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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,410.07303 | Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow | ['Fu-Yun Wang', 'Ling Yang', 'Zhaoyang Huang', 'Mengdi Wang', 'Hongsheng Li'] | ['cs.CV'] | Diffusion models have greatly improved visual generation but are hindered by
slow generation speed due to the computationally intensive nature of solving
generative ODEs. Rectified flow, a widely recognized solution, improves
generation speed by straightening the ODE path. Its key components include: 1)
using the diffu... | 2024-10-09T17:43:38Z | null | null | null | Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow | ['Fu-Yun Wang', 'Ling Yang', 'Zhaoyang Huang', 'Mengdi Wang', 'Hongsheng Li'] | 2,024 | International Conference on Learning Representations | 21 | 71 | ['Computer Science'] |
2,410.07348 | MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation
Experts | ['Peng Jin', 'Bo Zhu', 'Li Yuan', 'Shuicheng Yan'] | ['cs.LG', 'cs.AI'] | In this work, we aim to simultaneously enhance the effectiveness and
efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose
MoE++, a general and heterogeneous MoE framework that integrates both
Feed-Forward Network~(FFN) and zero-computation experts. Specifically, we
introduce three types of zero-c... | 2024-10-09T18:01:27Z | 23 pages, Code: https://github.com/SkyworkAI/MoE-plus-plus | null | null | MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts | ['Peng Jin', 'Bo Zhu', 'Li Yuan', 'Shuicheng Yan'] | 2,024 | International Conference on Learning Representations | 6 | 64 | ['Computer Science'] |
2,410.0752 | News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News | ['Tarun Jain', 'Yufei Gao', 'Sridhar Vanga', 'Karan Singla'] | ['cs.CL'] | Large Language Models (LLMs) have fast become an essential tools to many
conversational chatbots due to their ability to provide coherent answers for
varied queries. Datasets used to train these LLMs are often a mix of generic
and synthetic samples, thus lacking the verification needed to provide correct
and verifiable... | 2024-10-10T01:21:48Z | 5 pages, under review at ICASSP 2025 | null | null | News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News | ['Tarun Jain', 'Yufei Gao', 'Sridhar Vanga', 'Karan Singla'] | 2,024 | arXiv.org | 0 | 22 | ['Computer Science'] |
2,410.07563 | PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency | ['Preferred Elements', ':', 'Kenshin Abe', 'Kaizaburo Chubachi', 'Yasuhiro Fujita', 'Yuta Hirokawa', 'Kentaro Imajo', 'Toshiki Kataoka', 'Hiroyoshi Komatsu', 'Hiroaki Mikami', 'Tsuguo Mogami', 'Shogo Murai', 'Kosuke Nakago', 'Daisuke Nishino', 'Toru Ogawa', 'Daisuke Okanohara', 'Yoshihiko Ozaki', 'Shotaro Sano', 'Shuji... | ['cs.CL', 'cs.AI', 'cs.LG'] | We introduce PLaMo-100B, a large-scale language model designed for Japanese
proficiency. The model was trained from scratch using 2 trillion tokens, with
architecture such as QK Normalization and Z-Loss to ensure training stability
during the training process. Post-training techniques, including Supervised
Fine-Tuning ... | 2024-10-10T02:59:36Z | null | null | null | PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency | ['Preferred Elements Kenshin Abe', 'Kaizaburo Chubachi', 'Yasuhiro Fujita', 'Yuta Hirokawa', 'Kentaro Imajo', 'Toshiki Kataoka', 'Hiroyoshi Komatsu', 'Hiroaki Mikami', 'Tsuguo Mogami', 'Shogo Murai', 'Kosuke Nakago', 'Daisuke Nishino', 'Toru Ogawa', 'Daisuke Okanohara', 'Yoshihiko Ozaki', 'Shotaro Sano', 'Shuji Suzuki'... | 2,024 | arXiv.org | 0 | 36 | ['Computer Science'] |
2,410.07718 | Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image
Animation | ['Jiahao Cui', 'Hui Li', 'Yao Yao', 'Hao Zhu', 'Hanlin Shang', 'Kaihui Cheng', 'Hang Zhou', 'Siyu Zhu', 'Jingdong Wang'] | ['cs.CV'] | Recent advances in latent diffusion-based generative models for portrait
image animation, such as Hallo, have achieved impressive results in
short-duration video synthesis. In this paper, we present updates to Hallo,
introducing several design enhancements to extend its capabilities. First, we
extend the method to prod... | 2024-10-10T08:34:41Z | null | null | null | null | null | null | null | null | null | null |
2,410.0783 | NusaMT-7B: Machine Translation for Low-Resource Indonesian Languages
with Large Language Models | ['William Tan', 'Kevin Zhu'] | ['cs.CL'] | Large Language Models (LLMs) have demonstrated exceptional promise in
translation tasks for high-resource languages. However, their performance in
low-resource languages is limited by the scarcity of both parallel and
monolingual corpora, as well as the presence of noise. Consequently, such LLMs
suffer with alignment a... | 2024-10-10T11:33:25Z | Accepted to SoLaR @ NeurIPS 2024 | null | null | null | null | null | null | null | null | null |
2,410.07864 | RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation | ['Songming Liu', 'Lingxuan Wu', 'Bangguo Li', 'Hengkai Tan', 'Huayu Chen', 'Zhengyi Wang', 'Ke Xu', 'Hang Su', 'Jun Zhu'] | ['cs.RO', 'cs.AI', 'cs.CV', 'cs.LG'] | Bimanual manipulation is essential in robotics, yet developing foundation
models is extremely challenging due to the inherent complexity of coordinating
two robot arms (leading to multi-modal action distributions) and the scarcity
of training data. In this paper, we present the Robotics Diffusion Transformer
(RDT), a p... | 2024-10-10T12:33:46Z | 10 pages, conference | null | null | null | null | null | null | null | null | null |
2,410.07896 | Executing Arithmetic: Fine-Tuning Large Language Models as Turing
Machines | ['Junyu Lai', 'Jiahe Xu', 'Yao Yang', 'Yunpeng Huang', 'Chun Cao', 'Jingwei Xu'] | ['cs.AI', 'I.2.7'] | Large Language Models (LLMs) have demonstrated remarkable capabilities across
a wide range of natural language processing and reasoning tasks. However, their
performance in the foundational domain of arithmetic remains unsatisfactory.
When dealing with arithmetic tasks, LLMs often memorize specific examples
rather than... | 2024-10-10T13:23:49Z | 30 pages | null | null | null | null | null | null | null | null | null |
2,410.07919 | InstructBioMol: Advancing Biomolecule Understanding and Design Following
Human Instructions | ['Xiang Zhuang', 'Keyan Ding', 'Tianwen Lyu', 'Yinuo Jiang', 'Xiaotong Li', 'Zhuoyi Xiang', 'Zeyuan Wang', 'Ming Qin', 'Kehua Feng', 'Jike Wang', 'Qiang Zhang', 'Huajun Chen'] | ['cs.CL', 'q-bio.BM'] | Understanding and designing biomolecules, such as proteins and small
molecules, is central to advancing drug discovery, synthetic biology, and
enzyme engineering. Recent breakthroughs in Artificial Intelligence (AI) have
revolutionized biomolecular research, achieving remarkable accuracy in
biomolecular prediction and ... | 2024-10-10T13:45:56Z | null | null | null | InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions | ['Zhuang Xiang', 'Keyan Ding', 'Tianwen Lyu', 'Yinuo Jiang', 'Xiaotong Li', 'Zhuoyi Xiang', 'Zeyuan Wang', 'Ming Qin', 'Kehua Feng', 'Jike Wang', 'Qiang Zhang', 'Huajun Chen'] | 2,024 | arXiv.org | 5 | 76 | ['Computer Science', 'Biology'] |
2,410.07985 | Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large
Language Models | ['Bofei Gao', 'Feifan Song', 'Zhe Yang', 'Zefan Cai', 'Yibo Miao', 'Qingxiu Dong', 'Lei Li', 'Chenghao Ma', 'Liang Chen', 'Runxin Xu', 'Zhengyang Tang', 'Benyou Wang', 'Daoguang Zan', 'Shanghaoran Quan', 'Ge Zhang', 'Lei Sha', 'Yichang Zhang', 'Xuancheng Ren', 'Tianyu Liu', 'Baobao Chang'] | ['cs.CL'] | Recent advancements in large language models (LLMs) have led to significant
breakthroughs in mathematical reasoning capabilities. However, existing
benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g.,
OpenAI o1 achieves 94.8\% on MATH dataset), indicating their inadequacy for
truly challenging t... | 2024-10-10T14:39:33Z | 30 pages | null | null | Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models | ['Bofei Gao', 'Feifan Song', 'Zhe Yang', 'Zefan Cai', 'Yibo Miao', 'Qingxiu Dong', 'Lei Li', 'Chenghao Ma', 'Liang Chen', 'Runxin Xu', 'Zhengyang Tang', 'Benyou Wang', 'Daoguang Zan', 'Shanghaoran Quan', 'Ge Zhang', 'Lei Sha', 'Yichang Zhang', 'Xuancheng Ren', 'Tianyu Liu', 'Baobao Chang'] | 2,024 | International Conference on Learning Representations | 66 | 24 | ['Computer Science'] |
2,410.08001 | Towards Synergistic, Generalized, and Efficient Dual-System for Robotic
Manipulation | ['Qingwen Bu', 'Hongyang Li', 'Li Chen', 'Jisong Cai', 'Jia Zeng', 'Heming Cui', 'Maoqing Yao', 'Yu Qiao'] | ['cs.RO', 'cs.AI'] | The increasing demand for versatile robotic systems to operate in diverse and
dynamic environments has emphasized the importance of a generalist policy,
which leverages a large cross-embodiment data corpus to facilitate broad
adaptability and high-level reasoning. However, the generalist would struggle
with inefficient... | 2024-10-10T14:57:51Z | Project page: https://opendrivelab.com/RoboDual/ | null | null | Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation | ['Qingwen Bu', 'Hongyang Li', 'Li Chen', 'Jisong Cai', 'Jia Zeng', 'Heming Cui', 'Maoqing Yao', 'Yu Qiao'] | 2,024 | arXiv.org | 11 | 74 | ['Computer Science'] |
2,410.08102 | Efficient Pretraining Data Selection for Language Models via Multi-Actor
Collaboration | ['Tianyi Bai', 'Ling Yang', 'Zhen Hao Wong', 'Fupeng Sun', 'Jiahui Peng', 'Xinlin Zhuang', 'Chi Zhang', 'Lijun Wu', 'Jiantao Qiu', 'Wentao Zhang', 'Binhang Yuan', 'Conghui He'] | ['cs.CL'] | Efficient data selection is crucial to accelerate the pretraining of language
model (LMs). While various methods have been proposed to enhance data
efficiency, limited research has addressed the inherent conflicts between these
approaches to achieve optimal data selection for LM pretraining. To tackle this
problem, we ... | 2024-10-10T16:45:28Z | null | null | null | null | null | null | null | null | null | null |
2,410.08119 | Q-VLM: Post-training Quantization for Large Vision-Language Models | ['Changyuan Wang', 'Ziwei Wang', 'Xiuwei Xu', 'Yansong Tang', 'Jie Zhou', 'Jiwen Lu'] | ['cs.CV'] | In this paper, we propose a post-training quantization framework of large
vision-language models (LVLMs) for efficient multi-modal inference.
