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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,501.14677 | MatAnyone: Stable Video Matting with Consistent Memory Propagation | ['Peiqing Yang', 'Shangchen Zhou', 'Jixin Zhao', 'Qingyi Tao', 'Chen Change Loy'] | ['cs.CV'] | Auxiliary-free human video matting methods, which rely solely on input
frames, often struggle with complex or ambiguous backgrounds. To address this,
we propose MatAnyone, a robust framework tailored for target-assigned video
matting. Specifically, building on a memory-based paradigm, we introduce a
consistent memory p... | 2025-01-24T17:56:24Z | Project page: https://pq-yang.github.io/projects/MatAnyone | null | null | null | null | null | null | null | null | null |
2,501.14693 | Rethinking Table Instruction Tuning | ['Naihao Deng', 'Rada Mihalcea'] | ['cs.CL', 'cs.AI'] | Recent advances in table understanding have focused on instruction-tuning
large language models (LLMs) for table-related tasks. However, existing
research has overlooked the impact of hyperparameter choices, and also lacks a
comprehensive evaluation of the out-of-domain table understanding ability and
the general capab... | 2025-01-24T18:06:07Z | Accepted to ACL 2025 Findings. Updates: 07/2025: We release the
TAMA-QWen2.5 and TAMA-QWen3 models. 06/2025: We release our project page:
https://lit.eecs.umich.edu/TAMA/, code: https://github.com/MichiganNLP/TAMA,
huggingface models:
https://huggingface.co/collections/MichiganNLP/tama-684eeb3e7f262362856eccd1,... | null | null | null | null | null | null | null | null | null |
2,501.14818 | Eagle 2: Building Post-Training Data Strategies from Scratch for
Frontier Vision-Language Models | ['Zhiqi Li', 'Guo Chen', 'Shilong Liu', 'Shihao Wang', 'Vibashan VS', 'Yishen Ji', 'Shiyi Lan', 'Hao Zhang', 'Yilin Zhao', 'Subhashree Radhakrishnan', 'Nadine Chang', 'Karan Sapra', 'Amala Sanjay Deshmukh', 'Tuomas Rintamaki', 'Matthieu Le', 'Ilia Karmanov', 'Lukas Voegtle', 'Philipp Fischer', 'De-An Huang', 'Timo Roma... | ['cs.CV', 'cs.AI', 'cs.LG'] | Recently, promising progress has been made by open-source vision-language
models (VLMs) in bringing their capabilities closer to those of proprietary
frontier models. However, most open-source models only publish their final
model weights, leaving the critical details of data strategies and
implementation largely opaqu... | 2025-01-20T18:40:47Z | null | null | null | null | null | null | null | null | null | null |
2,501.1514 | Analyzing and Boosting the Power of Fine-Grained Visual Recognition for
Multi-modal Large Language Models | ['Hulingxiao He', 'Geng Li', 'Zijun Geng', 'Jinglin Xu', 'Yuxin Peng'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Multi-modal large language models (MLLMs) have shown remarkable abilities in
various visual understanding tasks. However, MLLMs still struggle with
fine-grained visual recognition (FGVR), which aims to identify
subordinate-level categories from images. This can negatively impact more
advanced capabilities of MLLMs, suc... | 2025-01-25T08:52:43Z | Published as a conference paper at ICLR 2025. The model is available
at https://huggingface.co/StevenHH2000/Finedefics | null | null | Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models | ['Hulingxiao He', 'Geng Li', 'Zijun Geng', 'Jinglin Xu', 'Yuxin Peng'] | 2,025 | International Conference on Learning Representations | 7 | 53 | ['Computer Science'] |
2,501.15187 | Uni-Sign: Toward Unified Sign Language Understanding at Scale | ['Zecheng Li', 'Wengang Zhou', 'Weichao Zhao', 'Kepeng Wu', 'Hezhen Hu', 'Houqiang Li'] | ['cs.CV'] | Sign language pre-training has gained increasing attention for its ability to
enhance performance across various sign language understanding (SLU) tasks.
However, existing methods often suffer from a gap between pre-training and
fine-tuning, leading to suboptimal results. To address this, we propose
Uni-Sign, a unified... | 2025-01-25T11:51:23Z | Accepted by ICLR 2025 | null | null | Uni-Sign: Toward Unified Sign Language Understanding at Scale | ['Zecheng Li', 'Wen-gang Zhou', 'Weichao Zhao', 'Kepeng Wu', 'Hezhen Hu', 'Houqiang Li'] | 2,025 | International Conference on Learning Representations | 6 | 72 | ['Computer Science'] |
2,501.15368 | Baichuan-Omni-1.5 Technical Report | ['Yadong Li', 'Jun Liu', 'Tao Zhang', 'Tao Zhang', 'Song Chen', 'Tianpeng Li', 'Zehuan Li', 'Lijun Liu', 'Lingfeng Ming', 'Guosheng Dong', 'Da Pan', 'Chong Li', 'Yuanbo Fang', 'Dongdong Kuang', 'Mingrui Wang', 'Chenglin Zhu', 'Youwei Zhang', 'Hongyu Guo', 'Fengyu Zhang', 'Yuran Wang', 'Bowen Ding', 'Wei Song', 'Xu Li',... | ['cs.CL', 'cs.SD', 'eess.AS'] | We introduce Baichuan-Omni-1.5, an omni-modal model that not only has
omni-modal understanding capabilities but also provides end-to-end audio
generation capabilities. To achieve fluent and high-quality interaction across
modalities without compromising the capabilities of any modality, we
prioritized optimizing three ... | 2025-01-26T02:19:03Z | null | null | null | Baichuan-Omni-1.5 Technical Report | ['Yadong Li', 'Jun Liu', 'Tao Zhang', 'Song Chen', 'Tianpeng Li', 'Zehuan Li', 'Lijun Liu', 'Lingfeng Ming', 'Guosheng Dong', 'Dawei Pan', 'Chong Li', 'Yuanbo Fang', 'Dong-Ling Kuang', 'Mingrui Wang', 'Chenglin Zhu', 'Youwei Zhang', 'Hongyu Guo', 'Fengyu Zhang', 'Yuran Wang', 'Bowen Ding', 'Wei Song', 'Xu Li', 'Yuqiu H... | 2,025 | arXiv.org | 23 | 184 | ['Computer Science', 'Engineering'] |
2,501.15369 | iFormer: Integrating ConvNet and Transformer for Mobile Application | ['Chuanyang Zheng'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We present a new family of mobile hybrid vision networks, called iFormer,
with a focus on optimizing latency and accuracy on mobile applications. iFormer
effectively integrates the fast local representation capacity of convolution
with the efficient global modeling ability of self-attention. The local
interactions are ... | 2025-01-26T02:34:58Z | Accepted to ICLR 2025. Code:
https://github.com/ChuanyangZheng/iFormer | null | null | iFormer: Integrating ConvNet and Transformer for Mobile Application | ['Chuanyang Zheng'] | 2,025 | International Conference on Learning Representations | 0 | 82 | ['Computer Science'] |
2,501.15383 | Qwen2.5-1M Technical Report | ['An Yang', 'Bowen Yu', 'Chengyuan Li', 'Dayiheng Liu', 'Fei Huang', 'Haoyan Huang', 'Jiandong Jiang', 'Jianhong Tu', 'Jianwei Zhang', 'Jingren Zhou', 'Junyang Lin', 'Kai Dang', 'Kexin Yang', 'Le Yu', 'Mei Li', 'Minmin Sun', 'Qin Zhu', 'Rui Men', 'Tao He', 'Weijia Xu', 'Wenbiao Yin', 'Wenyuan Yu', 'Xiafei Qiu', 'Xingzh... | ['cs.CL'] | We introduce Qwen2.5-1M, a series of models that extend the context length to
1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series
have significantly enhanced long-context capabilities through long-context
pre-training and post-training. Key techniques such as long data synthesis,
progressive ... | 2025-01-26T03:47:25Z | null | null | null | Qwen2.5-1M Technical Report | ['An Yang', 'Bowen Yu', 'Chengyuan Li', 'Dayiheng Liu', 'Fei Huang', 'Haoyan Huang', 'Jiandong Jiang', 'Jianhong Tu', 'Jianwei Zhang', 'Jingren Zhou', 'Junyang Lin', 'Kai Dang', 'Kexin Yang', 'Le Yu', 'Mei Li', 'Minmin Sun', 'Qin Zhu', 'Rui Men', 'Tao He', 'Weijia Xu', 'Wenbiao Yin', 'Wenyuan Yu', 'Xiafei Qiu', 'Xingzh... | 2,025 | arXiv.org | 29 | 46 | ['Computer Science'] |
2,501.15415 | OCSU: Optical Chemical Structure Understanding for Molecule-centric
Scientific Discovery | ['Siqi Fan', 'Yuguang Xie', 'Bowen Cai', 'Ailin Xie', 'Gaochao Liu', 'Mu Qiao', 'Jie Xing', 'Zaiqing Nie'] | ['cs.CV'] | Understanding the chemical structure from a graphical representation of a
molecule is a challenging image caption task that would greatly benefit
molecule-centric scientific discovery. Variations in molecular images and
caption subtasks pose a significant challenge in both image representation
learning and task modelin... | 2025-01-26T06:14:29Z | null | null | null | null | null | null | null | null | null | null |
2,501.15442 | Overview of the Amphion Toolkit (v0.2) | ['Jiaqi Li', 'Xueyao Zhang', 'Yuancheng Wang', 'Haorui He', 'Chaoren Wang', 'Li Wang', 'Huan Liao', 'Junyi Ao', 'Zeyu Xie', 'Yiqiao Huang', 'Junan Zhang', 'Zhizheng Wu'] | ['cs.SD', 'cs.AI', 'eess.AS'] | Amphion is an open-source toolkit for Audio, Music, and Speech Generation,
