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2,505.20161 | Prismatic Synthesis: Gradient-based Data Diversification Boosts
Generalization in LLM Reasoning | ['Jaehun Jung', 'Seungju Han', 'Ximing Lu', 'Skyler Hallinan', 'David Acuna', 'Shrimai Prabhumoye', 'Mostafa Patwary', 'Mohammad Shoeybi', 'Bryan Catanzaro', 'Yejin Choi'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Effective generalization in language models depends critically on the
diversity of their training data. Yet existing diversity metrics often fall
short of this goal, relying on surface-level heuristics that are decoupled from
model behavior. This motivates us to ask: What kind of diversity in training
data actually dri... | 2025-05-26T16:05:10Z | null | null | null | Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning | ['Jaehun Jung', 'Seungju Han', 'Ximing Lu', 'Skyler Hallinan', 'David Acuna', 'Shrimai Prabhumoye', 'Mostafa Patwary', 'M. Shoeybi', 'Bryan Catanzaro', 'Yejin Choi'] | 2,025 | arXiv.org | 1 | 54 | ['Computer Science'] |
2,505.20192 | FunReason: Enhancing Large Language Models' Function Calling via
Self-Refinement Multiscale Loss and Automated Data Refinement | ['Bingguang Hao', 'Maolin Wang', 'Zengzhuang Xu', 'Cunyin Peng', 'Yicheng Chen', 'Xiangyu Zhao', 'Jinjie Gu', 'Chenyi Zhuang'] | ['cs.LG', 'cs.IR'] | The integration of large language models (LLMs) with function calling has
emerged as a crucial capability for enhancing their practical utility in
real-world applications. However, effectively combining reasoning processes
with accurate function execution remains a significant challenge. Traditional
training approaches... | 2025-05-26T16:38:06Z | null | null | null | FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement | ['Bingguang Hao', 'Maolin Wang', 'Zengzhuang Xu', 'Cunyin Peng', 'Yicheng Chen', 'Xiangyu Zhao', 'Jinjie Gu', 'Chenyi Zhuang'] | 2,025 | arXiv.org | 0 | 44 | ['Computer Science'] |
2,505.20225 | FLAME-MoE: A Transparent End-to-End Research Platform for
Mixture-of-Experts Language Models | ['Hao Kang', 'Zichun Yu', 'Chenyan Xiong'] | ['cs.CL', 'cs.LG'] | Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4
increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong
efficiency-performance trade-offs by activating only a fraction of the model
per token. Yet academic researchers still lack a fully open, end-to-end MoE
platform for in... | 2025-05-26T17:06:25Z | All code, training logs, and model checkpoints are available at
https://github.com/cmu-flame/FLAME-MoE | null | null | FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models | ['Hao Kang', 'Zichun Yu', 'Chenyan Xiong'] | 2,025 | arXiv.org | 0 | 39 | ['Computer Science'] |
2,505.20255 | AniCrafter: Customizing Realistic Human-Centric Animation via
Avatar-Background Conditioning in Video Diffusion Models | ['Muyao Niu', 'Mingdeng Cao', 'Yifan Zhan', 'Qingtian Zhu', 'Mingze Ma', 'Jiancheng Zhao', 'Yanhong Zeng', 'Zhihang Zhong', 'Xiao Sun', 'Yinqiang Zheng'] | ['cs.CV'] | Recent advances in video diffusion models have significantly improved
character animation techniques. However, current approaches rely on basic
structural conditions such as DWPose or SMPL-X to animate character images,
limiting their effectiveness in open-domain scenarios with dynamic backgrounds
or challenging human ... | 2025-05-26T17:32:10Z | Homepage: https://myniuuu.github.io/AniCrafter ; Codes:
https://github.com/MyNiuuu/AniCrafter | null | null | null | null | null | null | null | null | null |
2,505.20256 | Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System
Collaboration | ['Hao Zhong', 'Muzhi Zhu', 'Zongze Du', 'Zheng Huang', 'Canyu Zhao', 'Mingyu Liu', 'Wen Wang', 'Hao Chen', 'Chunhua Shen'] | ['cs.CV'] | Long-horizon video-audio reasoning and fine-grained pixel understanding
impose conflicting requirements on omnimodal models: dense temporal coverage
demands many low-resolution frames, whereas precise grounding calls for
high-resolution inputs. We tackle this trade-off with a two-system
architecture: a Global Reasoning... | 2025-05-26T17:34:06Z | Project page: https://aim-uofa.github.io/OmniR1 | null | null | null | null | null | null | null | null | null |
2,505.20282 | One-shot Entropy Minimization | ['Zitian Gao', 'Lynx Chen', 'Joey Zhou', 'Bryan Dai'] | ['cs.CL'] | We trained 13,440 large language models and found that entropy minimization
requires only a single unlabeled data and 10 steps optimization to achieve
performance improvements comparable to or even greater than those obtained
using thousands of data and carefully designed rewards in rule-based
reinforcement learning. T... | 2025-05-26T17:58:30Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,505.20287 | MotionPro: A Precise Motion Controller for Image-to-Video Generation | ['Zhongwei Zhang', 'Fuchen Long', 'Zhaofan Qiu', 'Yingwei Pan', 'Wu Liu', 'Ting Yao', 'Tao Mei'] | ['cs.CV', 'cs.MM'] | Animating images with interactive motion control has garnered popularity for
image-to-video (I2V) generation. Modern approaches typically rely on large
Gaussian kernels to extend motion trajectories as condition without explicitly
defining movement region, leading to coarse motion control and failing to
disentangle obj... | 2025-05-26T17:59:03Z | CVPR 2025. Project page: https://zhw-zhang.github.io/MotionPro-page/ | null | null | null | null | null | null | null | null | null |
2,505.20292 | OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for
Subject-to-Video Generation | ['Shenghai Yuan', 'Xianyi He', 'Yufan Deng', 'Yang Ye', 'Jinfa Huang', 'Bin Lin', 'Jiebo Luo', 'Li Yuan'] | ['cs.CV', 'cs.AI'] | Subject-to-Video (S2V) generation aims to create videos that faithfully
incorporate reference content, providing enhanced flexibility in the production
of videos. To establish the infrastructure for S2V generation, we propose
OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and
(ii) OpenS2V-5M, ... | 2025-05-26T17:59:46Z | Code and Dataset: https://github.com/PKU-YuanGroup/OpenS2V-Nexus | null | null | null | null | null | null | null | null | null |
2,505.20298 | MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
Manga Understanding | ['Jeonghun Baek', 'Kazuki Egashira', 'Shota Onohara', 'Atsuyuki Miyai', 'Yuki Imajuku', 'Hikaru Ikuta', 'Kiyoharu Aizawa'] | ['cs.CL', 'cs.AI', 'cs.CV'] | Manga, or Japanese comics, is a richly multimodal narrative form that blends
images and text in complex ways. Teaching large multimodal models (LMMs) to
understand such narratives at a human-like level could help manga creators
reflect on and refine their stories. To this end, we introduce two benchmarks
for multimodal... | 2025-05-26T17:59:59Z | 20 pages, 11 figures | null | null | null | null | null | null | null | null | null |
2,505.20302 | VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning
and Formal Verification | ['Patrick Yubeaton', 'Andre Nakkab', 'Weihua Xiao', 'Luca Collini', 'Ramesh Karri', 'Chinmay Hegde', 'Siddharth Garg'] | ['cs.PL', 'cs.AI', 'cs.LO'] | This paper introduces VeriThoughts, a novel dataset designed for
reasoning-based Verilog code generation. We establish a new benchmark framework
grounded in formal verification methods to evaluate the quality and correctness
of generated hardware descriptions. Additionally, we present a suite of
specialized small-scale... | 2025-05-16T21:33:14Z | null | null | null | null | null | null | null | null | null | null |
2,505.20315 | Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL | ['Zhewei Yao', 'Guoheng Sun', 'Lukasz Borchmann', 'Zheyu Shen', 'Minghang Deng', 'Bohan Zhai', 'Hao Zhang', 'Ang Li', 'Yuxiong He'] | ['cs.CL', 'cs.AI'] | Translating natural language into SQL (Test2SQL) is a longstanding challenge
at the intersection of natural language understanding and structured data
access. While large language models (LLMs) have significantly improved fluency
in SQL generation, producing correct and executable SQL--particularly for
complex queries-... | 2025-05-22T23:33:47Z | 22 pages, 2 figures | null | null | null | null | null | null | null | null | null |
2,505.20325 | Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic
Confidence | ['Amirhosein Ghasemabadi', 'Keith G. Mills', 'Baochun Li', 'Di Niu'] | ['cs.CL', 'cs.AI'] | Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM)
reasoning often incur substantial computational costs, primarily due to
extensive reliance on external Process Reward Models (PRMs) or sampling methods
like Best-of-N (BoN). This paper introduces Guided by Gut (GG), an efficient
self-guided TTS fr... | 2025-05-23T18:19:09Z | null | null | null | null | null | null | null | null | null | null |
2,505.20715 | MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware
Multi-Segment Grounding | ['Fuwen Luo', 'Shengfeng Lou', 'Chi Chen', 'Ziyue Wang', 'Chenliang Li', 'Weizhou Shen', 'Jiyue Guo', 'Peng Li', 'Ming Yan', 'Ji Zhang', 'Fei Huang', 'Yang Liu'] | ['cs.CV', 'cs.CL'] | Video temporal understanding is crucial for multimodal large language models
(MLLMs) to reason over events in videos. Despite recent advances in general
video understanding, current MLLMs still struggle with fine-grained temporal
reasoning. While reinforcement learning (RL) has been explored to address this
issue recen... | 2025-05-27T04:50:07Z | null | null | null | null | null | null | null | null | null | null |
2,505.20767 | CogniBench: A Legal-inspired Framework and Dataset for Assessing
Cognitive Faithfulness of Large Language Models | ['Xiaqiang Tang', 'Jian Li', 'Keyu Hu', 'Du Nan', 'Xiaolong Li', 'Xi Zhang', 'Weigao Sun', 'Sihong Xie'] | ['cs.CL', 'cs.AI'] | Faithfulness hallucinations are claims generated by a Large Language Model
(LLM) not supported by contexts provided to the LLM. Lacking assessment
standards, existing benchmarks focus on "factual statements" that rephrase
source materials while overlooking "cognitive statements" that involve making
inferences from the ... | 2025-05-27T06:16:27Z | ACL 2025 | null | null | CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models | ['Xiaqiang Tang', 'Jian Li', 'Ke-Bang Hu', 'Du Nan', 'Xiaolong Li', 'Xi Zhang', 'Weigao Sun', 'Sihong Xie'] | 2,025 | arXiv.org | 0 | 40 | ['Computer Science'] |
