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2,504.11536 | ReTool: Reinforcement Learning for Strategic Tool Use in LLMs | ['Jiazhan Feng', 'Shijue Huang', 'Xingwei Qu', 'Ge Zhang', 'Yujia Qin', 'Baoquan Zhong', 'Chengquan Jiang', 'Jinxin Chi', 'Wanjun Zhong'] | ['cs.CL', 'cs.AI'] | While reasoning models (e.g., DeepSeek R1) trained with reinforcement
learning (RL), excel in textual reasoning, they struggle in scenarios requiring
structured problem-solving, such as geometric reasoning, concise computation,
or complex equation solving-areas where computational tools like code
interpreters (CI) demo... | 2025-04-15T18:10:22Z | fix typos | null | null | null | null | null | null | null | null | null |
2,504.11622 | Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with
LLM-Assisted Spectrograms' "Typo" Correction | ['Seyyed Ali Ayati', 'Jin Hyun Park', 'Yichen Cai', 'Marcus Botacin'] | ['cs.CR', 'cs.SD', 'eess.AS'] | The large integration of microphones into devices increases the opportunities
for Acoustic Side-Channel Attacks (ASCAs), as these can be used to capture
keystrokes' audio signals that might reveal sensitive information. However, the
current State-Of-The-Art (SOTA) models for ASCAs, including Convolutional
Neural Networ... | 2025-04-15T21:23:25Z | Length: 13 pages Figures: 5 figures Tables: 7 tables Keywords:
Acoustic side-channel attacks, machine learning, Visual Transformers, Large
Language Models (LLMs), security Conference: Accepted at the 19th USENIX WOOT
Conference on Offensive Technologies (WOOT '25). Licensing: This paper is
submitted under the C... | null | null | Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with LLM-Assisted Spectrograms' "Typo" Correction | ['Seyyed Ali Ayati', 'Jin Hyun Park', 'Yichen Cai', 'Marcus Botacin'] | 2,025 | arXiv.org | 0 | 45 | ['Computer Science', 'Engineering'] |
2,504.11651 | 70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU
Inference via Dynamic-Length Float | ['Tianyi Zhang', 'Yang Sui', 'Shaochen Zhong', 'Vipin Chaudhary', 'Xia Hu', 'Anshumali Shrivastava'] | ['cs.LG', 'cs.DC'] | Large Language Models (LLMs) have grown rapidly in size, creating significant
challenges for efficient deployment on resource-constrained hardware. In this
paper, we introduce Dynamic-Length Float (DFloat11), a lossless compression
framework that reduces LLM size by 30% while preserving outputs that are
bit-for-bit ide... | 2025-04-15T22:38:38Z | null | null | null | null | null | null | null | null | null | null |
2,504.11713 | Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint
Matching | ['Aaron Havens', 'Benjamin Kurt Miller', 'Bing Yan', 'Carles Domingo-Enrich', 'Anuroop Sriram', 'Brandon Wood', 'Daniel Levine', 'Bin Hu', 'Brandon Amos', 'Brian Karrer', 'Xiang Fu', 'Guan-Horng Liu', 'Ricky T. Q. Chen'] | ['cs.LG', 'cs.AI'] | We introduce Adjoint Sampling, a highly scalable and efficient algorithm for
learning diffusion processes that sample from unnormalized densities, or energy
functions. It is the first on-policy approach that allows significantly more
gradient updates than the number of energy evaluations and model samples,
allowing us ... | 2025-04-16T02:20:06Z | null | null | null | null | null | null | null | null | null | null |
2,504.11749 | SkeletonX: Data-Efficient Skeleton-based Action Recognition via
Cross-sample Feature Aggregation | ['Zongye Zhang', 'Wenrui Cai', 'Qingjie Liu', 'Yunhong Wang'] | ['cs.CV', 'I.4.9'] | While current skeleton action recognition models demonstrate impressive
performance on large-scale datasets, their adaptation to new application
scenarios remains challenging. These challenges are particularly pronounced
when facing new action categories, diverse performers, and varied skeleton
layouts, leading to sign... | 2025-04-16T04:01:42Z | Accepted by IEEE Transactions on Multimedia (TMM). 13 pages, 7
figures, 11 tables | null | null | null | null | null | null | null | null | null |
2,504.11919 | Rethinking the Generation of High-Quality CoT Data from the Perspective
of LLM-Adaptive Question Difficulty Grading | ['Qianjin Yu', 'Keyu Wu', 'Zihan Chen', 'Chushu Zhang', 'Manlin Mei', 'Lingjun Huang', 'Fang Tan', 'Yongsheng Du', 'Kunlin Liu', 'Yurui Zhu'] | ['cs.AI'] | Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its
excellent reasoning ability in complex tasks and has publiclyshared its
methodology. This provides potentially high-quality chain-of-thought (CoT) data
for stimulating the reasoning abilities of small-sized large language models
(LLMs). To gene... | 2025-04-16T09:55:34Z | null | null | null | Rethinking the Generation of High-Quality CoT Data from the Perspective of LLM-Adaptive Question Difficulty Grading | ['Qianjin Yu', 'Keyu Wu', 'Zihan Chen', 'Chushu Zhang', 'Manlin Mei', 'Lingjun Huang', 'Fang Tan', 'Yongsheng Du', 'Kunlin Liu', 'Yurui Zhu'] | 2,025 | arXiv.org | 3 | 26 | ['Computer Science'] |
2,504.12083 | Self-alignment of Large Video Language Models with Refined Regularized
Preference Optimization | ['Pritam Sarkar', 'Ali Etemad'] | ['cs.CV'] | Despite recent advances in Large Video Language Models (LVLMs), they still
struggle with fine-grained temporal understanding, hallucinate, and often make
simple mistakes on even simple video question-answering tasks, all of which
pose significant challenges to their safe and reliable deployment in real-world
applicatio... | 2025-04-16T13:43:56Z | null | null | null | null | null | null | null | null | null | null |
2,504.1224 | Cobra: Efficient Line Art COlorization with BRoAder References | ['Junhao Zhuang', 'Lingen Li', 'Xuan Ju', 'Zhaoyang Zhang', 'Chun Yuan', 'Ying Shan'] | ['cs.CV'] | The comic production industry requires reference-based line art colorization
with high accuracy, efficiency, contextual consistency, and flexible control. A
comic page often involves diverse characters, objects, and backgrounds, which
complicates the coloring process. Despite advancements in diffusion models for
image ... | 2025-04-16T16:45:19Z | Project page with code: https://zhuang2002.github.io/Cobra/ | null | null | Cobra: Efficient Line Art COlorization with BRoAder References | ['Junhao Zhuang', 'Lingen Li', 'Xu Ju', 'Zhaoyang Zhang', 'Chun Yuan', 'Ying Shan'] | 2,025 | arXiv.org | 0 | 53 | ['Computer Science'] |
2,504.12285 | BitNet b1.58 2B4T Technical Report | ['Shuming Ma', 'Hongyu Wang', 'Shaohan Huang', 'Xingxing Zhang', 'Ying Hu', 'Ting Song', 'Yan Xia', 'Furu Wei'] | ['cs.CL', 'cs.LG'] | We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large
Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4
trillion tokens, the model has been rigorously evaluated across benchmarks
covering language understanding, mathematical reasoning, coding proficiency,
and conversati... | 2025-04-16T17:51:43Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,504.12322 | A Strategic Coordination Framework of Small LLMs Matches Large LLMs in
Data Synthesis | ['Xin Gao', 'Qizhi Pei', 'Zinan Tang', 'Yu Li', 'Honglin Lin', 'Jiang Wu', 'Lijun Wu', 'Conghui He'] | ['cs.CL', 'cs.AI', 'cs.LG'] | While data synthesis and distillation are promising strategies to enhance
small language models, current approaches heavily rely on Large Language Models
(LLMs), which suffer from high computational costs, environmental inefficiency,
and potential biases inherited from monolithic architectures. In contrast,
smaller LLM... | 2025-04-11T06:13:43Z | null | null | null | A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis | ['Xin Gao', 'Qizhi Pei', 'Zinan Tang', 'Yu Li', 'Honglin Lin', 'Jiang Wu', 'Conghui He', 'Lijun Wu'] | 2,025 | arXiv.org | 0 | 47 | ['Computer Science'] |
2,504.12343 | Transforming Simulation to Data Without Pairing | ['Eli Gendreau-Distler', 'Luc Le Pottier', 'Haichen Wang'] | ['physics.data-an', 'hep-ex', 'hep-ph'] | We explore a generative machine learning-based approach for estimating
multi-dimensional probability density functions (PDFs) in a target sample using
a statistically independent but related control sample - a common challenge in
particle physics data analysis. The generative model must accurately reproduce
individual ... | 2025-04-15T08:12:54Z | 5 pages, 3 figures. Conference paper for NEURIPS 2024 | null | null | null | null | null | null | null | null | null |
2,504.12364 | DMM: Building a Versatile Image Generation Model via Distillation-Based
Model Merging | ['Tianhui Song', 'Weixin Feng', 'Shuai Wang', 'Xubin Li', 'Tiezheng Ge', 'Bo Zheng', 'Limin Wang'] | ['cs.CV'] | The success of text-to-image (T2I) generation models has spurred a
proliferation of numerous model checkpoints fine-tuned from the same base model
on various specialized datasets. This overwhelming specialized model production
introduces new challenges for high parameter redundancy and huge storage cost,
thereby necess... | 2025-04-16T15:09:45Z | null | null | null | null | null | null | null | null | null | null |
2,504.12397 | Activated LoRA: Fine-tuned LLMs for Intrinsics | ['Kristjan Greenewald', 'Luis Lastras', 'Thomas Parnell', 'Vraj Shah', 'Lucian Popa', 'Giulio Zizzo', 'Chulaka Gunasekara', 'Ambrish Rawat', 'David Cox'] | ['cs.LG', 'cs.AI'] | Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for
finetuning the weights of large foundation models, and has become the go-to
method for data-driven customization of LLMs. Despite the promise of highly
customized behaviors and capabilities, switching between relevant LoRAs in a
multiturn settin... | 2025-04-16T18:03:21Z | null | null | null | null | null | null | null | null | null | null |
2,504.12626 | Packing Input Frame Context in Next-Frame Prediction Models for Video
Generation | ['Lvmin Zhang', 'Maneesh Agrawala'] | ['cs.CV'] | We present a neural network structure, FramePack, to train next-frame (or
next-frame-section) prediction models for video generation. The FramePack
compresses input frames to make the transformer context length a fixed number
regardless of the video length. As a result, we are able to process a large
number of frames u... | 2025-04-17T04:02:31Z | https://github.com/lllyasviel/FramePack | null | null | Packing Input Frame Context in Next-Frame Prediction Models for Video Generation | ['Lvmin Zhang', 'Maneesh Agrawala'] | 2,025 | arXiv.org | 12 | 61 | ['Computer Science'] |
2,504.1268 | Embodied-R: Collaborative Framework for Activating Embodied Spatial
Reasoning in Foundation Models via Reinforcement Learning | ['Baining Zhao', 'Ziyou Wang', 'Jianjie Fang', 'Chen Gao', 'Fanhang Man', 'Jinqiang Cui', 'Xin Wang', 'Xinlei Chen', 'Yong Li', 'Wenwu Zhu'] | ['cs.AI', 'cs.CV'] | Humans can perceive and reason about spatial relationships from sequential
visual observations, such as egocentric video streams. However, how pretrained
models acquire such abilities, especially high-level reasoning, remains
unclear. This paper introduces Embodied-R, a collaborative framework combining
large-scale Vis... | 2025-04-17T06:16:11Z | 12 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,504.12739 | Mask Image Watermarking | ['Runyi Hu', 'Jie Zhang', 'Shiqian Zhao', 'Nils Lukas', 'Jiwei Li', 'Qing Guo', 'Han Qiu', 'Tianwei Zhang'] | ['cs.CV'] | We present MaskMark, a simple, efficient, and flexible framework for image
watermarking. MaskMark has two variants: (1) MaskMark-D, which supports global
watermark embedding, watermark localization, and local watermark extraction for
applications such as tamper detection; (2) MaskMark-ED, which focuses on local
waterma... | 2025-04-17T08:29:00Z | 26 pages, 20 figures | null | null | null | null | null | null | null | null | null |
2,504.1288 | Can Masked Autoencoders Also Listen to Birds? | ['Lukas Rauch', 'René Heinrich', 'Ilyass Moummad', 'Alexis Joly', 'Bernhard Sick', 'Christoph Scholz'] | ['cs.LG', 'cs.SD', 'eess.AS'] | Masked Autoencoders (MAEs) have shown competitive results in audio
classification by learning rich semantic representations through an efficient
self-supervised reconstruction task. However, general-purpose models fail to
generalize well when applied directly to fine-grained audio domains.