designed to lower the entry barrier for junior researchers and engineers in
these fields. It provides a versatile framework that supports a variety of
generation tasks and models. In this report, we introduce Amphion v0.2, the
second major relea... | 2025-01-26T08:10:13Z | Github: https://github.com/open-mmlab/Amphion | null | null | null | null | null | null | null | null | null |
2,501.15513 | TinyLLaVA-Video: Towards Smaller LMMs for Video Understanding with Group
Resampler | ['Xingjian Zhang', 'Xi Weng', 'Yihao Yue', 'Zhaoxin Fan', 'Wenjun Wu', 'Lei Huang'] | ['cs.CV'] | Video behavior recognition and scene understanding are fundamental tasks in
multimodal intelligence, serving as critical building blocks for numerous
real-world applications. Through large multimodal models (LMMs) have achieved
remarkable progress in video understanding, most existing open-source models
rely on over 7B... | 2025-01-26T13:10:12Z | code and training recipes are available at
https://github.com/ZhangXJ199/TinyLLaVA-Video | null | null | TinyLLaVA-Video: Towards Smaller LMMs for Video Understanding with Group Resampler | ['Xingjian Zhang', 'Xi Weng', 'Yihao Yue', 'Zhaoxin Fan', 'Wenjun Wu', 'Lei Huang'] | 2,025 | null | 0 | 0 | ['Computer Science'] |
2,501.1557 | ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language
Model Born from Transformer | ['Lin Yueyu', 'Li Zhiyuan', 'Peter Yue', 'Liu Xiao'] | ['cs.CL'] | As is known, hybrid quadratic and subquadratic attention models in multi-head
architectures have surpassed both Transformer and Linear RNN models , with
these works primarily focusing on reducing KV complexity and improving
efficiency. For further research on expressiveness, we introduce our series of
models distilled ... | 2025-01-26T15:56:56Z | null | null | null | null | null | null | null | null | null | null |
2,501.15579 | An Explainable Biomedical Foundation Model via Large-Scale
Concept-Enhanced Vision-Language Pre-training | ['Yuxiang Nie', 'Sunan He', 'Yequan Bie', 'Yihui Wang', 'Zhixuan Chen', 'Shu Yang', 'Zhiyuan Cai', 'Hongmei Wang', 'Xi Wang', 'Luyang Luo', 'Mingxiang Wu', 'Xian Wu', 'Ronald Cheong Kin Chan', 'Yuk Ming Lau', 'Yefeng Zheng', 'Pranav Rajpurkar', 'Hao Chen'] | ['cs.CV', 'cs.CL'] | The clinical adoption of artificial intelligence (AI) in medical imaging
requires models that are both diagnostically accurate and interpretable to
clinicians. While current multimodal biomedical foundation models prioritize
performance, their black-box nature hinders explaining the decision-making
process in clinicall... | 2025-01-26T16:07:11Z | null | null | null | null | null | null | null | null | null | null |
2,501.15588 | Tumor Detection, Segmentation and Classification Challenge on Automated
3D Breast Ultrasound: The TDSC-ABUS Challenge | ['Gongning Luo', 'Mingwang Xu', 'Hongyu Chen', 'Xinjie Liang', 'Xing Tao', 'Dong Ni', 'Hyunsu Jeong', 'Chulhong Kim', 'Raphael Stock', 'Michael Baumgartner', 'Yannick Kirchhoff', 'Maximilian Rokuss', 'Klaus Maier-Hein', 'Zhikai Yang', 'Tianyu Fan', 'Nicolas Boutry', 'Dmitry Tereshchenko', 'Arthur Moine', 'Maximilien Ch... | ['eess.IV', 'cs.CV'] | Breast cancer is one of the most common causes of death among women
worldwide. Early detection helps in reducing the number of deaths. Automated 3D
Breast Ultrasound (ABUS) is a newer approach for breast screening, which has
many advantages over handheld mammography such as safety, speed, and higher
detection rate of b... | 2025-01-26T16:30:30Z | null | null | null | null | null | null | null | null | null | null |
2,501.1583 | SpatialVLA: Exploring Spatial Representations for Visual-Language-Action
Model | ['Delin Qu', 'Haoming Song', 'Qizhi Chen', 'Yuanqi Yao', 'Xinyi Ye', 'Yan Ding', 'Zhigang Wang', 'JiaYuan Gu', 'Bin Zhao', 'Dong Wang', 'Xuelong Li'] | ['cs.RO', 'cs.AI'] | In this paper, we claim that spatial understanding is the keypoint in robot
manipulation, and propose SpatialVLA to explore effective spatial
representations for the robot foundation model. Specifically, we introduce
Ego3D Position Encoding to inject 3D information into the input observations of
the visual-language-act... | 2025-01-27T07:34:33Z | null | Robotics: Science and Systems, 2025 | null | null | null | null | null | null | null | null |
2,501.16011 | MEL: Legal Spanish Language Model | ['David Betancur Sánchez', 'Nuria Aldama García', 'Álvaro Barbero Jiménez', 'Marta Guerrero Nieto', 'Patricia Marsà Morales', 'Nicolás Serrano Salas', 'Carlos García Hernán', 'Pablo Haya Coll', 'Elena Montiel Ponsoda', 'Pablo Calleja Ibáñez'] | ['cs.CL'] | Legal texts, characterized by complex and specialized terminology, present a
significant challenge for Language Models. Adding an underrepresented language,
such as Spanish, to the mix makes it even more challenging. While pre-trained
models like XLM-RoBERTa have shown capabilities in handling multilingual
corpora, the... | 2025-01-27T12:50:10Z | 8 pages, 6 figures, 3 tables | null | null | MEL: Legal Spanish Language Model | ['David Betancur Sánchez', 'Nuria Aldama-García', 'Álvaro Barbero Jiménez', 'Marta Guerrero Nieto', 'Patricia Marsa Morales', "Nicol'as Serrano Salas", "Carlos Garc'ia Hern'an", 'Pablo Haya Coll', 'Elena Montiel-Ponsoda', 'Pablo Calleja-Ibáñez'] | 2,025 | arXiv.org | 0 | 16 | ['Computer Science'] |
2,501.16207 | From Informal to Formal -- Incorporating and Evaluating LLMs on Natural
Language Requirements to Verifiable Formal Proofs | ['Jialun Cao', 'Yaojie Lu', 'Meiziniu Li', 'Haoyang Ma', 'Haokun Li', 'Mengda He', 'Cheng Wen', 'Le Sun', 'Hongyu Zhang', 'Shengchao Qin', 'Shing-Chi Cheung', 'Cong Tian'] | ['cs.AI', 'cs.CL', 'cs.PL'] | The research in AI-based formal mathematical reasoning has shown an
unstoppable growth trend. These studies have excelled in mathematical
competitions like IMO and have made significant progress. This paper focuses on
formal verification, an immediate application scenario of formal reasoning, and
breaks it down into su... | 2025-01-27T17:00:56Z | 20 pages | null | null | null | null | null | null | null | null | null |
2,501.16214 | Provence: efficient and robust context pruning for retrieval-augmented
generation | ['Nadezhda Chirkova', 'Thibault Formal', 'Vassilina Nikoulina', 'Stéphane Clinchant'] | ['cs.CL', 'cs.IR'] | Retrieval-augmented generation improves various aspects of large language
models (LLMs) generation, but suffers from computational overhead caused by
long contexts as well as the propagation of irrelevant retrieved information
into generated responses. Context pruning deals with both aspects, by removing
irrelevant par... | 2025-01-27T17:06:56Z | Accepted to ICLR 2025 | null | null | Provence: efficient and robust context pruning for retrieval-augmented generation | ['Nadezhda Chirkova', 'Thibault Formal', 'Vassilina Nikoulina', 'S. Clinchant'] | 2,025 | International Conference on Learning Representations | 1 | 0 | ['Computer Science'] |
2,501.16239 | Distilling foundation models for robust and efficient models in digital
pathology | ['Alexandre Filiot', 'Nicolas Dop', 'Oussama Tchita', 'Auriane Riou', 'Rémy Dubois', 'Thomas Peeters', 'Daria Valter', 'Marin Scalbert', 'Charlie Saillard', 'Geneviève Robin', 'Antoine Olivier'] | ['cs.CV', '68T45', 'I.4.9; J.3'] | In recent years, the advent of foundation models (FM) for digital pathology
has relied heavily on scaling the pre-training datasets and the model size,
yielding large and powerful models. While it resulted in improving the
performance on diverse downstream tasks, it also introduced increased
computational cost and infe... | 2025-01-27T17:35:39Z | Preprint | null | null | null | null | null | null | null | null | null |
2,501.16255 | A foundation model for human-AI collaboration in medical literature
mining | ['Zifeng Wang', 'Lang Cao', 'Qiao Jin', 'Joey Chan', 'Nicholas Wan', 'Behdad Afzali', 'Hyun-Jin Cho', 'Chang-In Choi', 'Mehdi Emamverdi', 'Manjot K. Gill', 'Sun-Hyung Kim', 'Yijia Li', 'Yi Liu', 'Hanley Ong', 'Justin Rousseau', 'Irfan Sheikh', 'Jenny J. Wei', 'Ziyang Xu', 'Christopher M. Zallek', 'Kyungsang Kim', 'Yifa... | ['cs.CL'] | Systematic literature review is essential for evidence-based medicine,
