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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,503.01774 | Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models | ['Jay Zhangjie Wu', 'Yuxuan Zhang', 'Haithem Turki', 'Xuanchi Ren', 'Jun Gao', 'Mike Zheng Shou', 'Sanja Fidler', 'Zan Gojcic', 'Huan Ling'] | ['cs.CV'] | Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D
reconstruction and novel-view synthesis task. However, achieving photorealistic
rendering from extreme novel viewpoints remains challenging, as artifacts
persist across representations. In this work, we introduce Difix3D+, a novel
pipeline designed... | 2025-03-03T17:58:33Z | CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,503.01776 | Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation | ['Tiansheng Wen', 'Yifei Wang', 'Zequn Zeng', 'Zhong Peng', 'Yudi Su', 'Xinyang Liu', 'Bo Chen', 'Hongwei Liu', 'Stefanie Jegelka', 'Chenyu You'] | ['cs.LG', 'cs.AI', 'cs.CV', 'cs.IR'] | Many large-scale systems rely on high-quality deep representations
(embeddings) to facilitate tasks like retrieval, search, and generative
modeling. Matryoshka Representation Learning (MRL) recently emerged as a
solution for adaptive embedding lengths, but it requires full model retraining
and suffers from noticeable p... | 2025-03-03T17:59:48Z | Accepted by ICML2025 | null | null | Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation | ['Tiansheng Wen', 'Yifei Wang', 'Zequn Zeng', 'Zhong Peng', 'Yudi Su', 'Xinyang Liu', 'Bo Chen', 'Hongwei Liu', 'Stefanie Jegelka', 'Chenyu You'] | 2,025 | arXiv.org | 4 | 75 | ['Computer Science'] |
2,503.01785 | Visual-RFT: Visual Reinforcement Fine-Tuning | ['Ziyu Liu', 'Zeyi Sun', 'Yuhang Zang', 'Xiaoyi Dong', 'Yuhang Cao', 'Haodong Duan', 'Dahua Lin', 'Jiaqi Wang'] | ['cs.CV'] | Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1
learns from feedback on its answers, which is especially useful in applications
when fine-tuning data is scarce. Recent open-source work like DeepSeek-R1
demonstrates that reinforcement learning with verifiable reward is one key
direction in repro... | 2025-03-03T18:16:32Z | project page: https://github.com/Liuziyu77/Visual-RFT | null | null | null | null | null | null | null | null | null |
2,503.01807 | Large-Scale Data Selection for Instruction Tuning | ['Hamish Ivison', 'Muru Zhang', 'Faeze Brahman', 'Pang Wei Koh', 'Pradeep Dasigi'] | ['cs.CL'] | Selecting high-quality training data from a larger pool is a crucial step
when instruction-tuning language models, as carefully curated datasets often
produce models that outperform those trained on much larger, noisier datasets.
Automated data selection approaches for instruction-tuning are typically tested
by selecti... | 2025-03-03T18:37:26Z | Updated, new baselines, removed some typos | null | null | Large-Scale Data Selection for Instruction Tuning | ['Hamish Ivison', 'Muru Zhang', 'Faeze Brahman', 'Pang Wei Koh', 'Pradeep Dasigi'] | 2,025 | arXiv.org | 5 | 64 | ['Computer Science'] |
2,503.01835 | Primus: Enforcing Attention Usage for 3D Medical Image Segmentation | ['Tassilo Wald', 'Saikat Roy', 'Fabian Isensee', 'Constantin Ulrich', 'Sebastian Ziegler', 'Dasha Trofimova', 'Raphael Stock', 'Michael Baumgartner', 'Gregor Köhler', 'Klaus Maier-Hein'] | ['cs.CV'] | Transformers have achieved remarkable success across multiple fields, yet
their impact on 3D medical image segmentation remains limited with
convolutional networks still dominating major benchmarks. In this work, we a)
analyze current Transformer-based segmentation models and identify critical
shortcomings, particularl... | 2025-03-03T18:56:29Z | Preprint | null | null | null | null | null | null | null | null | null |
2,503.01874 | CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model
Merging | ['Zongzhen Yang', 'Binhang Qi', 'Hailong Sun', 'Wenrui Long', 'Ruobing Zhao', 'Xiang Gao'] | ['cs.LG', 'cs.AI'] | Model merging based on task vectors, i.e., the parameter differences between
fine-tuned models and a shared base model, provides an efficient way to
integrate multiple task-specific models into a multitask model without
retraining. Recent works have endeavored to address the conflicts between task
vectors, one of the s... | 2025-02-26T12:38:55Z | null | null | null | CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging | ['Zongzhen Yang', 'Binhang Qi', 'Hailong Sun', 'Wenrui Long', 'Ruobing Zhao', 'Xiang Gao'] | 2,025 | arXiv.org | 0 | 56 | ['Computer Science'] |
2,503.0194 | AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification | ['Xuan Zhang', 'Yongliang Shen', 'Zhe Zheng', 'Linjuan Wu', 'Wenqi Zhang', 'Yuchen Yan', 'Qiuying Peng', 'Jun Wang', 'Weiming Lu'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) have demonstrated remarkable capabilities in
tool learning. In real-world scenarios, user queries are often ambiguous and
incomplete, requiring effective clarification. However, existing interactive
clarification approaches face two critical limitations: reliance on manually
constructed dat... | 2025-03-03T12:55:49Z | null | null | null | AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification | ['Xuan Zhang', 'Yongliang Shen', 'Zhe Zheng', 'Linjuan Wu', 'Wenqi Zhang', 'Yuchen Yan', 'Qiuying Peng', 'Jun Wang', 'Weiming Lu'] | 2,025 | arXiv.org | 2 | 49 | ['Computer Science'] |
2,503.0198 | Recurrence-Enhanced Vision-and-Language Transformers for Robust
Multimodal Document Retrieval | ['Davide Caffagni', 'Sara Sarto', 'Marcella Cornia', 'Lorenzo Baraldi', 'Rita Cucchiara'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.MM'] | Cross-modal retrieval is gaining increasing efficacy and interest from the
research community, thanks to large-scale training, novel architectural and
learning designs, and its application in LLMs and multimodal LLMs. In this
paper, we move a step forward and design an approach that allows for multimodal
queries, compo... | 2025-03-03T19:01:17Z | CVPR 2025 | null | null | Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval | ['Davide Caffagni', 'Sara Sarto', 'Marcella Cornia', 'L. Baraldi', 'Rita Cucchiara'] | 2,025 | Computer Vision and Pattern Recognition | 1 | 58 | ['Computer Science'] |
2,503.0213 | Forgetting Transformer: Softmax Attention with a Forget Gate | ['Zhixuan Lin', 'Evgenii Nikishin', 'Xu Owen He', 'Aaron Courville'] | ['cs.LG', 'cs.AI', 'cs.CL'] | An essential component of modern recurrent sequence models is the forget
gate. While Transformers do not have an explicit recurrent form, we show that a
forget gate can be naturally incorporated into Transformers by down-weighting
the unnormalized attention scores in a data-dependent way. We name this
attention mechani... | 2025-03-03T23:35:23Z | Published as a conference paper at ICLR 2025; Fixed an issue with the
attention map visualization | null | null | Forgetting Transformer: Softmax Attention with a Forget Gate | ['Zhixuan Lin', 'Evgenii Nikishin', 'Xu Owen He', 'Aaron C. Courville'] | 2,025 | International Conference on Learning Representations | 13 | 73 | ['Computer Science'] |
2,503.02152 | Tabby: Tabular Data Synthesis with Language Models | ['Sonia Cromp', 'Satya Sai Srinath Namburi GNVV', 'Mohammed Alkhudhayri', 'Catherine Cao', 'Samuel Guo', 'Nicholas Roberts', 'Frederic Sala'] | ['cs.LG', 'cs.CL', 'I.2.6'] | While advances in large language models (LLMs) have greatly improved the
quality of synthetic text data in recent years, synthesizing tabular data has
received relatively less attention. We address this disparity with Tabby, a
simple but powerful post-training modification to the standard Transformer
language model arc... | 2025-03-04T00:32:15Z | 21 pages, 8 figures | null | null | Tabby: Tabular Data Synthesis with Language Models | ['Sonia Cromp', 'Satya Sai Srinath Namburi Gnvv', 'Mohammed Alkhudhayri', 'Catherine Cao', 'Samuel Guo', 'Nicholas Roberts', 'Frederic Sala'] | 2,025 | arXiv.org | 0 | 38 | ['Computer Science'] |
2,503.0224 | OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale | ['Haoyang Li', 'Shang Wu', 'Xiaokang Zhang', 'Xinmei Huang', 'Jing Zhang', 'Fuxin Jiang', 'Shuai Wang', 'Tieying Zhang', 'Jianjun Chen', 'Rui Shi', 'Hong Chen', 'Cuiping Li'] | ['cs.CL', 'cs.DB'] | Text-to-SQL, the task of translating natural language questions into SQL
queries, plays a crucial role in enabling non-experts to interact with
databases. While recent advancements in large language models (LLMs) have
significantly enhanced text-to-SQL performance, existing approaches face
notable limitations in real-w... | 2025-03-04T03:30:56Z | null | null | null | null | null | null | null | null | null | null |
2,503.02304 | A Token-level Text Image Foundation Model for Document Understanding | ['Tongkun Guan', 'Zining Wang', 'Pei Fu', 'Zhengtao Guo', 'Wei Shen', 'Kai Zhou', 'Tiezhu Yue', 'Chen Duan', 'Hao Sun', 'Qianyi Jiang', 'Junfeng Luo', 'Xiaokang Yang'] | ['cs.CV'] | In recent years, general visual foundation models (VFMs) have witnessed
increasing adoption, particularly as image encoders for popular multi-modal
large language models (MLLMs). However, without semantically fine-grained
supervision, these models still encounter fundamental prediction errors in the
context of downstre... | 2025-03-04T06:05:33Z | 23 pages | null | null | null | null | null | null | null | null | null |
2,503.02318 | Audio-Reasoner: Improving Reasoning Capability in Large Audio Language
Models | ['Zhifei Xie', 'Mingbao Lin', 'Zihang Liu', 'Pengcheng Wu', 'Shuicheng Yan', 'Chunyan Miao'] | ['cs.SD', 'cs.AI', 'cs.CL', 'cs.LG', 'cs.MM', 'eess.AS'] | Recent advancements in multimodal reasoning have largely overlooked the audio
modality. We introduce Audio-Reasoner, a large-scale audio language model for
