 • arxiv.org • Ziyu Zhao, Tao Shen, Didi Zhu, Zexi Li, Jing Su, Xuwu Wang, Kun Kuang, Fei Wu
Low-Rank Adaptation (LoRA) has emerged as a popular technique for fine-tuning large language models (LLMs) to various domains due to its modular design and widespread availability on platforms like...
Paper • Oct 2, 2024 • arxiv.org • Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, Chuan Wu
Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node represents computation of a ne...
Paper • Sep 19, 2024 • arxiv.org • Ye Bai, Haonan Chen, Jitong Chen, Zhuo Chen, Yi Deng, Xiaohong Dong, Lamtharn Hantrakul, Weituo Hao, Qingqing Huang, Zhongyi Huang, Dongya Jia, Feihu La, Duc Le, Bochen Li, Chumin Li, Hui Li, Xingxing Li, Shouda Liu, Wei-Tsung Lu, Yiqing Lu, Andrew Shaw, Janne Spijkervet, Yakun Sun, Bo Wang, Ju-Chiang Wang, Yuping Wang, Yuxuan Wang, Ling Xu, Yifeng Yang, Chao Yao, Shuo Zhang, Yang Zhang, Yilin Zhang, Hang Zhao, Ziyi Zhao, Dejian Zhong, Shicen Zhou, Pei Zou
We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language m...
Paper • Sep 3, 2024 • arxiv.org • Xiurui Zhao, Stefano Marchesi, Marco Ajello, Francesca Civano, Roberto Gilli, Giorgio Lanzuisi, Iván E. López, Ross Silver, Nuria Torres-Albà, Peter G. Boorman, Andrealuna Pizzetti
We present a comprehensive X-ray analysis and spectral energy distribution (SED) fitting of WISEA J171419.96+602724.6, an extremely luminous type 2 quasar at $z$ = 2.99. The source was suggested as...
Paper • Apr 2, 2025 • arxiv.org • Borui Wan, Mingji Han, Yiyao Sheng, Yanghua Peng, Haibin Lin, Mofan Zhang, Zhichao Lai, Menghan Yu, Junda Zhang, Zuquan Song, Xin Liu, Chuan Wu
Checkpointing to preserve training states is crucial during the development of Large Foundation Models (LFMs), for training resumption upon various failures or changes in GPU resources and parallel...
It ranks public content for llm using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to llm topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to llm topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use the linked entities section to jump to labs, companies, and experts connected to this topic and explore their timelines. This guidance is specific to llm topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to llm topic page on Attendemia and is written so it still makes sense without reading other sections on the page.