requiring comprehensive analysis of clinical trial publications. However, the
application of artificial intelligence (AI) models for medical literature
mining has been limited by insufficient training and evaluation across broad
therapeutic areas an... | 2025-01-27T17:55:37Z | null | null | null | null | null | null | null | null | null | null |
2,501.16372 | Low-Rank Adapters Meet Neural Architecture Search for LLM Compression | ['J. Pablo Muñoz', 'Jinjie Yuan', 'Nilesh Jain'] | ['cs.LG', 'cs.AI', 'cs.CL'] | The rapid expansion of Large Language Models (LLMs) has posed significant
challenges regarding the computational resources required for fine-tuning and
deployment. Recent advancements in low-rank adapters have demonstrated their
efficacy in parameter-efficient fine-tuning (PEFT) of these models. This
retrospective pape... | 2025-01-23T02:14:08Z | AAAI-25 Workshop on Connecting Low-rank Representations in AI | null | null | Low-Rank Adapters Meet Neural Architecture Search for LLM Compression | ['J. P. Munoz', 'Jinjie Yuan', 'Nilesh Jain'] | 2,025 | arXiv.org | 0 | 9 | ['Computer Science'] |
2,501.16764 | DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian
Splat Generation | ['Chenguo Lin', 'Panwang Pan', 'Bangbang Yang', 'Zeming Li', 'Yadong Mu'] | ['cs.CV'] | Recent advancements in 3D content generation from text or a single image
struggle with limited high-quality 3D datasets and inconsistency from 2D
multi-view generation. We introduce DiffSplat, a novel 3D generative framework
that natively generates 3D Gaussian splats by taming large-scale text-to-image
diffusion models... | 2025-01-28T07:38:59Z | Accepted to ICLR 2025; Project page:
https://chenguolin.github.io/projects/DiffSplat | null | null | DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation | ['Chenguo Lin', 'Panwang Pan', 'Bangbang Yang', 'Zeming Li', 'Yadong Mu'] | 2,025 | International Conference on Learning Representations | 9 | 94 | ['Computer Science'] |
2,501.16899 | RDMM: Fine-Tuned LLM Models for On-Device Robotic Decision Making with
Enhanced Contextual Awareness in Specific Domains | ['Shady Nasrat', 'Myungsu Kim', 'Seonil Lee', 'Jiho Lee', 'Yeoncheol Jang', 'Seung-joon Yi'] | ['cs.RO', 'cs.AI'] | Large language models (LLMs) represent a significant advancement in
integrating physical robots with AI-driven systems. We showcase the
capabilities of our framework within the context of the real-world household
competition. This research introduces a framework that utilizes RDMM (Robotics
Decision-Making Models), whi... | 2025-01-28T12:35:06Z | null | null | null | null | null | null | null | null | null | null |
2,501.16937 | TAID: Temporally Adaptive Interpolated Distillation for Efficient
Knowledge Transfer in Language Models | ['Makoto Shing', 'Kou Misaki', 'Han Bao', 'Sho Yokoi', 'Takuya Akiba'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Causal language models have demonstrated remarkable capabilities, but their
size poses significant challenges for deployment in resource-constrained
environments. Knowledge distillation, a widely-used technique for transferring
knowledge from a large teacher model to a small student model, presents a
promising approach... | 2025-01-28T13:31:18Z | To appear at the 13th International Conference on Learning
Representations (ICLR 2025) as a Spotlight presentation | null | null | null | null | null | null | null | null | null |
2,501.17088 | Mamba-Shedder: Post-Transformer Compression for Efficient Selective
Structured State Space Models | ['J. Pablo Muñoz', 'Jinjie Yuan', 'Nilesh Jain'] | ['cs.LG', 'cs.AI', 'cs.CL', 'I.2.0'] | Large pre-trained models have achieved outstanding results in sequence
modeling. The Transformer block and its attention mechanism have been the main
drivers of the success of these models. Recently, alternative architectures,
such as Selective Structured State Space Models (SSMs), have been proposed to
address the ine... | 2025-01-28T17:22:01Z | NAACL-25 - Main track | null | null | null | null | null | null | null | null | null |
2,501.17144 | FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data | ['Deren Lei', 'Yaxi Li', 'Siyao Li', 'Mengya Hu', 'Rui Xu', 'Ken Archer', 'Mingyu Wang', 'Emily Ching', 'Alex Deng'] | ['cs.CL', 'cs.AI'] | Prior research on training grounded factuality classification models to
detect hallucinations in large language models (LLMs) has relied on public
natural language inference (NLI) data and synthetic data. However, conventional
NLI datasets are not well-suited for document-level reasoning, which is
critical for detectin... | 2025-01-28T18:45:07Z | NAACL 2025 | null | null | FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data | ['Deren Lei', 'Yaxi Li', 'Siyao Li', 'Mengya Hu', 'Rui Xu', 'Ken Archer', 'Mingyu Wang', 'Emily Ching', 'Alex Deng'] | 2,025 | North American Chapter of the Association for Computational Linguistics | 2 | 36 | ['Computer Science'] |
2,501.17161 | SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model
Post-training | ['Tianzhe Chu', 'Yuexiang Zhai', 'Jihan Yang', 'Shengbang Tong', 'Saining Xie', 'Dale Schuurmans', 'Quoc V. Le', 'Sergey Levine', 'Yi Ma'] | ['cs.AI', 'cs.CV', 'cs.LG'] | Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used
post-training techniques for foundation models. However, their roles in
enhancing model generalization capabilities remain unclear. This paper studies
the difference between SFT and RL on generalization and memorization, focusing
on text-based... | 2025-01-28T18:59:44Z | Website at https://tianzhechu.com/SFTvsRL | null | null | null | null | null | null | null | null | null |
2,501.17195 | Atla Selene Mini: A General Purpose Evaluation Model | ['Andrei Alexandru', 'Antonia Calvi', 'Henry Broomfield', 'Jackson Golden', 'Kyle Dai', 'Mathias Leys', 'Maurice Burger', 'Max Bartolo', 'Roman Engeler', 'Sashank Pisupati', 'Toby Drane', 'Young Sun Park'] | ['cs.CL', 'cs.AI'] | We introduce Atla Selene Mini, a state-of-the-art small language
model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that
outperforms the best SLMJs and GPT-4o-mini on overall performance across 11
out-of-distribution benchmarks, spanning absolute scoring, classification, and
pairwise preference tasks. ... | 2025-01-27T15:09:08Z | null | null | null | null | null | null | null | null | null | null |
2,501.17703 | Critique Fine-Tuning: Learning to Critique is More Effective than
Learning to Imitate | ['Yubo Wang', 'Xiang Yue', 'Wenhu Chen'] | ['cs.CL'] | Supervised Fine-Tuning (SFT) is commonly used to train language models to
imitate annotated responses for given instructions. In this paper, we propose
Critique Fine-Tuning (CFT), a method more effective than SFT for reasoning
tasks. Instead of simply imitating correct responses, CFT trains models to
critique noisy res... | 2025-01-29T15:20:30Z | null | null | null | null | null | null | null | null | null | null |
2,501.1779 | BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone
Disambiguation -- Challenges and Insights | ['Chan-Jan Hsu', 'Yi-Cheng Lin', 'Chia-Chun Lin', 'Wei-Chih Chen', 'Ho Lam Chung', 'Chen-An Li', 'Yi-Chang Chen', 'Chien-Yu Yu', 'Ming-Ji Lee', 'Chien-Cheng Chen', 'Ru-Heng Huang', 'Hung-yi Lee', 'Da-Shan Shiu'] | ['cs.CL', 'cs.AI'] | We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted
for Taiwanese Mandarin, highlighting phonetic control abilities to address the
unique challenges of polyphone disambiguation in the language. Building upon
CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an
optimal-t... | 2025-01-29T17:31:26Z | null | null | null | null | null | null | null | null | null | null |
2,501.17811 | Janus-Pro: Unified Multimodal Understanding and Generation with Data and
Model Scaling | ['Xiaokang Chen', 'Zhiyu Wu', 'Xingchao Liu', 'Zizheng Pan', 'Wen Liu', 'Zhenda Xie', 'Xingkai Yu', 'Chong Ruan'] | ['cs.AI', 'cs.CL', 'cs.CV'] | In this work, we introduce Janus-Pro, an advanced version of the previous
work Janus. Specifically, Janus-Pro incorporates (1) an optimized training
strategy, (2) expanded training data, and (3) scaling to larger model size.