deep reasoning in audio tasks. We meticulously curated a large-scale and
diverse multi-task audio dataset with simple annotations. Then, we leverage
closed-source m... | 2025-03-04T06:18:34Z | Technical report, in process | null | null | null | null | null | null | null | null | null |
2,503.02324 | PromptCoT: Synthesizing Olympiad-level Problems for Mathematical
Reasoning in Large Language Models | ['Xueliang Zhao', 'Wei Wu', 'Jian Guan', 'Lingpeng Kong'] | ['cs.CL', 'cs.AI', 'cs.LG'] | The ability of large language models to solve complex mathematical problems
has progressed significantly, particularly for tasks requiring advanced
reasoning. However, the scarcity of sufficiently challenging problems,
particularly at the Olympiad level, hinders further advancements. In this work,
we introduce PromptCo... | 2025-03-04T06:32:30Z | Preprint | null | null | null | null | null | null | null | null | null |
2,503.02502 | LADM: Long-context Training Data Selection with Attention-based
Dependency Measurement for LLMs | ['Jianghao Chen', 'Junhong Wu', 'Yangyifan Xu', 'Jiajun Zhang'] | ['cs.CL'] | Long-context modeling has drawn more and more attention in the area of Large
Language Models (LLMs). Continual training with long-context data becomes the
de-facto method to equip LLMs with the ability to process long inputs. However,
it still remains an open challenge to measure the quality of long-context
training da... | 2025-03-04T11:10:13Z | ACL 2025, our code is available at https://github.com/ZNLP/LADM | null | null | null | null | null | null | null | null | null |
2,503.02505 | ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment | ['Shaofei Cai', 'Zhancun Mu', 'Anji Liu', 'Yitao Liang'] | ['cs.AI', 'cs.CV', 'cs.LG', 'cs.RO'] | We aim to develop a goal specification method that is semantically clear,
spatially sensitive, domain-agnostic, and intuitive for human users to guide
agent interactions in 3D environments. Specifically, we propose a novel
cross-view goal alignment framework that allows users to specify target objects
using segmentatio... | 2025-03-04T11:16:46Z | null | null | null | ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment | ['Shaofei Cai', 'Zhancun Mu', 'Anji Liu', 'Yitao Liang'] | 2,025 | arXiv.org | 6 | 35 | ['Computer Science'] |
2,503.02572 | RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour | ['Valerii Serpiva', 'Artem Lykov', 'Artyom Myshlyaev', 'Muhammad Haris Khan', 'Ali Alridha Abdulkarim', 'Oleg Sautenkov', 'Dzmitry Tsetserukou'] | ['cs.RO', 'cs.AI'] | RaceVLA presents an innovative approach for autonomous racing drone
navigation by leveraging Visual-Language-Action (VLA) to emulate human-like
behavior. This research explores the integration of advanced algorithms that
enable drones to adapt their navigation strategies based on real-time
environmental feedback, mimic... | 2025-03-04T12:54:05Z | 6 pages, 6 figures. Submitted to IROS 2025 | null | null | null | null | null | null | null | null | null |
2,503.02579 | MM-OR: A Large Multimodal Operating Room Dataset for Semantic
Understanding of High-Intensity Surgical Environments | ['Ege Özsoy', 'Chantal Pellegrini', 'Tobias Czempiel', 'Felix Tristram', 'Kun Yuan', 'David Bani-Harouni', 'Ulrich Eck', 'Benjamin Busam', 'Matthias Keicher', 'Nassir Navab'] | ['cs.CV'] | Operating rooms (ORs) are complex, high-stakes environments requiring precise
understanding of interactions among medical staff, tools, and equipment for
enhancing surgical assistance, situational awareness, and patient safety.
Current datasets fall short in scale, realism and do not capture the multimodal
nature of OR... | 2025-03-04T13:00:52Z | null | null | null | null | null | null | null | null | null | null |
2,503.02597 | Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual
Attention for Multimodal LLMs | ['Wei-Yao Wang', 'Zhao Wang', 'Helen Suzuki', 'Yoshiyuki Kobayashi'] | ['cs.CV', 'cs.AI'] | Recent Multimodal Large Language Models (MLLMs) have demonstrated significant
progress in perceiving and reasoning over multimodal inquiries, ushering in a
new research era for foundation models. However, vision-language misalignment
in MLLMs has emerged as a critical challenge, where the textual responses
generated by... | 2025-03-04T13:18:33Z | Preprint | null | null | null | null | null | null | null | null | null |
2,503.02682 | MPO: Boosting LLM Agents with Meta Plan Optimization | ['Weimin Xiong', 'Yifan Song', 'Qingxiu Dong', 'Bingchan Zhao', 'Feifan Song', 'Xun Wang', 'Sujian Li'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Recent advancements in large language models (LLMs) have enabled LLM-based
agents to successfully tackle interactive planning tasks. However, despite
their successes, existing approaches often suffer from planning hallucinations
and require retraining for each new agent. To address these challenges, we
propose the Meta... | 2025-03-04T14:54:45Z | null | null | null | MPO: Boosting LLM Agents with Meta Plan Optimization | ['Weimin Xiong', 'Yifan Song', 'Qingxiu Dong', 'Bingchan Zhao', 'Feifan Song', 'Xun Wang', 'Sujian Li'] | 2,025 | arXiv.org | 3 | 36 | ['Computer Science'] |
2,503.02823 | A Multimodal Symphony: Integrating Taste and Sound through Generative AI | ['Matteo Spanio', 'Massimiliano Zampini', 'Antonio Rodà', 'Franco Pierucci'] | ['cs.SD', 'cs.AI', 'cs.MM', 'eess.AS', 'I.2.6; J.5'] | In recent decades, neuroscientific and psychological research has traced
direct relationships between taste and auditory perceptions. This article
explores multimodal generative models capable of converting taste information
into music, building on this foundational research. We provide a brief review
of the state of t... | 2025-03-04T17:48:48Z | 17 pages, 6 figures (2 + 2 figures with 2 subfigures each) | null | null | null | null | null | null | null | null | null |
2,503.02876 | SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
Baseline Models | ['Dmitry Nechaev', 'Alexey Pchelnikov', 'Ekaterina Ivanova'] | ['eess.IV', 'cs.CV'] | Advancing AI in computational pathology requires large, high-quality, and
diverse datasets, yet existing public datasets are often limited in organ
diversity, class coverage, or annotation quality. To bridge this gap, we
introduce SPIDER (Supervised Pathology Image-DEscription Repository), the
largest publicly availabl... | 2025-03-04T18:57:12Z | null | null | null | null | null | null | null | null | null | null |
2,503.02881 | Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for
Contact-Rich Manipulation | ['Han Xue', 'Jieji Ren', 'Wendi Chen', 'Gu Zhang', 'Yuan Fang', 'Guoying Gu', 'Huazhe Xu', 'Cewu Lu'] | ['cs.RO', 'cs.AI', 'cs.LG'] | Humans can accomplish complex contact-rich tasks using vision and touch, with
highly reactive capabilities such as fast response to external changes and
adaptive control of contact forces; however, this remains challenging for
robots. Existing visual imitation learning (IL) approaches rely on action
chunking to model c... | 2025-03-04T18:58:21Z | Accepted to RSS 2025. Project page:
https://reactive-diffusion-policy.github.io | null | null | Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation | ['Han Xue', 'Jieji Ren', 'Wendi Chen', 'Gu Zhang', 'Yuan Fang', 'Guoying Gu', 'Huazhe Xu', 'Cewu Lu'] | 2,025 | arXiv.org | 12 | 70 | ['Computer Science'] |
2,503.03008 | MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings | ['Andrea Gurioli', 'Federico Pennino', 'João Monteiro', 'Maurizio Gabbrielli'] | ['cs.CL', 'cs.AI', 'cs.PL', 'cs.SE'] | Deploying language models often requires navigating accuracy vs. performance
trade-offs to meet latency constraints while preserving utility. Traditional
model distillation reduces size but incurs substantial costs through training
separate models. We introduce ModularStarEncoder (MoSE), a 1-billion-parameter
multi-exi... | 2025-03-04T21:08:17Z | null | null | null | MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings | ['Andrea Gurioli', 'Federico Pennino', 'Joao Monteiro', 'Maurizio Gabbrielli'] | 2,025 | null | 0 | 40 | ['Computer Science'] |
2,503.0304 | SAGE: Steering Dialog Generation with Future-Aware State-Action
Augmentation | ['Yizhe Zhang', 'Navdeep Jaitly'] | ['cs.CL', 'cs.AI'] | Recent advances in large language models have demonstrated impressive
capabilities in task-oriented applications, yet building emotionally
intelligent chatbots that can engage in natural, strategic conversations
remains a challenge. We present a novel approach called SAGE that uses latent
variables to control long-hori... | 2025-03-04T22:45:24Z | 9 pages main text | null | null | null | null | null | null | null | null | null |
2,503.03196 | SpiritSight Agent: Advanced GUI Agent with One Look | ['Zhiyuan Huang', 'Ziming Cheng', 'Junting Pan', 'Zhaohui Hou', 'Mingjie Zhan'] | ['cs.CV', 'cs.HC', 'cs.RO'] | Graphical User Interface (GUI) agents show amazing abilities in assisting
human-computer interaction, automating human user's navigation on digital
devices. An ideal GUI agent is expected to achieve high accuracy, low latency,
and compatibility for different GUI platforms. Recent vision-based approaches
have shown prom... | 2025-03-05T05:30:22Z | Paper accepted to CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,503.03205 | MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning
enhances Formal Theorem Proving | ['Ruida Wang', 'Rui Pan', 'Yuxin Li', 'Jipeng Zhang', 'Yizhen Jia', 'Shizhe Diao', 'Renjie Pi', 'Junjie Hu', 'Tong Zhang'] | ['cs.CL', 'cs.AI'] | Solving mathematical problems using computer-verifiable languages like Lean
has significantly impacted the mathematical and computer science communities.