Specifically, bird-sound clas... | 2025-04-17T12:13:25Z | under review @TMLR | null | null | Can Masked Autoencoders Also Listen to Birds? | ['Lukas Rauch', 'Ilyass Moummad', "Ren'e Heinrich", 'Alexis Joly', 'Bernhard Sick', 'Christoph Scholz'] | 2,025 | arXiv.org | 0 | 67 | ['Computer Science', 'Engineering'] |
2,504.129 | FashionDPO:Fine-tune Fashion Outfit Generation Model using Direct
Preference Optimization | ['Mingzhe Yu', 'Yunshan Ma', 'Lei Wu', 'Changshuo Wang', 'Xue Li', 'Lei Meng'] | ['cs.MM', 'cs.IR'] | Personalized outfit generation aims to construct a set of compatible and
personalized fashion items as an outfit. Recently, generative AI models have
received widespread attention, as they can generate fashion items for users to
complete an incomplete outfit or create a complete outfit. However, they have
limitations i... | 2025-04-17T12:41:41Z | Accepted by SIGIR'25 | null | null | null | null | null | null | null | null | null |
2,504.13059 | RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins | ['Yao Mu', 'Tianxing Chen', 'Zanxin Chen', 'Shijia Peng', 'Zhiqian Lan', 'Zeyu Gao', 'Zhixuan Liang', 'Qiaojun Yu', 'Yude Zou', 'Mingkun Xu', 'Lunkai Lin', 'Zhiqiang Xie', 'Mingyu Ding', 'Ping Luo'] | ['cs.RO', 'cs.AI', 'cs.CL'] | In the rapidly advancing field of robotics, dual-arm coordination and complex
object manipulation are essential capabilities for developing advanced
autonomous systems. However, the scarcity of diverse, high-quality
demonstration data and real-world-aligned evaluation benchmarks severely limits
such development. To add... | 2025-04-17T16:14:24Z | CVPR 2025 Highlight. 22 pages. Project page:
https://robotwin-benchmark.github.io/ | null | null | RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins | ['Yao Mu', 'Tianxing Chen', 'Zanxin Chen', 'Shijia Peng', 'Zhiqian Lan', 'Zeyu Gao', 'Zhixuan Liang', 'Qiaojun Yu', 'Yude Zou', 'Min Xu', 'Lunkai Lin', 'Zhiqiang Xie', 'Mingyu Ding', 'Ping Luo'] | 2,025 | arXiv.org | 8 | 73 | ['Computer Science'] |
2,504.13074 | SkyReels-V2: Infinite-length Film Generative Model | ['Guibin Chen', 'Dixuan Lin', 'Jiangping Yang', 'Chunze Lin', 'Junchen Zhu', 'Mingyuan Fan', 'Hao Zhang', 'Sheng Chen', 'Zheng Chen', 'Chengcheng Ma', 'Weiming Xiong', 'Wei Wang', 'Nuo Pang', 'Kang Kang', 'Zhiheng Xu', 'Yuzhe Jin', 'Yupeng Liang', 'Yubing Song', 'Peng Zhao', 'Boyuan Xu', 'Di Qiu', 'Debang Li', 'Zhengco... | ['cs.CV'] | Recent advances in video generation have been driven by diffusion models and
autoregressive frameworks, yet critical challenges persist in harmonizing
prompt adherence, visual quality, motion dynamics, and duration: compromises in
motion dynamics to enhance temporal visual quality, constrained video duration
(5-10 seco... | 2025-04-17T16:37:27Z | 31 pages,10 figures | null | null | null | null | null | null | null | null | null |
2,504.13077 | Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts
of Labeled Data | ['Prasanna Reddy Pulakurthi', 'Majid Rabbani', 'Celso M. de Melo', 'Sohail A. Dianat', 'Raghuveer M. Rao'] | ['cs.CV'] | This paper introduces a novel dual-region augmentation approach designed to
reduce reliance on large-scale labeled datasets while improving model
robustness and adaptability across diverse computer vision tasks, including
source-free domain adaptation (SFDA) and person re-identification (ReID). Our
method performs targ... | 2025-04-17T16:42:33Z | 9 pages, 2 figures, 4 tables, Accepted to SPIE DSC 2025 Conference:
Synthetic Data for Artificial Intelligence and Machine Learning: Tools,
Techniques, and Applications III | Proc. SPIE 13459, Synthetic Data for Artificial Intelligence and
Machine Learning: Tools, Techniques, and Applications III, 134590I (2025) | 10.1117/12.3058627 | Effective dual-region augmentation for reduced reliance on large amounts of labeled data | ['Prasanna Reddy Pulakurthi', 'Majid Rabbani', 'Celso M. de Melo', 'S. Dianat', 'Raghuveer Rao'] | 2,025 | Defense + Security | 0 | 34 | ['Engineering', 'Computer Science'] |
2,504.13078 | MGT: Extending Virtual Try-Off to Multi-Garment Scenarios | ['Riza Velioglu', 'Petra Bevandic', 'Robin Chan', 'Barbara Hammer'] | ['cs.CV', 'cs.AI'] | Computer vision is transforming fashion industry through Virtual Try-On
(VTON) and Virtual Try-Off (VTOFF). VTON generates images of a person in a
specified garment using a target photo and a standardized garment image, while
a more challenging variant, Person-to-Person Virtual Try-On (p2p-VTON), uses a
photo of anothe... | 2025-04-17T16:45:18Z | Accepted at ICCVW'25 | null | null | null | null | null | null | null | null | null |
2,504.13129 | Science-T2I: Addressing Scientific Illusions in Image Synthesis | ['Jialuo Li', 'Wenhao Chai', 'Xingyu Fu', 'Haiyang Xu', 'Saining Xie'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We present a novel approach to integrating scientific knowledge into
generative models, enhancing their realism and consistency in image synthesis.