Conventional quantization methods sequentially search the layer-wise rounding
functions by minimizing activation discretization errors, which fails to
acquire optimal quantizatio... | 2024-10-10T17:02:48Z | null | null | null | null | null | null | null | null | null | null |
2,410.08168 | ZeroComp: Zero-shot Object Compositing from Image Intrinsics via
Diffusion | ['Zitian Zhang', 'Frédéric Fortier-Chouinard', 'Mathieu Garon', 'Anand Bhattad', 'Jean-François Lalonde'] | ['cs.CV'] | We present ZeroComp, an effective zero-shot 3D object compositing approach
that does not require paired composite-scene images during training. Our method
leverages ControlNet to condition from intrinsic images and combines it with a
Stable Diffusion model to utilize its scene priors, together operating as an
effective... | 2024-10-10T17:45:12Z | Project page: https://lvsn.github.io/ZeroComp, Code:
https://github.com/lvsn/ZeroComp | null | null | Zerocomp: Zero-Shot Object Compositing from Image Intrinsics via Diffusion | ['Zitian Zhang', "Fr'ed'eric Fortier-Chouinard", 'Mathieu Garon', 'Anand Bhattad', 'Jean-Franccois Lalonde'] | 2,024 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 4 | 96 | ['Computer Science'] |
2,410.08193 | GenARM: Reward Guided Generation with Autoregressive Reward Model for
Test-time Alignment | ['Yuancheng Xu', 'Udari Madhushani Sehwag', 'Alec Koppel', 'Sicheng Zhu', 'Bang An', 'Furong Huang', 'Sumitra Ganesh'] | ['cs.CL'] | Large Language Models (LLMs) exhibit impressive capabilities but require
careful alignment with human preferences. Traditional training-time methods
finetune LLMs using human preference datasets but incur significant training
costs and require repeated training to handle diverse user preferences.
Test-time alignment me... | 2024-10-10T17:58:24Z | Published at the Thirteenth International Conference on Learning
Representations (ICLR 2025) | null | null | GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment | ['Yuancheng Xu', 'Udari Madhushani Sehwag', 'Alec Koppel', 'Sicheng Zhu', 'Bang An', 'Furong Huang', 'Sumitra Ganesh'] | 2,024 | International Conference on Learning Representations | 14 | 54 | ['Computer Science'] |
2,410.08196 | MathCoder2: Better Math Reasoning from Continued Pretraining on
Model-translated Mathematical Code | ['Zimu Lu', 'Aojun Zhou', 'Ke Wang', 'Houxing Ren', 'Weikang Shi', 'Junting Pan', 'Mingjie Zhan', 'Hongsheng Li'] | ['cs.CL', 'cs.AI', 'cs.CV'] | Code has been shown to be effective in enhancing the mathematical reasoning
abilities of large language models due to its precision and accuracy. Previous
works involving continued mathematical pretraining often include code that
utilizes math-related packages, which are primarily designed for fields such as
engineerin... | 2024-10-10T17:58:40Z | https://github.com/mathllm/MathCoder2 | null | null | null | null | null | null | null | null | null |
2,410.08202 | Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large
Language Models with Endogenous Visual Pre-training | ['Gen Luo', 'Xue Yang', 'Wenhan Dou', 'Zhaokai Wang', 'Jiawen Liu', 'Jifeng Dai', 'Yu Qiao', 'Xizhou Zhu'] | ['cs.CV', 'cs.CL'] | In this paper, we focus on monolithic Multimodal Large Language Models
(MLLMs) that integrate visual encoding and language decoding into a single LLM.
In particular, we identify that existing pre-training strategies for monolithic
MLLMs often suffer from unstable optimization or catastrophic forgetting. To
address this... | 2024-10-10T17:59:22Z | Accepted by CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,410.08208 | SPA: 3D Spatial-Awareness Enables Effective Embodied Representation | ['Haoyi Zhu', 'Honghui Yang', 'Yating Wang', 'Jiange Yang', 'Limin Wang', 'Tong He'] | ['cs.CV', 'cs.AI', 'cs.LG', 'cs.RO'] | In this paper, we introduce SPA, a novel representation learning framework
that emphasizes the importance of 3D spatial awareness in embodied AI. Our
approach leverages differentiable neural rendering on multi-view images to
endow a vanilla Vision Transformer (ViT) with intrinsic spatial understanding.
We present the m... | 2024-10-10T17:59:51Z | Project Page: https://haoyizhu.github.io/spa/ | null | null | null | null | null | null | null | null | null |
2,410.08261 | Meissonic: Revitalizing Masked Generative Transformers for Efficient
High-Resolution Text-to-Image Synthesis | ['Jinbin Bai', 'Tian Ye', 'Wei Chow', 'Enxin Song', 'Xiangtai Li', 'Zhen Dong', 'Lei Zhu', 'Shuicheng Yan'] | ['cs.CV'] | We present Meissonic, which elevates non-autoregressive masked image modeling
(MIM) text-to-image to a level comparable with state-of-the-art diffusion
models like SDXL. By incorporating a comprehensive suite of architectural
innovations, advanced positional encoding strategies, and optimized sampling
conditions, Meiss... | 2024-10-10T17:59:17Z | Accepted to ICLR 2025. Codes and Supplementary Material:
https://github.com/viiika/Meissonic | null | null | Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis | ['Jinbin Bai', 'Tian Ye', 'Wei Chow', 'Enxin Song', 'Qing-Guo Chen', 'Xiangtai Li', 'Zhen Dong', 'Lei Zhu', 'Shuicheng Yan'] | 2,024 | arXiv.org | 19 | 64 | ['Computer Science'] |
2,410.08388 | The GUS Framework: Benchmarking Social Bias Classification with
Discriminative (Encoder-Only) and Generative (Decoder-Only) Language Models | ['Maximus Powers', 'Shaina Raza', 'Alex Chang', 'Umang Mavani', 'Harshitha Reddy Jonala', 'Ansh Tiwari', 'Hua Wei'] | ['cs.CL', 'cs.AI'] | The detection of social bias in text is a critical challenge, particularly
due to the limitations of binary classification methods. These methods often
oversimplify nuanced biases, leading to high emotional impact when content is
misclassified as either "biased" or "fair." To address these shortcomings, we
propose a mo... | 2024-10-10T21:51:22Z | null | null | null | The GUS Framework: Benchmarking Social Bias Classification with Discriminative (Encoder-Only) and Generative (Decoder-Only) Language Models | ['Maximus Powers', 'Shaina Raza', 'Alex Chang', 'Umang Mavani', 'Harshitha Reddy Jonala', 'Ansh Tiwari', 'Hua Wei'] | 2,024 | null | 1 | 46 | ['Computer Science'] |
2,410.088 | Data Processing for the OpenGPT-X Model Family | ["Nicolo' Brandizzi", 'Hammam Abdelwahab', 'Anirban Bhowmick', 'Lennard Helmer', 'Benny Jörg Stein', 'Pavel Denisov', 'Qasid Saleem', 'Michael Fromm', 'Mehdi Ali', 'Richard Rutmann', 'Farzad Naderi', 'Mohamad Saif Agy', 'Alexander Schwirjow', 'Fabian Küch', 'Luzian Hahn', 'Malte Ostendorff', 'Pedro Ortiz Suarez', 'Geor... | ['cs.CL', 'H.3.1; I.2.7'] | This paper presents a comprehensive overview of the data preparation pipeline
developed for the OpenGPT-X project, a large-scale initiative aimed at creating
open and high-performance multilingual large language models (LLMs). The
project goal is to deliver models that cover all major European languages, with
a particu... | 2024-10-11T13:34:24Z | null | null | null | Data Processing for the OpenGPT-X Model Family | ['Nicolo’ Brandizzi', 'Hammam Abdelwahab', 'Anirban Bhowmick', 'Lennard Helmer', 'Benny Stein', 'Pavel Denisov', 'Qasid Saleem', 'Michael Fromm', 'Mehdi Ali', 'Richard Rutmann', 'Farzad Naderi', 'Mohamad Saif Agy', 'Alexander Schwirjow', 'Fabian Küch', 'Luzian Hahn', 'Malte Ostendorff', 'Pedro Ortiz Suarez', 'Georg Reh... | 2,024 | arXiv.org | 2 | 85 | ['Computer Science'] |
2,410.08928 | Towards Multilingual LLM Evaluation for European Languages | ['Klaudia Thellmann', 'Bernhard Stadler', 'Michael Fromm', 'Jasper Schulze Buschhoff', 'Alex Jude', 'Fabio Barth', 'Johannes Leveling', 'Nicolas Flores-Herr', 'Joachim Köhler', 'René Jäkel', 'Mehdi Ali'] | ['cs.CL', 'cs.AI', 'cs.LG'] | The rise of Large Language Models (LLMs) has revolutionized natural language
processing across numerous languages and tasks. However, evaluating LLM
performance in a consistent and meaningful way across multiple European
languages remains challenging, especially due to the scarcity of
language-parallel multilingual ben... | 2024-10-11T15:53:24Z | null | null | null | Towards Multilingual LLM Evaluation for European Languages | ['Klaudia Thellmann', 'Bernhard Stadler', 'Michael Fromm', 'Jasper Schulze Buschhoff', 'Alex Jude', 'Fabio Barth', 'Johannes Leveling', 'Nicolas Flores-Herr', 'Joachim Köhler', 'René Jäkel', 'Mehdi Ali'] | 2,024 | arXiv.org | 10 | 0 | ['Computer Science'] |
2,410.09008 | SuperCorrect: Advancing Small LLM Reasoning with Thought Template
Distillation and Self-Correction | ['Ling Yang', 'Zhaochen Yu', 'Tianjun Zhang', 'Minkai Xu', 'Joseph E. Gonzalez', 'Bin Cui', 'Shuicheng Yan'] | ['cs.CL'] | Large language models (LLMs) like GPT-4, DeepSeek-R1, and ReasonFlux have
shown significant improvements in various reasoning tasks. However, smaller
LLMs still struggle with complex mathematical reasoning because they fail to
effectively identify and correct reasoning errors. Recent reflection-based
methods aim to add... | 2024-10-11T17:25:52Z | ICLR 2025. Project: https://github.com/YangLing0818/SuperCorrect-llm | null | null | null | null | null | null | null | null | null |
2,410.09019 | MedMobile: A mobile-sized language model with expert-level clinical