With these improvements, Janus-Pro achieves significant advancements in both
multimodal underst... | 2025-01-29T18:00:19Z | Research paper. arXiv admin note: text overlap with arXiv:2410.13848 | null | null | Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling | ['Xi-aokang Chen', 'Zhiyu Wu', 'Xingchao Liu', 'Zizheng Pan', 'Wen Liu', 'Zhenda Xie', 'Xingkai Yu', 'C. Ruan'] | 2,025 | arXiv.org | 160 | 0 | ['Computer Science'] |
2,501.17821 | SSF: Sparse Long-Range Scene Flow for Autonomous Driving | ['Ajinkya Khoche', 'Qingwen Zhang', 'Laura Pereira Sanchez', 'Aron Asefaw', 'Sina Sharif Mansouri', 'Patric Jensfelt'] | ['cs.CV'] | Scene flow enables an understanding of the motion characteristics of the
environment in the 3D world. It gains particular significance in the
long-range, where object-based perception methods might fail due to sparse
observations far away. Although significant advancements have been made in
scene flow pipelines to hand... | 2025-01-29T18:14:16Z | 7 pages, 3 figures, accepted to International Conference on Robotics
and Automation (ICRA) 2025 | null | null | null | null | null | null | null | null | null |
2,501.18052 | SAeUron: Interpretable Concept Unlearning in Diffusion Models with
Sparse Autoencoders | ['Bartosz Cywiński', 'Kamil Deja'] | ['cs.LG', 'cs.AI'] | Diffusion models, while powerful, can inadvertently generate harmful or
undesirable content, raising significant ethical and safety concerns. Recent
machine unlearning approaches offer potential solutions but often lack
transparency, making it difficult to understand the changes they introduce to
the base model. In thi... | 2025-01-29T23:29:47Z | null | null | null | SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders | ["Bartosz Cywi'nski", 'Kamil Deja'] | 2,025 | arXiv.org | 9 | 62 | ['Computer Science'] |
2,501.18107 | Scaling Inference-Efficient Language Models | ['Song Bian', 'Minghao Yan', 'Shivaram Venkataraman'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Scaling laws are powerful tools to predict the performance of large language
models. However, current scaling laws fall short of accounting for inference
costs. In this work, we first show that model architecture affects inference
latency, where models of the same size can have up to 3.5x difference in
latency. To tack... | 2025-01-30T03:16:44Z | 21 pages, 18 figures, ICML 2025 | null | null | null | null | null | null | null | null | null |
2,501.18251 | How to Select Datapoints for Efficient Human Evaluation of NLG Models? | ['Vilém Zouhar', 'Peng Cui', 'Mrinmaya Sachan'] | ['cs.CL'] | Human evaluation is the gold standard for evaluating text generation models.
However, it is expensive. In order to fit budgetary constraints, a random
subset of the test data is often chosen in practice for human evaluation.
However, randomly selected data may not accurately represent test performance,
making this appr... | 2025-01-30T10:33:26Z | null | null | null | How to Select Datapoints for Efficient Human Evaluation of NLG Models? | ['Vilém Zouhar', 'Peng Cui', 'Mrinmaya Sachan'] | 2,025 | arXiv.org | 1 | 64 | ['Computer Science'] |
2,501.18362 | MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
Understanding | ['Yuxin Zuo', 'Shang Qu', 'Yifei Li', 'Zhangren Chen', 'Xuekai Zhu', 'Ermo Hua', 'Kaiyan Zhang', 'Ning Ding', 'Bowen Zhou'] | ['cs.AI', 'cs.CL', 'cs.CV', 'cs.LG'] | We introduce MedXpertQA, a highly challenging and comprehensive benchmark to
evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA
includes 4,460 questions spanning 17 specialties and 11 body systems. It
includes two subsets, Text for text evaluation and MM for multimodal
evaluation. Notably, MM in... | 2025-01-30T14:07:56Z | ICML 2025 | null | null | null | null | null | null | null | null | null |
2,501.18427 | SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute
in Linear Diffusion Transformer | ['Enze Xie', 'Junsong Chen', 'Yuyang Zhao', 'Jincheng Yu', 'Ligeng Zhu', 'Chengyue Wu', 'Yujun Lin', 'Zhekai Zhang', 'Muyang Li', 'Junyu Chen', 'Han Cai', 'Bingchen Liu', 'Daquan Zhou', 'Song Han'] | ['cs.CV'] | This paper presents SANA-1.5, a linear Diffusion Transformer for efficient
scaling in text-to-image generation. Building upon SANA-1.0, we introduce three
key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that
enables scaling from 1.6B to 4.8B parameters with significantly reduced
computational r... | 2025-01-30T15:31:48Z | null | null | null | null | null | null | null | null | null | null |
2,501.18435 | GENIE: Generative Note Information Extraction model for structuring EHR
data | ['Huaiyuan Ying', 'Hongyi Yuan', 'Jinsen Lu', 'Zitian Qu', 'Yang Zhao', 'Zhengyun Zhao', 'Isaac Kohane', 'Tianxi Cai', 'Sheng Yu'] | ['cs.CL'] | Electronic Health Records (EHRs) hold immense potential for advancing
healthcare, offering rich, longitudinal data that combines structured
information with valuable insights from unstructured clinical notes. However,
the unstructured nature of clinical text poses significant challenges for
secondary applications. Trad... | 2025-01-30T15:42:24Z | null | null | null | null | null | null | null | null | null | null |
2,501.18492 | GuardReasoner: Towards Reasoning-based LLM Safeguards | ['Yue Liu', 'Hongcheng Gao', 'Shengfang Zhai', 'Jun Xia', 'Tianyi Wu', 'Zhiwei Xue', 'Yulin Chen', 'Kenji Kawaguchi', 'Jiaheng Zhang', 'Bryan Hooi'] | ['cs.CR', 'cs.AI', 'cs.LG'] | As LLMs increasingly impact safety-critical applications, ensuring their
safety using guardrails remains a key challenge. This paper proposes
GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to
reason. Concretely, we first create the GuardReasonerTrain dataset, which
consists of 127K samples... | 2025-01-30T17:06:06Z | 22 pages, 18 figures | null | null | null | null | null | null | null | null | null |
2,501.18511 | WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
Post-Training | ['Benjamin Feuer', 'Chinmay Hegde'] | ['cs.LG', 'cs.CL'] | Language model (LLM) post-training, from DPO to distillation, can refine
behaviors and unlock new skills, but the open science supporting these
post-training techniques is still in its infancy. One limiting factor has been
the difficulty of conducting large-scale comparative analyses of synthetic data
generating models... | 2025-01-30T17:21:44Z | ICML 2025 | null | null | WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training | ['Ben Feuer', 'Chinmay Hegde'] | 2,025 | arXiv.org | 0 | 53 | ['Computer Science'] |