State-of-the-art methods utilize a single Large Language Model (LLM) to
generate complete proof or perform tree search, but they fail to balance these
tasks. We propo... | 2025-03-05T05:50:31Z | null | null | null | null | null | null | null | null | null | null |
2,503.03225 | Targeted Distillation for Sentiment Analysis | ['Yice Zhang', 'Guangyu Xie', 'Jingjie Lin', 'Jianzhu Bao', 'Qianlong Wang', 'Xi Zeng', 'Ruifeng Xu'] | ['cs.CL'] | This paper presents a compact model that achieves strong sentiment analysis
capabilities through targeted distillation from advanced large language models
(LLMs). Our methodology decouples the distillation target into two key
components: sentiment-related knowledge and task alignment. To transfer these
components, we p... | 2025-03-05T06:45:25Z | null | null | null | Targeted Distillation for Sentiment Analysis | ['Yice Zhang', 'Guangyu Xie', 'Jingjie Lin', 'Jianzhu Bao', 'Qianlong Wang', 'Xi Zeng', 'Ruifeng Xu'] | 2,025 | arXiv.org | 0 | 53 | ['Computer Science'] |
2,503.03272 | Towards Effective and Sparse Adversarial Attack on Spiking Neural
Networks via Breaking Invisible Surrogate Gradients | ['Li Lun', 'Kunyu Feng', 'Qinglong Ni', 'Ling Liang', 'Yuan Wang', 'Ying Li', 'Dunshan Yu', 'Xiaoxin Cui'] | ['cs.CV'] | Spiking neural networks (SNNs) have shown their competence in handling
spatial-temporal event-based data with low energy consumption. Similar to
conventional artificial neural networks (ANNs), SNNs are also vulnerable to
gradient-based adversarial attacks, wherein gradients are calculated by
spatial-temporal back-propa... | 2025-03-05T08:52:55Z | Accepted by CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,503.03278 | Enhancing Abnormality Grounding for Vision Language Models with
Knowledge Descriptions | ['Jun Li', 'Che Liu', 'Wenjia Bai', 'Rossella Arcucci', 'Cosmin I. Bercea', 'Julia A. Schnabel'] | ['cs.CV', 'cs.CL'] | Visual Language Models (VLMs) have demonstrated impressive capabilities in
visual grounding tasks. However, their effectiveness in the medical domain,
particularly for abnormality detection and localization within medical images,
remains underexplored. A major challenge is the complex and abstract nature of
medical ter... | 2025-03-05T09:02:33Z | 11 pages, 3 figures | null | null | null | null | null | null | null | null | null |
2,503.0334 | EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with
Neural Knowledge Base of Entity States | ['Hainiu Xu', 'Siya Qi', 'Jiazheng Li', 'Yuxiang Zhou', 'Jinhua Du', 'Caroline Catmur', 'Yulan He'] | ['cs.CL'] | Theory-of-Mind (ToM), the ability to infer others' perceptions and mental
states, is fundamental to human interaction but remains challenging for Large
Language Models (LLMs). While existing ToM reasoning methods show promise with
reasoning via perceptual perspective-taking, they often rely excessively on
off-the-shelf... | 2025-03-05T10:13:05Z | Findings of ACL 2025 | null | null | null | null | null | null | null | null | null |
2,503.0336 | Transformers for molecular property prediction: Domain adaptation
efficiently improves performance | ['Afnan Sultan', 'Max Rausch-Dupont', 'Shahrukh Khan', 'Olga Kalinina', 'Dietrich Klakow', 'Andrea Volkamer'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Over the past six years, molecular transformer models have become key tools
in drug discovery. Most existing models are pre-trained on large, unlabeled
datasets such as ZINC or ChEMBL. However, the extent to which large-scale
pre-training improves molecular property prediction remains unclear. This study
evaluates tran... | 2025-03-05T10:40:09Z | null | null | null | Transformers for molecular property prediction: Domain adaptation efficiently improves performance | ['Afnan Sultan', 'Max Rausch-Dupont', 'Shahrukh Khan', 'Olga Kalinina', 'Andrea Volkamer', 'Dietrich Klakow'] | 2,025 | arXiv.org | 0 | 69 | ['Computer Science'] |
2,503.03708 | Rethinking Video Tokenization: A Conditioned Diffusion-based Approach | ['Nianzu Yang', 'Pandeng Li', 'Liming Zhao', 'Yang Li', 'Chen-Wei Xie', 'Yehui Tang', 'Xudong Lu', 'Zhihang Liu', 'Yun Zheng', 'Yu Liu', 'Junchi Yan'] | ['cs.CV', 'cs.AI'] | Existing video tokenizers typically use the traditional Variational
Autoencoder (VAE) architecture for video compression and reconstruction.
However, to achieve good performance, its training process often relies on
complex multi-stage training tricks that go beyond basic reconstruction loss
and KL regularization. Amon... | 2025-03-05T17:59:19Z | null | null | null | null | null | null | null | null | null | null |
2,503.03751 | GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera
Control | ['Xuanchi Ren', 'Tianchang Shen', 'Jiahui Huang', 'Huan Ling', 'Yifan Lu', 'Merlin Nimier-David', 'Thomas Müller', 'Alexander Keller', 'Sanja Fidler', 'Jun Gao'] | ['cs.CV', 'cs.GR'] | We present GEN3C, a generative video model with precise Camera Control and
temporal 3D Consistency. Prior video models already generate realistic videos,
but they tend to leverage little 3D information, leading to inconsistencies,
such as objects popping in and out of existence. Camera control, if implemented
at all, i... | 2025-03-05T18:59:50Z | To appear in CVPR 2025. Website:
https://research.nvidia.com/labs/toronto-ai/GEN3C/ | null | null | null | null | null | null | null | null | null |
2,503.03773 | A Phylogenetic Approach to Genomic Language Modeling | ['Carlos Albors', 'Jianan Canal Li', 'Gonzalo Benegas', 'Chengzhong Ye', 'Yun S. Song'] | ['q-bio.GN', 'cs.LG'] | Genomic language models (gLMs) have shown mostly modest success in
identifying evolutionarily constrained elements in mammalian genomes. To
address this issue, we introduce a novel framework for training gLMs that
explicitly models nucleotide evolution on phylogenetic trees using multispecies
whole-genome alignments. O... | 2025-03-04T06:53:03Z | 15 pages, 7 figures | null | null | A Phylogenetic Approach to Genomic Language Modeling | ['Carlos Albors', 'Jianan Canal Li', 'Gonzalo Benegas', 'Chengzhong Ye', 'Yun S. Song'] | 2,025 | Annual International Conference on Research in Computational Molecular Biology | 0 | 45 | ['Computer Science', 'Medicine', 'Biology'] |
2,503.03803 | EgoLife: Towards Egocentric Life Assistant | ['Jingkang Yang', 'Shuai Liu', 'Hongming Guo', 'Yuhao Dong', 'Xiamengwei Zhang', 'Sicheng Zhang', 'Pengyun Wang', 'Zitang Zhou', 'Binzhu Xie', 'Ziyue Wang', 'Bei Ouyang', 'Zhengyu Lin', 'Marco Cominelli', 'Zhongang Cai', 'Yuanhan Zhang', 'Peiyuan Zhang', 'Fangzhou Hong', 'Joerg Widmer', 'Francesco Gringoli', 'Lei Yang'... | ['cs.CV'] | We introduce EgoLife, a project to develop an egocentric life assistant that
accompanies and enhances personal efficiency through AI-powered wearable
glasses. To lay the foundation for this assistant, we conducted a comprehensive
data collection study where six participants lived together for one week,
continuously rec... | 2025-03-05T18:54:16Z | Accepted to CVPR 2025. Project Page: https://egolife-ai.github.io/.