capabilities | ['Krithik Vishwanath', 'Jaden Stryker', 'Anton Alyakin', 'Daniel Alexander Alber', 'Eric Karl Oermann'] | ['cs.CL'] | Language models (LMs) have demonstrated expert-level reasoning and recall
abilities in medicine. However, computational costs and privacy concerns are
mounting barriers to wide-scale implementation. We introduce a parsimonious
adaptation of phi-3-mini, MedMobile, a 3.8 billion parameter LM capable of
running on a mobil... | 2024-10-11T17:32:59Z | 13 pages, 5 figures (2 main, 3 supplementary) | null | null | MedMobile: A mobile-sized language model with expert-level clinical capabilities | ['Krithik Vishwanath', 'Jaden Stryker', 'Anton Alyakin', 'D. Alber', 'E. Oermann'] | 2,024 | arXiv.org | 3 | 26 | ['Computer Science'] |
2,410.09049 | SceneCraft: Layout-Guided 3D Scene Generation | ['Xiuyu Yang', 'Yunze Man', 'Jun-Kun Chen', 'Yu-Xiong Wang'] | ['cs.CV'] | The creation of complex 3D scenes tailored to user specifications has been a
tedious and challenging task with traditional 3D modeling tools. Although some
pioneering methods have achieved automatic text-to-3D generation, they are
generally limited to small-scale scenes with restricted control over the shape
and textur... | 2024-10-11T17:59:58Z | NeurIPS 2024. Code: https://github.com/OrangeSodahub/SceneCraft
Project Page: https://orangesodahub.github.io/SceneCraft | null | null | null | null | null | null | null | null | null |
2,410.09347 | Toward Guidance-Free AR Visual Generation via Condition Contrastive
Alignment | ['Huayu Chen', 'Hang Su', 'Peize Sun', 'Jun Zhu'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Classifier-Free Guidance (CFG) is a critical technique for enhancing the
sample quality of visual generative models. However, in autoregressive (AR)
multi-modal generation, CFG introduces design inconsistencies between language
and visual content, contradicting the design philosophy of unifying different
modalities for... | 2024-10-12T03:31:25Z | null | null | null | null | null | null | null | null | null | null |
2,410.09401 | A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and
Feature Fusion | ['Lixia Zhang', 'Tianxu Liu', 'Kaihui Shen', 'Cheng Chen'] | ['cs.CR', 'cs.AI'] | With the rapid advancement of Internet technology, the threat of malware to
computer systems and network security has intensified. Malware affects
individual privacy and security and poses risks to critical infrastructures of
enterprises and nations. The increasing quantity and complexity of malware,
along with its con... | 2024-10-12T07:10:44Z | null | null | null | A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion | ['Lixia Zhang', 'Tianxu Liu', 'Kaihui Shen', 'Cheng Chen'] | 2,024 | International Conference on Robotics, Intelligent Control and Artificial Intelligence | 1 | 63 | ['Computer Science'] |
2,410.09426 | FlatQuant: Flatness Matters for LLM Quantization | ['Yuxuan Sun', 'Ruikang Liu', 'Haoli Bai', 'Han Bao', 'Kang Zhao', 'Yuening Li', 'Jiaxin Hu', 'Xianzhi Yu', 'Lu Hou', 'Chun Yuan', 'Xin Jiang', 'Wulong Liu', 'Jun Yao'] | ['cs.CL', 'cs.LG'] | Recently, quantization has been widely used for the compression and
acceleration of large language models (LLMs). Due to the outliers in LLMs, it
is crucial to flatten weights and activations to minimize quantization error
with equally spaced quantization points. Prior research explores various
pre-quantization transfo... | 2024-10-12T08:10:28Z | 27 pages, accepted to ICML 20205 | null | null | null | null | null | null | null | null | null |
2,410.09575 | Reconstructive Visual Instruction Tuning | ['Haochen Wang', 'Anlin Zheng', 'Yucheng Zhao', 'Tiancai Wang', 'Zheng Ge', 'Xiangyu Zhang', 'Zhaoxiang Zhang'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | This paper introduces reconstructive visual instruction tuning (ROSS), a
family of Large Multimodal Models (LMMs) that exploit vision-centric
supervision signals. In contrast to conventional visual instruction tuning
approaches that exclusively supervise text outputs, ROSS prompts LMMs to
supervise visual outputs via r... | 2024-10-12T15:54:29Z | null | null | null | null | null | null | null | null | null | null |
2,410.09644 | Adapters for Altering LLM Vocabularies: What Languages Benefit the Most? | ['HyoJung Han', 'Akiko Eriguchi', 'Haoran Xu', 'Hieu Hoang', 'Marine Carpuat', 'Huda Khayrallah'] | ['cs.CL'] | Vocabulary adaptation, which integrates new vocabulary into pre-trained
language models, enables expansion to new languages and mitigates token
over-fragmentation. However, existing approaches are limited by their reliance
on heuristics or external embeddings. We propose VocADT, a novel method for
vocabulary adaptation... | 2024-10-12T20:45:24Z | ICLR2025 | null | null | null | null | null | null | null | null | null |
2,410.09671 | OpenR: An Open Source Framework for Advanced Reasoning with Large
Language Models | ['Jun Wang', 'Meng Fang', 'Ziyu Wan', 'Muning Wen', 'Jiachen Zhu', 'Anjie Liu', 'Ziqin Gong', 'Yan Song', 'Lei Chen', 'Lionel M. Ni', 'Linyi Yang', 'Ying Wen', 'Weinan Zhang'] | ['cs.AI', 'cs.CL'] | In this technical report, we introduce OpenR, an open-source framework
designed to integrate key components for enhancing the reasoning capabilities
of large language models (LLMs). OpenR unifies data acquisition, reinforcement
learning training (both online and offline), and non-autoregressive decoding
into a cohesive... | 2024-10-12T23:42:16Z | null | null | null | OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models | ['Jun Wang', 'Meng Fang', 'Ziyu Wan', 'Muning Wen', 'Jiachen Zhu', 'Anjie Liu', 'Ziqin Gong', 'Yan Song', 'Lei Chen', 'Lionel M. Ni', 'Linyi Yang', 'Ying Wen', 'Weinan Zhang'] | 2,024 | arXiv.org | 39 | 30 | ['Computer Science'] |
2,410.09724 | Taming Overconfidence in LLMs: Reward Calibration in RLHF | ['Jixuan Leng', 'Chengsong Huang', 'Banghua Zhu', 'Jiaxin Huang'] | ['cs.CL'] | Language model calibration refers to the alignment between the confidence of
the model and the actual performance of its responses. While previous studies
point out the overconfidence phenomenon in Large Language Models (LLMs) and
show that LLMs trained with Reinforcement Learning from Human Feedback (RLHF)
are overcon... | 2024-10-13T04:48:40Z | null | null | null | null | null | null | null | null | null | null |
2,410.0989 | Large-Scale 3D Medical Image Pre-training with Geometric Context Priors | ['Linshan Wu', 'Jiaxin Zhuang', 'Hao Chen'] | ['cs.CV', 'cs.AI'] | The scarcity of annotations poses a significant challenge in medical image
analysis. Large-scale pre-training has emerged as a promising label-efficient
solution, owing to the utilization of large-scale data, large models, and
advanced pre-training techniques. However, its development in medical images
remains underexp... | 2024-10-13T15:59:26Z | CVPR 2024 Extension | null | null | null | null | null | null | null | null | null |
2,410.10076 | VideoAgent: Self-Improving Video Generation | ['Achint Soni', 'Sreyas Venkataraman', 'Abhranil Chandra', 'Sebastian Fischmeister', 'Percy Liang', 'Bo Dai', 'Sherry Yang'] | ['cs.AI', 'cs.LG'] | Video generation has been used to generate visual plans for controlling
robotic systems. Given an image observation and a language instruction,
previous work has generated video plans which are then converted to robot
controls to be executed. However, a major bottleneck in leveraging video
generation for control lies i... | 2024-10-14T01:39:56Z | null | null | null | null | null | null | null | null | null | null |
2,410.10122 | MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal
Sampling | ['Yue Zhang', 'Zhizhou Zhong', 'Minhao Liu', 'Zhaokang Chen', 'Bin Wu', 'Yubin Zeng', 'Chao Zhan', 'Yingjie He', 'Junxin Huang', 'Wenjiang Zhou'] | ['cs.CV'] | Real-time video dubbing that preserves identity consistency while achieving
accurate lip synchronization remains a critical challenge. Existing approaches
face a trilemma: diffusion-based methods achieve high visual fidelity but
suffer from prohibitive computational costs, while GAN-based solutions
sacrifice lip-sync a... | 2024-10-14T03:22:26Z | 15 pages, 4 figures | null | null | null | null | null | null | null | null | null |
2,410.10139 | MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large
Vision-Language Models | ['Peng Xia', 'Siwei Han', 'Shi Qiu', 'Yiyang Zhou', 'Zhaoyang Wang', 'Wenhao Zheng', 'Zhaorun Chen', 'Chenhang Cui', 'Mingyu Ding', 'Linjie Li', 'Lijuan Wang', 'Huaxiu Yao'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Interleaved multimodal comprehension and generation, enabling models to
produce and interpret both images and text in arbitrary sequences, have become
a pivotal area in multimodal learning. Despite significant advancements, the
evaluation of this capability remains insufficient. Existing benchmarks suffer
from limitati... | 2024-10-14T04:15:00Z | ICLR 2025 Oral | null | null | null | null | null | null | null | null | null |
2,410.1021 | Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as
the Key | ['Yingda Chen', 'Xingjun Wang', 'Jintao Huang', 'Yunlin Mao', 'Daoze Zhang', 'Yuze Zhao'] | ['cs.CL'] | As large language models rapidly evolve to support longer context, there is a
notable disparity in their capability to generate output at greater lengths.