2,501.1859 | DiffusionRenderer: Neural Inverse and Forward Rendering with Video
Diffusion Models | ['Ruofan Liang', 'Zan Gojcic', 'Huan Ling', 'Jacob Munkberg', 'Jon Hasselgren', 'Zhi-Hao Lin', 'Jun Gao', 'Alexander Keller', 'Nandita Vijaykumar', 'Sanja Fidler', 'Zian Wang'] | ['cs.CV', 'cs.GR'] | Understanding and modeling lighting effects are fundamental tasks in computer
vision and graphics. Classic physically-based rendering (PBR) accurately
simulates the light transport, but relies on precise scene
representations--explicit 3D geometry, high-quality material properties, and
lighting conditions--that are oft... | 2025-01-30T18:59:11Z | CVPR 2025; project page:
research.nvidia.com/labs/toronto-ai/DiffusionRenderer/ | null | null | DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models | ['Ruofan Liang', 'Zan Gojcic', 'Huan Ling', 'Jacob Munkberg', 'J. Hasselgren', 'Zhi-Hao Lin', 'Jun Gao', 'Alexander Keller', 'Nandita Vijaykumar', 'Sanja Fidler', 'Zian Wang'] | 2,025 | arXiv.org | 11 | 91 | ['Computer Science'] |
2,501.1867 | High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA
Fine-Tuning with Multimodal LLaMA 3.2 | ['Nandakishor M', 'Anjali M'] | ['cs.CV', 'cs.AI'] | Electrocardiogram (ECG) interpretation is a cornerstone of cardiac
diagnostics. This paper explores a practical approach to enhance ECG image
interpretation using the multimodal LLaMA 3.2 model. We used a
parameter-efficient fine-tuning strategy, Low-Rank Adaptation (LoRA),
specifically designed to boost the model's ab... | 2025-01-30T17:55:27Z | null | null | null | null | null | null | null | null | null | null |
2,501.18898 | GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with
Spatial-Temporal Modeling | ['Pinxin Liu', 'Luchuan Song', 'Junhua Huang', 'Haiyang Liu', 'Chenliang Xu'] | ['cs.CV', 'cs.GR'] | Generating full-body human gestures based on speech signals remains
challenges on quality and speed. Existing approaches model different body
regions such as body, legs and hands separately, which fail to capture the
spatial interactions between them and result in unnatural and disjointed
movements. Additionally, their... | 2025-01-31T05:34:59Z | null | null | null | null | null | null | null | null | null | null |
2,501.18954 | LLMDet: Learning Strong Open-Vocabulary Object Detectors under the
Supervision of Large Language Models | ['Shenghao Fu', 'Qize Yang', 'Qijie Mo', 'Junkai Yan', 'Xihan Wei', 'Jingke Meng', 'Xiaohua Xie', 'Wei-Shi Zheng'] | ['cs.CV'] | Recent open-vocabulary detectors achieve promising performance with abundant
region-level annotated data. In this work, we show that an open-vocabulary
detector co-training with a large language model by generating image-level
detailed captions for each image can further improve performance. To achieve
the goal, we fir... | 2025-01-31T08:27:31Z | null | null | null | null | null | null | null | null | null | null |
2,501.19054 | Text-to-CAD Generation Through Infusing Visual Feedback in Large
Language Models | ['Ruiyu Wang', 'Yu Yuan', 'Shizhao Sun', 'Jiang Bian'] | ['cs.CV', 'cs.LG'] | Creating Computer-Aided Design (CAD) models requires significant expertise
and effort. Text-to-CAD, which converts textual descriptions into CAD
parametric sequences, is crucial in streamlining this process. Recent studies
have utilized ground-truth parametric sequences, known as sequential signals,
as supervision to a... | 2025-01-31T11:28:16Z | ICML 2025 camera ready | null | null | null | null | null | null | null | null | null |
2,501.19374 | Fixing the Double Penalty in Data-Driven Weather Forecasting Through a
Modified Spherical Harmonic Loss Function | ['Christopher Subich', 'Syed Zahid Husain', 'Leo Separovic', 'Jing Yang'] | ['cs.LG', 'physics.ao-ph', 'I.2.6; I.2.1; J.2'] | Recent advancements in data-driven weather forecasting models have delivered
deterministic models that outperform the leading operational forecast systems
based on traditional, physics-based models. However, these data-driven models
are typically trained with a mean squared error loss function, which causes
smoothing o... | 2025-01-31T18:23:45Z | Accepted at ICML 2025 | null | null | Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function | ['Christopher Subich', 'S. Husain', 'L. Šeparović', 'Jing Yang'] | 2,025 | arXiv.org | 5 | 38 | ['Computer Science', 'Physics'] |
2,501.19393 | s1: Simple test-time scaling | ['Niklas Muennighoff', 'Zitong Yang', 'Weijia Shi', 'Xiang Lisa Li', 'Li Fei-Fei', 'Hannaneh Hajishirzi', 'Luke Zettlemoyer', 'Percy Liang', 'Emmanuel Candès', 'Tatsunori Hashimoto'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Test-time scaling is a promising new approach to language modeling that uses
extra test-time compute to improve performance. Recently, OpenAI's o1 model
showed this capability but did not publicly share its methodology, leading to
many replication efforts. We seek the simplest approach to achieve test-time
scaling and ... | 2025-01-31T18:48:08Z | 46 pages (9 main), 10 figures, 15 tables | null | null | s1: Simple test-time scaling | ['Niklas Muennighoff', 'Zitong Yang', 'Weijia Shi', 'Xiang Lisa Li', 'Fei-Fei Li', 'Hanna Hajishirzi', 'Luke S. Zettlemoyer', 'Percy Liang', 'Emmanuel J. Candes', 'Tatsunori Hashimoto'] | 2,025 | arXiv.org | 392 | 72 | ['Computer Science'] |
2,501.194 | Vintix: Action Model via In-Context Reinforcement Learning | ['Andrey Polubarov', 'Nikita Lyubaykin', 'Alexander Derevyagin', 'Ilya Zisman', 'Denis Tarasov', 'Alexander Nikulin', 'Vladislav Kurenkov'] | ['cs.LG', 'cs.AI', 'cs.RO'] | In-Context Reinforcement Learning (ICRL) represents a promising paradigm for
developing generalist agents that learn at inference time through
trial-and-error interactions, analogous to how large language models adapt
contextually, but with a focus on reward maximization. However, the scalability
of ICRL beyond toy tas... | 2025-01-31T18:57:08Z | Preprint. In review | null | null | Vintix: Action Model via In-Context Reinforcement Learning | ['Andrey Polubarov', 'Nikita Lyubaykin', 'Alexander Derevyagin', 'Ilya Zisman', 'Denis Tarasov', 'Alexander Nikulin', 'Vladislav Kurenkov'] | 2,025 | arXiv.org | 3 | 0 | ['Computer Science'] |
2,501.99999 | null | [] | [''] | null | null | null | null | null | null | null | null | null | null | null | null |
2,502.00094 | AIN: The Arabic INclusive Large Multimodal Model | ['Ahmed Heakl', 'Sara Ghaboura', 'Omkar Thawkar', 'Fahad Shahbaz Khan', 'Hisham Cholakkal', 'Rao Muhammad Anwer', 'Salman Khan'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.HC', 'cs.LG'] | Amid the swift progress of large language models (LLMs) and their evolution