Code: https://github.com/EvolvingLMMs-Lab/EgoLife | null | null | EgoLife: Towards Egocentric Life Assistant | ['Jingkang Yang', 'Shuai Liu', 'Hongming Guo', 'Yuhao Dong', 'Xiamengwei Zhang', 'Sicheng Zhang', 'Pengyun Wang', 'Zitang Zhou', 'Binzhu Xie', 'Ziyue Wang', 'Bei Ouyang', 'Zhengyu Lin', 'Marco Cominelli', 'Zhongang Cai', 'Yuanhan Zhang', 'Peiyuan Zhang', 'Fangzhou Hong', 'Joerg Widmer', 'Francesco Gringoli', 'Lei Yang'... | 2,025 | arXiv.org | 6 | 170 | ['Computer Science'] |
2,503.03848 | Nexar Dashcam Collision Prediction Dataset and Challenge | ['Daniel C. Moura', 'Shizhan Zhu', 'Orly Zvitia'] | ['cs.CV'] | This paper presents the Nexar Dashcam Collision Prediction Dataset and
Challenge, designed to support research in traffic event analysis, collision
prediction, and autonomous vehicle safety. The dataset consists of 1,500
annotated video clips, each approximately 40 seconds long, capturing a diverse
range of real-world ... | 2025-03-05T19:20:28Z | null | null | null | null | null | null | null | null | null | null |
2,503.03962 | On the Acquisition of Shared Grammatical Representations in Bilingual
Language Models | ['Catherine Arnett', 'Tyler A. Chang', 'James A. Michaelov', 'Benjamin K. Bergen'] | ['cs.CL'] | Crosslingual transfer is crucial to contemporary language models'
multilingual capabilities, but how it occurs is not well understood. We ask
what happens to a monolingual language model when it begins to be trained on a
second language. Specifically, we train small bilingual models for which we
control the amount of d... | 2025-03-05T23:27:58Z | 9 pages, 5 figures. Accepted at ACL 2025 | null | null | On the Acquisition of Shared Grammatical Representations in Bilingual Language Models | ['Catherine Arnett', 'Tyler A. Chang', 'J. Michaelov', 'Benjamin Bergen'] | 2,025 | arXiv.org | 0 | 57 | ['Computer Science'] |
2,503.03965 | All-atom Diffusion Transformers: Unified generative modelling of
molecules and materials | ['Chaitanya K. Joshi', 'Xiang Fu', 'Yi-Lun Liao', 'Vahe Gharakhanyan', 'Benjamin Kurt Miller', 'Anuroop Sriram', 'Zachary W. Ulissi'] | ['cs.LG', 'cs.AI'] | Diffusion models are the standard toolkit for generative modelling of 3D
atomic systems. However, for different types of atomic systems -- such as
molecules and materials -- the generative processes are usually highly specific
to the target system despite the underlying physics being the same. We
introduce the All-atom... | 2025-03-05T23:35:44Z | ICML 2025 | null | null | null | null | null | null | null | null | null |
2,503.03983 | Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding
and Expert Reasoning Abilities | ['Sreyan Ghosh', 'Zhifeng Kong', 'Sonal Kumar', 'S Sakshi', 'Jaehyeon Kim', 'Wei Ping', 'Rafael Valle', 'Dinesh Manocha', 'Bryan Catanzaro'] | ['cs.SD', 'cs.CL', 'cs.LG', 'eess.AS'] | Understanding and reasoning over non-speech sounds and music are crucial for
both humans and AI agents to interact effectively with their environments. In
this paper, we introduce Audio Flamingo 2 (AF2), an Audio-Language Model (ALM)
with advanced audio understanding and reasoning capabilities. AF2 leverages (i)
a cust... | 2025-03-06T00:10:26Z | null | null | null | null | null | null | null | null | null | null |
2,503.04065 | PP-DocBee: Improving Multimodal Document Understanding Through a Bag of
Tricks | ['Feng Ni', 'Kui Huang', 'Yao Lu', 'Wenyu Lv', 'Guanzhong Wang', 'Zeyu Chen', 'Yi Liu'] | ['cs.CV', 'cs.AI', 'cs.CL'] | With the rapid advancement of digitalization, various document images are
being applied more extensively in production and daily life, and there is an
increasingly urgent need for fast and accurate parsing of the content in
document images. Therefore, this report presents PP-DocBee, a novel multimodal
large language mo... | 2025-03-06T03:43:21Z | null | null | null | null | null | null | null | null | null | null |
2,503.04222 | FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion | ['Ziyi Yang', 'Fanqi Wan', 'Longguang Zhong', 'Canbin Huang', 'Guosheng Liang', 'Xiaojun Quan'] | ['cs.CL'] | We introduce FuseChat-3.0, a suite of large language models (LLMs) developed
by integrating the strengths of heterogeneous source LLMs into more compact
target LLMs. Our source models include the powerful Gemma-2-27B-it,
Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct.
For target models, ... | 2025-03-06T09:03:36Z | Technical report | null | null | null | null | null | null | null | null | null |
2,503.04378 | HelpSteer3: Human-Annotated Feedback and Edit Data to Empower
Inference-Time Scaling in Open-Ended General-Domain Tasks | ['Zhilin Wang', 'Jiaqi Zeng', 'Olivier Delalleau', 'Daniel Egert', 'Ellie Evans', 'Hoo-Chang Shin', 'Felipe Soares', 'Yi Dong', 'Oleksii Kuchaiev'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Inference-Time Scaling has been critical to the success of recent models such
as OpenAI o1 and DeepSeek R1. However, many techniques used to train models for
inference-time scaling require tasks to have answers that can be verified,
limiting their application to domains such as math, coding and logical
reasoning. We ta... | 2025-03-06T12:30:24Z | 23 pages, 2 figures, Accepted to ACL 2025 Main | null | null | null | null | null | null | null | null | null |
2,503.04459 | Question-Aware Gaussian Experts for Audio-Visual Question Answering | ['Hongyeob Kim', 'Inyoung Jung', 'Dayoon Suh', 'Youjia Zhang', 'Sangmin Lee', 'Sungeun Hong'] | ['cs.CV'] | Audio-Visual Question Answering (AVQA) requires not only question-based
multimodal reasoning but also precise temporal grounding to capture subtle
dynamics for accurate prediction. However, existing methods mainly use question
information implicitly, limiting focus on question-specific details.
Furthermore, most studie... | 2025-03-06T14:11:46Z | CVPR 2025. Code is available at https://github.com/AIM-SKKU/QA-TIGER | null | null | null | null | null | null | null | null | null |
2,503.04482 | Generalized Interpolating Discrete Diffusion | ['Dimitri von Rütte', 'Janis Fluri', 'Yuhui Ding', 'Antonio Orvieto', 'Bernhard Schölkopf', 'Thomas Hofmann'] | ['cs.CL', 'cs.AI', 'cs.LG'] | While state-of-the-art language models achieve impressive results through
next-token prediction, they have inherent limitations such as the inability to
revise already generated tokens. This has prompted exploration of alternative
approaches such as discrete diffusion. However, masked diffusion, which has
emerged as a ... | 2025-03-06T14:30:55Z | Published at ICML 2025; Code available at
https://github.com/dvruette/gidd | null | null | null | null | null | null | null | null | null |
2,503.04606 | The Best of Both Worlds: Integrating Language Models and Diffusion
Models for Video Generation | ['Aoxiong Yin', 'Kai Shen', 'Yichong Leng', 'Xu Tan', 'Xinyu Zhou', 'Juncheng Li', 'Siliang Tang'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Recent advancements in text-to-video (T2V) generation have been driven by two
competing paradigms: autoregressive language models and diffusion models.
However, each paradigm has intrinsic limitations: language models struggle with
visual quality and error accumulation, while diffusion models lack semantic
understandin... | 2025-03-06T16:53:14Z | Our code is available at https://github.com/LanDiff/LanDiff | null | null | null | null | null | null | null | null | null |
2,503.04713 | Scaling Rich Style-Prompted Text-to-Speech Datasets | ['Anuj Diwan', 'Zhisheng Zheng', 'David Harwath', 'Eunsol Choi'] | ['eess.AS', 'cs.AI', 'cs.CL', 'cs.LG', 'cs.SD'] | We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale
dataset that annotates speech utterances with rich style captions. While rich
abstract tags (e.g. guttural, nasal, pained) have been explored in small-scale
human-annotated datasets, existing large-scale datasets only cover basic tags
(e.g. low... | 2025-03-06T18:57:40Z | null | null | null | null | null | null | null | null | null | null |
2,503.0472 | FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video | ['Yue Gao', 'Hong-Xing Yu', 'Bo Zhu', 'Jiajun Wu'] | ['cs.CV'] | We study reconstructing and predicting 3D fluid appearance and velocity from
a single video. Current methods require multi-view videos for fluid
reconstruction. We present FluidNexus, a novel framework that bridges video
generation and physics simulation to tackle this task. Our key insight is to
synthesize multiple no... | 2025-03-06T18:59:06Z | CVPR 2025 (oral). The first two authors contributed equally. Project
website: https://yuegao.me/FluidNexus | null | null | null | null | null | null | null | null | null |
2,503.04724 | LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM | ['Sambal Shikhar', 'Mohammed Irfan Kurpath', 'Sahal Shaji Mullappilly', 'Jean Lahoud', 'Fahad Khan', 'Rao Muhammad Anwer', 'Salman Khan', 'Hisham Cholakkal'] | ['cs.CL'] | Recent advancements in speech-to-speech dialogue systems leverage LLMs for
multimodal interactions, yet they remain hindered by fine-tuning requirements,
high computational overhead, and text-speech misalignment. Existing
speech-enabled LLMs often degrade conversational quality by modifying the LLM,
thereby compromisin... | 2025-03-06T18:59:38Z | null | null | null | null | null | null | null | null | null | null |
2,503.04789 | Ext2Gen: Alignment through Unified Extraction and Generation for Robust
Retrieval-Augmented Generation | ['Hwanjun Song', 'Jeonghwan Choi', 'Minseok Kim'] | ['cs.CL', 'cs.AI'] | Retrieval-augmented generation (RAG) enhances LLMs by integrating external
knowledge, but generation remains fragile due to the uncertain placement of
relevant chunks and retrieval-induced information overload, leading to
hallucinations. We propose Ext2Gen, a novel extract-then-generate model that
enhances RAG robustne... | 2025-02-28T06:46:53Z | null | null | null | Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation | ['Hwanjun Song', 'Jeonghwan Choi', 'Minseok Kim'] | 2,025 | arXiv.org | 0 | 45 | ['Computer Science'] |
2,503.04812 | LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted
Contrastive Learning | ['Zhibin Lan', 'Liqiang Niu', 'Fandong Meng', 'Jie Zhou', 'Jinsong Su'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Universal multimodal embedding models play a critical role in tasks such as
interleaved image-text retrieval, multimodal RAG, and multimodal clustering.