Recent study suggests that the primary cause for this imbalance may arise from
the lack of data with long-output during alignment training. In light of this
observat... | 2024-10-14T07:09:02Z | null | null | null | Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as the Key | ['Yingda Chen', 'Xingjun Wang', 'Jintao Huang', 'Yunlin Mao', 'Daoze Zhang', 'Yuze Zhao'] | 2,024 | arXiv.org | 0 | 15 | ['Computer Science'] |
2,410.10254 | LoLCATs: On Low-Rank Linearizing of Large Language Models | ['Michael Zhang', 'Simran Arora', 'Rahul Chalamala', 'Alan Wu', 'Benjamin Spector', 'Aaryan Singhal', 'Krithik Ramesh', 'Christopher Ré'] | ['cs.LG', 'cs.AI', 'cs.CL', 'stat.ML'] | Recent works show we can linearize large language models (LLMs) -- swapping
the quadratic attentions of popular Transformer-based LLMs with subquadratic
analogs, such as linear attention -- avoiding the expensive pretraining costs.
However, linearizing LLMs often significantly degrades model quality, still
requires tra... | 2024-10-14T08:10:34Z | 58 pages, 25 figures, 26 tables, ICLR 2025 | null | null | null | null | null | null | null | null | null |
2,410.10306 | Animate-X: Universal Character Image Animation with Enhanced Motion
Representation | ['Shuai Tan', 'Biao Gong', 'Xiang Wang', 'Shiwei Zhang', 'Dandan Zheng', 'Ruobing Zheng', 'Kecheng Zheng', 'Jingdong Chen', 'Ming Yang'] | ['cs.CV'] | Character image animation, which generates high-quality videos from a
reference image and target pose sequence, has seen significant progress in
recent years. However, most existing methods only apply to human figures, which
usually do not generalize well on anthropomorphic characters commonly used in
industries like g... | 2024-10-14T09:06:55Z | null | null | null | null | null | null | null | null | null | null |
2,410.10323 | MentalGLM Series: Explainable Large Language Models for Mental Health
Analysis on Chinese Social Media | ['Wei Zhai', 'Nan Bai', 'Qing Zhao', 'Jianqiang Li', 'Fan Wang', 'Hongzhi Qi', 'Meng Jiang', 'Xiaoqin Wang', 'Bing Xiang Yang', 'Guanghui Fu'] | ['cs.CL'] | As the prevalence of mental health challenges, social media has emerged as a
key platform for individuals to express their emotions.Deep learning tends to
be a promising solution for analyzing mental health on social media. However,
black box models are often inflexible when switching between tasks, and their
results t... | 2024-10-14T09:29:27Z | null | null | null | MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media | ['Wei Zhai', 'Nan Bai', 'Qing Zhao', 'Jianqiang Li', 'Fan Wang', 'Hongzhi Qi', 'Meng Jiang', 'Xiaoqin Wang', 'Bing Xiang Yang', 'Guanghui Fu'] | 2,024 | arXiv.org | 0 | 39 | ['Computer Science'] |
2,410.10356 | FasterDiT: Towards Faster Diffusion Transformers Training without
Architecture Modification | ['Jingfeng Yao', 'Wang Cheng', 'Wenyu Liu', 'Xinggang Wang'] | ['cs.CV'] | Diffusion Transformers (DiT) have attracted significant attention in
research. However, they suffer from a slow convergence rate. In this paper, we
aim to accelerate DiT training without any architectural modification. We
identify the following issues in the training process: firstly, certain
training strategies do not... | 2024-10-14T10:17:24Z | NeurIPS 2024 (poster); update to camera-ready version | null | null | FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification | ['Jingfeng Yao', 'Wang Cheng', 'Wenyu Liu', 'Xinggang Wang'] | 2,024 | Neural Information Processing Systems | 13 | 45 | ['Computer Science'] |
2,410.10594 | VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality
Documents | ['Shi Yu', 'Chaoyue Tang', 'Bokai Xu', 'Junbo Cui', 'Junhao Ran', 'Yukun Yan', 'Zhenghao Liu', 'Shuo Wang', 'Xu Han', 'Zhiyuan Liu', 'Maosong Sun'] | ['cs.IR', 'cs.AI', 'cs.CL', 'cs.CV'] | Retrieval-augmented generation (RAG) is an effective technique that enables
large language models (LLMs) to utilize external knowledge sources for
generation. However, current RAG systems are solely based on text, rendering it
impossible to utilize vision information like layout and images that play
crucial roles in re... | 2024-10-14T15:04:18Z | null | null | null | null | null | null | null | null | null | null |
2,410.10626 | Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of
Language Family Experts | ['Guorui Zheng', 'Xidong Wang', 'Juhao Liang', 'Nuo Chen', 'Yuping Zheng', 'Benyou Wang'] | ['cs.CL'] | Adapting medical Large Language Models to local languages can reduce barriers
to accessing healthcare services, but data scarcity remains a significant
challenge, particularly for low-resource languages. To address this, we first
construct a high-quality medical dataset and conduct analysis to ensure its
quality. In or... | 2024-10-14T15:31:54Z | null | null | null | Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts | ['Guorui Zheng', 'Xidong Wang', 'Juhao Liang', 'Nuo Chen', 'Yuping Zheng', 'Benyou Wang'] | 2,024 | International Conference on Learning Representations | 5 | 80 | ['Computer Science'] |
2,410.10629 | SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion
Transformers | ['Enze Xie', 'Junsong Chen', 'Junyu Chen', 'Han Cai', 'Haotian Tang', 'Yujun Lin', 'Zhekai Zhang', 'Muyang Li', 'Ligeng Zhu', 'Yao Lu', 'Song Han'] | ['cs.CV'] | We introduce Sana, a text-to-image framework that can efficiently generate
images up to 4096$\times$4096 resolution. Sana can synthesize high-resolution,
high-quality images with strong text-image alignment at a remarkably fast
speed, deployable on laptop GPU. Core designs include: (1) Deep compression
autoencoder: unl... | 2024-10-14T15:36:42Z | Technical Report | null | null | SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers | ['Enze Xie', 'Junsong Chen', 'Junyu Chen', 'Han Cai', 'Haotian Tang', 'Yujun Lin', 'Zhekai Zhang', 'Muyang Li', 'Ligeng Zhu', 'Yao Lu', 'Song Han'] | 2,024 | arXiv.org | 88 | 61 | ['Computer Science'] |
2,410.10733 | Deep Compression Autoencoder for Efficient High-Resolution Diffusion
Models | ['Junyu Chen', 'Han Cai', 'Junsong Chen', 'Enze Xie', 'Shang Yang', 'Haotian Tang', 'Muyang Li', 'Yao Lu', 'Song Han'] | ['cs.CV', 'cs.AI'] | We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder
models for accelerating high-resolution diffusion models. Existing autoencoder
models have demonstrated impressive results at a moderate spatial compression
ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for
high s... | 2024-10-14T17:15:07Z | ICLR 2025. The first two authors contributed equally to this work.
Fix Typo | null | null | Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models | ['Junyu Chen', 'Han Cai', 'Junsong Chen', 'Enze Xie', 'Shang Yang', 'Haotian Tang', 'Muyang Li', 'Yao Lu', 'Song Han'] | 2,024 | International Conference on Learning Representations | 53 | 58 | ['Computer Science'] |
2,410.10739 | Balancing Continuous Pre-Training and Instruction Fine-Tuning:
Optimizing Instruction-Following in LLMs | ['Ishan Jindal', 'Chandana Badrinath', 'Pranjal Bharti', 'Lakkidi Vinay', 'Sachin Dev Sharma'] | ['cs.CL'] | Large Language Models (LLMs) for public use require continuous pre-training
to remain up-to-date with the latest data. The models also need to be
fine-tuned with specific instructions to maintain their ability to follow
instructions accurately. Typically, LLMs are released in two versions: the Base
LLM, pre-trained on ... | 2024-10-14T17:20:30Z | null | null | null | Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs | ['Ishan Jindal', 'Chandana Badrinath', 'Pranjal Bharti', 'Lakkidi Vinay', 'Sachin Dev Sharma'] | 2,024 | arXiv.org | 1 | 43 | ['Computer Science'] |
2,410.10801 | Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning | ['Aakanksha', 'Arash Ahmadian', 'Seraphina Goldfarb-Tarrant', 'Beyza Ermis', 'Marzieh Fadaee', 'Sara Hooker'] | ['cs.CL', 'cs.LG'] | Large Language Models (LLMs) have been adopted and deployed worldwide for a
broad variety of applications. However, ensuring their safe use remains a
significant challenge. Preference training and safety measures often overfit to
harms prevalent in Western-centric datasets, and safety protocols frequently
fail to exten... | 2024-10-14T17:58:01Z | null | null | null | null | null | null | null | null | null | null |
2,410.10812 | HART: Efficient Visual Generation with Hybrid Autoregressive Transformer | ['Haotian Tang', 'Yecheng Wu', 'Shang Yang', 'Enze Xie', 'Junsong Chen', 'Junyu Chen', 'Zhuoyang Zhang', 'Han Cai', 'Yao Lu', 'Song Han'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We introduce Hybrid Autoregressive Transformer (HART), an autoregressive (AR)
visual generation model capable of directly generating 1024x1024 images,
rivaling diffusion models in image generation quality. Existing AR models face
limitations due to the poor image reconstruction quality of their discrete
tokenizers and ... | 2024-10-14T17:59:42Z | Demo: https://hart.mit.edu. The first two authors contributed equally
to this work | null | null | HART: Efficient Visual Generation with Hybrid Autoregressive Transformer | ['Haotian Tang', 'Yecheng Wu', 'Shang Yang', 'Enze Xie', 'Junsong Chen', 'Junyu Chen', 'Zhuoyang Zhang', 'Han Cai', 'Yao Lu', 'Song Han'] | 2,024 | International Conference on Learning Representations | 48 | 76 | ['Computer Science'] |
2,410.10815 | Depth Any Video with Scalable Synthetic Data | ['Honghui Yang', 'Di Huang', 'Wei Yin', 'Chunhua Shen', 'Haifeng Liu', 'Xiaofei He', 'Binbin Lin', 'Wanli Ouyang', 'Tong He'] | ['cs.CV', 'cs.AI'] | Video depth estimation has long been hindered by the scarcity of consistent
and scalable ground truth data, leading to inconsistent and unreliable results.