2,505.20779 | CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature | ['Noy Sternlicht', 'Tom Hope'] | ['cs.CL'] | A hallmark of human innovation is the process of recombination -- creating
original ideas by integrating elements of existing mechanisms and concepts. In
this work, we automatically mine the scientific literature and build CHIMERA: a
large-scale knowledge base (KB) of recombination examples. CHIMERA can be used
to empi... | 2025-05-27T06:36:04Z | Project page: https://noy-sternlicht.github.io/CHIMERA-Web | null | null | null | null | null | null | null | null | null |
2,505.20793 | Rendering-Aware Reinforcement Learning for Vector Graphics Generation | ['Juan A. Rodriguez', 'Haotian Zhang', 'Abhay Puri', 'Aarash Feizi', 'Rishav Pramanik', 'Pascal Wichmann', 'Arnab Mondal', 'Mohammad Reza Samsami', 'Rabiul Awal', 'Perouz Taslakian', 'Spandana Gella', 'Sai Rajeswar', 'David Vazquez', 'Christopher Pal', 'Marco Pedersoli'] | ['cs.CV', 'cs.AI'] | Scalable Vector Graphics (SVG) offer a powerful format for representing
visual designs as interpretable code. Recent advances in vision-language models
(VLMs) have enabled high-quality SVG generation by framing the problem as a
code generation task and leveraging large-scale pretraining. VLMs are
particularly suitable ... | 2025-05-27T06:56:00Z | null | null | null | null | null | null | null | null | null | null |
2,505.20979 | MelodySim: Measuring Melody-aware Music Similarity for Plagiarism
Detection | ['Tongyu Lu', 'Charlotta-Marlena Geist', 'Jan Melechovsky', 'Abhinaba Roy', 'Dorien Herremans'] | ['cs.SD', 'cs.AI', 'eess.AS'] | We propose MelodySim, a melody-aware music similarity model and dataset for
plagiarism detection. First, we introduce a novel method to construct a dataset
with focus on melodic similarity. By augmenting Slakh2100; an existing MIDI
dataset, we generate variations of each piece while preserving the melody
through modifi... | 2025-05-27T10:14:03Z | null | null | null | null | null | null | null | null | null | null |
2,505.20993 | Who Reasons in the Large Language Models? | ['Jie Shao', 'Jianxin Wu'] | ['cs.CL', 'cs.AI'] | Despite the impressive performance of large language models (LLMs), the
process of endowing them with new capabilities--such as mathematical
reasoning--remains largely empirical and opaque. A critical open question is
whether reasoning abilities stem from the entire model, specific modules, or
are merely artifacts of o... | 2025-05-27T10:26:47Z | null | null | null | Who Reasons in the Large Language Models? | ['Jie Shao', 'Jianxin Wu'] | 2,025 | arXiv.org | 0 | 51 | ['Computer Science'] |
2,505.2102 | NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation | ['Yuan Gao', 'Ruiqi Shu', 'Hao Wu', 'Fan Xu', 'Yanfei Xiang', 'Ruijian Gou', 'Qingsong Wen', 'Xian Wu', 'Xiaomeng Huang'] | ['cs.LG', 'physics.ao-ph'] | Accurate Subseasonal-to-Seasonal (S2S) ocean simulation is critically
important for marine research, yet remains challenging due to its substantial
thermal inertia and extended time delay. Machine learning (ML)-based models
have demonstrated significant advancements in simulation accuracy and
computational efficiency c... | 2025-05-27T10:54:40Z | null | null | null | null | null | null | null | null | null | null |
2,505.21062 | Inverse Virtual Try-On: Generating Multi-Category Product-Style Images
from Clothed Individuals | ['Davide Lobba', 'Fulvio Sanguigni', 'Bin Ren', 'Marcella Cornia', 'Rita Cucchiara', 'Nicu Sebe'] | ['cs.CV'] | While virtual try-on (VTON) systems aim to render a garment onto a target
person image, this paper tackles the novel task of virtual try-off (VTOFF),
which addresses the inverse problem: generating standardized product images of
garments from real-world photos of clothed individuals. Unlike VTON, which must
resolve div... | 2025-05-27T11:47:51Z | null | null | null | Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals | ['Davide Lobba', 'Fulvio Sanguigni', 'Bin Ren', 'Marcella Cornia', 'Rita Cucchiara', 'N. Sebe'] | 2,025 | arXiv.org | 0 | 59 | ['Computer Science'] |
2,505.21115 | Will It Still Be True Tomorrow? Multilingual Evergreen Question
Classification to Improve Trustworthy QA | ['Sergey Pletenev', 'Maria Marina', 'Nikolay Ivanov', 'Daria Galimzianova', 'Nikita Krayko', 'Mikhail Salnikov', 'Vasily Konovalov', 'Alexander Panchenko', 'Viktor Moskvoretskii'] | ['cs.CL'] | Large Language Models (LLMs) often hallucinate in question answering (QA)
tasks. A key yet underexplored factor contributing to this is the temporality
of questions -- whether they are evergreen (answers remain stable over time) or
mutable (answers change). In this work, we introduce EverGreenQA, the first
multilingual... | 2025-05-27T12:35:13Z | null | null | null | null | null | null | null | null | null | null |
2,505.21136 | SageAttention2++: A More Efficient Implementation of SageAttention2 | ['Jintao Zhang', 'Xiaoming Xu', 'Jia Wei', 'Haofeng Huang', 'Pengle Zhang', 'Chendong Xiang', 'Jun Zhu', 'Jianfei Chen'] | ['cs.LG', 'cs.AI', 'cs.AR', 'cs.CV'] | The efficiency of attention is critical because its time complexity grows
quadratically with sequence length. SageAttention2 addresses this by utilizing
quantization to accelerate matrix multiplications (Matmul) in attention. To
further accelerate SageAttention2, we propose to utilize the faster instruction
of FP8 Matm... | 2025-05-27T12:50:36Z | null | null | null | null | null | null | null | null | null | null |
2,505.21172 | TAT-R1: Terminology-Aware Translation with Reinforcement Learning and
Word Alignment | ['Zheng Li', 'Mao Zheng', 'Mingyang Song', 'Wenjie Yang'] | ['cs.CL'] | Recently, deep reasoning large language models(LLMs) like DeepSeek-R1 have
made significant progress in tasks such as mathematics and coding. Inspired by
this, several studies have employed reinforcement learning(RL) to enhance
models' deep reasoning capabilities and improve machine translation(MT)
quality. However, th... | 2025-05-27T13:26:02Z | null | null | null | null | null | null | null | null | null | null |
2,505.21178 | Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning | ['Mingyang Song', 'Mao Zheng'] | ['cs.CL'] | As test-time scaling becomes a pivotal research frontier in Large Language
Models (LLMs) development, contemporary and advanced post-training
methodologies increasingly focus on extending the generation length of long
Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward
DeepSeek R1-like performance... | 2025-05-27T13:29:51Z | Ongoing Work | null | null | null | null | null | null | null | null | null |
2,505.21325 | MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving
Video Virtual Try-on | ['Guangyuan Li', 'Siming Zheng', 'Hao Zhang', 'Jinwei Chen', 'Junsheng Luan', 'Binkai Ou', 'Lei Zhao', 'Bo Li', 'Peng-Tao Jiang'] | ['cs.CV'] | Video Virtual Try-On (VVT) aims to simulate the natural appearance of
garments across consecutive video frames, capturing their dynamic variations
and interactions with human body motion. However, current VVT methods still
face challenges in terms of spatiotemporal consistency and garment content
preservation. First, t... | 2025-05-27T15:22:02Z | null | null | null | MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving Video Virtual Try-on | ['Guangyuan Li', 'Siming Zheng', 'Hao Zhang', 'Jinwei Chen', 'Junsheng Luan', 'Binkai Ou', 'Lei Zhao', 'Bo Li', 'Peng-Tao Jiang'] | 2,025 | arXiv.org | 0 | 58 | ['Computer Science'] |
2,505.21411 | Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity | ['Yehui Tang', 'Xiaosong Li', 'Fangcheng Liu', 'Wei Guo', 'Hang Zhou', 'Yaoyuan Wang', 'Kai Han', 'Xianzhi Yu', 'Jinpeng Li', 'Hui Zang', 'Fei Mi', 'Xiaojun Meng', 'Zhicheng Liu', 'Hanting Chen', 'Binfan Zheng', 'Can Chen', 'Youliang Yan', 'Ruiming Tang', 'Peifeng Qin', 'Xinghao Chen', 'Dacheng Tao', 'Yunhe Wang'] | ['cs.CL'] | The surgence of Mixture of Experts (MoE) in Large Language Models promises a
small price of execution cost for a much larger model parameter count and
learning capacity, because only a small fraction of parameters are activated
for each input token. However, it is commonly observed that some experts are
activated far m... | 2025-05-27T16:40:21Z | null | null | null | Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity | ['Yehui Tang', 'Xiaosong Li', 'Fangcheng Liu', 'Wei Guo', 'Hang Zhou', 'Yaoyuan Wang', 'Kai Han', 'Xian Yu', 'Jinpeng Li', 'Hui Zang', 'Fei Mi', 'Xiaojun Meng', 'Zhicheng Liu', 'Hanting Chen', 'Binfan Zheng', 'Can Chen', 'Youliang Yan', 'Ruiming Tang', 'Peifeng Qin', 'Xinghao Chen', 'Dacheng Tao', 'Yunhe Wang'] | 2,025 | arXiv.org | 0 | 49 | ['Computer Science'] |
2,505.21432 | Hume: Introducing System-2 Thinking in Visual-Language-Action Model | ['Haoming Song', 'Delin Qu', 'Yuanqi Yao', 'Qizhi Chen', 'Qi Lv', 'Yiwen Tang', 'Modi Shi', 'Guanghui Ren', 'Maoqing Yao', 'Bin Zhao', 'Dong Wang', 'Xuelong Li'] | ['cs.RO', 'cs.AI'] | Humans practice slow thinking before performing actual actions when handling
complex tasks in the physical world. This thinking paradigm, recently, has
achieved remarkable advancement in boosting Large Language Models (LLMs) to
solve complex tasks in digital domains. However, the potential of slow thinking
remains larg... | 2025-05-27T17:04:21Z | null | null | null | null | null | null | null | null | null | null |
2,505.21496 | UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based
Mobile GUI Agents | ['Han Xiao', 'Guozhi Wang', 'Yuxiang Chai', 'Zimu Lu', 'Weifeng Lin', 'Hao He', 'Lue Fan', 'Liuyang Bian', 'Rui Hu', 'Liang Liu', 'Shuai Ren', 'Yafei Wen', 'Xiaoxin Chen', 'Aojun Zhou', 'Hongsheng Li'] | ['cs.CL', 'cs.CV', 'cs.LG'] | In this paper, we introduce UI-Genie, a self-improving framework addressing
two key challenges in GUI agents: verification of trajectory outcome is
challenging and high-quality training data are not scalable. These challenges
are addressed by a reward model and a self-improving pipeline, respectively.