However, our empirical results indicate that existing LMM-based embedding
models trained with the standard InfoNCE loss exhibit a high degree of overlap
in similarity ... | 2025-03-04T10:21:57Z | Preprint | null | null | LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning | ['Zhibin Lan', 'Liqiang Niu', 'Fandong Meng', 'Jie Zhou', 'Jinsong Su'] | 2,025 | arXiv.org | 6 | 37 | ['Computer Science'] |
2,503.04872 | TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation | ['Lin Sun', 'Guangxiang Zhao', 'Xiaoqi Jian', 'Yuhan Wu', 'Weihong Lin', 'Yongfu Zhu', 'Change Jia', 'Linglin Zhang', 'Jinzhu Wu', 'Junfeng Ran', 'Sai-er Hu', 'Zihan Jiang', 'Junting Zhou', 'Wenrui Liu', 'Bin Cui', 'Tong Yang', 'Xiangzheng Zhang'] | ['cs.CL', 'cs.AI'] | The challenge of reducing the size of Large Language Models (LLMs) while
maintaining their performance has gained significant attention. However,
existing methods, such as model distillation and transfer learning, often fail
to achieve high accuracy. To address this limitation, we introduce the
Branch-Merge distillatio... | 2025-03-06T16:25:53Z | Preprint | null | null | null | null | null | null | null | null | null |
2,503.0506 | ModernBERT is More Efficient than Conventional BERT for Chest CT
Findings Classification in Japanese Radiology Reports | ['Yosuke Yamagishi', 'Tomohiro Kikuchi', 'Shouhei Hanaoka', 'Takeharu Yoshikawa', 'Osamu Abe'] | ['cs.CL'] | Objective: This study aims to evaluate and compare the performance of two
Japanese language models-conventional Bidirectional Encoder Representations
from Transformers (BERT) and the newer ModernBERT-in classifying findings from
chest CT reports, with a focus on tokenization efficiency, processing time, and
classificat... | 2025-03-07T00:28:08Z | 23 pages, 8 figures | null | null | ModernBERT is More Efficient than Conventional BERT for Chest CT Findings Classification in Japanese Radiology Reports | ['Yosuke Yamagishi', 'Tomohiro Kikuchi', 'S. Hanaoka', 'Takeharu Yoshikawa', 'O. Abe'] | 2,025 | arXiv.org | 1 | 5 | ['Computer Science'] |
2,503.05132 | R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model | ['Hengguang Zhou', 'Xirui Li', 'Ruochen Wang', 'Minhao Cheng', 'Tianyi Zhou', 'Cho-Jui Hsieh'] | ['cs.AI', 'cs.CV', 'cs.LG'] | Recently DeepSeek R1 demonstrated how reinforcement learning with simple
rule-based incentives can enable autonomous development of complex reasoning in
large language models, characterized by the "aha moment", in which the model
manifest self-reflection and increased response length during training.
However, attempts ... | 2025-03-07T04:21:47Z | 10 pages, 6 figures | null | null | null | null | null | null | null | null | null |
2,503.05139 | Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without
Premium GPUs | ['Ling Team', 'Binwei Zeng', 'Chao Huang', 'Chao Zhang', 'Changxin Tian', 'Cong Chen', 'Dingnan Jin', 'Feng Yu', 'Feng Zhu', 'Feng Yuan', 'Fakang Wang', 'Gangshan Wang', 'Guangyao Zhai', 'Haitao Zhang', 'Huizhong Li', 'Jun Zhou', 'Jia Liu', 'Junpeng Fang', 'Junjie Ou', 'Jun Hu', 'Ji Luo', 'Ji Zhang', 'Jian Liu', 'Jian ... | ['cs.LG', 'cs.AI', 'cs.CL'] | In this technical report, we tackle the challenges of training large-scale
Mixture of Experts (MoE) models, focusing on overcoming cost inefficiency and
resource limitations prevalent in such systems. To address these issues, we
present two differently sized MoE large language models (LLMs), namely
Ling-Lite and Ling-P... | 2025-03-07T04:43:39Z | 34 pages | null | null | null | null | null | null | null | null | null |
2,503.05179 | Sketch-of-Thought: Efficient LLM Reasoning with Adaptive
Cognitive-Inspired Sketching | ['Simon A. Aytes', 'Jinheon Baek', 'Sung Ju Hwang'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Recent advances in large language models (LLMs) have enabled strong reasoning
capabilities through Chain-of-Thought (CoT) prompting, which elicits
step-by-step problem solving, but often at the cost of excessive verbosity in
intermediate outputs, leading to increased computational overhead. We propose
Sketch-of-Thought... | 2025-03-07T06:57:17Z | null | null | null | Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching | ['Simon A. Aytes', 'Jinheon Baek', 'Sung Ju Hwang'] | 2,025 | arXiv.org | 44 | 52 | ['Computer Science'] |
2,503.05236 | Unified Reward Model for Multimodal Understanding and Generation | ['Yibin Wang', 'Yuhang Zang', 'Hao Li', 'Cheng Jin', 'Jiaqi Wang'] | ['cs.CV'] | Recent advances in human preference alignment have significantly enhanced
multimodal generation and understanding. A key approach is training reward
models to guide preference optimization. However, existing models are often
task-specific, limiting their adaptability across diverse visual applications.
We also argue th... | 2025-03-07T08:36:05Z | project page: https://codegoat24.github.io/UnifiedReward/ | null | null | Unified Reward Model for Multimodal Understanding and Generation | ['Yibin Wang', 'Yuhang Zang', 'Hao Li', 'Cheng Jin', 'Jiaqi Wang'] | 2,025 | arXiv.org | 11 | 53 | ['Computer Science'] |
2,503.05244 | WritingBench: A Comprehensive Benchmark for Generative Writing | ['Yuning Wu', 'Jiahao Mei', 'Ming Yan', 'Chenliang Li', 'Shaopeng Lai', 'Yuran Ren', 'Zijia Wang', 'Ji Zhang', 'Mengyue Wu', 'Qin Jin', 'Fei Huang'] | ['cs.AI', 'cs.CL'] | Recent advancements in large language models (LLMs) have significantly
enhanced text generation capabilities, yet evaluating their performance in
generative writing remains a challenge. Existing benchmarks primarily focus on
generic text generation or limited in writing tasks, failing to capture the
diverse requirement... | 2025-03-07T08:56:20Z | null | null | null | null | null | null | null | null | null | null |
2,503.05379 | R1-Omni: Explainable Omni-Multimodal Emotion Recognition with
Reinforcement Learning | ['Jiaxing Zhao', 'Xihan Wei', 'Liefeng Bo'] | ['cs.LG', 'cs.CV'] | In this work, we present the first application of Reinforcement Learning with
Verifiable Reward (RLVR) to an Omni-multimodal large language model in the
context of emotion recognition, a task where both visual and audio modalities
play crucial roles. We leverage RLVR to optimize the Omni model, significantly
enhancing ... | 2025-03-07T12:46:42Z | null | null | null | null | null | null | null | null | null | null |
2,503.055 | EuroBERT: Scaling Multilingual Encoders for European Languages | ['Nicolas Boizard', 'Hippolyte Gisserot-Boukhlef', 'Duarte M. Alves', 'André Martins', 'Ayoub Hammal', 'Caio Corro', 'Céline Hudelot', 'Emmanuel Malherbe', 'Etienne Malaboeuf', 'Fanny Jourdan', 'Gabriel Hautreux', 'João Alves', 'Kevin El-Haddad', 'Manuel Faysse', 'Maxime Peyrard', 'Nuno M. Guerreiro', 'Patrick Fernande... | ['cs.CL', 'cs.AI'] | General-purpose multilingual vector representations, used in retrieval,
regression and classification, are traditionally obtained from bidirectional
encoder models. Despite their wide applicability, encoders have been recently
overshadowed by advances in generative decoder-only models. However, many
innovations driving... | 2025-03-07T15:13:58Z | 28 pages, 8 figures, 13 tables | null | null | null | null | null | null | null | null | null |
2,503.05507 | Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? | ['Qingyuan Liang', 'Zhao Zhang', 'Zeyu Sun', 'Zheng Lin', 'Qi Luo', 'Yueyi Xiao', 'Yizhou Chen', 'Yuqun Zhang', 'Haotian Zhang', 'Lu Zhang', 'Bin Chen', 'Yingfei Xiong'] | ['cs.PL', 'cs.AI'] | Grammar serves as a cornerstone in programming languages and software
engineering, providing frameworks to define the syntactic space and program
structure. Existing research demonstrates the effectiveness of grammar-based
code representations in small-scale models, showing their ability to reduce
syntax errors and enh... | 2025-03-07T15:23:13Z | null | null | null | Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? | ['Qing-Lin Liang', 'Zhao Zhang', 'Zeyu Sun', 'Zheng Lin', 'Qi Luo', 'Yueyi Xiao', 'Yizhou Chen', 'Yuqun Zhang', 'Haotian Zhang', 'Lu Zhang', 'Bin Chen', 'Yingfei Xiong'] | 2,025 | arXiv.org | 1 | 35 | ['Computer Science'] |
2,503.05638 | TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos
via Diffusion Models | ['Mark YU', 'Wenbo Hu', 'Jinbo Xing', 'Ying Shan'] | ['cs.CV', 'cs.AI', 'cs.GR'] | We present TrajectoryCrafter, a novel approach to redirect camera
trajectories for monocular videos. By disentangling deterministic view
transformations from stochastic content generation, our method achieves precise
control over user-specified camera trajectories. We propose a novel dual-stream
conditional video diffu... | 2025-03-07T17:57:53Z | Project webpage: https://trajectorycrafter.github.io/ | null | null | TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models | ['YU Mark', 'Wenbo Hu', 'Jinbo Xing', 'Ying Shan'] | 2,025 | arXiv.org | 12 | 100 | ['Computer Science'] |
2,503.05639 | VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play
Context Control | ['Yuxuan Bian', 'Zhaoyang Zhang', 'Xuan Ju', 'Mingdeng Cao', 'Liangbin Xie', 'Ying Shan', 'Qiang Xu'] | ['cs.CV', 'cs.AI', 'cs.MM'] | Video inpainting, which aims to restore corrupted video content, has
experienced substantial progress. Despite these advances, existing methods,
whether propagating unmasked region pixels through optical flow and receptive
field priors, or extending image-inpainting models temporally, face challenges
in generating full... | 2025-03-07T17:59:46Z | Project page available at
https://yxbian23.github.io/project/video-painter | null | null | null | null | null | null | null | null | null |
2,503.05689 | GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
Generation in End-to-End Autonomous Driving | ['Zebin Xing', 'Xingyu Zhang', 'Yang Hu', 'Bo Jiang', 'Tong He', 'Qian Zhang', 'Xiaoxiao Long', 'Wei Yin'] | ['cs.CV'] | We propose GoalFlow, an end-to-end autonomous driving method for generating
high-quality multimodal trajectories. In autonomous driving scenarios, there is
rarely a single suitable trajectory. Recent methods have increasingly focused
on modeling multimodal trajectory distributions. However, they suffer from
trajectory ... | 2025-03-07T18:52:08Z | null | null | null | null | null | null | null | null | null | null |
2,503.05731 | AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark
from MLCommons | ['Shaona Ghosh', 'Heather Frase', 'Adina Williams', 'Sarah Luger', 'Paul Röttger', 'Fazl Barez', 'Sean McGregor', 'Kenneth Fricklas', 'Mala Kumar', 'Quentin Feuillade--Montixi', 'Kurt Bollacker', 'Felix Friedrich', 'Ryan Tsang', 'Bertie Vidgen', 'Alicia Parrish', 'Chris Knotz', 'Eleonora Presani', 'Jonathan Bennion', '... | ['cs.CY', 'cs.AI'] | The rapid advancement and deployment of AI systems have created an urgent
need for standard safety-evaluation frameworks. This paper introduces
AILuminate v1.0, the first comprehensive industry-standard benchmark for
assessing AI-product risk and reliability. Its development employed an open
process that included parti... | 2025-02-19T05:58:52Z | 51 pages, 8 figures and an appendix | null | null | null | null | null | null | null | null | null |
2,503.05931 | Training and Inference Efficiency of Encoder-Decoder Speech Models | ['Piotr Żelasko', 'Kunal Dhawan', 'Daniel Galvez', 'Krishna C. Puvvada', 'Ankita Pasad', 'Nithin Rao Koluguri', 'Ke Hu', 'Vitaly Lavrukhin', 'Jagadeesh Balam', 'Boris Ginsburg'] | ['cs.CL', 'eess.AS'] | Attention encoder-decoder model architecture is the backbone of several
recent top performing foundation speech models: Whisper, Seamless, OWSM, and
Canary-1B. However, the reported data and compute requirements for their
training are prohibitive for many in the research community. In this work, we
focus on the efficie... | 2025-03-07T20:57:43Z | null | null | null | null | null | null | null | null | null | null |
2,503.0599 | HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image
Synthesis for Interpretable Compression Fracture Grading | ['Qi Zhang', 'Shunan Zhang', 'Ziqi Zhao', 'Kun Wang', 'Jun Xu', 'Jianqi Sun'] | ['eess.IV', 'cs.CV'] | Osteoporotic vertebral compression fractures (VCFs) are prevalent in the
elderly population, typically assessed on computed tomography (CT) scans by
evaluating vertebral height loss. This assessment helps determine the
fracture's impact on spinal stability and the need for surgical intervention.