In this paper, we introduce Depth Any Video, a model that tackles the challenge
through two key innovations. First, we develop a scalable synthetic data
pipeline, c... | 2024-10-14T17:59:46Z | Project Page: https://depthanyvideo.github.io/ | null | null | null | null | null | null | null | null | null |
2,410.10989 | Liger Kernel: Efficient Triton Kernels for LLM Training | ['Pin-Lun Hsu', 'Yun Dai', 'Vignesh Kothapalli', 'Qingquan Song', 'Shao Tang', 'Siyu Zhu', 'Steven Shimizu', 'Shivam Sahni', 'Haowen Ning', 'Yanning Chen'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.DC'] | Training Large Language Models (LLMs) efficiently at scale presents a
formidable challenge, driven by their ever-increasing computational demands and
the need for enhanced performance. In this work, we introduce Liger-Kernel, an
open-sourced set of Triton kernels developed specifically for LLM training.
With kernel opt... | 2024-10-14T18:17:01Z | 17 pages, 12 figures | null | null | null | null | null | null | null | null | null |
2,410.11081 | Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models | ['Cheng Lu', 'Yang Song'] | ['cs.LG', 'stat.ML'] | Consistency models (CMs) are a powerful class of diffusion-based generative
models optimized for fast sampling. Most existing CMs are trained using
discretized timesteps, which introduce additional hyperparameters and are prone
to discretization errors. While continuous-time formulations can mitigate these
issues, thei... | 2024-10-14T20:43:25Z | ICLR 2025 Oral | null | null | null | null | null | null | null | null | null |
2,410.1119 | Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex
Capabilities | ['Zhifei Xie', 'Changqiao Wu'] | ['eess.AS', 'cs.AI', 'cs.CV', 'cs.LG', 'cs.SD'] | GPT-4o, an all-encompassing model, represents a milestone in the development
of large multi-modal language models. It can understand visual, auditory, and
textual modalities, directly output audio, and support flexible duplex
interaction. Models from the open-source community often achieve some
functionalities of GPT-4... | 2024-10-15T02:10:45Z | Technical report, work in progress. Demo and code:
https://github.com/gpt-omni/mini-omni2 | null | null | null | null | null | null | null | null | null |
2,410.11419 | GS^3: Efficient Relighting with Triple Gaussian Splatting | ['Zoubin Bi', 'Yixin Zeng', 'Chong Zeng', 'Fan Pei', 'Xiang Feng', 'Kun Zhou', 'Hongzhi Wu'] | ['cs.CV', 'cs.GR'] | We present a spatial and angular Gaussian based representation and a triple
splatting process, for real-time, high-quality novel lighting-and-view
synthesis from multi-view point-lit input images. To describe complex
appearance, we employ a Lambertian plus a mixture of angular Gaussians as an
effective reflectance func... | 2024-10-15T09:11:30Z | Accepted to SIGGRAPH Asia 2024. Project page:
https://gsrelight.github.io/ | ACM SIGGRAPH Asia 2024 Conference Papers | 10.1145/3680528.3687576 | null | null | null | null | null | null | null |
2,410.11439 | A Simple Approach to Unifying Diffusion-based Conditional Generation | ['Xirui Li', 'Charles Herrmann', 'Kelvin C. K. Chan', 'Yinxiao Li', 'Deqing Sun', 'Chao Ma', 'Ming-Hsuan Yang'] | ['cs.CV'] | Recent progress in image generation has sparked research into controlling
these models through condition signals, with various methods addressing
specific challenges in conditional generation. Instead of proposing another
specialized technique, we introduce a simple, unified framework to handle
diverse conditional gene... | 2024-10-15T09:41:43Z | Project page: https://lixirui142.github.io/unicon-diffusion/ | null | null | null | null | null | null | null | null | null |
2,410.11584 | DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object
Manipulation via Preference-based Action Alignment | ['Wendi Chen', 'Han Xue', 'Fangyuan Zhou', 'Yuan Fang', 'Cewu Lu'] | ['cs.RO', 'cs.AI', 'cs.CV'] | In recent years, imitation learning has made progress in the field of robotic
manipulation. However, it still faces challenges when addressing complex
long-horizon tasks with deformable objects, such as high-dimensional state
spaces, complex dynamics, and multimodal action distributions. Traditional
imitation learning ... | 2024-10-15T13:19:16Z | Accepted to ICRA 2025. Project page: https://deform-pam.robotflow.ai | null | null | DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment | ['Wendi Chen', 'Han Xue', 'Fangyuan Zhou', 'Yuan Fang', 'Cewu Lu'] | 2,024 | arXiv.org | 1 | 44 | ['Computer Science'] |
2,410.11654 | Transformer Layer Injection: A Novel Approach for Efficient Upscaling of
Large Language Models | ['James Vo'] | ['cs.CL'] | In this paper, we propose Transformer Layer Injection (TLI), a novel method
for efficiently upscaling large language models (LLMs) while minimizing
computational costs and maintaining model performance. Model scale is a key
factor in enhancing the quality of machine learning models, and TLI addresses
the challenge of s... | 2024-10-15T14:41:44Z | null | null | null | null | null | null | null | null | null | null |
2,410.11666 | DORNet: A Degradation Oriented and Regularized Network for Blind Depth
Super-Resolution | ['Zhengxue Wang', 'Zhiqiang Yan', 'Jinshan Pan', 'Guangwei Gao', 'Kai Zhang', 'Jian Yang'] | ['cs.CV'] | Recent RGB-guided depth super-resolution methods have achieved impressive
performance under the assumption of fixed and known degradation (e.g., bicubic
downsampling). However, in real-world scenarios, captured depth data often
suffer from unconventional and unknown degradation due to sensor limitations
and complex ima... | 2024-10-15T14:53:07Z | CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,410.11758 | Latent Action Pretraining from Videos | ['Seonghyeon Ye', 'Joel Jang', 'Byeongguk Jeon', 'Sejune Joo', 'Jianwei Yang', 'Baolin Peng', 'Ajay Mandlekar', 'Reuben Tan', 'Yu-Wei Chao', 'Bill Yuchen Lin', 'Lars Liden', 'Kimin Lee', 'Jianfeng Gao', 'Luke Zettlemoyer', 'Dieter Fox', 'Minjoon Seo'] | ['cs.RO', 'cs.CL', 'cs.CV', 'cs.LG'] | We introduce Latent Action Pretraining for general Action models (LAPA), an
unsupervised method for pretraining Vision-Language-Action (VLA) models without
ground-truth robot action labels. Existing Vision-Language-Action models
require action labels typically collected by human teleoperators during
pretraining, which ... | 2024-10-15T16:28:09Z | ICLR 2025 Website: https://latentactionpretraining.github.io | null | null | null | null | null | null | null | null | null |
2,410.11761 | SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology
Image Understanding | ['Ying Chen', 'Guoan Wang', 'Yuanfeng Ji', 'Yanjun Li', 'Jin Ye', 'Tianbin Li', 'Ming Hu', 'Rongshan Yu', 'Yu Qiao', 'Junjun He'] | ['cs.CV', 'cs.AI'] | Despite the progress made by multimodal large language models (MLLMs) in
computational pathology, they remain limited by a predominant focus on
patch-level analysis, missing essential contextual information at the
whole-slide level. The lack of large-scale instruction datasets and the
gigapixel scale of whole slide ima... | 2024-10-15T16:33:33Z | Accepted by CVPR2025 | null | null | SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding | ['Ying Chen', 'Guoan Wang', 'Yuanfeng Ji', 'Yanjun Li', 'Jin Ye', 'Tian-Xin Li', 'Bin Zhang', 'Nana Pei', 'Rongshan Yu', 'Yu Qiao', 'Junjun He'] | 2,024 | Computer Vision and Pattern Recognition | 5 | 35 | ['Computer Science'] |
2,410.11786 | Selection-p: Self-Supervised Task-Agnostic Prompt Compression for
Faithfulness and Transferability | ['Tsz Ting Chung', 'Leyang Cui', 'Lemao Liu', 'Xinting Huang', 'Shuming Shi', 'Dit-Yan Yeung'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) have demonstrated impressive capabilities in a
wide range of natural language processing tasks when leveraging in-context
learning. To mitigate the additional computational and financial costs
associated with in-context learning, several prompt compression methods have
been proposed to comp... | 2024-10-15T17:05:25Z | 14 pages, 5 figures, 10 tables, EMNLP 2024 Findings | null | null | null | null | null | null | null | null | null |
2,410.11817 | Improving Long-Text Alignment for Text-to-Image Diffusion Models | ['Luping Liu', 'Chao Du', 'Tianyu Pang', 'Zehan Wang', 'Chongxuan Li', 'Dong Xu'] | ['cs.CV', 'cs.LG', 'cs.MM'] | The rapid advancement of text-to-image (T2I) diffusion models has enabled
them to generate unprecedented results from given texts. However, as text
inputs become longer, existing encoding methods like CLIP face limitations, and
aligning the generated images with long texts becomes challenging. To tackle
these issues, w... | 2024-10-15T17:46:31Z | null | International Conference on Learning Representations (ICLR 2025) | null | Improving Long-Text Alignment for Text-to-Image Diffusion Models | ['Luping Liu', 'Chao Du', 'Tianyu Pang', 'Zehan Wang', 'Chongxuan Li', 'Dong Xu'] | 2,024 | International Conference on Learning Representations | 8 | 70 | ['Computer Science'] |
2,410.11831 | CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real
Videos | ['Nikita Karaev', 'Iurii Makarov', 'Jianyuan Wang', 'Natalia Neverova', 'Andrea Vedaldi', 'Christian Rupprecht'] | ['cs.CV'] | Most state-of-the-art point trackers are trained on synthetic data due to the
difficulty of annotating real videos for this task. However, this can result in
suboptimal performance due to the statistical gap between synthetic and real
videos. In order to understand these issues better, we introduce CoTracker3,
comprisi... | 2024-10-15T17:56:32Z | null | null | null | CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos | ['Nikita Karaev', 'Iurii Makarov', 'Jianyuan Wang', 'Natalia Neverova', 'Andrea Vedaldi', 'Christian Rupprecht'] | 2,024 | arXiv.org | 68 | 41 | ['Computer Science'] |