The reward model,... | 2025-05-27T17:58:06Z | https://github.com/Euphoria16/UI-Genie | null | null | null | null | null | null | null | null | null |
2,505.216 | R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large
Model Token Routing | ['Tianyu Fu', 'Yi Ge', 'Yichen You', 'Enshu Liu', 'Zhihang Yuan', 'Guohao Dai', 'Shengen Yan', 'Huazhong Yang', 'Yu Wang'] | ['cs.CL', 'cs.AI', 'cs.LG', 'cs.PF', 'I.2.7'] | Large Language Models (LLMs) achieve impressive reasoning capabilities at the
cost of substantial inference overhead, posing substantial deployment
challenges. Although distilled Small Language Models (SLMs) significantly
enhance efficiency, their performance suffers as they fail to follow LLMs'
reasoning paths. Luckil... | 2025-05-27T16:57:20Z | null | null | null | null | null | null | null | null | null | null |
2,505.21668 | R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised
and Reinforcement Learning | ['Yongchao Chen', 'Yueying Liu', 'Junwei Zhou', 'Yilun Hao', 'Jingquan Wang', 'Yang Zhang', 'Chuchu Fan'] | ['cs.AI', 'cs.CL', 'cs.SC'] | Despite advances in reasoning and planning of R1-like models, Large Language
Models (LLMs) still struggle with tasks requiring precise computation, symbolic
manipulation, optimization, and algorithmic reasoning, in which textual
reasoning lacks the rigor of code execution. A key challenge is enabling LLMs
to decide whe... | 2025-05-27T18:47:33Z | 33 pages, 8 figures | null | null | R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning | ['Yongchao Chen', 'Yueying Liu', 'Junwei Zhou', 'Yilun Hao', 'Jingquan Wang', 'Yang Zhang', 'Chuchu Fan'] | 2,025 | arXiv.org | 0 | 49 | ['Computer Science'] |
2,505.21847 | RePaViT: Scalable Vision Transformer Acceleration via Structural
Reparameterization on Feedforward Network Layers | ['Xuwei Xu', 'Yang Li', 'Yudong Chen', 'Jiajun Liu', 'Sen Wang'] | ['cs.CV', 'cs.AI'] | We reveal that feedforward network (FFN) layers, rather than attention
layers, are the primary contributors to Vision Transformer (ViT) inference
latency, with their impact signifying as model size increases. This finding
highlights a critical opportunity for optimizing the efficiency of large-scale
ViTs by focusing on... | 2025-05-28T00:27:18Z | Accepted to ICML2025 | null | null | RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers | ['Xuwei Xu', 'Yang Li', 'Yudong Chen', 'Jiajun Liu', 'Sen Wang'] | 2,025 | arXiv.org | 0 | 73 | ['Computer Science'] |
2,505.21925 | RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with
Global Illumination | ['Chong Zeng', 'Yue Dong', 'Pieter Peers', 'Hongzhi Wu', 'Xin Tong'] | ['cs.GR', 'cs.CV', 'cs.LG'] | We present RenderFormer, a neural rendering pipeline that directly renders an
image from a triangle-based representation of a scene with full global
illumination effects and that does not require per-scene training or
fine-tuning. Instead of taking a physics-centric approach to rendering, we
formulate rendering as a se... | 2025-05-28T03:20:46Z | Accepted to SIGGRAPH 2025. Project page:
https://microsoft.github.io/renderformer | ACM SIGGRAPH 2025 Conference Papers | 10.1145/3721238.3730595 | null | null | null | null | null | null | null |
2,505.2196 | One-Way Ticket:Time-Independent Unified Encoder for Distilling
Text-to-Image Diffusion Models | ['Senmao Li', 'Lei Wang', 'Kai Wang', 'Tao Liu', 'Jiehang Xie', 'Joost van de Weijer', 'Fahad Shahbaz Khan', 'Shiqi Yang', 'Yaxing Wang', 'Jian Yang'] | ['cs.CV'] | Text-to-Image (T2I) diffusion models have made remarkable advancements in
generative modeling; however, they face a trade-off between inference speed and
image quality, posing challenges for efficient deployment. Existing distilled
T2I models can generate high-fidelity images with fewer sampling steps, but
often strugg... | 2025-05-28T04:23:22Z | Accepted at CVPR2025, Code: https://github.com/sen-mao/Loopfree | null | null | null | null | null | null | null | null | null |
2,505.22019 | VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich
Information Understanding via Iterative Reasoning with Reinforcement Learning | ['Qiuchen Wang', 'Ruixue Ding', 'Yu Zeng', 'Zehui Chen', 'Lin Chen', 'Shihang Wang', 'Pengjun Xie', 'Fei Huang', 'Feng Zhao'] | ['cs.CL', 'cs.AI', 'cs.CV'] | Effectively retrieving, reasoning and understanding visually rich information
remains a challenge for RAG methods. Traditional text-based methods cannot
handle visual-related information. On the other hand, current vision-based RAG
approaches are often limited by fixed pipelines and frequently struggle to
reason effect... | 2025-05-28T06:30:51Z | null | null | null | null | null | null | null | null | null | null |
2,505.22232 | Judging Quality Across Languages: A Multilingual Approach to Pretraining
Data Filtering with Language Models | ['Mehdi Ali', 'Manuel Brack', 'Max Lübbering', 'Elias Wendt', 'Abbas Goher Khan', 'Richard Rutmann', 'Alex Jude', 'Maurice Kraus', 'Alexander Arno Weber', 'David Kaczér', 'Florian Mai', 'Lucie Flek', 'Rafet Sifa', 'Nicolas Flores-Herr', 'Joachim Köhler', 'Patrick Schramowski', 'Michael Fromm', 'Kristian Kersting'] | ['cs.CL', 'cs.AI', 'cs.LG'] | High-quality multilingual training data is essential for effectively
pretraining large language models (LLMs). Yet, the availability of suitable
open-source multilingual datasets remains limited. Existing state-of-the-art
datasets mostly rely on heuristic filtering methods, restricting both their
cross-lingual transfer... | 2025-05-28T11:06:54Z | Project page available at https://huggingface.co/spaces/Jackal-AI/JQL | null | null | Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models | ['Mehdi Ali', 'Manuel Brack', 'Max Lubbering', 'Elias Wendt', 'Abbas Goher Khan', 'Richard Rutmann', 'Alex Jude', 'Maurice Kraus', 'Alexander Arno Weber', 'Felix Stollenwerk', "David Kacz'er", 'Florian Mai', 'Lucie Flek', 'R. Sifa', 'Nicolas Flores-Herr', 'Joachim Kohler', 'P. Schramowski', 'Michael Fromm', 'K. Kerstin... | 2,025 | arXiv.org | 0 | 0 | ['Computer Science'] |
2,505.22312 | Skywork Open Reasoner 1 Technical Report | ['Jujie He', 'Jiacai Liu', 'Chris Yuhao Liu', 'Rui Yan', 'Chaojie Wang', 'Peng Cheng', 'Xiaoyu Zhang', 'Fuxiang Zhang', 'Jiacheng Xu', 'Wei Shen', 'Siyuan Li', 'Liang Zeng', 'Tianwen Wei', 'Cheng Cheng', 'Bo An', 'Yang Liu', 'Yahui Zhou'] | ['cs.LG', 'cs.AI', 'cs.CL'] | The success of DeepSeek-R1 underscores the significant role of reinforcement
learning (RL) in enhancing the reasoning capabilities of large language models
(LLMs). In this work, we present Skywork-OR1, an effective and scalable RL
implementation for long Chain-of-Thought (CoT) models. Building on the
DeepSeek-R1-Distil... | 2025-05-28T12:56:04Z | null | null | null | Skywork Open Reasoner 1 Technical Report | ['Jujie He', 'Jiacai Liu', 'Chris Liu', 'Rui Yan', 'Chaojie Wang', 'Peng Cheng', 'Xiaoyu Zhang', 'Fuxiang Zhang', 'Jiacheng Xu', 'Wei Shen', 'Siyuan Li', 'Liang Zeng', 'Tianwen Wei', 'Cheng Cheng', 'Bo An', 'Yang Liu', 'Yahui Zhou'] | 2,025 | arXiv.org | 7 | 0 | ['Computer Science'] |
2,505.22334 | Advancing Multimodal Reasoning via Reinforcement Learning with Cold
Start | ['Lai Wei', 'Yuting Li', 'Kaipeng Zheng', 'Chen Wang', 'Yue Wang', 'Linghe Kong', 'Lichao Sun', 'Weiran Huang'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | Recent advancements in large language models (LLMs) have demonstrated
impressive chain-of-thought reasoning capabilities, with reinforcement learning
(RL) playing a crucial role in this progress. While "aha moment"
patterns--where models exhibit self-correction through reflection--are often
attributed to emergent prope... | 2025-05-28T13:21:38Z | null | null | null | null | null | null | null | null | null | null |
2,505.22425 | Scaling Reasoning without Attention | ['Xueliang Zhao', 'Wei Wu', 'Lingpeng Kong'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Large language models (LLMs) have made significant advances in complex
reasoning tasks, yet they remain bottlenecked by two core challenges:
architectural inefficiency due to reliance on Transformers, and a lack of
structured fine-tuning for high-difficulty domains. We introduce \ourmodel, an
attention-free language mo... | 2025-05-28T14:52:15Z | preprint | null | null | Scaling Reasoning without Attention | ['Xueliang Zhao', 'Wei Wu', 'Lingpeng Kong'] | 2,025 | arXiv.org | 0 | 41 | ['Computer Science'] |