First, we introduce Science-T2I, an expert-annotated adversarial dataset
comprising adversarial 20k image pairs with 9k prompts, covering wide distinct
scientific knowledge... | 2025-04-17T17:44:19Z | Accepted to CVPR 2025. Code, docs, weight, benchmark and training
data are all avaliable at https://jialuo-li.github.io/Science-T2I-Web | null | null | null | null | null | null | null | null | null |
2,504.13157 | AerialMegaDepth: Learning Aerial-Ground Reconstruction and View
Synthesis | ['Khiem Vuong', 'Anurag Ghosh', 'Deva Ramanan', 'Srinivasa Narasimhan', 'Shubham Tulsiani'] | ['cs.CV'] | We explore the task of geometric reconstruction of images captured from a
mixture of ground and aerial views. Current state-of-the-art learning-based
approaches fail to handle the extreme viewpoint variation between aerial-ground
image pairs. Our hypothesis is that the lack of high-quality, co-registered
aerial-ground ... | 2025-04-17T17:57:05Z | Appearing in CVPR 2025. Project page:
https://aerial-megadepth.github.io | null | null | AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis | ['Khiem Vuong', 'Anurag Ghosh', 'Deva Ramanan', 'Srinivasa Narasimhan', 'Shubham Tulsiani'] | 2,025 | arXiv.org | 3 | 78 | ['Computer Science'] |
2,504.13169 | Generate, but Verify: Reducing Hallucination in Vision-Language Models
with Retrospective Resampling | ['Tsung-Han Wu', 'Heekyung Lee', 'Jiaxin Ge', 'Joseph E. Gonzalez', 'Trevor Darrell', 'David M. Chan'] | ['cs.CV'] | Vision-Language Models (VLMs) excel at visual understanding but often suffer
from visual hallucinations, where they generate descriptions of nonexistent
objects, actions, or concepts, posing significant risks in safety-critical
applications. Existing hallucination mitigation methods typically follow one of
two paradigm... | 2025-04-17T17:59:22Z | Preprint. Project Page: https://reverse-vlm.github.io | null | null | Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling | ['Tsung-Han Wu', 'Heekyung Lee', 'Jiaxin Ge', 'Joseph Gonzalez', 'Trevor Darrell', 'David M. Chan'] | 2,025 | arXiv.org | 0 | 54 | ['Computer Science'] |
2,504.13176 | IMAGGarment-1: Fine-Grained Garment Generation for Controllable Fashion
Design | ['Fei Shen', 'Jian Yu', 'Cong Wang', 'Xin Jiang', 'Xiaoyu Du', 'Jinhui Tang'] | ['cs.CV'] | This paper presents IMAGGarment-1, a fine-grained garment generation (FGG)
framework that enables high-fidelity garment synthesis with precise control
over silhouette, color, and logo placement. Unlike existing methods that are
limited to single-condition inputs, IMAGGarment-1 addresses the challenges of
multi-conditio... | 2025-04-17T17:59:47Z | null | null | null | IMAGGarment-1: Fine-Grained Garment Generation for Controllable Fashion Design | ['Fei Shen', 'Jian Yu', 'Cong Wang', 'Xin Jiang', 'Xiaoyu Du', 'Jinhui Tang'] | 2,025 | arXiv.org | 9 | 41 | ['Computer Science'] |
2,504.1318 | PerceptionLM: Open-Access Data and Models for Detailed Visual
Understanding | ['Jang Hyun Cho', 'Andrea Madotto', 'Effrosyni Mavroudi', 'Triantafyllos Afouras', 'Tushar Nagarajan', 'Muhammad Maaz', 'Yale Song', 'Tengyu Ma', 'Shuming Hu', 'Suyog Jain', 'Miguel Martin', 'Huiyu Wang', 'Hanoona Rasheed', 'Peize Sun', 'Po-Yao Huang', 'Daniel Bolya', 'Nikhila Ravi', 'Shashank Jain', 'Tammy Stark', 'Sh... | ['cs.CV', 'cs.AI', 'cs.LG'] | Vision-language models are integral to computer vision research, yet many
high-performing models remain closed-source, obscuring their data, design and
training recipe. The research community has responded by using distillation
from black-box models to label training data, achieving strong benchmark
results, at the cos... | 2025-04-17T17:59:56Z | Technical Report | null | null | PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding | ['Jang Hyun Cho', 'Andrea Madotto', 'E. Mavroudi', 'Triantafyllos Afouras', 'Tushar Nagarajan', 'Muhammad Maaz', 'Yale Song', 'Tengyu Ma', 'Shuming Hu', 'S. Jain', 'Miguel Martin', 'Huiyu Wang', 'H. Rasheed', 'Peize Sun', 'Po-Yao Huang', 'Daniel Bolya', 'Nikhila Ravi', 'Shashank Jain', 'Tammy Stark', 'Shane Moon', 'Bab... | 2,025 | arXiv.org | 6 | 0 | ['Computer Science'] |
2,504.13181 | Perception Encoder: The best visual embeddings are not at the output of
the network | ['Daniel Bolya', 'Po-Yao Huang', 'Peize Sun', 'Jang Hyun Cho', 'Andrea Madotto', 'Chen Wei', 'Tengyu Ma', 'Jiale Zhi', 'Jathushan Rajasegaran', 'Hanoona Rasheed', 'Junke Wang', 'Marco Monteiro', 'Hu Xu', 'Shiyu Dong', 'Nikhila Ravi', 'Daniel Li', 'Piotr Dollár', 'Christoph Feichtenhofer'] | ['cs.CV'] | We introduce Perception Encoder (PE), a state-of-the-art vision encoder for
image and video understanding trained via simple vision-language learning.
Traditionally, vision encoders have relied on a variety of pretraining
objectives, each tailored to specific downstream tasks such as classification,
captioning, or loca... | 2025-04-17T17:59:57Z | Updated refs, fixed typos, and added new COCO SotA: 66.0 val mAP!
Code, models, and data at
https://github.com/facebookresearch/perception_models | null | null | null | null | null | null | null | null | null |
2,504.13297 | Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding
Boxes | ['Andreas Lau Hansen', 'Lukas Wanzeck', 'Dim P. Papadopoulos'] | ['cs.CV', 'I.4'] | Monocular 3D object detection is an essential task in computer vision, and it
has several applications in robotics and virtual reality. However, 3D object
detectors are typically trained in a fully supervised way, relying extensively
on 3D labeled data, which is labor-intensive and costly to annotate. This work
focuses... | 2025-04-17T19:13:42Z | 14 pages, 5 figures. Accepted for 23rd Scandinavian Conference, SCIA
2025, Reykjavik, Iceland | null | null | null | null | null | null | null | null | null |
2,504.13617 | Compile Scene Graphs with Reinforcement Learning | ['Zuyao Chen', 'Jinlin Wu', 'Zhen Lei', 'Marc Pollefeys', 'Chang Wen Chen'] | ['cs.CV'] | Next-token prediction is the fundamental principle for training large
language models (LLMs), and reinforcement learning (RL) further enhances their
reasoning performance. As an effective way to model language, image, video, and
other modalities, the use of LLMs for end-to-end extraction of structured
visual representa... | 2025-04-18T10:46:22Z | null | null | null | Compile Scene Graphs with Reinforcement Learning | ['Zuyao Chen', 'Jinlin Wu', 'Zhen Lei', 'Marc Pollefeys', 'Changwen Chen'] | 2,025 | arXiv.org | 3 | 45 | ['Computer Science'] |
2,504.1363 | Remedy: Learning Machine Translation Evaluation from Human Preferences
with Reward Modeling | ['Shaomu Tan', 'Christof Monz'] | ['cs.CL'] | A key challenge in MT evaluation is the inherent noise and inconsistency of
human ratings. Regression-based neural metrics struggle with this noise, while
prompting LLMs shows promise at system-level evaluation but performs poorly at
segment level. In this work, we propose ReMedy, a novel MT metric framework
that refor... | 2025-04-18T11:11:14Z | null | null | null | Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling | ['Shaomu Tan', 'C. Monz'] | 2,025 | arXiv.org | 0 | 52 | ['Computer Science'] |
2,504.13835 | MIG: Automatic Data Selection for Instruction Tuning by Maximizing
Information Gain in Semantic Space | ['Yicheng Chen', 'Yining Li', 'Kai Hu', 'Zerun Ma', 'Haochen Ye', 'Kai Chen'] | ['cs.CL', 'cs.AI'] | Data quality and diversity are key to the construction of effective
instruction-tuning datasets. % With the increasing availability of open-source
instruction-tuning datasets, it is advantageous to automatically select
high-quality and diverse subsets from a vast amount of data. % Existing methods
typically prioritize ... | 2025-04-18T17:59:46Z | null | null | null | null | null | null | null | null | null | null |
2,504.14011 | Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented
Generation | ['Fulvio Sanguigni', 'Davide Morelli', 'Marcella Cornia', 'Rita Cucchiara'] | ['cs.CV', 'cs.AI', 'cs.MM'] | In recent years, the fashion industry has increasingly adopted AI
technologies to enhance customer experience, driven by the proliferation of
e-commerce platforms and virtual applications. Among the various tasks, virtual
try-on and multimodal fashion image editing -- which utilizes diverse input
modalities such as tex... | 2025-04-18T18:02:33Z | IJCNN 2025 | null | null | Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation | ['Fulvio Sanguigni', 'Davide Morelli', 'Marcella Cornia', 'Rita Cucchiara'] | 2,025 | arXiv.org | 1 | 55 | ['Computer Science'] |
2,504.14032 | LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision
Foundation Models | ['Haiwen Huang', 'Anpei Chen', 'Volodymyr Havrylov', 'Andreas Geiger', 'Dan Zhang'] | ['cs.CV', 'cs.AI', 'cs.LG', 'eess.IV'] | Vision foundation models (VFMs) such as DINOv2 and CLIP have achieved
impressive results on various downstream tasks, but their limited feature
resolution hampers performance in applications requiring pixel-level
understanding. Feature upsampling offers a promising direction to address this
challenge. In this work, we ... | 2025-04-18T18:46:08Z | null | null | null | LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models | ['Haiwen Huang', 'Anpei Chen', 'Volodymyr Havrylov', 'Andreas Geiger', 'Dan Zhang'] | 2,025 | arXiv.org | 2 | 63 | ['Computer Science', 'Engineering'] |
2,504.14131 | Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep
Learning Approach | ['Ole-Christian Galbo Engstrøm', 'Michela Albano-Gaglio', 'Erik Schou Dreier', 'Yamine Bouzembrak', 'Maria Font-i-Furnols', 'Puneet Mishra', 'Kim Steenstrup Pedersen'] | ['cs.CV', 'cs.LG', 'q-bio.QM'] | Current approaches to chemical map generation from hyperspectral images are
based on models such as partial least squares (PLS) regression, generating
pixel-wise predictions that do not consider spatial context and suffer from a
high degree of noise. This study proposes an end-to-end deep learning approach
using a modi... | 2025-04-19T01:27:19Z | null | null | null | null | null | null | null | null | null | null |
2,504.14194 | Meta-rater: A Multi-dimensional Data Selection Method for Pre-training
Language Models | ['Xinlin Zhuang', 'Jiahui Peng', 'Ren Ma', 'Yinfan Wang', 'Tianyi Bai', 'Xingjian Wei', 'Jiantao Qiu', 'Chi Zhang', 'Ying Qian', 'Conghui He'] | ['cs.CL'] | The composition of pre-training datasets for large language models (LLMs)
remains largely undisclosed, hindering transparency and efforts to optimize
data quality, a critical driver of model performance. Current data selection
methods, such as natural language quality assessments, diversity-based filters,
and classifie... | 2025-04-19T06:12:33Z | Accepted by ACL 2025 | null | null | null | null | null | null | null | null | null |
2,504.14239 | InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to
Deliberative Reasoners | ['Yuhang Liu', 'Pengxiang Li', 'Congkai Xie', 'Xavier Hu', 'Xiaotian Han', 'Shengyu Zhang', 'Hongxia Yang', 'Fei Wu'] | ['cs.AI', 'cs.CL'] | Multimodal Large Language Models (MLLMs) have powered Graphical User
Interface (GUI) Agents, showing promise in automating tasks on computing
devices. Recent works have begun exploring reasoning in GUI tasks with
encouraging results. However, many current approaches rely on manually designed
reasoning templates, which ... | 2025-04-19T09:25:55Z | 10 pages, 3 figures, work in progress | null | null | null | null | null | null | null | null | null |
2,504.14286 | SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement
Learning on LLM | ['Xiaojiang Zhang', 'Jinghui Wang', 'Zifei Cheng', 'Wenhao Zhuang', 'Zheng Lin', 'Minglei Zhang', 'Shaojie Wang', 'Yinghan Cui', 'Chao Wang', 'Junyi Peng', 'Shimiao Jiang', 'Shiqi Kuang', 'Shouyu Yin', 'Chaohang Wen', 'Haotian Zhang', 'Bin Chen', 'Bing Yu'] | ['cs.LG'] | Recent advances of reasoning models, exemplified by OpenAI's o1 and
DeepSeek's R1, highlight the significant potential of Reinforcement Learning
(RL) to enhance the reasoning capabilities of Large Language Models (LLMs).