2,410.11842 | MoH: Multi-Head Attention as Mixture-of-Head Attention | ['Peng Jin', 'Bo Zhu', 'Li Yuan', 'Shuicheng Yan'] | ['cs.CV', 'cs.AI', 'cs.LG'] | In this work, we upgrade the multi-head attention mechanism, the core of the
Transformer model, to improve efficiency while maintaining or surpassing the
previous accuracy level. We show that multi-head attention can be expressed in
the summation form. Drawing on the insight that not all attention heads hold
equal sign... | 2024-10-15T17:59:44Z | Accepted by ICML 2025, code: https://github.com/SkyworkAI/MoH | null | null | MoH: Multi-Head Attention as Mixture-of-Head Attention | ['Peng Jin', 'Bo Zhu', 'Li Yuan', 'Shuicheng Yan'] | 2,024 | arXiv.org | 19 | 103 | ['Computer Science'] |
2,410.11888 | Aharonov-Bohm effects on the GUP framework | ['Baoyu Tan'] | ['quant-ph', 'gr-qc'] | Modifying the fundamental commutation relation of quantum mechanics to
reflect the influence of gravity is an important approach to reconcile the
contradiction between quantum field theory and general relativity. In the past
two decades, researchers have conducted extensive research on geometric phase
problems in non-c... | 2024-10-12T09:09:06Z | null | null | null | null | null | null | null | null | null | null |
2,410.12288 | A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context
Reasoning | ['Yuanning Cui', 'Zequn Sun', 'Wei Hu'] | ['cs.AI', 'cs.CL'] | Extensive knowledge graphs (KGs) have been constructed to facilitate
knowledge-driven tasks across various scenarios. However, existing work usually
develops separate reasoning models for different KGs, lacking the ability to
generalize and transfer knowledge across diverse KGs and reasoning settings. In
this paper, we... | 2024-10-16T06:47:18Z | Accepted in the 38th Conference on Neural Information Processing
Systems (NeurIPS 2024) | null | null | A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning | ['Yuanning Cui', 'Zequn Sun', 'Wei Hu'] | 2,024 | Neural Information Processing Systems | 8 | 81 | ['Computer Science'] |
2,410.1236 | Towards Neural Scaling Laws for Time Series Foundation Models | ['Qingren Yao', 'Chao-Han Huck Yang', 'Renhe Jiang', 'Yuxuan Liang', 'Ming Jin', 'Shirui Pan'] | ['cs.LG', 'cs.AI'] | Scaling laws offer valuable insights into the design of time series
foundation models (TSFMs). However, previous research has largely focused on
the scaling laws of TSFMs for in-distribution (ID) data, leaving their
out-of-distribution (OOD) scaling behavior and the influence of model
architectures less explored. In th... | 2024-10-16T08:23:39Z | Accepted by the 13th International Conference on Learning
Representations (ICLR 2025) | null | null | null | null | null | null | null | null | null |
2,410.12375 | PRefLexOR: Preference-based Recursive Language Modeling for Exploratory
Optimization of Reasoning and Agentic Thinking | ['Markus J. Buehler'] | ['cs.AI', 'cond-mat.dis-nn', 'cond-mat.mes-hall', 'cond-mat.mtrl-sci', 'cs.CL'] | PRefLexOR (Preference-based Recursive Language Modeling for Exploratory
Optimization of Reasoning) combines preference optimization with concepts from
Reinforcement Learning to enable models to self-teach through iterative
reasoning improvements. We propose a recursive learning approach that engages
the model in multi-... | 2024-10-16T08:46:26Z | null | null | null | null | null | null | null | null | null | null |
2,410.12377 | HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying
Real-World Claims | ['Yejun Yoon', 'Jaeyoon Jung', 'Seunghyun Yoon', 'Kunwoo Park'] | ['cs.CL', 'cs.CY'] | To tackle the AVeriTeC shared task hosted by the FEVER-24, we introduce a
system that only employs publicly available large language models (LLMs) for
each step of automated fact-checking, dubbed the Herd of Open LLMs for
verifying real-world claims (HerO). For evidence retrieval, a language model is
used to enhance a ... | 2024-10-16T08:49:17Z | A system description paper for the AVeriTeC shared task, hosted by
the seventh FEVER workshop (co-located with EMNLP 2024) | null | null | HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims | ['Yejun Yoon', 'Jaeyoon Jung', 'Seunghyun Yoon', 'Kunwoo Park'] | 2,024 | FEVER | 2 | 23 | ['Computer Science'] |
2,410.12381 | HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex
Diagrams in Coding Tasks | ['Fengji Zhang', 'Linquan Wu', 'Huiyu Bai', 'Guancheng Lin', 'Xiao Li', 'Xiao Yu', 'Yue Wang', 'Bei Chen', 'Jacky Keung'] | ['cs.CV', 'cs.AI'] | Understanding and reasoning over diagrams is a fundamental aspect of human
intelligence. While Large Multimodal Models (LMMs) have demonstrated impressive
capabilities across various tasks, existing benchmarks lack comprehensive
evaluation of their diagram interpretation and reasoning abilities,
particularly in coding ... | 2024-10-16T09:04:57Z | homepage https://humaneval-v.github.io/ | null | null | HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks | ['Fengji Zhang', 'Linquan Wu', 'Huiyu Bai', 'Guancheng Lin', 'Xiao Li', 'Xiao Yu', 'Yue Wang', 'Bei Chen', 'J. Keung'] | 2,024 | null | 0 | 49 | ['Computer Science'] |
2,410.12391 | Tracking Universal Features Through Fine-Tuning and Model Merging | ['Niels Horn', 'Desmond Elliott'] | ['cs.CL', 'cs.LG'] | We study how features emerge, disappear, and persist across models fine-tuned
on different domains of text. More specifically, we start from a base one-layer
Transformer language model that is trained on a combination of the BabyLM
corpus, and a collection of Python code from The Stack. This base model is
adapted to tw... | 2024-10-16T09:18:39Z | null | null | null | null | null | null | null | null | null | null |
2,410.12409 | Revealing the Barriers of Language Agents in Planning | ['Jian Xie', 'Kexun Zhang', 'Jiangjie Chen', 'Siyu Yuan', 'Kai Zhang', 'Yikai Zhang', 'Lei Li', 'Yanghua Xiao'] | ['cs.AI', 'cs.CL'] | Autonomous planning has been an ongoing pursuit since the inception of
artificial intelligence. Based on curated problem solvers, early planning
agents could deliver precise solutions for specific tasks but lacked
generalization. The emergence of large language models (LLMs) and their
powerful reasoning capabilities ha... | 2024-10-16T09:44:38Z | Work in Progress | null | null | Revealing the Barriers of Language Agents in Planning | ['Jian Xie', 'Kexun Zhang', 'Jiangjie Chen', 'Siyu Yuan', 'Kai Zhang', 'Yikai Zhang', 'Lei Li', 'Yanghua Xiao'] | 2,024 | North American Chapter of the Association for Computational Linguistics | 12 | 49 | ['Computer Science'] |
2,410.1249 | Stabilize the Latent Space for Image Autoregressive Modeling: A Unified
Perspective | ['Yongxin Zhu', 'Bocheng Li', 'Hang Zhang', 'Xin Li', 'Linli Xu', 'Lidong Bing'] | ['cs.CV', 'cs.AI'] | Latent-based image generative models, such as Latent Diffusion Models (LDMs)
and Mask Image Models (MIMs), have achieved notable success in image generation
tasks. These models typically leverage reconstructive autoencoders like VQGAN
or VAE to encode pixels into a more compact latent space and learn the data
distribut... | 2024-10-16T12:13:17Z | Accepted at NeurIPS 2024 | null | null | Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective | ['Yongxin Zhu', 'Bocheng Li', 'Hang Zhang', 'Xin Li', 'Linli Xu', 'Li Bing'] | 2,024 | Neural Information Processing Systems | 9 | 42 | ['Computer Science'] |
2,410.12628 | DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
Synthetic Data and Global-to-Local Adaptive Perception | ['Zhiyuan Zhao', 'Hengrui Kang', 'Bin Wang', 'Conghui He'] | ['cs.CV'] | Document Layout Analysis is crucial for real-world document understanding
systems, but it encounters a challenging trade-off between speed and accuracy:
multimodal methods leveraging both text and visual features achieve higher
accuracy but suffer from significant latency, whereas unimodal methods relying
solely on vis... | 2024-10-16T14:50:47Z | Github Repo: https://github.com/opendatalab/DocLayout-YOLO | null | null | null | null | null | null | null | null | null |
2,410.12669 | 3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image
Generation | ['Dewei Zhou', 'Ji Xie', 'Zongxin Yang', 'Yi Yang'] | ['cs.CV'] | The increasing demand for controllable outputs in text-to-image generation
has spurred advancements in multi-instance generation (MIG), allowing users to
define both instance layouts and attributes. However, unlike image-conditional
generation methods such as ControlNet, MIG techniques have not been widely
adopted in s... | 2024-10-16T15:34:13Z | 10 pages | null | null | null | null | null | null | null | null | null |
2,410.12722 | WorldMedQA-V: a multilingual, multimodal medical examination dataset for
multimodal language models evaluation | ['João Matos', 'Shan Chen', 'Siena Placino', 'Yingya Li', 'Juan Carlos Climent Pardo', 'Daphna Idan', 'Takeshi Tohyama', 'David Restrepo', 'Luis F. Nakayama', 'Jose M. M. Pascual-Leone', 'Guergana Savova', 'Hugo Aerts', 'Leo A. Celi', 'A. Ian Wong', 'Danielle S. Bitterman', 'Jack Gallifant'] | ['cs.CL'] | Multimodal/vision language models (VLMs) are increasingly being deployed in
healthcare settings worldwide, necessitating robust benchmarks to ensure their
safety, efficacy, and fairness. Multiple-choice question and answer (QA)
datasets derived from national medical examinations have long served as