2,505.22453 | Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO | ['Lai Wei', 'Yuting Li', 'Chen Wang', 'Yue Wang', 'Linghe Kong', 'Weiran Huang', 'Lichao Sun'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | Improving Multi-modal Large Language Models (MLLMs) in the post-training
stage typically relies on supervised fine-tuning (SFT) or reinforcement
learning (RL). However, these supervised methods require expensive and manually
annotated multi-modal data--an ultimately unsustainable resource. While recent
efforts have exp... | 2025-05-28T15:11:16Z | null | null | null | null | null | null | null | null | null | null |
2,505.22569 | ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion
Models | ['Dmitrii Sorokin', 'Maksim Nakhodnov', 'Andrey Kuznetsov', 'Aibek Alanov'] | ['cs.CV'] | Recent advances in diffusion models have led to impressive image generation
capabilities, but aligning these models with human preferences remains
challenging. Reward-based fine-tuning using models trained on human feedback
improves alignment but often harms diversity, producing less varied outputs. In
this work, we ad... | 2025-05-28T16:45:07Z | The source code can be found at
https://github.com/ControlGenAI/ImageReFL | null | null | null | null | null | null | null | null | null |
2,505.22636 | ObjectClear: Complete Object Removal via Object-Effect Attention | ['Jixin Zhao', 'Shangchen Zhou', 'Zhouxia Wang', 'Peiqing Yang', 'Chen Change Loy'] | ['cs.CV'] | Object removal requires eliminating not only the target object but also its
effects, such as shadows and reflections. However, diffusion-based inpainting
methods often produce artifacts, hallucinate content, alter background, and
struggle to remove object effects accurately. To address this challenge, we
introduce a ne... | 2025-05-28T17:51:17Z | Project page: https://zjx0101.github.io/projects/ObjectClear/ | null | null | null | null | null | null | null | null | null |
2,505.22647 | Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation | ['Zhe Kong', 'Feng Gao', 'Yong Zhang', 'Zhuoliang Kang', 'Xiaoming Wei', 'Xunliang Cai', 'Guanying Chen', 'Wenhan Luo'] | ['cs.CV'] | Audio-driven human animation methods, such as talking head and talking body
generation, have made remarkable progress in generating synchronized facial
movements and appealing visual quality videos. However, existing methods
primarily focus on single human animation and struggle with multi-stream audio
inputs, facing i... | 2025-05-28T17:57:06Z | Homepage: https://meigen-ai.github.io/multi-talk Github:
https://github.com/MeiGen-AI/MultiTalk | null | null | null | null | null | null | null | null | null |
2,505.22648 | WebDancer: Towards Autonomous Information Seeking Agency | ['Jialong Wu', 'Baixuan Li', 'Runnan Fang', 'Wenbiao Yin', 'Liwen Zhang', 'Zhengwei Tao', 'Dingchu Zhang', 'Zekun Xi', 'Gang Fu', 'Yong Jiang', 'Pengjun Xie', 'Fei Huang', 'Jingren Zhou'] | ['cs.CL'] | Addressing intricate real-world problems necessitates in-depth information
seeking and multi-step reasoning. Recent progress in agentic systems,
exemplified by Deep Research, underscores the potential for autonomous
multi-step research. In this work, we present a cohesive paradigm for building
end-to-end agentic inform... | 2025-05-28T17:57:07Z | null | null | null | null | null | null | null | null | null | null |
2,505.22651 | Sherlock: Self-Correcting Reasoning in Vision-Language Models | ['Yi Ding', 'Ruqi Zhang'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Reasoning Vision-Language Models (VLMs) have shown promising performance on
complex multimodal tasks. However, they still face significant challenges: they
are highly sensitive to reasoning errors, require large volumes of annotated
data or accurate verifiers, and struggle to generalize beyond specific domains.
To addr... | 2025-05-28T17:58:03Z | 27 pages | null | null | null | null | null | null | null | null | null |
2,505.22653 | The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in
Learning to Reason | ['Ang Lv', 'Ruobing Xie', 'Xingwu Sun', 'Zhanhui Kang', 'Rui Yan'] | ['cs.CL'] | Recent studies on post-training large language models (LLMs) for reasoning
through reinforcement learning (RL) typically focus on tasks that can be
accurately verified and rewarded, such as solving math problems. In contrast,
our research investigates the impact of reward noise, a more practical
consideration for real-... | 2025-05-28T17:59:03Z | Preprint | null | null | The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason | ['Ang Lv', 'Ruobing Xie', 'Xingwu Sun', 'Zhanhui Kang', 'Rui Yan'] | 2,025 | arXiv.org | 0 | 42 | ['Computer Science'] |
2,505.22662 | AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models | ['Feng Luo', 'Yu-Neng Chuang', 'Guanchu Wang', 'Hoang Anh Duy Le', 'Shaochen Zhong', 'Hongyi Liu', 'Jiayi Yuan', 'Yang Sui', 'Vladimir Braverman', 'Vipin Chaudhary', 'Xia Hu'] | ['cs.CL', 'cs.LG'] | The reasoning-capable large language models (LLMs) demonstrate strong
performance on complex reasoning tasks but often suffer from overthinking,
generating unnecessarily long chain-of-thought (CoT) reasoning paths for easy
reasoning questions, thereby increasing inference cost and latency. Recent
approaches attempt to ... | 2025-05-28T17:59:53Z | null | null | null | null | null | null | null | null | null | null |
2,505.22664 | Zero-Shot Vision Encoder Grafting via LLM Surrogates | ['Kaiyu Yue', 'Vasu Singla', 'Menglin Jia', 'John Kirchenbauer', 'Rifaa Qadri', 'Zikui Cai', 'Abhinav Bhatele', 'Furong Huang', 'Tom Goldstein'] | ['cs.CV'] | Vision language models (VLMs) typically pair a modestly sized vision encoder
with a large language model (LLM), e.g., Llama-70B, making the decoder the
primary computational burden during training. To reduce costs, a potential
promising strategy is to first train the vision encoder using a small language
model before t... | 2025-05-28T17:59:59Z | 15 pages | null | null | Zero-Shot Vision Encoder Grafting via LLM Surrogates | ['Kaiyu Yue', 'Vasu Singla', 'Menglin Jia', 'John Kirchenbauer', 'Rifaa Qadri', 'Zikui Cai', 'A. Bhatele', 'Furong Huang', 'Tom Goldstein'] | 2,025 | arXiv.org | 0 | 50 | ['Computer Science'] |
2,505.22705 | HiDream-I1: A High-Efficient Image Generative Foundation Model with
Sparse Diffusion Transformer | ['Qi Cai', 'Jingwen Chen', 'Yang Chen', 'Yehao Li', 'Fuchen Long', 'Yingwei Pan', 'Zhaofan Qiu', 'Yiheng Zhang', 'Fengbin Gao', 'Peihan Xu', 'Yimeng Wang', 'Kai Yu', 'Wenxuan Chen', 'Ziwei Feng', 'Zijian Gong', 'Jianzhuang Pan', 'Yi Peng', 'Rui Tian', 'Siyu Wang', 'Bo Zhao', 'Ting Yao', 'Tao Mei'] | ['cs.CV', 'cs.MM'] | Recent advancements in image generative foundation models have prioritized
quality improvements but often at the cost of increased computational
complexity and inference latency. To address this critical trade-off, we
introduce HiDream-I1, a new open-source image generative foundation model with
17B parameters that ach... | 2025-05-28T17:59:15Z | Source codes and models are available at
https://github.com/HiDream-ai/HiDream-I1 and
https://github.com/HiDream-ai/HiDream-E1 | null | null | null | null | null | null | null | null | null |
2,505.22759 | FAMA: The First Large-Scale Open-Science Speech Foundation Model for
English and Italian | ['Sara Papi', 'Marco Gaido', 'Luisa Bentivogli', 'Alessio Brutti', 'Mauro Cettolo', 'Roberto Gretter', 'Marco Matassoni', 'Mohamed Nabih', 'Matteo Negri'] | ['cs.CL', 'cs.AI', 'cs.SD'] | The development of speech foundation models (SFMs) like Whisper and
SeamlessM4T has significantly advanced the field of speech processing. However,
their closed nature--with inaccessible training data and code--poses major
reproducibility and fair evaluation challenges. While other domains have made
substantial progres... | 2025-05-28T18:19:34Z | null | null | null | FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian | ['Sara Papi', 'Marco Gaido', 'L. Bentivogli', 'A. Brutti', 'Mauro Cettolo', 'Roberto Gretter', 'M. Matassoni', 'Mohamed Nabih', 'Matteo Negri'] | 2,025 | arXiv.org | 0 | 42 | ['Computer Science'] |
2,505.22765 | StressTest: Can YOUR Speech LM Handle the Stress? | ['Iddo Yosha', 'Gallil Maimon', 'Yossi Adi'] | ['cs.CL', 'cs.SD', 'eess.AS'] | Sentence stress refers to emphasis, placed on specific words within a spoken
utterance to highlight or contrast an idea, or to introduce new information. It
is often used to imply an underlying intention that is not explicitly stated.