into large multimodal models (LMMs), significant strides have been made in
high-resource languages such as English and Chinese. While Arabic LLMs have
seen notable progress, Arabic LMMs remain largely unexplored, often narrowly
focusing on a fe... | 2025-01-31T18:58:20Z | 20 pages, 16 figures, ACL | null | null | AIN: The Arabic INclusive Large Multimodal Model | ['Ahmed Heakl', 'Sara Ghaboura', 'Omkar Thawakar', 'F. Khan', 'Hisham Cholakkal', 'R. Anwer', 'Salman H. Khan'] | 2,025 | arXiv.org | 1 | 0 | ['Computer Science'] |
2,502.00196 | DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access
Dermatology Datasets | ['Abdurrahim Yilmaz', 'Furkan Yuceyalcin', 'Ece Gokyayla', 'Donghee Choi', 'Ozan Erdem', 'Ali Anil Demircali', 'Rahmetullah Varol', 'Ufuk Gorkem Kirabali', 'Gulsum Gencoglan', 'Joram M. Posma', 'Burak Temelkuran'] | ['cs.CV', 'cs.AI', 'cs.CL'] | A major barrier to developing vision large language models (LLMs) in
dermatology is the lack of large image--text pairs dataset. We introduce
DermaSynth, a dataset comprising of 92,020 synthetic image--text pairs curated
from 45,205 images (13,568 clinical and 35,561 dermatoscopic) for
dermatology-related clinical task... | 2025-01-31T22:26:33Z | 12 pages, 4 figures | null | null | DermaSynth: Rich Synthetic Image-Text Pairs Using Open Access Dermatology Datasets | ['Abdurrahim Yilmaz', 'Furkan Yuceyalcin', 'Ece Gokyayla', 'Donghee Choi', 'Ozan Erdem Ali Anil Demircali', 'Rahmetullah Varol', 'Ufuk Gorkem Kirabali', 'G. Gencoglan', 'J. Posma', 'Burak Temelkuran'] | 2,025 | arXiv.org | 0 | 19 | ['Computer Science'] |
2,502.00203 | Reward-aware Preference Optimization: A Unified Mathematical Framework
for Model Alignment | ['Shengyang Sun', 'Yian Zhang', 'Alexander Bukharin', 'David Mosallanezhad', 'Jiaqi Zeng', 'Soumye Singhal', 'Gerald Shen', 'Adithya Renduchintala', 'Tugrul Konuk', 'Yi Dong', 'Zhilin Wang', 'Dmitry Chichkov', 'Olivier Delalleau', 'Oleksii Kuchaiev'] | ['cs.LG', 'cs.CL'] | The rapid development of large language model (LLM) alignment algorithms has
resulted in a complex and fragmented landscape, with limited clarity on the
effectiveness of different methods and their inter-connections. This paper
introduces Reward-Aware Preference Optimization (RPO), a mathematical framework
that unifies... | 2025-01-31T22:39:04Z | 8 pages, 4 figures; update author names | null | null | null | null | null | null | null | null | null |
2,502.00212 | STP: Self-play LLM Theorem Provers with Iterative Conjecturing and
Proving | ['Kefan Dong', 'Tengyu Ma'] | ['cs.LG', 'cs.AI', 'cs.LO'] | A fundamental challenge in formal theorem proving by LLMs is the lack of
high-quality training data. Although reinforcement learning or expert iteration
partially mitigates this issue by alternating between LLM generating proofs and
finetuning them on correctly generated ones, performance quickly plateaus due
to the sc... | 2025-01-31T23:01:48Z | 25 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,502.00258 | ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for
Pretrained LLMs | ['Hongyi Liu', 'Rajarshi Saha', 'Zhen Jia', 'Youngsuk Park', 'Jiaji Huang', 'Shoham Sabach', 'Yu-Xiang Wang', 'George Karypis'] | ['cs.LG', 'cs.CL'] | Large Language Models (LLMs) have demonstrated exceptional performance in
natural language processing tasks, yet their massive size makes serving them
inefficient and costly. Semi-structured pruning has emerged as an effective
method for model acceleration, but existing approaches are suboptimal because
they focus on l... | 2025-02-01T01:35:23Z | ICML25 | null | null | ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs | ['Hongyi Liu', 'Rajarshi Saha', 'Zhen Jia', 'Youngsuk Park', 'Jiaji Huang', 'Shoham Sabach', 'Yu-xiang Wang', 'George Karypis'] | 2,025 | arXiv.org | 0 | 0 | ['Computer Science'] |
2,502.00366 | Prostate-Specific Foundation Models for Enhanced Detection of Clinically
Significant Cancer | ['Jeong Hoon Lee', 'Cynthia Xinran Li', 'Hassan Jahanandish', 'Indrani Bhattacharya', 'Sulaiman Vesal', 'Lichun Zhang', 'Shengtian Sang', 'Moon Hyung Choi', 'Simon John Christoph Soerensen', 'Steve Ran Zhou', 'Elijah Richard Sommer', 'Richard Fan', 'Pejman Ghanouni', 'Yuze Song', 'Tyler M. Seibert', 'Geoffrey A. Sonn',... | ['eess.IV', 'cs.CV'] | Accurate prostate cancer diagnosis remains challenging. Even when using MRI,
radiologists exhibit low specificity and significant inter-observer
variability, leading to potential delays or inaccuracies in identifying
clinically significant cancers. This leads to numerous unnecessary biopsies and
risks of missing clinic... | 2025-02-01T08:42:33Z | 44pages | null | null | null | null | null | null | null | null | null |
2,502.00592 | M+: Extending MemoryLLM with Scalable Long-Term Memory | ['Yu Wang', 'Dmitry Krotov', 'Yuanzhe Hu', 'Yifan Gao', 'Wangchunshu Zhou', 'Julian McAuley', 'Dan Gutfreund', 'Rogerio Feris', 'Zexue He'] | ['cs.CL'] | Equipping large language models (LLMs) with latent-space memory has attracted
increasing attention as they can extend the context window of existing language
models. However, retaining information from the distant past remains a
challenge. For example, MemoryLLM (Wang et al., 2024a), as a representative
work with laten... | 2025-02-01T23:13:10Z | null | null | null | null | null | null | null | null | null | null |
2,502.00816 | Sundial: A Family of Highly Capable Time Series Foundation Models | ['Yong Liu', 'Guo Qin', 'Zhiyuan Shi', 'Zhi Chen', 'Caiyin Yang', 'Xiangdong Huang', 'Jianmin Wang', 'Mingsheng Long'] | ['cs.LG'] | We introduce Sundial, a family of native, flexible, and scalable time series
foundation models. To predict the next-patch's distribution, we propose a
TimeFlow Loss based on flow-matching, which facilitates native pre-training of
Transformers on continuous-valued time series without discrete tokenization.
Conditioned o... | 2025-02-02T14:52:50Z | null | null | null | null | null | null | null | null | null | null |
2,502.00857 | HintEval: A Comprehensive Framework for Hint Generation and Evaluation
for Questions | ['Jamshid Mozafari', 'Bhawna Piryani', 'Abdelrahman Abdallah', 'Adam Jatowt'] | ['cs.CL', 'cs.IR'] | Large Language Models (LLMs) are transforming how people find information,
and many users turn nowadays to chatbots to obtain answers to their questions.