valuable evaluation ... | 2024-10-16T16:31:24Z | submitted for review, total of 14 pages | null | null | WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation | ['João Matos', 'Shan Chen', 'Siena Placino', 'Yingya Li', 'Juan Carlos Climent Pardo', 'Daphna Idan', 'Takeshi Tohyama', 'David Restrepo', 'L. F. Nakayama', 'Jose M. M. Pascual-Leone', 'G. Savova', 'Hugo J. W. L. Aerts', 'L. Celi', 'A. I. Wong', 'Danielle S. Bitterman', 'Jack Gallifant'] | 2,024 | North American Chapter of the Association for Computational Linguistics | 3 | 38 | ['Computer Science'] |
2,410.12757 | StyleDistance: Stronger Content-Independent Style Embeddings with
Synthetic Parallel Examples | ['Ajay Patel', 'Jiacheng Zhu', 'Justin Qiu', 'Zachary Horvitz', 'Marianna Apidianaki', 'Kathleen McKeown', 'Chris Callison-Burch'] | ['cs.CL', 'cs.LG'] | Style representations aim to embed texts with similar writing styles closely
and texts with different styles far apart, regardless of content. However, the
contrastive triplets often used for training these representations may vary in
both style and content, leading to potential content leakage in the
representations. ... | 2024-10-16T17:25:25Z | To appear at NAACL 2025 | null | null | null | null | null | null | null | null | null |
2,410.12771 | Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models | ['Luis Barroso-Luque', 'Muhammed Shuaibi', 'Xiang Fu', 'Brandon M. Wood', 'Misko Dzamba', 'Meng Gao', 'Ammar Rizvi', 'C. Lawrence Zitnick', 'Zachary W. Ulissi'] | ['cond-mat.mtrl-sci', 'cs.AI', 'physics.comp-ph'] | The ability to discover new materials with desirable properties is critical
for numerous applications from helping mitigate climate change to advances in
next generation computing hardware. AI has the potential to accelerate
materials discovery and design by more effectively exploring the chemical space
compared to oth... | 2024-10-16T17:48:34Z | 19 pages | null | null | null | null | null | null | null | null | null |
2,410.12788 | Meta-Chunking: Learning Text Segmentation and Semantic Completion via
Logical Perception | ['Jihao Zhao', 'Zhiyuan Ji', 'Yuchen Feng', 'Pengnian Qi', 'Simin Niu', 'Bo Tang', 'Feiyu Xiong', 'Zhiyu Li'] | ['cs.CL'] | While Retrieval-Augmented Generation (RAG) has emerged as a promising
paradigm for boosting large language models (LLMs) in knowledge-intensive
tasks, it often overlooks the crucial aspect of text chunking within its
workflow. This paper proposes the Meta-Chunking framework, which specifically
enhances chunking quality... | 2024-10-16T17:59:32Z | null | null | null | null | null | null | null | null | null | null |
2,410.12844 | TextLap: Customizing Language Models for Text-to-Layout Planning | ['Jian Chen', 'Ruiyi Zhang', 'Yufan Zhou', 'Jennifer Healey', 'Jiuxiang Gu', 'Zhiqiang Xu', 'Changyou Chen'] | ['cs.CL', 'cs.LG'] | Automatic generation of graphical layouts is crucial for many real-world
applications, including designing posters, flyers, advertisements, and
graphical user interfaces. Given the incredible ability of Large language
models (LLMs) in both natural language understanding and generation, we believe
that we could customiz... | 2024-10-09T19:51:38Z | Accepted to the EMNLP Findings | null | null | TextLap: Customizing Language Models for Text-to-Layout Planning | ['Jian Chen', 'Ruiyi Zhang', 'Yufan Zhou', 'Jennifer Healey', 'Jiuxiang Gu', 'Zhiqiang Xu', 'Changyou Chen'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 3 | 43 | ['Computer Science'] |
2,410.12855 | JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent
Enhanced Explanation Evaluation Framework | ['Fan Liu', 'Yue Feng', 'Zhao Xu', 'Lixin Su', 'Xinyu Ma', 'Dawei Yin', 'Hao Liu'] | ['cs.CL', 'cs.AI'] | Despite advancements in enhancing LLM safety against jailbreak attacks,
evaluating LLM defenses remains a challenge, with current methods often lacking
explainability and generalization to complex scenarios, leading to incomplete
assessments (e.g., direct judgment without reasoning, low F1 score of GPT-4 in
complex cas... | 2024-10-11T14:56:28Z | null | null | null | null | null | null | null | null | null | null |
2,410.13116 | Learning to Summarize from LLM-generated Feedback | ['Hwanjun Song', 'Taewon Yun', 'Yuho Lee', 'Jihwan Oh', 'Gihun Lee', 'Jason Cai', 'Hang Su'] | ['cs.CL', 'cs.AI'] | Developing effective text summarizers remains a challenge due to issues like
hallucinations, key information omissions, and verbosity in LLM-generated
summaries. This work explores using LLM-generated feedback to improve summary
quality by aligning the summaries with human preferences for faithfulness,
completeness, an... | 2024-10-17T01:01:09Z | Accepted at NAACL 2025 (main, long) | null | null | null | null | null | null | null | null | null |
2,410.13136 | Unlocking the Capabilities of Masked Generative Models for Image
Synthesis via Self-Guidance | ['Jiwan Hur', 'Dong-Jae Lee', 'Gyojin Han', 'Jaehyun Choi', 'Yunho Jeon', 'Junmo Kim'] | ['cs.CV'] | Masked generative models (MGMs) have shown impressive generative ability
while providing an order of magnitude efficient sampling steps compared to
continuous diffusion models. However, MGMs still underperform in image
synthesis compared to recent well-developed continuous diffusion models with
similar size in terms of... | 2024-10-17T01:48:05Z | NeurIPS 2024. Code is available at:
https://github.com/JiwanHur/UnlockMGM | null | null | Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance | ['Jiwan Hur', 'Dong-Jae Lee', 'Gyojin Han', 'Jaehyun Choi', 'Yunho Jeon', 'Junmo Kim'] | 2,024 | Neural Information Processing Systems | 0 | 64 | ['Computer Science'] |
2,410.13213 | LLMOPT: Learning to Define and Solve General Optimization Problems from
Scratch | ['Caigao Jiang', 'Xiang Shu', 'Hong Qian', 'Xingyu Lu', 'Jun Zhou', 'Aimin Zhou', 'Yang Yu'] | ['cs.AI', 'cs.LG'] | Optimization problems are prevalent across various scenarios. Formulating and
then solving optimization problems described by natural language often requires
highly specialized human expertise, which could block the widespread
application of optimization-based decision making. To automate problem
formulation and solvin... | 2024-10-17T04:37:37Z | null | null | null | null | null | null | null | null | null | null |
2,410.1323 | Starbucks-v2: Improved Training for 2D Matryoshka Embeddings | ['Shengyao Zhuang', 'Shuai Wang', 'Fabio Zheng', 'Bevan Koopman', 'Guido Zuccon'] | ['cs.IR'] | 2D Matryoshka training enables a single embedding model to generate
sub-network representations across different layers and embedding dimensions,
offering adaptability to diverse computational and task constraints. However,
its effectiveness remains well below that of individually trained models of
equivalent sizes. To... | 2024-10-17T05:33:50Z | Updated Version of Starbucks, add (1) Generalisation to E5 model (2)
Out-of-domain zero-shot effectiveness (3) Propose Depth-wise Starbucks and
Hybrid-Starbucks | null | null | null | null | null | null | null | null | null |
2,410.13267 | CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages
Using Large Language Models | ['Shangda Wu', 'Yashan Wang', 'Ruibin Yuan', 'Zhancheng Guo', 'Xu Tan', 'Ge Zhang', 'Monan Zhou', 'Jing Chen', 'Xuefeng Mu', 'Yuejie Gao', 'Yuanliang Dong', 'Jiafeng Liu', 'Xiaobing Li', 'Feng Yu', 'Maosong Sun'] | ['cs.SD', 'cs.CL', 'eess.AS'] | Challenges in managing linguistic diversity and integrating various musical
modalities are faced by current music information retrieval systems. These
limitations reduce their effectiveness in a global, multimodal music
environment. To address these issues, we introduce CLaMP 2, a system compatible
with 101 languages t... | 2024-10-17T06:43:54Z | 17 pages, 10 figures, 4 tables, accepted by NAACL 2025 | null | null | CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models | ['Shangda Wu', 'Yashan Wang', 'Ruibin Yuan', 'Zhancheng Guo', 'Xu Tan', 'Ge Zhang', 'Monan Zhou', 'Jing Chen', 'Xuefeng Mu', 'Yuejie Gao', 'Yuanliang Dong', 'Jiafeng Liu', 'Xiaobing Li', 'Feng Yu', 'Maosong Sun'] | 2,024 | North American Chapter of the Association for Computational Linguistics | 5 | 46 | ['Computer Science', 'Engineering'] |
2,410.13276 | SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs | ['Yizhao Gao', 'Zhichen Zeng', 'Dayou Du', 'Shijie Cao', 'Peiyuan Zhou', 'Jiaxing Qi', 'Junjie Lai', 'Hayden Kwok-Hay So', 'Ting Cao', 'Fan Yang', 'Mao Yang'] | ['cs.CL'] | Attention is the cornerstone of modern Large Language Models (LLMs). Yet its
quadratic complexity hinders efficiency and scalability, especially for
long-context processing. A promising approach is to leverage sparsity in
attention. However, existing sparsity-based solutions predominantly rely on
predefined patterns or... | 2024-10-17T07:07:09Z | null | null | null | null | null | null | null | null | null | null |
2,410.13394 | Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs | ['Sumanth Doddapaneni', 'Mohammed Safi Ur Rahman Khan', 'Dilip Venkatesh', 'Raj Dabre', 'Anoop Kunchukuttan', 'Mitesh M. Khapra'] | ['cs.CL'] | Evaluating machine-generated text remains a significant challenge in NLP,
especially for non-English languages. Current methodologies, including
automated metrics, human assessments, and LLM-based evaluations, predominantly
focus on English, revealing a significant gap in multilingual evaluation
frameworks. We introduc... | 2024-10-17T09:45:32Z | null | null | null | null | null | null | null | null | null | null |
2,410.13458 | MedINST: Meta Dataset of Biomedical Instructions | ['Wenhan Han', 'Meng Fang', 'Zihan Zhang', 'Yu Yin', 'Zirui Song', 'Ling Chen', 'Mykola Pechenizkiy', 'Qingyu Chen'] | ['cs.CL'] | The integration of large language model (LLM) techniques in the field of
medical analysis has brought about significant advancements, yet the scarcity
of large, diverse, and well-annotated datasets remains a major challenge.