However, replicating these advancements across diverse domains remains
challenging due to limited m... | 2025-04-19T13:06:03Z | null | null | null | null | null | null | null | null | null | null |
2,504.14305 | Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning | ['Jiyuan Shi', 'Xinzhe Liu', 'Dewei Wang', 'Ouyang Lu', 'Sören Schwertfeger', 'Fuchun Sun', 'Chenjia Bai', 'Xuelong Li'] | ['cs.RO'] | Humans exhibit diverse and expressive whole-body movements. However,
attaining human-like whole-body coordination in humanoid robots remains
challenging, as conventional approaches that mimic whole-body motions often
neglect the distinct roles of upper and lower body. This oversight leads to
computationally intensive p... | 2025-04-19T14:03:57Z | Code: https://github.com/TeleHuman/ALMI-Open, Dataset:
https://huggingface.co/datasets/TeleEmbodied/ALMI-X | null | null | null | null | null | null | null | null | null |
2,504.1444 | SG-Reg: Generalizable and Efficient Scene Graph Registration | ['Chuhao Liu', 'Zhijian Qiao', 'Jieqi Shi', 'Ke Wang', 'Peize Liu', 'Shaojie Shen'] | ['cs.RO', 'cs.CV'] | This paper addresses the challenges of registering two rigid semantic scene
graphs, an essential capability when an autonomous agent needs to register its
map against a remote agent, or against a prior map. The hand-crafted
descriptors in classical semantic-aided registration, or the ground-truth
annotation reliance in... | 2025-04-20T01:22:40Z | IEEE Transactions Robotics Regular Paper | null | null | null | null | null | null | null | null | null |
2,504.14655 | LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
Training of Code LLMs | ['Yunhui Xia', 'Wei Shen', 'Yan Wang', 'Jason Klein Liu', 'Huifeng Sun', 'Siyue Wu', 'Jian Hu', 'Xiaolong Xu'] | ['cs.LG', 'cs.CL', 'cs.SE'] | We introduce LeetCodeDataset, a high-quality benchmark for evaluating and
training code-generation models, addressing two key challenges in LLM research:
the lack of reasoning-focused coding benchmarks and self-contained training
testbeds. By curating LeetCode Python problems with rich metadata, broad
coverage, 100+ te... | 2025-04-20T15:28:16Z | null | null | null | LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs | ['Yunhui Xia', 'Wei Shen', 'Yan Wang', 'Jason Klein Liu', 'Huifeng Sun', 'Siyue Wu', 'Jian Hu', 'Xiaolong Xu'] | 2,025 | arXiv.org | 3 | 24 | ['Computer Science'] |
2,504.14666 | Generative Multimodal Pretraining with Discrete Diffusion Timestep
Tokens | ['Kaihang Pan', 'Wang Lin', 'Zhongqi Yue', 'Tenglong Ao', 'Liyu Jia', 'Wei Zhao', 'Juncheng Li', 'Siliang Tang', 'Hanwang Zhang'] | ['cs.CV'] | Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify
visual comprehension and generation by combining LLM and diffusion models, the
state-of-the-art in each task, respectively. Existing approaches rely on
spatial visual tokens, where image patches are encoded and arranged according
to a spatial ord... | 2025-04-20T16:14:28Z | Accepted by CVPR 2025 (Oral) | null | null | null | null | null | null | null | null | null |
2,504.14717 | TAPIP3D: Tracking Any Point in Persistent 3D Geometry | ['Bowei Zhang', 'Lei Ke', 'Adam W. Harley', 'Katerina Fragkiadaki'] | ['cs.CV', 'cs.LG'] | We introduce TAPIP3D, a novel approach for long-term 3D point tracking in
monocular RGB and RGB-D videos. TAPIP3D represents videos as camera-stabilized
spatio-temporal feature clouds, leveraging depth and camera motion information
to lift 2D video features into a 3D world space where camera movement is
effectively can... | 2025-04-20T19:09:43Z | Long-term feed-forward 3D point tracking in persistent 3D point maps.
Code:https://github.com/zbw001/TAPIP3D | null | null | null | null | null | null | null | null | null |
2,504.14738 | PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom
Production Large Language Model Pipelines | ['Reya Vir', 'Shreya Shankar', 'Harrison Chase', 'Will Fu-Hinthorn', 'Aditya Parameswaran'] | ['cs.CL'] | Large language models (LLMs) are increasingly deployed in specialized
production data processing pipelines across diverse domains -- such as finance,
marketing, and e-commerce. However, when running them in production across many
inputs, they often fail to follow instructions or meet developer expectations.
To improve ... | 2025-04-20T21:04:23Z | Accepted to NAACL 2025 Main Conference | null | null | PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines | ['Reya Vir', 'Shreya Shankar', 'Harrison Chase', 'Will Fu-Hinthorn', 'Aditya G. Parameswaran'] | 2,025 | North American Chapter of the Association for Computational Linguistics | 0 | 56 | ['Computer Science'] |
2,504.14839 | Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers | ['Xinjie Shen', 'Zhichao Geng', 'Yang Yang'] | ['cs.IR', 'cs.AI'] | With increasing demands for efficiency, information retrieval has developed a
branch of sparse retrieval, further advancing towards inference-free retrieval
where the documents are encoded during indexing time and there is no
model-inference for queries. Existing sparse retrieval models rely on FLOPS
regularization for... | 2025-04-21T03:40:43Z | Accepted by SIGIR 2025 | null | 10.1145/3726302.3730192 | Exploring ℓ0 Sparsification for Inference-free Sparse Retrievers | ['Xinjie Shen', 'Zhichao Geng', 'Yang Yang'] | 2,025 | arXiv.org | 0 | 22 | ['Computer Science'] |
2,504.14886 | Zero Day Malware Detection with Alpha: Fast DBI with Transformer Models
for Real World Application | ['Matthew Gaber', 'Mohiuddin Ahmed', 'Helge Janicke'] | ['cs.CR'] | The effectiveness of an AI model in accurately classifying novel malware
hinges on the quality of the features it is trained on, which in turn depends
on the effectiveness of the analysis tool used. Peekaboo, a Dynamic Binary
Instrumentation (DBI) tool, defeats malware evasion techniques to capture
authentic behavior a... | 2025-04-21T06:30:21Z | null | null | null | null | null | null | null | null | null | null |
2,504.14906 | OmniAudio: Generating Spatial Audio from 360-Degree Video | ['Huadai Liu', 'Tianyi Luo', 'Kaicheng Luo', 'Qikai Jiang', 'Peiwen Sun', 'Jialei Wang', 'Rongjie Huang', 'Qian Chen', 'Wen Wang', 'Xiangtai Li', 'Shiliang Zhang', 'Zhijie Yan', 'Zhou Zhao', 'Wei Xue'] | ['eess.AS', 'cs.CV', 'cs.SD'] | Traditional video-to-audio generation techniques primarily focus on
perspective video and non-spatial audio, often missing the spatial cues
necessary for accurately representing sound sources in 3D environments. To
address this limitation, we introduce a novel task, 360V2SA, to generate
spatial audio from 360-degree vi... | 2025-04-21T07:21:28Z | ICML 2025 | null | null | OmniAudio: Generating Spatial Audio from 360-Degree Video | ['Huadai Liu', 'Tianyi Luo', 'Qikai Jiang', 'Kaicheng Luo', 'Peiwen Sun', 'Jialei Wan', 'Rongjie Huang', 'Qian Chen', 'Wen Wang', 'Xiangtai Li', 'Shiliang Zhang', 'Zhijie Yan', 'Zhou Zhao', 'Wei Xue'] | 2,025 | arXiv.org | 1 | 58 | ['Engineering', 'Computer Science'] |
2,504.14945 | Learning to Reason under Off-Policy Guidance | ['Jianhao Yan', 'Yafu Li', 'Zican Hu', 'Zhi Wang', 'Ganqu Cui', 'Xiaoye Qu', 'Yu Cheng', 'Yue Zhang'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Recent advances in large reasoning models (LRMs) demonstrate that
sophisticated behaviors such as multi-step reasoning and self-reflection can
emerge via reinforcement learning with verifiable rewards~(\textit{RLVR}).
However, existing \textit{RLVR} approaches are inherently ``on-policy'',
limiting learning to a model'... | 2025-04-21T08:09:13Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,504.14977 | RealisDance-DiT: Simple yet Strong Baseline towards Controllable
Character Animation in the Wild | ['Jingkai Zhou', 'Yifan Wu', 'Shikai Li', 'Min Wei', 'Chao Fan', 'Weihua Chen', 'Wei Jiang', 'Fan Wang'] | ['cs.CV'] | Controllable character animation remains a challenging problem, particularly
in handling rare poses, stylized characters, character-object interactions,
complex illumination, and dynamic scenes. To tackle these issues, prior work
has largely focused on injecting pose and appearance guidance via elaborate
bypass network... | 2025-04-21T09:09:21Z | Project Page:
https://thefoxofsky.github.io/project_pages_new/RealisDance-DiT/index | null | null | null | null | null | null | null | null | null |
2,504.15009 | Insert Anything: Image Insertion via In-Context Editing in DiT | ['Wensong Song', 'Hong Jiang', 'Zongxing Yang', 'Ruijie Quan', 'Yi Yang'] | ['cs.CV'] | This work presents Insert Anything, a unified framework for reference-based
image insertion that seamlessly integrates objects from reference images into
target scenes under flexible, user-specified control guidance. Instead of
training separate models for individual tasks, our approach is trained once on
our new AnyIn... | 2025-04-21T10:19:12Z | null | null | null | Insert Anything: Image Insertion via In-Context Editing in DiT | ['Wensong Song', 'Hong Jiang', 'Zongxin Yang', 'Ruijie Quan', 'Yi Yang'] | 2,025 | arXiv.org | 4 | 55 | ['Computer Science'] |
2,504.15027 | DistilQwen2.5: Industrial Practices of Training Distilled Open
Lightweight Language Models | ['Chengyu Wang', 'Junbing Yan', 'Yuanhao Yue', 'Jun Huang'] | ['cs.CL'] | Enhancing computational efficiency and reducing deployment costs for large
language models (LLMs) have become critical challenges in various
resource-constrained scenarios. In this work, we present DistilQwen2.5, a
family of distilled, lightweight LLMs derived from the public Qwen2.5 models.