Recent advances in speech-aware language models (SLMs) have enabled direct
processing... | 2025-05-28T18:32:56Z | null | null | null | null | null | null | null | null | null | null |
2,505.22914 | cadrille: Multi-modal CAD Reconstruction with Online Reinforcement
Learning | ['Maksim Kolodiazhnyi', 'Denis Tarasov', 'Dmitrii Zhemchuzhnikov', 'Alexander Nikulin', 'Ilya Zisman', 'Anna Vorontsova', 'Anton Konushin', 'Vladislav Kurenkov', 'Danila Rukhovich'] | ['cs.CV', 'cs.LG'] | Computer-Aided Design (CAD) plays a central role in engineering and
manufacturing, making it possible to create precise and editable 3D models.
Using a variety of sensor or user-provided data as inputs for CAD
reconstruction can democratize access to design applications. However, existing
methods typically focus on a s... | 2025-05-28T22:32:31Z | null | null | null | null | null | null | null | null | null | null |
2,505.22943 | Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of
Pre-trained Multimodal Representation via Text Updates | ['Jaewoo Ahn', 'Heeseung Yun', 'Dayoon Ko', 'Gunhee Kim'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG', 'cs.SD'] | While pre-trained multimodal representations (e.g., CLIP) have shown
impressive capabilities, they exhibit significant compositional vulnerabilities
leading to counterintuitive judgments. We introduce Multimodal Adversarial
Compositionality (MAC), a benchmark that leverages large language models (LLMs)
to generate dece... | 2025-05-28T23:45:55Z | ACL 2025 Main. Code is released at
https://vision.snu.ac.kr/projects/mac | null | null | null | null | null | null | null | null | null |
2,505.22944 | ATI: Any Trajectory Instruction for Controllable Video Generation | ['Angtian Wang', 'Haibin Huang', 'Jacob Zhiyuan Fang', 'Yiding Yang', 'Chongyang Ma'] | ['cs.CV', 'cs.AI'] | We propose a unified framework for motion control in video generation that
seamlessly integrates camera movement, object-level translation, and
fine-grained local motion using trajectory-based inputs. In contrast to prior
methods that address these motion types through separate modules or
task-specific designs, our app... | 2025-05-28T23:49:18Z | null | null | null | null | null | null | null | null | null | null |
2,505.22961 | ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind | ['Peixuan Han', 'Zijia Liu', 'Jiaxuan You'] | ['cs.CL', 'cs.LG'] | Large language models (LLMs) have shown promising potential in persuasion,
but existing works on training LLM persuaders are still preliminary. Notably,
while humans are skilled in modeling their opponent's thoughts and opinions
proactively and dynamically, current LLMs struggle with such Theory of Mind
(ToM) reasoning... | 2025-05-29T01:03:41Z | null | null | null | null | null | null | null | null | null | null |
2,505.22977 | HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex
Motions | ['Shuolin Xu', 'Siming Zheng', 'Ziyi Wang', 'HC Yu', 'Jinwei Chen', 'Huaqi Zhang', 'Bo Li', 'Peng-Tao Jiang'] | ['cs.CV'] | Recent advances in diffusion models have significantly improved conditional
video generation, particularly in the pose-guided human image animation task.
Although existing methods are capable of generating high-fidelity and
time-consistent animation sequences in regular motions and static scenes, there
are still obviou... | 2025-05-29T01:30:46Z | 17 pages, 7 figures | null | null | HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex Motions | ['Shuolin Xu', 'Siming Zheng', 'Ziyi Wang', 'HC Yu', 'Jinwei Chen', 'Huaqi Zhang', 'Bo Li', 'Peng-Tao Jiang'] | 2,025 | arXiv.org | 0 | 54 | ['Computer Science'] |
2,505.2306 | Self-Correcting Code Generation Using Small Language Models | ['Jeonghun Cho', 'Deokhyung Kang', 'Hyounghun Kim', 'Gary Geunbae Lee'] | ['cs.CL'] | Self-correction has demonstrated potential in code generation by allowing
language models to revise and improve their outputs through successive
refinement. Recent studies have explored prompting-based strategies that
incorporate verification or feedback loops using proprietary models, as well as
training-based methods... | 2025-05-29T04:04:44Z | null | null | null | Self-Correcting Code Generation Using Small Language Models | ['Jeonghun Cho', 'Deokhyung Kang', 'Hyounghun Kim', 'G. Lee'] | 2,025 | arXiv.org | 0 | 32 | ['Computer Science'] |
2,505.23091 | Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased
Reinforcement Learning in Multimodal Small Language Models | ['Zeyu Liu', 'Yuhang Liu', 'Guanghao Zhu', 'Congkai Xie', 'Zhen Li', 'Jianbo Yuan', 'Xinyao Wang', 'Qing Li', 'Shing-Chi Cheung', 'Shengyu Zhang', 'Fei Wu', 'Hongxia Yang'] | ['cs.AI', 'cs.CL'] | Recent advancements in large language models (LLMs) have demonstrated
substantial progress in reasoning capabilities, such as DeepSeek-R1, which
leverages rule-based reinforcement learning to enhance logical reasoning
significantly. However, extending these achievements to multimodal large
language models (MLLMs) prese... | 2025-05-29T04:51:56Z | null | null | null | Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models | ['Zeyu Liu', 'Yuhang Liu', 'Guanghao Zhu', 'Congkai Xie', 'Zhen Li', 'Jianbo Yuan', 'Xinyao Wang', 'Qing Li', 'Shing-Chi Cheung', 'Sheng Zhang', 'Fei Wu', 'Hongxia Yang'] | 2,025 | arXiv.org | 0 | 35 | ['Computer Science'] |
2,505.23253 | UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes | ['Yixun Liang', 'Kunming Luo', 'Xiao Chen', 'Rui Chen', 'Hongyu Yan', 'Weiyu Li', 'Jiarui Liu', 'Ping Tan'] | ['cs.CV'] | We present UniTEX, a novel two-stage 3D texture generation framework to
create high-quality, consistent textures for 3D assets. Existing approaches
predominantly rely on UV-based inpainting to refine textures after reprojecting
the generated multi-view images onto the 3D shapes, which introduces challenges
related to t... | 2025-05-29T08:58:41Z | 10 pages, 9 figures | null | null | null | null | null | null | null | null | null |
2,505.23277 | Sentinel: Attention Probing of Proxy Models for LLM Context Compression
with an Understanding Perspective | ['Yong Zhang', 'Yanwen Huang', 'Ning Cheng', 'Yang Guo', 'Yun Zhu', 'Yanmeng Wang', 'Shaojun Wang', 'Jing Xiao'] | ['cs.CL', 'cs.AI'] | Retrieval-augmented generation (RAG) enhances large language models (LLMs)
with external context, but retrieved passages are often lengthy, noisy, or
exceed input limits. Existing compression methods typically require supervised
training of dedicated compression models, increasing cost and reducing
portability. We prop... | 2025-05-29T09:24:12Z | Preprint. 17 pages including appendix | null | null | null | null | null | null | null | null | null |
2,505.23297 | EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian | ['Daryna Dementieva', 'Nikolay Babakov', 'Alexander Fraser'] | ['cs.CL'] | While Ukrainian NLP has seen progress in many texts processing tasks, emotion
classification remains an underexplored area with no publicly available
benchmark to date. In this work, we introduce EmoBench-UA, the first annotated
dataset for emotion detection in Ukrainian texts. Our annotation schema is
adapted from the... | 2025-05-29T09:49:57Z | null | null | null | null | null | null | null | null | null | null |
2,505.23325 | Dimension-Reduction Attack! Video Generative Models are Experts on
Controllable Image Synthesis | ['Hengyuan Cao', 'Yutong Feng', 'Biao Gong', 'Yijing Tian', 'Yunhong Lu', 'Chuang Liu', 'Bin Wang'] | ['cs.CV'] | Video generative models can be regarded as world simulators due to their
ability to capture dynamic, continuous changes inherent in real-world
environments. These models integrate high-dimensional information across
visual, temporal, spatial, and causal dimensions, enabling predictions of
subjects in various status. A ... | 2025-05-29T10:34:45Z | null | null | null | null | null | null | null | null | null | null |
2,505.23604 | Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software
Engineering | ['Guangtao Zeng', 'Maohao Shen', 'Delin Chen', 'Zhenting Qi', 'Subhro Das', 'Dan Gutfreund', 'David Cox', 'Gregory Wornell', 'Wei Lu', 'Zhang-Wei Hong', 'Chuang Gan'] | ['cs.CL', 'cs.AI', 'cs.SE'] | Language models (LMs) perform well on standardized coding benchmarks but
struggle with real-world software engineering tasks such as resolving GitHub
issues in SWE-Bench, especially when model parameters are less than 100B. While