However, clinical data ... | 2025-03-08T00:05:39Z | null | null | null | null | null | null | null | null | null | null |
2,503.06053 | DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal
Consistent Video Generation | ['Runze Zhang', 'Guoguang Du', 'Xiaochuan Li', 'Qi Jia', 'Liang Jin', 'Lu Liu', 'Jingjing Wang', 'Cong Xu', 'Zhenhua Guo', 'Yaqian Zhao', 'Xiaoli Gong', 'Rengang Li', 'Baoyu Fan'] | ['cs.CV', 'cs.AI'] | Spatio-temporal consistency is a critical research topic in video generation.
A qualified generated video segment must ensure plot plausibility and coherence
while maintaining visual consistency of objects and scenes across varying
viewpoints. Prior research, especially in open-source projects, primarily
focuses on eit... | 2025-03-08T04:37:38Z | null | null | null | DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation | ['Runze Zhang', 'Guoguang Du', 'Xiaochuan Li', 'Qi Jia', 'Liang Jin', 'Lu Liu', 'Jingjing Wang', 'Cong Xu', 'Zhenhua Guo', 'Yaqian Zhao', 'Xiaoli Gong', 'Rengang Li', 'Baoyu Fan'] | 2,025 | arXiv.org | 2 | 70 | ['Computer Science'] |
2,503.06073 | GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and
Images | ['Xiang Lan', 'Feng Wu', 'Kai He', 'Qinghao Zhao', 'Shenda Hong', 'Mengling Feng'] | ['cs.CL', 'cs.AI', 'cs.CV'] | While recent multimodal large language models (MLLMs) have advanced automated
ECG interpretation, they still face two key limitations: (1) insufficient
multimodal synergy between time series signals and visual ECG representations,
and (2) limited explainability in linking diagnoses to granular waveform
evidence. We int... | 2025-03-08T05:48:53Z | null | null | null | null | null | null | null | null | null | null |
2,503.06134 | X2I: Seamless Integration of Multimodal Understanding into Diffusion
Transformer via Attention Distillation | ['Jian Ma', 'Qirong Peng', 'Xu Guo', 'Chen Chen', 'Haonan Lu', 'Zhenyu Yang'] | ['cs.CV'] | Text-to-image (T2I) models are well known for their ability to produce highly
realistic images, while multimodal large language models (MLLMs) are renowned
for their proficiency in understanding and integrating multiple modalities.
However, currently there is no straightforward and efficient framework to
transfer the m... | 2025-03-08T09:07:45Z | https://github.com/OPPO-Mente-Lab/X2I | null | null | X2I: Seamless Integration of Multimodal Understanding into Diffusion Transformer via Attention Distillation | ['Jiancang Ma', 'Qirong Peng', 'Xu Guo', 'Chen Chen', 'H. Lu', 'Zhenyu Yang'] | 2,025 | arXiv.org | 1 | 82 | ['Computer Science'] |
2,503.06252 | Can Atomic Step Decomposition Enhance the Self-structured Reasoning of
Multimodal Large Models? | ['Kun Xiang', 'Zhili Liu', 'Zihao Jiang', 'Yunshuang Nie', 'Kaixin Cai', 'Yiyang Yin', 'Runhui Huang', 'Haoxiang Fan', 'Hanhui Li', 'Weiran Huang', 'Yihan Zeng', 'Yu-Jie Yuan', 'Jianhua Han', 'Lanqing Hong', 'Hang Xu', 'Xiaodan Liang'] | ['cs.CV', 'cs.AI'] | In this paper, we address the challenging task of multimodal mathematical
reasoning by incorporating the ability of "slow thinking" into multimodal large
language models (MLLMs). Our core idea is that different levels of reasoning
abilities can be combined dynamically to tackle questions with different
complexity. To t... | 2025-03-08T15:23:47Z | null | null | null | null | null | null | null | null | null | null |
2,503.06505 | DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial
Editability | ['Xirui Hu', 'Jiahao Wang', 'Hao Chen', 'Weizhan Zhang', 'Benqi Wang', 'Yikun Li', 'Haishun Nan'] | ['cs.CV', 'cs.AI'] | Recent advancements in text-to-image generation have spurred interest in
personalized human image generation, which aims to create novel images
featuring specific human identities as reference images indicate. Although
existing methods achieve high-fidelity identity preservation, they often
struggle with limited multi-... | 2025-03-09T08:16:19Z | ICCV 2025 | null | null | null | null | null | null | null | null | null |
2,503.0651 | Less is More: Adaptive Program Repair with Bug Localization and
Preference Learning | ['Zhenlong Dai', 'Bingrui Chen', 'Zhuoluo Zhao', 'Xiu Tang', 'Sai Wu', 'Chang Yao', 'Zhipeng Gao', 'Jingyuan Chen'] | ['cs.SE', 'cs.CL'] | Automated Program Repair (APR) is a task to automatically generate patches
for the buggy code. However, most research focuses on generating correct
patches while ignoring the consistency between the fixed code and the original
buggy code. How to conduct adaptive bug fixing and generate patches with
minimal modification... | 2025-03-09T08:32:38Z | accepted by AAAI2025 Oral | null | null | null | null | null | null | null | null | null |
2,503.0652 | Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive
Reinforcement | ['Yuqi Liu', 'Bohao Peng', 'Zhisheng Zhong', 'Zihao Yue', 'Fanbin Lu', 'Bei Yu', 'Jiaya Jia'] | ['cs.CV', 'cs.MM'] | Traditional methods for reasoning segmentation rely on supervised fine-tuning
with categorical labels and simple descriptions, limiting its out-of-domain
generalization and lacking explicit reasoning processes. To address these
limitations, we propose Seg-Zero, a novel framework that demonstrates
remarkable generalizab... | 2025-03-09T08:48:51Z | null | null | null | null | null | null | null | null | null | null |
2,503.06594 | Beyond Decoder-only: Large Language Models Can be Good Encoders for
Machine Translation | ['Yingfeng Luo', 'Tong Zheng', 'Yongyu Mu', 'Bei Li', 'Qinghong Zhang', 'Yongqi Gao', 'Ziqiang Xu', 'Peinan Feng', 'Xiaoqian Liu', 'Tong Xiao', 'Jingbo Zhu'] | ['cs.CL'] | The field of neural machine translation (NMT) has changed with the advent of
large language models (LLMs). Much of the recent emphasis in natural language
processing (NLP) has been on modeling machine translation and many other
problems using a single pre-trained Transformer decoder, while encoder-decoder
architectures... | 2025-03-09T12:54:05Z | Accepted to ACL Findings 2025. Please cite the ACL version. Code and
data are available at: https://github.com/NiuTrans/LaMaTE | null | null | Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation | ['Yingfeng Luo', 'Tong Zheng', 'Yongyu Mu', 'Bei Li', 'Qinghong Zhang', 'Yongqi Gao', 'Ziqiang Xu', 'Peinan Feng', 'Xiaoqian Liu', 'Tong Xiao', 'Jingbo Zhu'] | 2,025 | arXiv.org | 3 | 105 | ['Computer Science'] |
2,503.06669 | AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable
and Intelligent Embodied Systems | ['AgiBot-World-Contributors', 'Qingwen Bu', 'Jisong Cai', 'Li Chen', 'Xiuqi Cui', 'Yan Ding', 'Siyuan Feng', 'Shenyuan Gao', 'Xindong He', 'Xuan Hu', 'Xu Huang', 'Shu Jiang', 'Yuxin Jiang', 'Cheng Jing', 'Hongyang Li', 'Jialu Li', 'Chiming Liu', 'Yi Liu', 'Yuxiang Lu', 'Jianlan Luo', 'Ping Luo', 'Yao Mu', 'Yuehan Niu',... | ['cs.RO', 'cs.CV', 'cs.LG'] | We explore how scalable robot data can address real-world challenges for
generalized robotic manipulation. Introducing AgiBot World, a large-scale
platform comprising over 1 million trajectories across 217 tasks in five
deployment scenarios, we achieve an order-of-magnitude increase in data scale
compared to existing d... | 2025-03-09T15:40:29Z | Project website: https://agibot-world.com/. Github repo:
https://github.com/OpenDriveLab/AgiBot-World. The author list is ordered
alphabetically by surname, with detailed contributions provided in the
appendix | null | null | null | null | null | null | null | null | null |
2,503.06674 | Learning Few-Step Diffusion Models by Trajectory Distribution Matching | ['Yihong Luo', 'Tianyang Hu', 'Jiacheng Sun', 'Yujun Cai', 'Jing Tang'] | ['cs.CV'] | Accelerating diffusion model sampling is crucial for efficient AIGC
deployment. While diffusion distillation methods -- based on distribution
matching and trajectory matching -- reduce sampling to as few as one step, they
fall short on complex tasks like text-to-image generation. Few-step generation
offers a better bal... | 2025-03-09T15:53:49Z | Project page: https://tdm-t2x.github.io/ | null | null | null | null | null | null | null | null | null |
2,503.07014 | Vib2Mol: from vibrational spectra to molecular structures-a versatile
deep learning model | ['Xinyu Lu', 'Hao Ma', 'Hui Li', 'Jia Li', 'Tong Zhu', 'Guokun Liu', 'Bin Ren'] | ['physics.chem-ph'] | There will be a paradigm shift in chemical and biological research, to be
enabled by autonomous, closed-loop, real-time self-directed decision-making
experimentation. Spectrum-to-structure correlation, which is to elucidate
molecular structures with spectral information, is the core step in
understanding the experiment... | 2025-03-10T07:53:58Z | null | null | null | Vib2Mol: from vibrational spectra to molecular structures-a versatile deep learning model | ['Xinyu Lu', 'Hao Ma', 'Hui Li', 'Jia Li', 'Tong Zhu', 'Guo-kun Liu', 'Bin Ren'] | 2,025 | null | 1 | 31 | ['Physics'] |
2,503.07027 | EasyControl: Adding Efficient and Flexible Control for Diffusion
Transformer | ['Yuxuan Zhang', 'Yirui Yuan', 'Yiren Song', 'Haofan Wang', 'Jiaming Liu'] | ['cs.CV'] | Recent advancements in Unet-based diffusion models, such as ControlNet and
IP-Adapter, have introduced effective spatial and subject control mechanisms.