These distilled models exhi... | 2025-04-21T11:26:02Z | null | null | null | null | null | null | null | null | null | null |
2,504.15071 | Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling | ['Louis Bradshaw', 'Simon Colton'] | ['cs.SD', 'cs.AI', 'cs.LG'] | We introduce an extensive new dataset of MIDI files, created by transcribing
audio recordings of piano performances into their constituent notes. The data
pipeline we use is multi-stage, employing a language model to autonomously
crawl and score audio recordings from the internet based on their metadata,
followed by a ... | 2025-04-21T12:59:40Z | null | International Conference on Learning Representations (ICLR), 2025 | null | Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling | ['Louis Bradshaw', 'Simon Colton'] | 2,025 | International Conference on Learning Representations | 1 | 52 | ['Computer Science'] |
2,504.15077 | Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL | ['Simone Papicchio', 'Simone Rossi', 'Luca Cagliero', 'Paolo Papotti'] | ['cs.LG', 'cs.DB'] | Large Language Models (LLMs) have shown impressive capabilities in
transforming natural language questions about relational databases into SQL
queries. Despite recent improvements, small LLMs struggle to handle questions
involving multiple tables and complex SQL patterns under a Zero-Shot Learning
(ZSL) setting. Superv... | 2025-04-21T13:05:26Z | 17 pages, work in progress | null | null | null | null | null | null | null | null | null |
2,504.1527 | An LMM for Efficient Video Understanding via Reinforced Compression of
Video Cubes | ['Ji Qi', 'Yuan Yao', 'Yushi Bai', 'Bin Xu', 'Juanzi Li', 'Zhiyuan Liu', 'Tat-Seng Chua'] | ['cs.CV', 'cs.CL'] | Large Multimodal Models (LMMs) uniformly perceive video frames, creating
computational inefficiency for videos with inherently varying temporal
information density. This paper present \textbf{Quicksviewer}, an LMM with new
perceiving paradigm that partitions a video of nonuniform density into varying
cubes using Gumbel... | 2025-04-21T17:57:21Z | null | null | null | An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes | ['Ji Qi', 'Yuan Yao', 'Yushi Bai', 'Bin Xu', 'Juanzi Li', 'Zhiyuan Liu', 'Tat-Seng Chua'] | 2,025 | arXiv.org | 0 | 54 | ['Computer Science'] |
2,504.15275 | Stop Summation: Min-Form Credit Assignment Is All Process Reward Model
Needs for Reasoning | ['Jie Cheng', 'Ruixi Qiao', 'Lijun Li', 'Chao Guo', 'Junle Wang', 'Gang Xiong', 'Yisheng Lv', 'Fei-Yue Wang'] | ['cs.AI', 'cs.LG'] | Process reward models (PRMs) have proven effective for test-time scaling of
Large Language Models (LLMs) on challenging reasoning tasks. However, reward
hacking issues with PRMs limit their successful application in reinforcement
fine-tuning. In this paper, we identify the main cause of PRM-induced reward
hacking: the ... | 2025-04-21T17:59:02Z | 18 pages | null | null | null | null | null | null | null | null | null |
2,504.15279 | VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal
Large Language Models | ['Weiye Xu', 'Jiahao Wang', 'Weiyun Wang', 'Zhe Chen', 'Wengang Zhou', 'Aijun Yang', 'Lewei Lu', 'Houqiang Li', 'Xiaohua Wang', 'Xizhou Zhu', 'Wenhai Wang', 'Jifeng Dai', 'Jinguo Zhu'] | ['cs.CV'] | Visual reasoning is a core component of human intelligence and a critical
capability for advanced multimodal models. Yet current reasoning evaluations of
multimodal large language models (MLLMs) often rely on text descriptions and
allow language-based reasoning shortcuts, failing to measure genuine
vision-centric reaso... | 2025-04-21T17:59:53Z | Code, data, and baselines are available at
https://visulogic-benchmark.github.io/VisuLogic | null | null | null | null | null | null | null | null | null |
2,504.15376 | Towards Understanding Camera Motions in Any Video | ['Zhiqiu Lin', 'Siyuan Cen', 'Daniel Jiang', 'Jay Karhade', 'Hewei Wang', 'Chancharik Mitra', 'Tiffany Ling', 'Yuhan Huang', 'Sifan Liu', 'Mingyu Chen', 'Rushikesh Zawar', 'Xue Bai', 'Yilun Du', 'Chuang Gan', 'Deva Ramanan'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG', 'cs.MM'] | We introduce CameraBench, a large-scale dataset and benchmark designed to
assess and improve camera motion understanding. CameraBench consists of ~3,000
diverse internet videos, annotated by experts through a rigorous multi-stage
quality control process. One of our contributions is a taxonomy of camera
motion primitive... | 2025-04-21T18:34:57Z | Project site: https://linzhiqiu.github.io/papers/camerabench/ | null | null | null | null | null | null | null | null | null |
2,504.15404 | Context Aware Grounded Teacher for Source Free Object Detection | ['Tajamul Ashraf', 'Rajes Manna', 'Partha Sarathi Purkayastha', 'Tavaheed Tariq', 'Janibul Bashir'] | ['cs.CV'] | We focus on the Source Free Object Detection (SFOD) problem, when source data
is unavailable during adaptation, and the model must adapt to the unlabeled
target domain. In medical imaging, several approaches have leveraged a
semi-supervised student-teacher architecture to bridge domain discrepancy.
Context imbalance in... | 2025-04-21T19:13:33Z | null | null | null | null | null | null | null | null | null | null |
2,504.15431 | Trillion 7B Technical Report | ['Sungjun Han', 'Juyoung Suk', 'Suyeong An', 'Hyungguk Kim', 'Kyuseok Kim', 'Wonsuk Yang', 'Seungtaek Choi', 'Jamin Shin'] | ['cs.CL', 'cs.AI', 'cs.LG'] | We introduce Trillion-7B, the most token-efficient Korean-centric
multilingual LLM available. Our novel Cross-lingual Document Attention (XLDA)
mechanism enables highly efficient and effective knowledge transfer from
English to target languages like Korean and Japanese. Combined with optimized
data mixtures, language-s... | 2025-04-21T20:54:44Z | Preview version | null | null | null | null | null | null | null | null | null |
2,504.15466 | Learning Adaptive Parallel Reasoning with Language Models | ['Jiayi Pan', 'Xiuyu Li', 'Long Lian', 'Charlie Snell', 'Yifei Zhou', 'Adam Yala', 'Trevor Darrell', 'Kurt Keutzer', 'Alane Suhr'] | ['cs.AI', 'cs.CL'] | Scaling inference-time computation has substantially improved the reasoning
capabilities of language models. However, existing methods have significant
limitations: serialized chain-of-thought approaches generate overly long
outputs, leading to increased latency and exhausted context windows, while
parallel methods suc... | 2025-04-21T22:29:02Z | Code, model, and data are available at
https://github.com/Parallel-Reasoning/APR. The first three authors
contributed equally to this work | null | null | null | null | null | null | null | null | null |
2,504.15471 | Bigram Subnetworks: Mapping to Next Tokens in Transformer Language
Models | ['Tyler A. Chang', 'Benjamin K. Bergen'] | ['cs.CL'] | In Transformer language models, activation vectors transform from current
token embeddings to next token predictions as they pass through the model. To
isolate a minimal form of this transformation, we identify language model
subnetworks that make bigram predictions, naive next token predictions based
only on the curre... | 2025-04-21T22:41:00Z | null | null | null | null | null | null | null | null | null | null |
2,504.15544 | llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese
Corpus with Long Context Length | ['Issa Sugiura', 'Kouta Nakayama', 'Yusuke Oda'] | ['cs.CL'] | Encoder-only transformer models like BERT are widely adopted as a pre-trained
backbone for tasks like sentence classification and retrieval. However,
pretraining of encoder models with large-scale corpora and long contexts has
been relatively underexplored compared to decoder-only transformers. In this
work, we present... | 2025-04-22T02:45:19Z | 9 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,504.15777 | Tina: Tiny Reasoning Models via LoRA | ['Shangshang Wang', 'Julian Asilis', 'Ömer Faruk Akgül', 'Enes Burak Bilgin', 'Ollie Liu', 'Willie Neiswanger'] | ['cs.CL', 'cs.AI', 'cs.LG'] | How cost-effectively can strong reasoning abilities be achieved in language
models? Driven by this fundamental question, we present Tina, a family of tiny
reasoning models achieved with high cost-efficiency. Notably, Tina demonstrates
that substantial reasoning performance can be developed using only minimal
resources,... | 2025-04-22T10:38:00Z | null | null | null | null | null | null | null | null | null | null |
2,504.1603 | LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale | ['Joya Chen', 'Ziyun Zeng', 'Yiqi Lin', 'Wei Li', 'Zejun Ma', 'Mike Zheng Shou'] | ['cs.CV'] | Recent video large language models (Video LLMs) often depend on costly human
annotations or proprietary model APIs (e.g., GPT-4o) to produce training data,
which limits their training at scale. In this paper, we explore large-scale
training for Video LLM with cheap automatic speech recognition (ASR)
transcripts. Specif... | 2025-04-22T16:52:09Z | CVPR 2025. If any references are missing, please contact
joyachen@u.nus.edu | null | null | null | null | null | null | null | null | null |
2,504.16064 | Boosting Generative Image Modeling via Joint Image-Feature Synthesis | ['Theodoros Kouzelis', 'Efstathios Karypidis', 'Ioannis Kakogeorgiou', 'Spyros Gidaris', 'Nikos Komodakis'] | ['cs.CV'] | Latent diffusion models (LDMs) dominate high-quality image generation, yet
integrating representation learning with generative modeling remains a
challenge. We introduce a novel generative image modeling framework that
seamlessly bridges this gap by leveraging a diffusion model to jointly model
low-level image latents ... | 2025-04-22T17:41:42Z | null | null | null | Boosting Generative Image Modeling via Joint Image-Feature Synthesis | ['Theodoros Kouzelis', 'Efstathios Karypidis', 'Ioannis Kakogeorgiou', 'Spyros Gidaris', 'Nikos Komodakis'] | 2,025 | arXiv.org | 0 | 69 | ['Computer Science'] |