Despite the instant access to abundant information that LLMs offer, it is still
important to promote critical thinking and problem-solving skills. Automatic
hint gene... | 2025-02-02T17:07:18Z | Submitted to SIGIR 2025 | null | null | HintEval: A Comprehensive Framework for Hint Generation and Evaluation for Questions | ['Jamshid Mozafari', 'Bhawna Piryani', 'Abdelrahman Abdallah', 'Adam Jatowt'] | 2,025 | arXiv.org | 1 | 0 | ['Computer Science'] |
2,502.00963 | PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs | ['Mauricio Soroco', 'Jialin Song', 'Mengzhou Xia', 'Kye Emond', 'Weiran Sun', 'Wuyang Chen'] | ['cs.LG'] | While recent AI-for-math has made strides in pure mathematics, areas of
applied mathematics, particularly PDEs, remain underexplored despite their
significant real-world applications. We present PDE-Controller, a framework
that enables large language models (LLMs) to control systems governed by
partial differential equ... | 2025-02-03T00:03:41Z | null | null | null | null | null | null | null | null | null | null |
2,502.01051 | Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level
Preference Optimization | ['Tao Zhang', 'Cheng Da', 'Kun Ding', 'Huan Yang', 'Kun Jin', 'Yan Li', 'Tingting Gao', 'Di Zhang', 'Shiming Xiang', 'Chunhong Pan'] | ['cs.CV'] | Preference optimization for diffusion models aims to align them with human
preferences for images. Previous methods typically use Vision-Language Models
(VLMs) as pixel-level reward models to approximate human preferences. However,
when used for step-level preference optimization, these models face challenges
in handli... | 2025-02-03T04:51:28Z | 25 pages, 26 tables, 15 figures | null | null | null | null | null | null | null | null | null |
2,502.01113 | GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation | ['Linhao Luo', 'Zicheng Zhao', 'Gholamreza Haffari', 'Dinh Phung', 'Chen Gong', 'Shirui Pan'] | ['cs.IR', 'cs.AI', 'cs.CL'] | Retrieval-augmented generation (RAG) has proven effective in integrating
knowledge into large language models (LLMs). However, conventional RAGs
struggle to capture complex relationships between pieces of knowledge, limiting
their performance in intricate reasoning that requires integrating knowledge
from multiple sour... | 2025-02-03T07:04:29Z | 19 pages, 6 figures | null | null | GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation | ['Linhao Luo', 'Zicheng Zhao', 'Gholamreza Haffari', 'D.Q. Phung', 'Chen Gong', 'Shirui Pan'] | 2,025 | arXiv.org | 5 | 0 | ['Computer Science'] |
2,502.01385 | Detecting Backdoor Samples in Contrastive Language Image Pretraining | ['Hanxun Huang', 'Sarah Erfani', 'Yige Li', 'Xingjun Ma', 'James Bailey'] | ['cs.LG', 'cs.CV'] | Contrastive language-image pretraining (CLIP) has been found to be vulnerable
to poisoning backdoor attacks where the adversary can achieve an almost perfect
attack success rate on CLIP models by poisoning only 0.01\% of the training
dataset. This raises security concerns on the current practice of pretraining
large-sc... | 2025-02-03T14:21:05Z | ICLR2025 | null | null | Detecting Backdoor Samples in Contrastive Language Image Pretraining | ['Hanxun Huang', 'S. Erfani', 'Yige Li', 'Xingjun Ma', 'James Bailey'] | 2,025 | International Conference on Learning Representations | 5 | 0 | ['Computer Science'] |
2,502.01406 | GRADIEND: Monosemantic Feature Learning within Neural Networks Applied
to Gender Debiasing of Transformer Models | ['Jonathan Drechsel', 'Steffen Herbold'] | ['cs.LG', 'cs.AI', 'cs.CL'] | AI systems frequently exhibit and amplify social biases, including gender
bias, leading to harmful consequences in critical areas. This study introduces
a novel encoder-decoder approach that leverages model gradients to learn a
single monosemantic feature neuron encoding gender information. We show that
our method can ... | 2025-02-03T14:38:27Z | null | null | null | null | null | null | null | null | null | null |
2,502.01416 | Categorical Schrödinger Bridge Matching | ['Grigoriy Ksenofontov', 'Alexander Korotin'] | ['cs.LG'] | The Schr\"odinger Bridge (SB) is a powerful framework for solving generative
modeling tasks such as unpaired domain translation. Most SB-related research
focuses on continuous data space $\mathbb{R}^{D}$ and leaves open theoretical
and algorithmic questions about applying SB methods to discrete data, e.g, on
finite spa... | 2025-02-03T14:55:28Z | null | null | null | Categorical Schrödinger Bridge Matching | ['Grigoriy Ksenofontov', 'Alexander Korotin'] | 2,025 | arXiv.org | 1 | 63 | ['Computer Science'] |
2,502.01456 | Process Reinforcement through Implicit Rewards | ['Ganqu Cui', 'Lifan Yuan', 'Zefan Wang', 'Hanbin Wang', 'Wendi Li', 'Bingxiang He', 'Yuchen Fan', 'Tianyu Yu', 'Qixin Xu', 'Weize Chen', 'Jiarui Yuan', 'Huayu Chen', 'Kaiyan Zhang', 'Xingtai Lv', 'Shuo Wang', 'Yuan Yao', 'Xu Han', 'Hao Peng', 'Yu Cheng', 'Zhiyuan Liu', 'Maosong Sun', 'Bowen Zhou', 'Ning Ding'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Dense process rewards have proven a more effective alternative to the sparse
outcome-level rewards in the inference-time scaling of large language models
(LLMs), particularly in tasks requiring complex multi-step reasoning. While
dense rewards also offer an appealing choice for the reinforcement learning
(RL) of LLMs s... | 2025-02-03T15:43:48Z | 20 pages. Model&Code&Data available at
https://github.com/PRIME-RL/PRIME | null | null | Process Reinforcement through Implicit Rewards | ['Ganqu Cui', 'Lifan Yuan', 'Zefan Wang', 'Hanbin Wang', 'Wendi Li', 'Bingxiang He', 'Yuchen Fan', 'Tianyu Yu', 'Qixin Xu', 'Weize Chen', 'Jiarui Yuan', 'Huayu Chen', 'Kaiyan Zhang', 'Xingtai Lv', 'Shuo Wang', 'Yuan Yao', 'Xu Han', 'Hao Peng', 'Yu Cheng', 'Zhiyuan Liu', 'Maosong Sun', 'Bowen Zhou', 'Ning Ding'] | 2,025 | arXiv.org | 103 | 54 | ['Computer Science'] |
2,502.01534 | Preference Leakage: A Contamination Problem in LLM-as-a-judge | ['Dawei Li', 'Renliang Sun', 'Yue Huang', 'Ming Zhong', 'Bohan Jiang', 'Jiawei Han', 'Xiangliang Zhang', 'Wei Wang', 'Huan Liu'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Large Language Models (LLMs) as judges and LLM-based data synthesis have
emerged as two fundamental LLM-driven data annotation methods in model
development. While their combination significantly enhances the efficiency of
model training and evaluation, little attention has been given to the potential
contamination brou... | 2025-02-03T17:13:03Z | 20 pages, 7 figures | null | null | null | null | null | null | null | null | null |
2,502.01562 | Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints
Internalization | ['Minttu Alakuijala', 'Ya Gao', 'Georgy Ananov', 'Samuel Kaski', 'Pekka Marttinen', 'Alexander Ilin', 'Harri Valpola'] | ['cs.LG'] | As the general capabilities of artificial intelligence (AI) agents continue
to evolve, their ability to learn to master multiple complex tasks through
experience remains a key challenge. Current LLM agents, particularly those
based on proprietary language models, typically rely on prompts to incorporate
knowledge about... | 2025-02-03T17:45:46Z | null | null | null | null | null | null | null | null | null | null |
2,502.01657 | Improving Rule-based Reasoning in LLMs using Neurosymbolic
Representations | ['Varun Dhanraj', 'Chris Eliasmith'] | ['cs.LG', 'cs.AI'] | Large language models (LLMs) continue to face challenges in reliably solving
reasoning tasks, particularly those that require precise rule following, as
often found in mathematical reasoning. This paper introduces a novel
neurosymbolic method that improves LLM reasoning by encoding hidden states into
neurosymbolic vect... | 2025-01-31T20:29:51Z | null | null | null | Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations | ['Varun Dhanraj', 'Chris Eliasmith'] | 2,025 | null | 0 | 39 | ['Computer Science'] |
2,502.01717 | Choose Your Model Size: Any Compression by a Single Gradient Descent | ['Martin Genzel', 'Patrick Putzky', 'Pengfei Zhao', 'Sebastian Schulze', 'Mattes Mollenhauer', 'Robert Seidel', 'Stefan Dietzel', 'Thomas Wollmann'] | ['cs.LG'] | The adoption of Foundation Models in resource-constrained environments
remains challenging due to their large size and inference costs. A promising
way to overcome these limitations is post-training compression, which aims to
balance reduced model size against performance degradation. This work presents
Any Compression... | 2025-02-03T18:40:58Z | null | null | null | null | null | null | null | null | null | null |
2,502.01718 | ACECODER: Acing Coder RL via Automated Test-Case Synthesis | ['Huaye Zeng', 'Dongfu Jiang', 'Haozhe Wang', 'Ping Nie', 'Xiaotong Chen', 'Wenhu Chen'] | ['cs.SE', 'cs.AI', 'cs.CL'] | Most progress in recent coder models has been driven by supervised
fine-tuning (SFT), while the potential of reinforcement learning (RL) remains
largely unexplored, primarily due to the lack of reliable reward data/model in
the code domain. In this paper, we address this challenge by leveraging
automated large-scale te... | 2025-02-03T18:46:04Z | 9 pages, 4 figure, 11 tables. Accepted to ACL 2025 main conference | null | null | null | null | null | null | null | null | null |
2,502.02016 | A Periodic Bayesian Flow for Material Generation | ['Hanlin Wu', 'Yuxuan Song', 'Jingjing Gong', 'Ziyao Cao', 'Yawen Ouyang', 'Jianbing Zhang', 'Hao Zhou', 'Wei-Ying Ma', 'Jingjing Liu'] | ['cs.LG', 'cs.AI'] | Generative modeling of crystal data distribution is an important yet
challenging task due to the unique periodic physical symmetry of crystals.