smaller models are preferable in practice due to their lower computational
cost, improving... | 2025-05-29T16:15:36Z | null | null | null | Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering | ['Guangtao Zeng', 'Maohao Shen', 'Delin Chen', 'Zhenting Qi', 'Subhro Das', 'Dan Gutfreund', 'David Cox', 'Greg Wornell', 'Wei Lu', 'Zhang-Wei Hong', 'Chuang Gan'] | 2,025 | arXiv.org | 0 | 35 | ['Computer Science'] |
2,505.23606 | Muddit: Liberating Generation Beyond Text-to-Image with a Unified
Discrete Diffusion Model | ['Qingyu Shi', 'Jinbin Bai', 'Zhuoran Zhao', 'Wenhao Chai', 'Kaidong Yu', 'Jianzong Wu', 'Shuangyong Song', 'Yunhai Tong', 'Xiangtai Li', 'Xuelong Li', 'Shuicheng Yan'] | ['cs.LG', 'cs.CV'] | Unified generation models aim to handle diverse tasks across modalities --
such as text generation, image generation, and vision-language reasoning --
within a single architecture and decoding paradigm. Autoregressive unified
models suffer from slow inference due to sequential decoding, and
non-autoregressive unified m... | 2025-05-29T16:15:48Z | The code and model are available at
https://github.com/M-E-AGI-Lab/Muddit | null | null | null | null | null | null | null | null | null |
2,505.23621 | Table-R1: Inference-Time Scaling for Table Reasoning | ['Zheyuan Yang', 'Lyuhao Chen', 'Arman Cohan', 'Yilun Zhao'] | ['cs.CL'] | In this work, we present the first study to explore inference-time scaling on
table reasoning tasks. We develop and evaluate two post-training strategies to
enable inference-time scaling: distillation from frontier model reasoning
traces and reinforcement learning with verifiable rewards (RLVR). For
distillation, we in... | 2025-05-29T16:28:50Z | null | null | null | null | null | null | null | null | null | null |
2,505.23678 | Grounded Reinforcement Learning for Visual Reasoning | ['Gabriel Sarch', 'Snigdha Saha', 'Naitik Khandelwal', 'Ayush Jain', 'Michael J. Tarr', 'Aviral Kumar', 'Katerina Fragkiadaki'] | ['cs.CV'] | While reinforcement learning (RL) over chains of thought has significantly
advanced language models in tasks such as mathematics and coding, visual
reasoning introduces added complexity by requiring models to direct visual
attention, interpret perceptual inputs, and ground abstract reasoning in
spatial evidence. We int... | 2025-05-29T17:20:26Z | Project website: https://visually-grounded-rl.github.io/ | null | null | Grounded Reinforcement Learning for Visual Reasoning | ['Gabriel Sarch', 'Snigdha Saha', 'Naitik Khandelwal', 'Ayush Jain', 'Michael J. Tarr', 'Aviral Kumar', 'Katerina Fragkiadaki'] | 2,025 | arXiv.org | 0 | 85 | ['Computer Science'] |
2,505.23716 | AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views | ['Lihan Jiang', 'Yucheng Mao', 'Linning Xu', 'Tao Lu', 'Kerui Ren', 'Yichen Jin', 'Xudong Xu', 'Mulin Yu', 'Jiangmiao Pang', 'Feng Zhao', 'Dahua Lin', 'Bo Dai'] | ['cs.CV'] | We introduce AnySplat, a feed forward network for novel view synthesis from
uncalibrated image collections. In contrast to traditional neural rendering
pipelines that demand known camera poses and per scene optimization, or recent
feed forward methods that buckle under the computational weight of dense views,
our model... | 2025-05-29T17:49:56Z | Project page: https://city-super.github.io/anysplat/ | null | null | null | null | null | null | null | null | null |
2,505.23719 | TiRex: Zero-Shot Forecasting Across Long and Short Horizons with
Enhanced In-Context Learning | ['Andreas Auer', 'Patrick Podest', 'Daniel Klotz', 'Sebastian Böck', 'Günter Klambauer', 'Sepp Hochreiter'] | ['cs.LG'] | In-context learning, the ability of large language models to perform tasks
using only examples provided in the prompt, has recently been adapted for time
series forecasting. This paradigm enables zero-shot prediction, where past
values serve as context for forecasting future values, making powerful
forecasting tools ac... | 2025-05-29T17:52:10Z | null | null | null | TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning | ['Andreas Auer', 'Patrick Podest', 'Daniel Klotz', 'Sebastian Bock', 'G. Klambauer', 'Sepp Hochreiter'] | 2,025 | arXiv.org | 0 | 40 | ['Computer Science'] |
2,505.23734 | ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS | ['Weijie Wang', 'Donny Y. Chen', 'Zeyu Zhang', 'Duochao Shi', 'Akide Liu', 'Bohan Zhuang'] | ['cs.CV'] | Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a
promising solution for novel view synthesis, enabling one-pass inference
without the need for per-scene 3DGS optimization. However, their scalability is
fundamentally constrained by the limited capacity of their encoders, leading to
degraded pe... | 2025-05-29T17:57:04Z | Project Page: https://lhmd.top/zpressor, Code:
https://github.com/ziplab/ZPressor | null | null | null | null | null | null | null | null | null |
2,505.23747 | Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial
Intelligence | ['Diankun Wu', 'Fangfu Liu', 'Yi-Hsin Hung', 'Yueqi Duan'] | ['cs.CV', 'cs.AI', 'cs.LG', 'I.2.6; I.2'] | Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced performance on 2D visual tasks. However, improving their
spatial intelligence remains a challenge. Existing 3D MLLMs always rely on
additional 3D or 2.5D data to incorporate spatial awareness, restricting their
utility in scenar... | 2025-05-29T17:59:04Z | 21 pages | null | null | Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence | ['Diankun Wu', 'Fangfu Liu', 'Yi-Hsin Hung', 'Yueqi Duan'] | 2,025 | arXiv.org | 1 | 67 | ['Computer Science'] |
2,505.23762 | ZeroGUI: Automating Online GUI Learning at Zero Human Cost | ['Chenyu Yang', 'Shiqian Su', 'Shi Liu', 'Xuan Dong', 'Yue Yu', 'Weijie Su', 'Xuehui Wang', 'Zhaoyang Liu', 'Jinguo Zhu', 'Hao Li', 'Wenhai Wang', 'Yu Qiao', 'Xizhou Zhu', 'Jifeng Dai'] | ['cs.AI', 'cs.CL', 'cs.CV'] | The rapid advancement of large Vision-Language Models (VLMs) has propelled
the development of pure-vision-based GUI Agents, capable of perceiving and
operating Graphical User Interfaces (GUI) to autonomously fulfill user
instructions. However, existing approaches usually adopt an offline learning
framework, which faces... | 2025-05-29T17:59:51Z | null | null | null | null | null | null | null | null | null | null |
2,505.23883 | BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive
Learning | ['Jianyang Gu', 'Samuel Stevens', 'Elizabeth G Campolongo', 'Matthew J Thompson', 'Net Zhang', 'Jiaman Wu', 'Andrei Kopanev', 'Zheda Mai', 'Alexander E. White', 'James Balhoff', 'Wasila Dahdul', 'Daniel Rubenstein', 'Hilmar Lapp', 'Tanya Berger-Wolf', 'Wei-Lun Chao', 'Yu Su'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Foundation models trained at scale exhibit remarkable emergent behaviors,
learning new capabilities beyond their initial training objectives. We find
such emergent behaviors in biological vision models via large-scale contrastive
vision-language training. To achieve this, we first curate TreeOfLife-200M,
comprising 214... | 2025-05-29T17:48:20Z | Project page: https://imageomics.github.io/bioclip-2/ | null | null | null | null | null | null | null | null | null |
2,505.23977 | VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL | ['Yichen Feng', 'Zhangchen Xu', 'Fengqing Jiang', 'Yuetai Li', 'Bhaskar Ramasubramanian', 'Luyao Niu', 'Bill Yuchen Lin', 'Radha Poovendran'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Vision language models (VLMs) are expected to perform effective multimodal
reasoning and make logically coherent decisions, which is critical to tasks
such as diagram understanding and spatial problem solving. However, current VLM
reasoning lacks large-scale and well-structured training datasets. To bridge
this gap, we... | 2025-05-29T20:08:36Z | Project page at https://visualsphinx.github.io/ | null | null | VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL | ['Yichen Feng', 'Zhangchen Xu', 'Fengqing Jiang', 'Yuetai Li', 'Bhaskar Ramasubramanian', 'Luyao Niu', 'Bill Yuchen Lin', 'Radha Poovendran'] | 2,025 | arXiv.org | 0 | 46 | ['Computer Science'] |
2,505.23987 | Large Language Models for Controllable Multi-property Multi-objective
Molecule Optimization | ['Vishal Dey', 'Xiao Hu', 'Xia Ning'] | ['cs.LG', 'cs.AI', 'cs.CL', 'q-bio.BM'] | In real-world drug design, molecule optimization requires selectively
improving multiple molecular properties up to pharmaceutically relevant levels,