2,504.16072 | Describe Anything: Detailed Localized Image and Video Captioning | ['Long Lian', 'Yifan Ding', 'Yunhao Ge', 'Sifei Liu', 'Hanzi Mao', 'Boyi Li', 'Marco Pavone', 'Ming-Yu Liu', 'Trevor Darrell', 'Adam Yala', 'Yin Cui'] | ['cs.CV', 'cs.AI'] | Generating detailed and accurate descriptions for specific regions in images
and videos remains a fundamental challenge for vision-language models. We
introduce the Describe Anything Model (DAM), a model designed for detailed
localized captioning (DLC). DAM preserves both local details and global context
through two ke... | 2025-04-22T17:51:41Z | Project page: https://describe-anything.github.io/ | null | null | null | null | null | null | null | null | null |
2,504.1608 | From Reflection to Perfection: Scaling Inference-Time Optimization for
Text-to-Image Diffusion Models via Reflection Tuning | ['Le Zhuo', 'Liangbing Zhao', 'Sayak Paul', 'Yue Liao', 'Renrui Zhang', 'Yi Xin', 'Peng Gao', 'Mohamed Elhoseiny', 'Hongsheng Li'] | ['cs.CV'] | Recent text-to-image diffusion models achieve impressive visual quality
through extensive scaling of training data and model parameters, yet they often
struggle with complex scenes and fine-grained details. Inspired by the
self-reflection capabilities emergent in large language models, we propose
ReflectionFlow, an inf... | 2025-04-22T17:58:07Z | All code, checkpoints, and datasets are available at
\url{https://diffusion-cot.github.io/reflection2perfection} | null | null | null | null | null | null | null | null | null |
2,504.16084 | TTRL: Test-Time Reinforcement Learning | ['Yuxin Zuo', 'Kaiyan Zhang', 'Li Sheng', 'Shang Qu', 'Ganqu Cui', 'Xuekai Zhu', 'Haozhan Li', 'Yuchen Zhang', 'Xinwei Long', 'Ermo Hua', 'Biqing Qi', 'Youbang Sun', 'Zhiyuan Ma', 'Lifan Yuan', 'Ning Ding', 'Bowen Zhou'] | ['cs.CL', 'cs.LG'] | This paper investigates Reinforcement Learning (RL) on data without explicit
labels for reasoning tasks in Large Language Models (LLMs). The core challenge
of the problem is reward estimation during inference while not having access to
ground-truth information. While this setting appears elusive, we find that
common pr... | 2025-04-22T17:59:56Z | null | null | null | null | null | null | null | null | null | null |
2,504.1646 | T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic
Understanding via Deep Triplet Loss Fine-Tuning | ['Vignesh Ethiraj', 'Sidhanth Menon', 'Divya Vijay'] | ['cs.CL', 'cs.AI', '68T50'] | The specialized vocabulary and complex concepts of the telecommunications
industry present significant challenges for standard Natural Language
Processing models. Generic text embeddings often fail to capture
telecom-specific semantics, hindering downstream task performance. We introduce
T-VEC (Telecom Vectorization Mo... | 2025-04-23T07:10:37Z | Introduces T-VEC, a telecom-specific text embedding model. Fine-tuned
gte-Qwen2-1.5B-instruct on curated telecom data points. Includes the first
open-source telecom tokenizer. Model available at
https://huggingface.co/NetoAISolutions/T-VEC | null | null | T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning | ['Vignesh Ethiraj', 'Sidhanth Menon', 'Divya Vijay'] | 2,025 | arXiv.org | 0 | 15 | ['Computer Science'] |
2,504.16656 | Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning | ['Peiyu Wang', 'Yichen Wei', 'Yi Peng', 'Xiaokun Wang', 'Weijie Qiu', 'Wei Shen', 'Tianyidan Xie', 'Jiangbo Pei', 'Jianhao Zhang', 'Yunzhuo Hao', 'Xuchen Song', 'Yang Liu', 'Yahui Zhou'] | ['cs.CV'] | We present Skywork R1V2, a next-generation multimodal reasoning model and a
major leap forward from its predecessor, Skywork R1V. At its core, R1V2
introduces a hybrid reinforcement learning paradigm that jointly leverages the
Mixed Preference Optimization (MPO) and the Group Relative Policy Optimization
(GRPO), which ... | 2025-04-23T12:24:10Z | null | null | null | null | null | null | null | null | null | null |
2,504.16828 | Process Reward Models That Think | ['Muhammad Khalifa', 'Rishabh Agarwal', 'Lajanugen Logeswaran', 'Jaekyeom Kim', 'Hao Peng', 'Moontae Lee', 'Honglak Lee', 'Lu Wang'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Step-by-step verifiers -- also known as process reward models (PRMs) -- are a
key ingredient for test-time scaling. PRMs require step-level supervision,
making them expensive to train. This work aims to build data-efficient PRMs as
verbalized step-wise reward models that verify every step in the solution by
generating ... | 2025-04-23T15:44:54Z | null | null | null | null | null | null | null | null | null | null |
2,504.16832 | GreenMind: A Next-Generation Vietnamese Large Language Model for
Structured and Logical Reasoning | ['Luu Quy Tung', 'Hoang Quoc Viet', 'Vo Trong Thu'] | ['cs.CL'] | Chain-of-Thought (CoT) is a robust approach for tackling LLM tasks that
require intermediate reasoning steps prior to generating a final answer. In
this paper, we present GreenMind-Medium-14B-R1, the Vietnamese reasoning model
inspired by the finetuning strategy based on Group Relative Policy
Optimization. We also leve... | 2025-04-23T15:48:55Z | null | null | null | null | null | null | null | null | null | null |
2,504.16856 | Emo Pillars: Knowledge Distillation to Support Fine-Grained
Context-Aware and Context-Less Emotion Classification | ['Alexander Shvets'] | ['cs.CL'] | Most datasets for sentiment analysis lack context in which an opinion was
expressed, often crucial for emotion understanding, and are mainly limited by a
few emotion categories. Foundation large language models (LLMs) like GPT-4
suffer from over-predicting emotions and are too resource-intensive. We design
an LLM-based... | 2025-04-23T16:23:17Z | null | null | null | null | null | null | null | null | null | null |
2,504.16891 | AIMO-2 Winning Solution: Building State-of-the-Art Mathematical
Reasoning Models with OpenMathReasoning dataset | ['Ivan Moshkov', 'Darragh Hanley', 'Ivan Sorokin', 'Shubham Toshniwal', 'Christof Henkel', 'Benedikt Schifferer', 'Wei Du', 'Igor Gitman'] | ['cs.AI', 'cs.CL', 'cs.LG'] | This paper presents our winning submission to the AI Mathematical Olympiad -
Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art
mathematical reasoning models relies on three key pillars. First, we create a
large-scale dataset comprising 540K unique high-quality math problems,
including olym... | 2025-04-23T17:13:04Z | Report of AIMO-2 winning submission | null | null | AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset | ['Ivan Moshkov', 'Darragh Hanley', 'Ivan Sorokin', 'Shubham Toshniwal', 'Christof Henkel', 'Benedikt D. Schifferer', 'Wei Du', 'Igor Gitman'] | 2,025 | arXiv.org | 16 | 39 | ['Computer Science'] |
2,504.16915 | DreamO: A Unified Framework for Image Customization | ['Chong Mou', 'Yanze Wu', 'Wenxu Wu', 'Zinan Guo', 'Pengze Zhang', 'Yufeng Cheng', 'Yiming Luo', 'Fei Ding', 'Shiwen Zhang', 'Xinghui Li', 'Mengtian Li', 'Mingcong Liu', 'Yi Zhang', 'Shaojin Wu', 'Songtao Zhao', 'Jian Zhang', 'Qian He', 'Xinglong Wu'] | ['cs.CV'] | Recently, extensive research on image customization (e.g., identity, subject,
style, background, etc.) demonstrates strong customization capabilities in
large-scale generative models. However, most approaches are designed for
specific tasks, restricting their generalizability to combine different types
of condition. De... | 2025-04-23T17:41:44Z | null | null | null | DreamO: A Unified Framework for Image Customization | ['Chong Mou', 'Yanze Wu', 'Wenxu Wu', 'Zinan Guo', 'Pengze Zhang', 'Yufeng Cheng', 'Yiming Luo', 'Fei Ding', 'Shiwen Zhang', 'Xinghui Li', 'Mengtian Li', 'Songtao Zhao', 'Jian Zhang', 'Qian He', 'Xinglong Wu'] | 2,025 | arXiv.org | 3 | 57 | ['Computer Science'] |
2,504.17025 | Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing
Efficiency Through Vocabulary Adaptation | ['Luca Moroni', 'Giovanni Puccetti', 'Pere-Lluis Huguet Cabot', 'Andrei Stefan Bejgu', 'Edoardo Barba', 'Alessio Miaschi', "Felice Dell'Orletta", 'Andrea Esuli', 'Roberto Navigli'] | ['cs.CL'] | The number of pretrained Large Language Models (LLMs) is increasing steadily,
though the majority are designed predominantly for the English language. While
state-of-the-art LLMs can handle other languages, due to language contamination
or some degree of multilingual pretraining data, they are not optimized for
non-Eng... | 2025-04-23T18:12:27Z | null | null | null | null | null | null | null | null | null | null |
2,504.17238 | Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn
Supportive Dialogues | ['Jinfeng Zhou', 'Yuxuan Chen', 'Jianing Yin', 'Yongkang Huang', 'Yihan Shi', 'Xikun Zhang', 'Libiao Peng', 'Rongsheng Zhang', 'Tangjie Lv', 'Zhipeng Hu', 'Hongning Wang', 'Minlie Huang'] | ['cs.CL', 'cs.HC'] | Cognitive Restructuring (CR) is a psychotherapeutic process aimed at
identifying and restructuring an individual's negative thoughts, arising from
mental health challenges, into more helpful and positive ones via multi-turn
dialogues. Clinician shortage and stigma urge the development of human-LLM
interactive psychothe... | 2025-04-24T04:22:00Z | null | null | null | Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues | ['Jinfeng Zhou', 'Yuxuan Chen', 'Jianing Yin', 'Yongkang Huang', 'Yihan Shi', 'Xikun Zhang', 'Libiao Peng', 'Rongsheng Zhang', 'Tangjie Lv', 'Zhipeng Hu', 'Hongning Wang', 'Minlie Huang'] | 2,025 | arXiv.org | 1 | 61 | ['Computer Science'] |
2,504.17343 | TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming
Videos | ['Linli Yao', 'Yicheng Li', 'Yuancheng Wei', 'Lei Li', 'Shuhuai Ren', 'Yuanxin Liu', 'Kun Ouyang', 'Lean Wang', 'Shicheng Li', 'Sida Li', 'Lingpeng Kong', 'Qi Liu', 'Yuanxing Zhang', 'Xu Sun'] | ['cs.CV'] | The rapid growth of online video platforms, particularly live streaming
services, has created an urgent need for real-time video understanding systems.