Medical data and tasks, which vary in format, size, and other parameters,
require extensive pre... | 2024-10-17T11:38:54Z | null | null | null | null | null | null | null | null | null | null |
2,410.13611 | H2OVL-Mississippi Vision Language Models Technical Report | ['Shaikat Galib', 'Shanshan Wang', 'Guanshuo Xu', 'Pascal Pfeiffer', 'Ryan Chesler', 'Mark Landry', 'Sri Satish Ambati'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Smaller vision-language models (VLMs) are becoming increasingly important for
privacy-focused, on-device applications due to their ability to run efficiently
on consumer hardware for processing enterprise commercial documents and images.
These models require strong language understanding and visual capabilities to
enha... | 2024-10-17T14:46:34Z | null | null | null | null | null | null | null | null | null | null |
2,410.13726 | DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework
for Talking Head Video Generation | ['Hanbo Cheng', 'Limin Lin', 'Chenyu Liu', 'Pengcheng Xia', 'Pengfei Hu', 'Jiefeng Ma', 'Jun Du', 'Jia Pan'] | ['cs.CV', 'cs.AI'] | Talking head generation intends to produce vivid and realistic talking head
videos from a single portrait and speech audio clip. Although significant
progress has been made in diffusion-based talking head generation, almost all
methods rely on autoregressive strategies, which suffer from limited context
utilization bey... | 2024-10-17T16:32:36Z | null | null | null | null | null | null | null | null | null | null |
2,410.13782 | DPLM-2: A Multimodal Diffusion Protein Language Model | ['Xinyou Wang', 'Zaixiang Zheng', 'Fei Ye', 'Dongyu Xue', 'Shujian Huang', 'Quanquan Gu'] | ['cs.LG', 'q-bio.QM'] | Proteins are essential macromolecules defined by their amino acid sequences,
which determine their three-dimensional structures and, consequently, their
functions in all living organisms. Therefore, generative protein modeling
necessitates a multimodal approach to simultaneously model, understand, and
generate both seq... | 2024-10-17T17:20:24Z | null | null | null | DPLM-2: A Multimodal Diffusion Protein Language Model | ['Xinyou Wang', 'Zaixiang Zheng', 'Fei Ye', 'Dongyu Xue', 'Shujian Huang', 'Quanquan Gu'] | 2,024 | International Conference on Learning Representations | 20 | 85 | ['Computer Science', 'Biology'] |
2,410.13824 | Harnessing Webpage UIs for Text-Rich Visual Understanding | ['Junpeng Liu', 'Tianyue Ou', 'Yifan Song', 'Yuxiao Qu', 'Wai Lam', 'Chenyan Xiong', 'Wenhu Chen', 'Graham Neubig', 'Xiang Yue'] | ['cs.CV', 'cs.CL'] | Text-rich visual understanding-the ability to process environments where
dense textual content is integrated with visuals-is crucial for multimodal
large language models (MLLMs) to interact effectively with structured
environments. To enhance this capability, we propose synthesizing general
multimodal instructions from... | 2024-10-17T17:48:54Z | null | null | null | null | null | null | null | null | null | null |
2,410.13842 | D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution
Refinement | ['Yansong Peng', 'Hebei Li', 'Peixi Wu', 'Yueyi Zhang', 'Xiaoyan Sun', 'Feng Wu'] | ['cs.CV'] | We introduce D-FINE, a powerful real-time object detector that achieves
outstanding localization precision by redefining the bounding box regression
task in DETR models. D-FINE comprises two key components: Fine-grained
Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation
(GO-LSD). FDR transf... | 2024-10-17T17:57:01Z | null | null | null | null | null | null | null | null | null | null |
2,410.13846 | LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with
Effortless Adaptation | ['Xuan Zhang', 'Fengzhuo Zhang', 'Cunxiao Du', 'Chao Du', 'Tianyu Pang', 'Wei Gao', 'Min Lin'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Scaling language models to handle longer contexts introduces substantial
memory challenges due to the growing cost of key-value (KV) caches. Motivated
by the efficiency gains of hybrid models and the broad availability of
pretrained large transformer backbones, we explore transitioning transformer
models into hybrid ar... | 2024-10-17T17:58:14Z | null | null | null | null | null | null | null | null | null | null |
2,410.13848 | Janus: Decoupling Visual Encoding for Unified Multimodal Understanding
and Generation | ['Chengyue Wu', 'Xiaokang Chen', 'Zhiyu Wu', 'Yiyang Ma', 'Xingchao Liu', 'Zizheng Pan', 'Wen Liu', 'Zhenda Xie', 'Xingkai Yu', 'Chong Ruan', 'Ping Luo'] | ['cs.CV', 'cs.AI', 'cs.CL'] | In this paper, we introduce Janus, an autoregressive framework that unifies
multimodal understanding and generation. Prior research often relies on a
single visual encoder for both tasks, such as Chameleon. However, due to the
differing levels of information granularity required by multimodal
understanding and generati... | 2024-10-17T17:58:37Z | Technical Report | null | null | null | null | null | null | null | null | null |
2,410.13852 | Retrospective Learning from Interactions | ['Zizhao Chen', 'Mustafa Omer Gul', 'Yiwei Chen', 'Gloria Geng', 'Anne Wu', 'Yoav Artzi'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | Multi-turn interactions between large language models (LLMs) and users
naturally include implicit feedback signals. If an LLM responds in an
unexpected way to an instruction, the user is likely to signal it by rephrasing
the request, expressing frustration, or pivoting to an alternative task. Such
signals are task-inde... | 2024-10-17T17:59:03Z | null | null | null | null | null | null | null | null | null | null |
2,410.13859 | $γ-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large
Language Models | ['Yaxin Luo', 'Gen Luo', 'Jiayi Ji', 'Yiyi Zhou', 'Xiaoshuai Sun', 'Zhiqiang Shen', 'Rongrong Ji'] | ['cs.CV'] | Despite the significant progress in multimodal large language models (MLLMs),
their high computational cost remains a barrier to real-world deployment.
Inspired by the mixture of depths (MoDs) in natural language processing, we aim
to address this limitation from the perspective of ``activated tokens''. Our
key insight... | 2024-10-17T17:59:53Z | null | null | null | null | null | null | null | null | null | null |
2,410.13861 | PUMA: Empowering Unified MLLM with Multi-granular Visual Generation | ['Rongyao Fang', 'Chengqi Duan', 'Kun Wang', 'Hao Li', 'Hao Tian', 'Xingyu Zeng', 'Rui Zhao', 'Jifeng Dai', 'Hongsheng Li', 'Xihui Liu'] | ['cs.CV'] | Recent advancements in multimodal foundation models have yielded significant
progress in vision-language understanding. Initial attempts have also explored
the potential of multimodal large language models (MLLMs) for visual content
generation. However, existing works have insufficiently addressed the varying
granulari... | 2024-10-17T17:59:57Z | Project page: https://rongyaofang.github.io/puma/ | null | null | null | null | null | null | null | null | null |
2,410.13862 | DepthSplat: Connecting Gaussian Splatting and Depth | ['Haofei Xu', 'Songyou Peng', 'Fangjinhua Wang', 'Hermann Blum', 'Daniel Barath', 'Andreas Geiger', 'Marc Pollefeys'] | ['cs.CV'] | Gaussian splatting and single-view depth estimation are typically studied in
isolation. In this paper, we present DepthSplat to connect Gaussian splatting
and depth estimation and study their interactions. More specifically, we first
contribute a robust multi-view depth model by leveraging pre-trained monocular
depth f... | 2024-10-17T17:59:58Z | CVPR 2025, Project page: https://haofeixu.github.io/depthsplat/,
Code: https://github.com/cvg/depthsplat | null | null | null | null | null | null | null | null | null |
2,410.13924 | ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding | ['Guangda Ji', 'Silvan Weder', 'Francis Engelmann', 'Marc Pollefeys', 'Hermann Blum'] | ['cs.CV', 'cs.AI'] | Neural network performance scales with both model size and data volume, as
shown in both language and image processing. This requires scaling-friendly
architectures and large datasets. While transformers have been adapted for 3D
vision, a `GPT-moment' remains elusive due to limited training data. We
introduce ARKit Lab... | 2024-10-17T14:44:35Z | null | null | null | null | null | null | null | null | null | null |
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