while maintaining others that already meet such criteria. However, existing
computational approaches and instruction-tuned LLMs fail to capture such
nuanced property-spec... | 2025-05-29T20:29:14Z | null | null | null | null | null | null | null | null | null | null |
2,505.24111 | Fine-tune Before Structured Pruning: Towards Compact and Accurate
Self-Supervised Models for Speaker Diarization | ['Jiangyu Han', 'Federico Landini', 'Johan Rohdin', 'Anna Silnova', 'Mireia Diez', 'Jan Cernocky', 'Lukas Burget'] | ['eess.AS'] | Self-supervised learning (SSL) models like WavLM can be effectively utilized
when building speaker diarization systems but are often large and slow,
limiting their use in resource constrained scenarios. Previous studies have
explored compression techniques, but usually for the price of degraded
performance at high prun... | 2025-05-30T01:19:58Z | Accepted by INTERSPEECH 2025 | null | null | Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization | ['Jiangyu Han', 'Federico Landini', 'Johan Rohdin', 'Anna Silnova', 'Mireia Díez', 'J. Černocký', 'Lukás Burget'] | 2,025 | null | 1 | 28 | ['Engineering'] |
2,505.24183 | CodeV-R1: Reasoning-Enhanced Verilog Generation | ['Yaoyu Zhu', 'Di Huang', 'Hanqi Lyu', 'Xiaoyun Zhang', 'Chongxiao Li', 'Wenxuan Shi', 'Yutong Wu', 'Jianan Mu', 'Jinghua Wang', 'Yang Zhao', 'Pengwei Jin', 'Shuyao Cheng', 'Shengwen Liang', 'Xishan Zhang', 'Rui Zhang', 'Zidong Du', 'Qi Guo', 'Xing Hu', 'Yunji Chen'] | ['cs.LG', 'cs.AR', 'cs.PL'] | Large language models (LLMs) trained via reinforcement learning with
verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit,
automatable verification, such as software programming and mathematical
problems. Extending RLVR to electronic design automation (EDA), especially
automatically generating ha... | 2025-05-30T03:51:06Z | null | null | null | null | null | null | null | null | null | null |
2,505.24216 | Shuffle PatchMix Augmentation with Confidence-Margin Weighted
Pseudo-Labels for Enhanced Source-Free Domain Adaptation | ['Prasanna Reddy Pulakurthi', 'Majid Rabbani', 'Jamison Heard', 'Sohail Dianat', 'Celso M. de Melo', 'Raghuveer Rao'] | ['cs.CV'] | This work investigates Source-Free Domain Adaptation (SFDA), where a model
adapts to a target domain without access to source data. A new augmentation
technique, Shuffle PatchMix (SPM), and a novel reweighting strategy are
introduced to enhance performance. SPM shuffles and blends image patches to
generate diverse and ... | 2025-05-30T05:02:42Z | 6 pages, 3 figures, 5 tables, Accepted to IEEE ICIP 2025 | null | null | null | null | null | null | null | null | null |
2,505.24219 | ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation | ['Lam Thanh Do', 'Aaditya Bodke', 'Pritom Saha Akash', 'Kevin Chen-Chuan Chang'] | ['cs.CL'] | Unsupervised keyphrase prediction has gained growing interest in recent
years. However, existing methods typically rely on heuristically defined
importance scores, which may lead to inaccurate informativeness estimation. In
addition, they lack consideration for time efficiency. To solve these problems,
we propose ERU-K... | 2025-05-30T05:09:53Z | Accepted to ACL 2025 | null | null | null | null | null | null | null | null | null |
2,505.24298 | AReaL: A Large-Scale Asynchronous Reinforcement Learning System for
Language Reasoning | ['Wei Fu', 'Jiaxuan Gao', 'Xujie Shen', 'Chen Zhu', 'Zhiyu Mei', 'Chuyi He', 'Shusheng Xu', 'Guo Wei', 'Jun Mei', 'Jiashu Wang', 'Tongkai Yang', 'Binhang Yuan', 'Yi Wu'] | ['cs.LG', 'cs.AI'] | Reinforcement learning (RL) has become a dominant paradigm for training large
language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs
requires massive parallelization and poses an urgent need for efficient
training systems. Most existing large-scale RL systems for LLMs are
synchronous, alternati... | 2025-05-30T07:18:25Z | null | null | null | null | null | null | null | null | null | null |
2,505.24421 | pyMEAL: A Multi-Encoder Augmentation-Aware Learning for Robust and
Generalizable Medical Image Translation | ['Abdul-mojeed Olabisi Ilyas', 'Adeleke Maradesa', 'Jamal Banzi', 'Jianpan Huang', 'Henry K. F. Mak', 'Kannie W. Y. Chan'] | ['eess.IV', 'cs.CV'] | Medical imaging is critical for diagnostics, but clinical adoption of
advanced AI-driven imaging faces challenges due to patient variability, image
artifacts, and limited model generalization. While deep learning has
transformed image analysis, 3D medical imaging still suffers from data scarcity
and inconsistencies due... | 2025-05-30T10:01:23Z | 36 pages, 9 figures, 2 tables | null | null | pyMEAL: A Multi-Encoder Augmentation-Aware Learning for Robust and Generalizable Medical Image Translation | ['A. Ilyas', 'Adeleke Maradesa', 'Jamal Banzi', 'Jianpan Huang', 'Henry K.F. Mak', 'Kannie W. Y. Chan'] | 2,025 | arXiv.org | 0 | 45 | ['Computer Science', 'Engineering'] |
2,505.24443 | Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised
Learning with Outliers | ['Heejo Kong', 'Sung-Jin Kim', 'Gunho Jung', 'Seong-Whan Lee'] | ['cs.CV', 'cs.LG'] | Conventional semi-supervised learning (SSL) ideally assumes that labeled and
unlabeled data share an identical class distribution, however in practice, this
assumption is easily violated, as unlabeled data often includes unknown class
data, i.e., outliers. The outliers are treated as noise, considerably degrading
the p... | 2025-05-30T10:24:30Z | Accepted by IEEE Transactions on Neural Networks and Learning Systems
(TNNLS) | null | 10.1109/TNNLS.2025.3547801 | Diversify and Conquer: Open-Set Disagreement for Robust Semi-Supervised Learning With Outliers | ['Heejo Kong', 'Sung-Jin Kim', 'Gunho Jung', 'Seong-Whan Lee'] | 2,025 | IEEE Transactions on Neural Networks and Learning Systems | 0 | 56 | ['Computer Science', 'Medicine'] |
2,505.24449 | When Large Multimodal Models Confront Evolving Knowledge:Challenges and
Pathways | ['Kailin Jiang', 'Yuntao Du', 'Yukai Ding', 'Yuchen Ren', 'Ning Jiang', 'Zhi Gao', 'Zilong Zheng', 'Lei Liu', 'Bin Li', 'Qing Li'] | ['cs.CL'] | Large language/multimodal models (LLMs/LMMs) store extensive pre-trained
knowledge but struggle to maintain consistency with real-world updates, making
it difficult to avoid catastrophic forgetting while acquiring evolving
knowledge. Previous work focused on constructing textual knowledge datasets and
exploring knowled... | 2025-05-30T10:36:19Z | null | null | null | When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways | ['Kailin Jiang', 'Yuntao Du', 'Yukai Ding', 'Yuchen Ren', 'Ning Jiang', 'Zhi Gao', 'Zilong Zheng', 'Lei Liu', 'Bin Li', 'Qing Li'] | 2,025 | arXiv.org | 0 | 78 | ['Computer Science'] |
2,505.24461 | Logits-Based Finetuning | ['Jingyao Li', 'Senqiao Yang', 'Sitong Wu', 'Han Shi', 'Chuanyang Zheng', 'Hong Xu', 'Jiaya Jia'] | ['cs.LG'] | In recent years, developing compact and efficient large language models
(LLMs) has emerged as a thriving area of research. Traditional Supervised
Fine-Tuning (SFT), which relies on singular ground truth labels, often fails to
capture token-level dependencies and linguistic diversity. To address these
limitations, we pr... | 2025-05-30T10:57:09Z | null | null | null | null | null | null | null | null | null | null |
2,505.24517 | un$^2$CLIP: Improving CLIP's Visual Detail Capturing Ability via
Inverting unCLIP | ['Yinqi Li', 'Jiahe Zhao', 'Hong Chang', 'Ruibing Hou', 'Shiguang Shan', 'Xilin Chen'] | ['cs.CV'] | Contrastive Language-Image Pre-training (CLIP) has become a foundation model
and has been applied to various vision and multimodal tasks. However, recent
works indicate that CLIP falls short in distinguishing detailed differences in
images and shows suboptimal performance on dense-prediction and vision-centric
multimod... | 2025-05-30T12:29:38Z | null | null | null | null | null | null | null | null | null | null |
2,505.24523 | Stress-testing Machine Generated Text Detection: Shifting Language
Models Writing Style to Fool Detectors | ['Andrea Pedrotti', 'Michele Papucci', 'Cristiano Ciaccio', 'Alessio Miaschi', 'Giovanni Puccetti', "Felice Dell'Orletta", 'Andrea Esuli'] | ['cs.CL', 'cs.AI'] | Recent advancements in Generative AI and Large Language Models (LLMs) have
enabled the creation of highly realistic synthetic content, raising concerns
about the potential for malicious use, such as misinformation and manipulation.