These systems must process continuous video streams and respond to user queries
instantaneously, presenting unique challenges for current Video Large Language
Models (V... | 2025-04-24T07:59:46Z | null | null | null | null | null | null | null | null | null | null |
2,504.17432 | Breaking the Modality Barrier: Universal Embedding Learning with
Multimodal LLMs | ['Tiancheng Gu', 'Kaicheng Yang', 'Ziyong Feng', 'Xingjun Wang', 'Yanzhao Zhang', 'Dingkun Long', 'Yingda Chen', 'Weidong Cai', 'Jiankang Deng'] | ['cs.CV'] | The Contrastive Language-Image Pre-training (CLIP) framework has become a
widely used approach for multimodal representation learning, particularly in
image-text retrieval and clustering. However, its efficacy is constrained by
three key limitations: (1) text token truncation, (2) isolated image-text
encoding, and (3) ... | 2025-04-24T10:51:52Z | 13 pages, 8 figures, Project page: https://garygutc.github.io/UniME | null | null | Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs | ['Tiancheng Gu', 'Kaicheng Yang', 'Ziyong Feng', 'Xingjun Wang', 'Yanzhao Zhang', 'Dingkun Long', 'Yingda Chen', 'Weidong Cai', 'Jiankang Deng'] | 2,025 | arXiv.org | 4 | 57 | ['Computer Science'] |
2,504.1767 | DiMeR: Disentangled Mesh Reconstruction Model | ['Lutao Jiang', 'Jiantao Lin', 'Kanghao Chen', 'Wenhang Ge', 'Xin Yang', 'Yifan Jiang', 'Yuanhuiyi Lyu', 'Xu Zheng', 'Yinchuan Li', 'Yingcong Chen'] | ['cs.CV'] | We propose DiMeR, a novel geometry-texture disentangled feed-forward model
with 3D supervision for sparse-view mesh reconstruction. Existing methods
confront two persistent obstacles: (i) textures can conceal geometric errors,
i.e., visually plausible images can be rendered even with wrong geometry,
producing multiple ... | 2025-04-24T15:39:20Z | Project Page: https://lutao2021.github.io/DiMeR_page/ | null | null | null | null | null | null | null | null | null |
2,504.17699 | Quadratic Interest Network for Multimodal Click-Through Rate Prediction | ['Honghao Li', 'Hanwei Li', 'Jing Zhang', 'Yi Zhang', 'Ziniu Yu', 'Lei Sang', 'Yiwen Zhang'] | ['cs.IR'] | Multimodal click-through rate (CTR) prediction is a key technique in
industrial recommender systems. It leverages heterogeneous modalities such as
text, images, and behavioral logs to capture high-order feature interactions
between users and items, thereby enhancing the system's understanding of user
interests and its ... | 2025-04-24T16:08:52Z | null | null | null | null | null | null | null | null | null | null |
2,504.17761 | Step1X-Edit: A Practical Framework for General Image Editing | ['Shiyu Liu', 'Yucheng Han', 'Peng Xing', 'Fukun Yin', 'Rui Wang', 'Wei Cheng', 'Jiaqi Liao', 'Yingming Wang', 'Honghao Fu', 'Chunrui Han', 'Guopeng Li', 'Yuang Peng', 'Quan Sun', 'Jingwei Wu', 'Yan Cai', 'Zheng Ge', 'Ranchen Ming', 'Lei Xia', 'Xianfang Zeng', 'Yibo Zhu', 'Binxing Jiao', 'Xiangyu Zhang', 'Gang Yu', 'Da... | ['cs.CV'] | In recent years, image editing models have witnessed remarkable and rapid
development. The recent unveiling of cutting-edge multimodal models such as
GPT-4o and Gemini2 Flash has introduced highly promising image editing
capabilities. These models demonstrate an impressive aptitude for fulfilling a
vast majority of use... | 2025-04-24T17:25:12Z | code: https://github.com/stepfun-ai/Step1X-Edit | null | null | Step1X-Edit: A Practical Framework for General Image Editing | ['Shiyu Liu', 'Yucheng Han', 'Peng Xing', 'Fukun Yin', 'Rui Wang', 'Wei Cheng', 'Jiaqi Liao', 'Yingming Wang', 'Honghao Fu', 'Chunrui Han', 'Guopeng Li', 'Yuang Peng', 'Quan Sun', 'Jingwei Wu', 'Yan Cai', 'Zheng Ge', 'Ranchen Ming', 'Lei Xia', 'Xianfang Zeng', 'Yibo Zhu', 'Binxing Jiao', 'Xiangyu Zhang', 'Gang Yu', 'Da... | 2,025 | arXiv.org | 24 | 75 | ['Computer Science'] |
2,504.1795 | Collaborating Action by Action: A Multi-agent LLM Framework for Embodied
Reasoning | ['Isadora White', 'Kolby Nottingham', 'Ayush Maniar', 'Max Robinson', 'Hansen Lillemark', 'Mehul Maheshwari', 'Lianhui Qin', 'Prithviraj Ammanabrolu'] | ['cs.MA', 'cs.CL'] | Collaboration is ubiquitous and essential in day-to-day life -- from
exchanging ideas, to delegating tasks, to generating plans together. This work
studies how LLMs can adaptively collaborate to perform complex embodied
reasoning tasks. To this end we introduce MINDcraft, an easily extensible
platform built to enable L... | 2025-04-24T21:28:16Z | 9 pages of main paper with 6 main figures, overall 28 pages | null | null | null | null | null | null | null | null | null |
2,504.1808 | Stabilizing Reasoning in Medical LLMs with Continued Pretraining and
Reasoning Preference Optimization | ['Wataru Kawakami', 'Keita Suzuki', 'Junichiro Iwasawa'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) show potential in medicine, yet clinical
adoption is hindered by concerns over factual accuracy, language-specific
limitations (e.g., Japanese), and critically, their reliability when required
to generate reasoning explanations -- a prerequisite for trust. This paper
introduces Preferred-Me... | 2025-04-25T05:15:31Z | null | null | null | Stabilizing Reasoning in Medical LLMs with Continued Pretraining and Reasoning Preference Optimization | ['Wataru Kawakami', 'Keita Suzuki', 'Junichiro Iwasawa'] | 2,025 | arXiv.org | 0 | 36 | ['Computer Science'] |
2,504.18087 | Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional
Talking Portrait Generation | ['Weipeng Tan', 'Chuming Lin', 'Chengming Xu', 'FeiFan Xu', 'Xiaobin Hu', 'Xiaozhong Ji', 'Junwei Zhu', 'Chengjie Wang', 'Yanwei Fu'] | ['cs.CV'] | Recent advances in Talking Head Generation (THG) have achieved impressive lip
synchronization and visual quality through diffusion models; yet existing
methods struggle to generate emotionally expressive portraits while preserving
speaker identity. We identify three critical limitations in current emotional
talking hea... | 2025-04-25T05:28:21Z | arXiv admin note: text overlap with arXiv:2409.03270 | null | null | Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation | ['Weipeng Tan', 'Chuming Lin', 'Chengming Xu', 'FeiFan Xu', 'Xiaobin Hu', 'Xiaozhong Ji', 'Junwei Zhu', 'Chengjie Wang', 'Yanwei Fu'] | 2,025 | arXiv.org | 0 | 44 | ['Computer Science'] |
2,504.18225 | Even Small Reasoners Should Quote Their Sources: Introducing the
Pleias-RAG Model Family | ['Pierre-Carl Langlais', 'Pavel Chizhov', 'Mattia Nee', 'Carlos Rosas Hinostroza', 'Matthieu Delsart', 'Irène Girard', 'Othman Hicheur', 'Anastasia Stasenko', 'Ivan P. Yamshchikov'] | ['cs.CL'] | We introduce a new generation of small reasoning models for RAG, search, and
source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a
large synthetic dataset emulating the retrieval of a wide variety of
multilingual open sources from the Common Corpus. They provide native support
for citation and gr... | 2025-04-25T10:17:04Z | null | null | null | null | null | null | null | null | null | null |
2,504.18256 | SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in
Ecology | ['Elena Plekhanova', 'Damien Robert', 'Johannes Dollinger', 'Emilia Arens', 'Philipp Brun', 'Jan Dirk Wegner', 'Niklaus Zimmermann'] | ['cs.CV'] | With the exacerbation of the biodiversity and climate crises, macroecological
pursuits such as global biodiversity mapping become more urgent. Remote sensing
offers a wealth of Earth observation data for ecological studies, but the
scarcity of labeled datasets remains a major challenge. Recently,
self-supervised learni... | 2025-04-25T10:58:44Z | CVPR 2025, EarthVision workshop | null | null | null | null | null | null | null | null | null |
2,504.18471 | Action Flow Matching for Continual Robot Learning | ['Alejandro Murillo-Gonzalez', 'Lantao Liu'] | ['cs.RO', 'cs.AI'] | Continual learning in robotics seeks systems that can constantly adapt to
changing environments and tasks, mirroring human adaptability. A key challenge
is refining dynamics models, essential for planning and control, while
addressing issues such as safe adaptation, catastrophic forgetting, outlier
management, data eff... | 2025-04-25T16:26:15Z | Robotics: Science and Systems 2025 | null | null | null | null | null | null | null | null | null |
2,504.18583 | PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model
Adaptation | ['Zihao An', 'Huajun Bai', 'Ziqiong Liu', 'Dong Li', 'Emad Barsoum'] | ['cs.LG', 'cs.PF'] | The autoregressive nature of large language models (LLMs) limits inference
speed. Each forward pass generates only a single token and is often
bottlenecked by memory bandwidth. Speculative decoding alleviates this issue
using a draft-then-verify approach to accelerate token generation. However, the