However, the DiT (Diffusion Transformer) architecture still struggles with
efficient and flexible control. To tackle this issue, we propose EasyControl, a
novel framew... | 2025-03-10T08:07:17Z | null | null | null | EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer | ['Yuxuan Zhang', 'Yirui Yuan', 'Yiren Song', 'Haofan Wang', 'Jiaming Liu'] | 2,025 | arXiv.org | 16 | 85 | ['Computer Science'] |
2,503.07091 | FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset | ['Shuhe Wang', 'Xiaoya Li', 'Jiwei Li', 'Guoyin Wang', 'Xiaofei Sun', 'Bob Zhu', 'Han Qiu', 'Mo Yu', 'Shengjie Shen', 'Tianwei Zhang', 'Eduard Hovy'] | ['cs.CV', 'cs.AI'] | Due to the data-driven nature of current face identity (FaceID) customization
methods, all state-of-the-art models rely on large-scale datasets containing
millions of high-quality text-image pairs for training. However, none of these
datasets are publicly available, which restricts transparency and hinders
further adva... | 2025-03-10T09:14:47Z | arXiv admin note: text overlap with arXiv:2501.15407 | null | null | null | null | null | null | null | null | null |
2,503.07111 | PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping
with VLM | ['Alan Dao', 'Dinh Bach Vu', 'Tuan Le Duc Anh', 'Bui Quang Huy'] | ['cs.RO', 'cs.CL'] | This paper introduces PoseLess, a novel framework for robot hand control that
eliminates the need for explicit pose estimation by directly mapping 2D images
to joint angles using projected representations. Our approach leverages
synthetic training data generated through randomized joint configurations,
enabling zero-sh... | 2025-03-10T09:34:05Z | null | null | null | PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping with VLM | ['Alan Dao', 'Dinh Bach Vu', 'Tuan Le Duc Anh', 'Bui Quang Huy'] | 2,025 | arXiv.org | 0 | 20 | ['Computer Science'] |
2,503.07197 | Effective and Efficient Masked Image Generation Models | ['Zebin You', 'Jingyang Ou', 'Xiaolu Zhang', 'Jun Hu', 'Jun Zhou', 'Chongxuan Li'] | ['cs.CV', 'cs.LG'] | Although masked image generation models and masked diffusion models are
designed with different motivations and objectives, we observe that they can be
unified within a single framework. Building upon this insight, we carefully
explore the design space of training and sampling, identifying key factors that
contribute t... | 2025-03-10T11:27:12Z | null | null | null | null | null | null | null | null | null | null |
2,503.07265 | WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image
Generation | ['Yuwei Niu', 'Munan Ning', 'Mengren Zheng', 'Weiyang Jin', 'Bin Lin', 'Peng Jin', 'Jiaqi Liao', 'Chaoran Feng', 'Kunpeng Ning', 'Bin Zhu', 'Li Yuan'] | ['cs.CV', 'cs.AI', 'cs.CL', 'I.2.7; I.2.10; I.4.9'] | Text-to-Image (T2I) models are capable of generating high-quality artistic
creations and visual content. However, existing research and evaluation
standards predominantly focus on image realism and shallow text-image
alignment, lacking a comprehensive assessment of complex semantic understanding
and world knowledge int... | 2025-03-10T12:47:53Z | Code, data and leaderboard: https://github.com/PKU-YuanGroup/WISE | null | null | null | null | null | null | null | null | null |
2,503.0733 | Mitigating Hallucinations in YOLO-based Object Detection Models: A
Revisit to Out-of-Distribution Detection | ['Weicheng He', 'Changshun Wu', 'Chih-Hong Cheng', 'Xiaowei Huang', 'Saddek Bensalem'] | ['cs.CV', 'cs.AI', 'cs.SE'] | Object detection systems must reliably perceive objects of interest without
being overly confident to ensure safe decision-making in dynamic environments.
Filtering techniques based on out-of-distribution (OoD) detection are commonly
added as an extra safeguard to filter hallucinations caused by overconfidence
in novel... | 2025-03-10T13:42:41Z | Camera-ready version for IROS 2025 | null | null | null | null | null | null | null | null | null |
2,503.07389 | TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image
Diffusion Models | ['Ruidong Chen', 'Honglin Guo', 'Lanjun Wang', 'Chenyu Zhang', 'Weizhi Nie', 'An-An Liu'] | ['cs.CV', 'cs.AI'] | Recent advances in text-to-image diffusion models enable photorealistic image
generation, but they also risk producing malicious content, such as NSFW
images. To mitigate risk, concept erasure methods are studied to facilitate the
model to unlearn specific concepts. However, current studies struggle to fully
erase mali... | 2025-03-10T14:37:53Z | null | null | null | TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models | ['Ruidong Chen', 'Honglin Guo', 'Lanjun Wang', 'Chenyu Zhang', 'Wei-zhi Nie', 'Anan Liu'] | 2,025 | arXiv.org | 2 | 46 | ['Computer Science'] |
2,503.07392 | SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion
Models | ['Ouxiang Li', 'Yuan Wang', 'Xinting Hu', 'Houcheng Jiang', 'Tao Liang', 'Yanbin Hao', 'Guojun Ma', 'Fuli Feng'] | ['cs.CV'] | Erasing concepts from large-scale text-to-image (T2I) diffusion models has
become increasingly crucial due to the growing concerns over copyright
infringement, offensive content, and privacy violations. However, existing
methods either require costly fine-tuning or degrade image quality for
non-target concepts (i.e., p... | 2025-03-10T14:40:01Z | null | null | null | SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models | ['Ouxiang Li', 'Yuan Wang', 'Xinting Hu', 'Houcheng Jiang', 'Tao Liang', 'Yanbin Hao', 'Guojun Ma', 'Fuli Feng'] | 2,025 | arXiv.org | 2 | 63 | ['Computer Science'] |
2,503.07465 | YOLOE: Real-Time Seeing Anything | ['Ao Wang', 'Lihao Liu', 'Hui Chen', 'Zijia Lin', 'Jungong Han', 'Guiguang Ding'] | ['cs.CV'] | Object detection and segmentation are widely employed in computer vision
applications, yet conventional models like YOLO series, while efficient and
accurate, are limited by predefined categories, hindering adaptability in open
scenarios. Recent open-set methods leverage text prompts, visual cues, or
prompt-free paradi... | 2025-03-10T15:42:59Z | 15 pages, 9 figures; | null | null | YOLOE: Real-Time Seeing Anything | ['Ao Wang', 'Lihao Liu', 'Hui Chen', 'Zijia Lin', 'Jungong Han', 'Guiguang Ding'] | 2,025 | arXiv.org | 6 | 76 | ['Computer Science'] |
2,503.07518 | TokenButler: Token Importance is Predictable | ['Yash Akhauri', 'Ahmed F AbouElhamayed', 'Yifei Gao', 'Chi-Chih Chang', 'Nilesh Jain', 'Mohamed S. Abdelfattah'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) rely on the Key-Value (KV) Cache to store token
history, enabling efficient decoding of tokens. As the KV-Cache grows, it
becomes a major memory and computation bottleneck, however, there is an
opportunity to alleviate this bottleneck, especially because prior research has
shown that only a... | 2025-03-10T16:41:14Z | null | null | null | null | null | null | null | null | null | null |
2,503.07519 | GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval | ['Justus-Jonas Erker', 'Nils Reimers', 'Iryna Gurevych'] | ['cs.IR', 'cs.CL'] | Decomposition-based multi-hop retrieval methods rely on many autoregressive
steps to break down complex queries, which breaks end-to-end differentiability
and is computationally expensive. Decomposition-free methods tackle this, but
current decomposition-free approaches struggle with longer multi-hop problems
and gener... | 2025-03-10T16:42:48Z | Under Review at ACL Rolling Review (ARR) | null | null | null | null | null | null | null | null | null |
2,503.07535 | LBM: Latent Bridge Matching for Fast Image-to-Image Translation | ['Clément Chadebec', 'Onur Tasar', 'Sanjeev Sreetharan', 'Benjamin Aubin'] | ['cs.CV'] | In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile
and scalable method that relies on Bridge Matching in a latent space to achieve
fast image-to-image translation. We show that the method can reach
state-of-the-art results for various image-to-image tasks using only a single
inference step. In a... | 2025-03-10T17:03:07Z | null | null | null | LBM: Latent Bridge Matching for Fast Image-to-Image Translation | ['Clément Chadebec', 'O. Tasar', 'Sanjeev Sreetharan', 'Benjamin Aubin'] | 2,025 | arXiv.org | 0 | 121 | ['Computer Science'] |
2,503.07536 | LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
Two-Stage Rule-Based RL | ['Yingzhe Peng', 'Gongrui Zhang', 'Miaosen Zhang', 'Zhiyuan You', 'Jie Liu', 'Qipeng Zhu', 'Kai Yang', 'Xingzhong Xu', 'Xin Geng', 'Xu Yang'] | ['cs.CL', 'cs.AI'] | Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges
from the complex interplay between visual perception and logical reasoning,
particularly in compact 3B-parameter architectures where architectural
constraints limit reasoning capacity and modality alignment.