Moreover, detecting Machine-Generated Text (MGT) remains challenging due to the
lack of ... | 2025-05-30T12:33:30Z | Accepted at Findings of ACL 2025 | null | null | null | null | null | null | null | null | null |
2,505.24527 | Optimal Density Functions for Weighted Convolution in Learning Models | ['Simone Cammarasana', 'Giuseppe Patanè'] | ['cs.CV', 'cs.LG', '42A85'] | The paper introduces the weighted convolution, a novel approach to the
convolution for signals defined on regular grids (e.g., 2D images) through the
application of an optimal density function to scale the contribution of
neighbouring pixels based on their distance from the central pixel. This choice
differs from the t... | 2025-05-30T12:36:36Z | 5 figures, 5 tables, 21 pages | null | null | null | null | null | null | null | null | null |
2,505.24558 | Optimal Weighted Convolution for Classification and Denosing | ['Simone Cammarasana', 'Giuseppe Patanè'] | ['cs.CV', '68T05'] | We introduce a novel weighted convolution operator that enhances traditional
convolutional neural networks (CNNs) by integrating a spatial density function
into the convolution operator. This extension enables the network to
differentially weight neighbouring pixels based on their relative position to
the reference pix... | 2025-05-30T13:10:46Z | 17 pages, 3 figures, 6 tables | null | null | Optimal Weighted Convolution for Classification and Denosing | ['Simone Cammarasana', 'Giuseppe Patané'] | 2,025 | arXiv.org | 0 | 39 | ['Computer Science'] |
2,505.24581 | GATE: General Arabic Text Embedding for Enhanced Semantic Textual
Similarity with Matryoshka Representation Learning and Hybrid Loss Training | ['Omer Nacar', 'Anis Koubaa', 'Serry Sibaee', 'Yasser Al-Habashi', 'Adel Ammar', 'Wadii Boulila'] | ['cs.CL'] | Semantic textual similarity (STS) is a critical task in natural language
processing (NLP), enabling applications in retrieval, clustering, and
understanding semantic relationships between texts. However, research in this
area for the Arabic language remains limited due to the lack of high-quality
datasets and pre-train... | 2025-05-30T13:29:03Z | null | null | null | null | null | null | null | null | null | null |
2,505.24616 | Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs
with POLLUX | ['Nikita Martynov', 'Anastasia Mordasheva', 'Dmitriy Gorbetskiy', 'Danil Astafurov', 'Ulyana Isaeva', 'Elina Basyrova', 'Sergey Skachkov', 'Victoria Berestova', 'Nikolay Ivanov', 'Valeriia Zanina', 'Alena Fenogenova'] | ['cs.CL', 'cs.AI'] | We introduce POLLUX, a comprehensive open-source benchmark designed to
evaluate the generative capabilities of large language models (LLMs) in
Russian. Our main contribution is a novel evaluation methodology that enhances
the interpretability of LLM assessment. For each task type, we define a set of
detailed criteria a... | 2025-05-30T14:08:17Z | 178 pages | null | null | Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX | ['Nikita Martynov', 'Anastasia Mordasheva', 'Dmitriy Gorbetskiy', 'Danil Astafurov', 'Ulyana Isaeva', 'Elina Basyrova', 'Sergey Skachkov', 'Victoria Berestova', 'Nikolay Ivanov', 'Valeriia Zanina', 'Alena Fenogenova'] | 2,025 | arXiv.org | 0 | 0 | ['Computer Science'] |
2,505.24713 | Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic
Dialect Identification | ['Badr M. Abdullah', 'Matthew Baas', 'Bernd Möbius', 'Dietrich Klakow'] | ['cs.CL', 'cs.SD', 'eess.AS'] | Arabic dialect identification (ADI) systems are essential for large-scale
data collection pipelines that enable the development of inclusive speech
technologies for Arabic language varieties. However, the reliability of current
ADI systems is limited by poor generalization to out-of-domain speech. In this
paper, we pre... | 2025-05-30T15:36:08Z | Accepted in Interspeech 2025 | null | null | null | null | null | null | null | null | null |
2,505.24717 | PDE-Transformer: Efficient and Versatile Transformers for Physics
Simulations | ['Benjamin Holzschuh', 'Qiang Liu', 'Georg Kohl', 'Nils Thuerey'] | ['cs.LG'] | We introduce PDE-Transformer, an improved transformer-based architecture for
surrogate modeling of physics simulations on regular grids. We combine recent
architectural improvements of diffusion transformers with adjustments specific
for large-scale simulations to yield a more scalable and versatile
general-purpose tra... | 2025-05-30T15:39:54Z | ICML 2025. Code available at
https://github.com/tum-pbs/pde-transformer | null | null | PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations | ['Benjamin Holzschuh', 'Qiang Liu', 'Georg Kohl', 'Nils Thuerey'] | 2,025 | arXiv.org | 1 | 92 | ['Computer Science'] |
2,505.24718 | Reinforcing Video Reasoning with Focused Thinking | ['Jisheng Dang', 'Jingze Wu', 'Teng Wang', 'Xuanhui Lin', 'Nannan Zhu', 'Hongbo Chen', 'Wei-Shi Zheng', 'Meng Wang', 'Tat-Seng Chua'] | ['cs.CV'] | Recent advancements in reinforcement learning, particularly through Group
Relative Policy Optimization (GRPO), have significantly improved multimodal
large language models for complex reasoning tasks. However, two critical
limitations persist: 1) they often produce unfocused, verbose reasoning chains
that obscure salie... | 2025-05-30T15:42:19Z | null | null | null | null | null | null | null | null | null | null |
2,505.2476 | REASONING GYM: Reasoning Environments for Reinforcement Learning with
Verifiable Rewards | ['Zafir Stojanovski', 'Oliver Stanley', 'Joe Sharratt', 'Richard Jones', 'Abdulhakeem Adefioye', 'Jean Kaddour', 'Andreas Köpf'] | ['cs.LG', 'cs.AI', 'cs.CL'] | We introduce Reasoning Gym (RG), a library of reasoning environments for
reinforcement learning with verifiable rewards. It provides over 100 data
generators and verifiers spanning multiple domains including algebra,
arithmetic, computation, cognition, geometry, graph theory, logic, and various
common games. Its key in... | 2025-05-30T16:20:18Z | For code, see https://github.com/open-thought/reasoning-gym | null | null | REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards | ['Zafir Stojanovski', 'Oliver Stanley', 'Joe Sharratt', 'Richard Jones', 'A. Adefioye', 'Jean Kaddour', 'Andreas Köpf'] | 2,025 | arXiv.org | 1 | 81 | ['Computer Science'] |
2,505.24782 | Context is Gold to find the Gold Passage: Evaluating and Training
Contextual Document Embeddings | ['Max Conti', 'Manuel Faysse', 'Gautier Viaud', 'Antoine Bosselut', 'Céline Hudelot', 'Pierre Colombo'] | ['cs.IR'] | A limitation of modern document retrieval embedding methods is that they
typically encode passages (chunks) from the same documents independently, often
overlooking crucial contextual information from the rest of the document that
could greatly improve individual chunk representations.
In this work, we introduce ConT... | 2025-05-30T16:43:28Z | Under Review | null | null | null | null | null | null | null | null | null |
2,505.2484 | Vision LLMs Are Bad at Hierarchical Visual Understanding, and LLMs Are
the Bottleneck | ['Yuwen Tan', 'Yuan Qing', 'Boqing Gong'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | This paper reveals that many state-of-the-art large language models (LLMs)
lack hierarchical knowledge about our visual world, unaware of even
well-established biology taxonomies. This shortcoming makes LLMs a bottleneck
for vision LLMs' hierarchical visual understanding (e.g., recognizing Anemone
Fish but not Vertebra... | 2025-05-30T17:40:46Z | 28 pages, 13 figures | null | null | null | null | null | null | null | null | null |
2,505.24864 | ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in
Large Language Models | ['Mingjie Liu', 'Shizhe Diao', 'Ximing Lu', 'Jian Hu', 'Xin Dong', 'Yejin Choi', 'Jan Kautz', 'Yi Dong'] | ['cs.CL', 'cs.AI'] | Recent advances in reasoning-centric language models have highlighted
reinforcement learning (RL) as a promising method for aligning models with
verifiable rewards. However, it remains contentious whether RL truly expands a
model's reasoning capabilities or merely amplifies high-reward outputs already
latent in the bas... | 2025-05-30T17:59:01Z | 26 pages, 17 figures | null | null | null | null | null | null | null | null | null |
2,505.24873 | MiniMax-Remover: Taming Bad Noise Helps Video Object Removal | ['Bojia Zi', 'Weixuan Peng', 'Xianbiao Qi', 'Jianan Wang', 'Shihao Zhao', 'Rong Xiao', 'Kam-Fai Wong'] | ['cs.CV'] | Recent advances in video diffusion models have driven rapid progress in video
editing techniques. However, video object removal, a critical subtask of video
editing, remains challenging due to issues such as hallucinated objects and
visual artifacts. Furthermore, existing methods often rely on computationally
expensive... | 2025-05-30T17:59:45Z | null | null | null | MiniMax-Remover: Taming Bad Noise Helps Video Object Removal | ['Bojia Zi', 'Weixuan Peng', 'Xianbiao Qi', 'Jianan Wang', 'Shihao Zhao', 'Rong Xiao', 'Kam-Fai Wong'] | 2,025 | arXiv.org | 0 | 53 | ['Computer Science'] |
2,505.24875 | ReasonGen-R1: CoT for Autoregressive Image generation models through SFT
and RL | ['Yu Zhang', 'Yunqi Li', 'Yifan Yang', 'Rui Wang', 'Yuqing Yang', 'Dai Qi', 'Jianmin Bao', 'Dongdong Chen', 'Chong Luo', 'Lili Qiu'] | ['cs.CV', 'cs.CL'] | Although chain-of-thought reasoning and reinforcement learning (RL) have
driven breakthroughs in NLP, their integration into generative vision models
remains underexplored. We introduce ReasonGen-R1, a two-stage framework that
first imbues an autoregressive image generator with explicit text-based
"thinking" skills via... | 2025-05-30T17:59:48Z | null | null | null | null | null | null | null | null | null | null |
2,506.00019 | Amadeus-Verbo Technical Report: The powerful Qwen2.5 family models
trained in Portuguese | ['William Alberto Cruz-Castañeda', 'Marcellus Amadeus'] | ['cs.CL', 'cs.AI'] | This report introduces the experience of developing Amadeus Verbo, a family
of large language models for Brazilian Portuguese. To handle diverse use cases,
Amadeus Verbo includes base-tuned, merged, and instruction-tuned models in
sizes of 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters. Thus, the main
objective is to... | 2025-05-20T22:40:00Z | null | null | null | null | null | null | null | null | null | null |
2,506.00129 | Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware
Sign Language Translation | ['Edward Fish', 'Richard Bowden'] | ['cs.CV', 'cs.LG'] | Recent progress in Sign Language Translation (SLT) has focussed primarily on
improving the representational capacity of large language models to incorporate
Sign Language features. This work explores an alternative direction: enhancing
the geometric properties of skeletal representations themselves. We propose
Geo-Sign... | 2025-05-30T18:05:33Z | Under Review | null | null | Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation | ['Edward Fish', 'Richard Bowden'] | 2,025 | arXiv.org | 1 | 76 | ['Computer Science'] |
2,506.00152 | Aligning Language Models with Observational Data: Opportunities and
Risks from a Causal Perspective | ['Erfan Loghmani'] | ['cs.LG', 'econ.EM', 'stat.ML', 'I.2.6; I.2.7; H.4.0; J.4'] | Large language models are being widely used across industries to generate
content that contributes directly to key performance metrics, such as
conversion rates. Pretrained models, however, often fall short when it comes to
aligning with human preferences or optimizing for business objectives. As a
result, fine-tuning ... | 2025-05-30T18:44:09Z | 10+12 pages, 8 figures | null | null | null | null | null | null | null | null | null |
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