overhead introduced ... | 2025-04-23T12:27:43Z | 15 pages, 6 figures | null | null | PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation | ['Zihao An', 'Huajun Bai', 'Ziqiong Liu', 'Dong Li', 'E. Barsoum'] | 2,025 | arXiv.org | 0 | 48 | ['Computer Science'] |
2,504.19144 | ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile
Hardware Development | ['Bowei Wang', 'Jiaran Gao', 'Yelai Feng', 'Renzhi Chen', 'Shanshan Li', 'Lei Wang'] | ['cs.AI', 'cs.AR', 'cs.SE'] | The growing demand for Domain-Specific Architecture (DSA) has driven the
development of Agile Hardware Development Methodology (AHDM). Hardware
Construction Language (HCL) like Chisel offers high-level abstraction features,
making it an ideal language for HCL-Based AHDM. While Large Language Models
(LLMs) excel in code... | 2025-04-27T07:56:49Z | null | null | null | null | null | null | null | null | null | null |
2,504.19162 | SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning | ['Jiaqi Chen', 'Bang Zhang', 'Ruotian Ma', 'Peisong Wang', 'Xiaodan Liang', 'Zhaopeng Tu', 'Xiaolong Li', 'Kwan-Yee K. Wong'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Evaluating the step-by-step reliability of large language model (LLM)
reasoning, such as Chain-of-Thought, remains challenging due to the difficulty
and cost of obtaining high-quality step-level supervision. In this paper, we
introduce Self-Play Critic (SPC), a novel approach where a critic model evolves
its ability to... | 2025-04-27T08:45:06Z | Project webpage: https://chen-judge.github.io/SPC/ | null | null | null | null | null | null | null | null | null |
2,504.19298 | AndroidGen: Building an Android Language Agent under Data Scarcity | ['Hanyu Lai', 'Junjie Gao', 'Xiao Liu', 'Yifan Xu', 'Shudan Zhang', 'Yuxiao Dong', 'Jie Tang'] | ['cs.CL'] | Large language models have opened up a world of possibilities for various NLP
tasks, sparking optimism for the future. Despite their potential, LLMs have yet
to be widely used as agents on real mobile devices. The main challenge is the
need for high-quality data sources. Time constraints and labor intensity often
hinde... | 2025-04-27T16:30:10Z | null | null | null | null | null | null | null | null | null | null |
2,504.19475 | Prisma: An Open Source Toolkit for Mechanistic Interpretability in
Vision and Video | ['Sonia Joseph', 'Praneet Suresh', 'Lorenz Hufe', 'Edward Stevinson', 'Robert Graham', 'Yash Vadi', 'Danilo Bzdok', 'Sebastian Lapuschkin', 'Lee Sharkey', 'Blake Aaron Richards'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Robust tooling and publicly available pre-trained models have helped drive
recent advances in mechanistic interpretability for language models. However,
similar progress in vision mechanistic interpretability has been hindered by
the lack of accessible frameworks and pre-trained weights. We present Prisma
(Access the c... | 2025-04-28T04:31:24Z | 4 pages, 3 figures, 9 tables. Oral and Tutorial at the CVPR
Mechanistic Interpretability for Vision (MIV) Workshop | null | null | null | null | null | null | null | null | null |
2,504.19675 | Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs | ['Osma Suominen', 'Juho Inkinen', 'Mona Lehtinen'] | ['cs.CL', 'cs.AI', 'cs.DL', 'cs.IR', 'cs.LG', 'I.2.7'] | This paper presents the Annif system in SemEval-2025 Task 5 (LLMs4Subjects),
which focussed on subject indexing using large language models (LLMs). The task
required creating subject predictions for bibliographic records from the
bilingual TIBKAT database using the GND subject vocabulary. Our approach
combines traditio... | 2025-04-28T11:04:23Z | 6 pages, 4 figures, submitted to SemEval-2025 workshop Task 5:
LLMs4Subjects | null | null | Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs | ['Osma Suominen', 'J. Inkinen', 'Mona Lehtinen'] | 2,025 | arXiv.org | 1 | 10 | ['Computer Science'] |
2,504.19724 | RepText: Rendering Visual Text via Replicating | ['Haofan Wang', 'Yujia Xu', 'Yimeng Li', 'Junchen Li', 'Chaowei Zhang', 'Jing Wang', 'Kejia Yang', 'Zhibo Chen'] | ['cs.CV'] | Although contemporary text-to-image generation models have achieved
remarkable breakthroughs in producing visually appealing images, their capacity
to generate precise and flexible typographic elements, especially non-Latin
alphabets, remains constrained. To address these limitations, we start from an
naive assumption ... | 2025-04-28T12:19:53Z | Technical Report. https://reptext.github.io/ | null | null | null | null | null | null | null | null | null |
2,504.19854 | NORA: A Small Open-Sourced Generalist Vision Language Action Model for
Embodied Tasks | ['Chia-Yu Hung', 'Qi Sun', 'Pengfei Hong', 'Amir Zadeh', 'Chuan Li', 'U-Xuan Tan', 'Navonil Majumder', 'Soujanya Poria'] | ['cs.RO', 'cs.AI', 'cs.CV'] | Existing Visual-Language-Action (VLA) models have shown promising performance
in zero-shot scenarios, demonstrating impressive task execution and reasoning
capabilities. However, a significant challenge arises from the limitations of
visual encoding, which can result in failures during tasks such as object
grasping. Mo... | 2025-04-28T14:47:34Z | null | null | null | NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks | ['Chia-Yu Hung', 'Qi Sun', 'Pengfei Hong', 'Amir Zadeh', 'Chuan Li', 'U-Xuan Tan', 'Navonil Majumder', 'Soujanya Poria'] | 2,025 | arXiv.org | 4 | 25 | ['Computer Science'] |
2,504.20114 | TreeHop: Generate and Filter Next Query Embeddings Efficiently for
Multi-hop Question Answering | ['Zhonghao Li', 'Kunpeng Zhang', 'Jinghuai Ou', 'Shuliang Liu', 'Xuming Hu'] | ['cs.IR', 'cs.AI', 'cs.HC', 'cs.LG'] | Retrieval-augmented generation (RAG) systems face significant challenges in
multi-hop question answering (MHQA), where complex queries require synthesizing
information across multiple document chunks. Existing approaches typically rely
on iterative LLM-based query rewriting and routing, resulting in high
computational ... | 2025-04-28T01:56:31Z | 9 pages | null | null | null | null | null | null | null | null | null |
2,504.20438 | PixelHacker: Image Inpainting with Structural and Semantic Consistency | ['Ziyang Xu', 'Kangsheng Duan', 'Xiaolei Shen', 'Zhifeng Ding', 'Wenyu Liu', 'Xiaohu Ruan', 'Xiaoxin Chen', 'Xinggang Wang'] | ['cs.CV'] | Image inpainting is a fundamental research area between image editing and
image generation. Recent state-of-the-art (SOTA) methods have explored novel
attention mechanisms, lightweight architectures, and context-aware modeling,
demonstrating impressive performance. However, they often struggle with complex
structure (e... | 2025-04-29T05:28:36Z | https://hustvl.github.io/PixelHacker | null | null | PixelHacker: Image Inpainting with Structural and Semantic Consistency | ['Ziyang Xu', 'Kangsheng Duan', 'Xiaolei Shen', 'Zhifeng Ding', 'Wenyu Liu', 'Xiaohu Ruan', 'Xiaoxin Chen', 'Xinggang Wang'] | 2,025 | arXiv.org | 0 | 40 | ['Computer Science'] |
2,504.20571 | Reinforcement Learning for Reasoning in Large Language Models with One
Training Example | ['Yiping Wang', 'Qing Yang', 'Zhiyuan Zeng', 'Liliang Ren', 'Liyuan Liu', 'Baolin Peng', 'Hao Cheng', 'Xuehai He', 'Kuan Wang', 'Jianfeng Gao', 'Weizhu Chen', 'Shuohang Wang', 'Simon Shaolei Du', 'Yelong Shen'] | ['cs.LG', 'cs.AI', 'cs.CL'] | We show that reinforcement learning with verifiable reward using one training
example (1-shot RLVR) is effective in incentivizing the mathematical reasoning
capabilities of large language models (LLMs). Applying RLVR to the base model
Qwen2.5-Math-1.5B, we identify a single example that elevates model performance
on MA... | 2025-04-29T09:24:30Z | 36 pages, link: https://github.com/ypwang61/One-Shot-RLVR | null | null | Reinforcement Learning for Reasoning in Large Language Models with One Training Example | ['Yiping Wang', 'Qing Yang', 'Zhiyuan Zeng', 'Liliang Ren', 'Lucas Liu', 'Baolin Peng', 'Hao Cheng', 'Xuehai He', 'Kuan Wang', 'Jianfeng Gao', 'Weizhu Chen', 'Shuohang Wang', 'S. Du', 'Yelong Shen'] | 2,025 | arXiv.org | 47 | 66 | ['Computer Science'] |
2,504.20595 | ReasonIR: Training Retrievers for Reasoning Tasks | ['Rulin Shao', 'Rui Qiao', 'Varsha Kishore', 'Niklas Muennighoff', 'Xi Victoria Lin', 'Daniela Rus', 'Bryan Kian Hsiang Low', 'Sewon Min', 'Wen-tau Yih', 'Pang Wei Koh', 'Luke Zettlemoyer'] | ['cs.AI', 'cs.CL', 'cs.IR', 'cs.LG'] | We present ReasonIR-8B, the first retriever specifically trained for general
reasoning tasks. Existing retrievers have shown limited gains on reasoning
tasks, in part because existing training datasets focus on short factual
queries tied to documents that straightforwardly answer them. We develop a
synthetic data gener... | 2025-04-29T09:49:28Z | Our code is released at
\url{https://github.com/facebookresearch/ReasonIR} | null | null | null | null | null | null | null | null | null |
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