While rule-based reinforcement l... | 2025-03-10T17:04:14Z | null | null | null | null | null | null | null | null | null | null |
2,503.07565 | Inductive Moment Matching | ['Linqi Zhou', 'Stefano Ermon', 'Jiaming Song'] | ['cs.LG', 'cs.AI', 'stat.ML'] | Diffusion models and Flow Matching generate high-quality samples but are slow
at inference, and distilling them into few-step models often leads to
instability and extensive tuning. To resolve these trade-offs, we propose
Inductive Moment Matching (IMM), a new class of generative models for one- or
few-step sampling wi... | 2025-03-10T17:37:39Z | null | null | null | Inductive Moment Matching | ['Linqi Zhou', 'Stefano Ermon', 'Jiaming Song'] | 2,025 | arXiv.org | 10 | 69 | ['Computer Science', 'Mathematics'] |
2,503.07572 | Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning | ['Yuxiao Qu', 'Matthew Y. R. Yang', 'Amrith Setlur', 'Lewis Tunstall', 'Edward Emanuel Beeching', 'Ruslan Salakhutdinov', 'Aviral Kumar'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Training models to effectively use test-time compute is crucial for improving
the reasoning performance of LLMs. Current methods mostly do so via fine-tuning
on search traces or running RL with 0/1 outcome reward, but do these approaches
efficiently utilize test-time compute? Would these approaches continue to scale
as... | 2025-03-10T17:40:43Z | null | null | null | Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning | ['Yuxiao Qu', 'Matthew Y. R. Yang', 'Amrith Rajagopal Setlur', 'Lewis Tunstall', 'Edward Beeching', 'Ruslan Salakhutdinov', 'Aviral Kumar'] | 2,025 | arXiv.org | 49 | 52 | ['Computer Science'] |
2,503.07598 | VACE: All-in-One Video Creation and Editing | ['Zeyinzi Jiang', 'Zhen Han', 'Chaojie Mao', 'Jingfeng Zhang', 'Yulin Pan', 'Yu Liu'] | ['cs.CV'] | Diffusion Transformer has demonstrated powerful capability and scalability in
generating high-quality images and videos. Further pursuing the unification of
generation and editing tasks has yielded significant progress in the domain of
image content creation. However, due to the intrinsic demands for consistency
across... | 2025-03-10T17:57:04Z | Project page: https://ali-vilab.github.io/VACE-Page/ | null | null | VACE: All-in-One Video Creation and Editing | ['Zeyinzi Jiang', 'Zhen Han', 'Chaojie Mao', 'Jingfeng Zhang', 'Yulin Pan', 'Yu Liu'] | 2,025 | arXiv.org | 23 | 74 | ['Computer Science'] |
2,503.07821 | Elderly Activity Recognition in the Wild: Results from the EAR Challenge | ['Anh-Kiet Duong'] | ['cs.CV'] | This paper presents our solution for the Elderly Action Recognition (EAR)
Challenge, part of the Computer Vision for Smalls Workshop at WACV 2025. The
competition focuses on recognizing Activities of Daily Living (ADLs) performed
by the elderly, covering six action categories with a diverse dataset. Our
approach builds... | 2025-03-10T20:07:05Z | 2 pages, EAR-CV4Smalls@WACV2025 | null | null | null | null | null | null | null | null | null |
2,503.08048 | LongProLIP: A Probabilistic Vision-Language Model with Long Context Text | ['Sanghyuk Chun', 'Sangdoo Yun'] | ['cs.CV', 'cs.LG'] | Recently, Probabilistic Language-Image Pre-Training (ProLIP) has been
proposed to tackle the multiplicity issue of vision-language (VL) tasks.
Despite their success in probabilistic representation learning at a scale, the
ProLIP models cannot handle long context texts longer than 64 context length,
which limits their a... | 2025-03-11T05:04:43Z | Accepted as a tiny paper at the 1st workshop of "Quantify Uncertainty
and Hallucination in Foundation Models: The Next Frontier in Reliable AI" at
ICLR 2025; code: https://github.com/naver-ai/prolip; models:
https://huggingface.co/collections/SanghyukChun/prolip-6712595dfc87fd8597350291 | null | null | LongProLIP: A Probabilistic Vision-Language Model with Long Context Text | ['Sanghyuk Chun', 'Sangdoo Yun'] | 2,025 | arXiv.org | 2 | 46 | ['Computer Science'] |
2,503.08067 | Context-aware Biases for Length Extrapolation | ['Ali Veisi', 'Hamidreza Amirzadeh', 'Amir Mansourian'] | ['cs.CL'] | Transformers often struggle to generalize to longer sequences than those seen
during training, a limitation known as length extrapolation. Most existing
Relative Positional Encoding (RPE) methods attempt to address this by
introducing either fixed linear biases or globally learned biases, which lack
the capacity to ada... | 2025-03-11T05:54:58Z | 13 pages, 6 figures, 4 table | null | null | null | null | null | null | null | null | null |
2,503.08153 | WISA: World Simulator Assistant for Physics-Aware Text-to-Video
Generation | ['Jing Wang', 'Ao Ma', 'Ke Cao', 'Jun Zheng', 'Zhanjie Zhang', 'Jiasong Feng', 'Shanyuan Liu', 'Yuhang Ma', 'Bo Cheng', 'Dawei Leng', 'Yuhui Yin', 'Xiaodan Liang'] | ['cs.CV'] | Recent rapid advancements in text-to-video (T2V) generation, such as SoRA and
Kling, have shown great potential for building world simulators. However,
current T2V models struggle to grasp abstract physical principles and generate
videos that adhere to physical laws. This challenge arises primarily from a
lack of clear... | 2025-03-11T08:10:03Z | null | null | null | null | null | null | null | null | null | null |
2,503.08161 | OASIS: Order-Augmented Strategy for Improved Code Search | ['Zuchen Gao', 'Zizheng Zhan', 'Xianming Li', 'Erxin Yu', 'Haotian Zhang', 'Bin Chen', 'Yuqun Zhang', 'Jing Li'] | ['cs.CL', 'cs.IR'] | Code embeddings capture the semantic representations of code and are crucial
for various code-related large language model (LLM) applications, such as code
search. Previous training primarily relies on optimizing the InfoNCE loss by
comparing positive natural language (NL)-code pairs with in-batch negatives.
However, d... | 2025-03-11T08:26:37Z | null | null | null | null | null | null | null | null | null | null |
2,503.08188 | RigoChat 2: an adapted language model to Spanish using a bounded dataset
and reduced hardware | ['Gonzalo Santamaría Gómez', 'Guillem García Subies', 'Pablo Gutiérrez Ruiz', 'Mario González Valero', 'Natàlia Fuertes', 'Helena Montoro Zamorano', 'Carmen Muñoz Sanz', 'Leire Rosado Plaza', 'Nuria Aldama García', 'David Betancur Sánchez', 'Kateryna Sushkova', 'Marta Guerrero Nieto', 'Álvaro Barbero Jiménez'] | ['cs.CL', 'cs.AI'] | Large Language Models (LLMs) have become a key element of modern artificial
intelligence, demonstrating the ability to address a wide range of language
processing tasks at unprecedented levels of accuracy without the need of
collecting problem-specific data. However, these versatile models face a
significant challenge:... | 2025-03-11T08:53:53Z | null | null | null | RigoChat 2: an adapted language model to Spanish using a bounded dataset and reduced hardware | ["Gonzalo Santamar'ia G'omez", 'Guillem García Subies', "Pablo Guti'errez Ruiz", "Mario Gonz'alez Valero", 'Natalia Fuertes', 'Helena Montoro Zamorano', 'Carmen Munoz Sanz', 'Leire Rosado Plaza', 'Nuria Aldama-García', 'David Betancur Sánchez', 'Kateryna Sushkova', 'Marta Guerrero Nieto', 'Álvaro Barbero Jiménez'] | 2,025 | arXiv.org | 0 | 49 | ['Computer Science'] |
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