Diffusion-based methods have shown early promise in modeling crystal
distribution. More recently, Bayesian Flow Networks were introduced to
aggregate noisy latent variables, re... | 2025-02-04T05:07:13Z | Accepted to ICLR25 | null | null | A Periodic Bayesian Flow for Material Generation | ['Hanlin Wu', 'Yuxuan Song', 'Jingjing Gong', 'Ziyao Cao', 'Yawen Ouyang', 'Jianbing Zhang', 'Hao Zhou', 'Wei-Ying Ma', 'Jingjing Liu'] | 2,025 | International Conference on Learning Representations | 3 | 121 | ['Computer Science'] |
2,502.02095 | LongDPO: Unlock Better Long-form Generation Abilities for LLMs via
Critique-augmented Stepwise Information | ['Bowen Ping', 'Jiali Zeng', 'Fandong Meng', 'Shuo Wang', 'Jie Zhou', 'Shanghang Zhang'] | ['cs.CL'] | Long-form generation is crucial for academic writing papers and repo-level
code generation. Despite this, current models, including GPT-4o, still exhibit
unsatisfactory performance. Existing methods that utilize preference learning
with outcome supervision often fail to provide detailed feedback for extended
contexts. ... | 2025-02-04T08:25:17Z | ACL 2025 | null | null | LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information | ['Bowen Ping', 'Jiali Zeng', 'Fandong Meng', 'Shuo Wang', 'Jie Zhou', 'Shanghang Zhang'] | 2,025 | arXiv.org | 2 | 54 | ['Computer Science'] |
2,502.02257 | UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic
Segmentation | ['Tao Zhang', 'Jinyong Wen', 'Zhen Chen', 'Kun Ding', 'Shiming Xiang', 'Chunhong Pan'] | ['cs.CV'] | Pre-training techniques significantly enhance the performance of semantic
segmentation tasks with limited training data. However, the efficacy under a
large domain gap between pre-training (e.g. RGB) and fine-tuning (e.g.
infrared) remains underexplored. In this study, we first benchmark the infrared
semantic segmentat... | 2025-02-04T12:08:20Z | ICLR 2025. 27 pages, 13 figures, 21 tables | null | null | UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation | ['Tao Zhang', 'Jinyong Wen', 'Zhen Chen', 'Kun Ding', 'Shiming Xiang', 'Chunhong Pan'] | 2,025 | International Conference on Learning Representations | 1 | 68 | ['Computer Science'] |
2,502.02358 | MotionLab: Unified Human Motion Generation and Editing via the
Motion-Condition-Motion Paradigm | ['Ziyan Guo', 'Zeyu Hu', 'Na Zhao', 'De Wen Soh'] | ['cs.CV'] | Human motion generation and editing are key components of computer graphics
and vision. However, current approaches in this field tend to offer isolated
solutions tailored to specific tasks, which can be inefficient and impractical
for real-world applications. While some efforts have aimed to unify
motion-related tasks... | 2025-02-04T14:43:26Z | null | null | null | null | null | null | null | null | null | null |
2,502.02384 | STAIR: Improving Safety Alignment with Introspective Reasoning | ['Yichi Zhang', 'Siyuan Zhang', 'Yao Huang', 'Zeyu Xia', 'Zhengwei Fang', 'Xiao Yang', 'Ranjie Duan', 'Dong Yan', 'Yinpeng Dong', 'Jun Zhu'] | ['cs.CL'] | Ensuring the safety and harmlessness of Large Language Models (LLMs) has
become equally critical as their performance in applications. However, existing
safety alignment methods typically suffer from safety-performance trade-offs
and the susceptibility to jailbreak attacks, primarily due to their reliance on
direct ref... | 2025-02-04T15:02:55Z | 22 pages, 8 figures, ICML2025 Oral | null | null | null | null | null | null | null | null | null |
2,502.02465 | Towards Consistent and Controllable Image Synthesis for Face Editing | ['Mengting Wei', 'Tuomas Varanka', 'Yante Li', 'Xingxun Jiang', 'Huai-Qian Khor', 'Guoying Zhao'] | ['cs.CV'] | Face editing methods, essential for tasks like virtual avatars, digital human
synthesis and identity preservation, have traditionally been built upon
GAN-based techniques, while recent focus has shifted to diffusion-based models
due to their success in image reconstruction. However, diffusion models still
face challeng... | 2025-02-04T16:36:07Z | null | null | null | null | null | null | null | null | null | null |
2,502.02481 | Multilingual Machine Translation with Open Large Language Models at
Practical Scale: An Empirical Study | ['Menglong Cui', 'Pengzhi Gao', 'Wei Liu', 'Jian Luan', 'Bin Wang'] | ['cs.CL'] | Large language models (LLMs) have shown continuously improving multilingual
capabilities, and even small-scale open-source models have demonstrated rapid
performance enhancement. In this paper, we systematically explore the abilities
of open LLMs with less than ten billion parameters to handle multilingual
machine tran... | 2025-02-04T16:57:03Z | Accept to NAACL2025 Main Conference | null | null | null | null | null | null | null | null | null |
2,502.02508 | Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM
Reasoning via Autoregressive Search | ['Maohao Shen', 'Guangtao Zeng', 'Zhenting Qi', 'Zhang-Wei Hong', 'Zhenfang Chen', 'Wei Lu', 'Gregory Wornell', 'Subhro Das', 'David Cox', 'Chuang Gan'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) have demonstrated remarkable reasoning
capabilities across diverse domains. Recent studies have shown that increasing
test-time computation enhances LLMs' reasoning capabilities. This typically
involves extensive sampling at inference time guided by an external LLM
verifier, resulting in a ... | 2025-02-04T17:26:58Z | null | null | null | null | null | null | null | null | null | null |
2,502.02631 | ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization | ['Zechun Liu', 'Changsheng Zhao', 'Hanxian Huang', 'Sijia Chen', 'Jing Zhang', 'Jiawei Zhao', 'Scott Roy', 'Lisa Jin', 'Yunyang Xiong', 'Yangyang Shi', 'Lin Xiao', 'Yuandong Tian', 'Bilge Soran', 'Raghuraman Krishnamoorthi', 'Tijmen Blankevoort', 'Vikas Chandra'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.CV'] | The optimal bit-width for achieving the best trade-off between quantized
model size and accuracy has been a subject of ongoing debate. While some
advocate for 4-bit quantization, others propose that 1.58-bit offers superior
results. However, the lack of a cohesive framework for different bits has left
such conclusions ... | 2025-02-04T18:59:26Z | null | null | null | null | null | null | null | null | null | null |
2,502.02708 | AsserT5: Test Assertion Generation Using a Fine-Tuned Code Language
Model | ['Severin Primbs', 'Benedikt Fein', 'Gordon Fraser'] | ['cs.SE'] | Writing good software tests can be challenging, therefore approaches that
support developers are desirable. While generating complete tests automatically
is such an approach commonly proposed in research, developers may already have
specific test scenarios in mind and thus just require help in selecting the
most suitab... | 2025-02-04T20:42:22Z | Accepted for AST 2025 (https://conf.researchr.org/home/ast-2025) | null | 10.1109/AST66626.2025.00008 | null | null | null | null | null | null | null |
2,502.02737 | SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language
Model | ['Loubna Ben Allal', 'Anton Lozhkov', 'Elie Bakouch', 'Gabriel Martín Blázquez', 'Guilherme Penedo', 'Lewis Tunstall', 'Andrés Marafioti', 'Hynek Kydlíček', 'Agustín Piqueres Lajarín', 'Vaibhav Srivastav', 'Joshua Lochner', 'Caleb Fahlgren', 'Xuan-Son Nguyen', 'Clémentine Fourrier', 'Ben Burtenshaw', 'Hugo Larcher', 'H... | ['cs.CL'] | While large language models have facilitated breakthroughs in many
applications of artificial intelligence, their inherent largeness makes them
computationally expensive and challenging to deploy in resource-constrained
settings. In this paper, we document the development of SmolLM2, a
state-of-the-art "small" (1.7 bil... | 2025-02-04T21:43:16Z | null | null | null | SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model | ['Loubna Ben Allal', 'Anton Lozhkov', 'Elie Bakouch', "Gabriel Mart'in Bl'azquez", 'Guilherme Penedo', 'Lewis Tunstall', 'Andrés Marafioti', "Hynek Kydl'ivcek", "Agust'in Piqueres Lajar'in", 'Vaibhav Srivastav', 'Joshua Lochner', 'Caleb Fahlgren', 'Xuan-Son Nguyen', 'Clémentine Fourrier', 'Ben Burtenshaw', 'Hugo Larche... | 2,025 | arXiv.org | 47 | 0 | ['Computer Science'] |
2,502.02904 | ScholaWrite: A Dataset of End-to-End Scholarly Writing Process | ['Linghe Wang', 'Minhwa Lee', 'Ross Volkov', 'Luan Tuyen Chau', 'Dongyeop Kang'] | ['cs.HC', 'cs.CL', 'q-bio.NC'] | Writing is a cognitively demanding task involving continuous decision-making,
heavy use of working memory, and frequent switching between multiple
activities. Scholarly writing is particularly complex as it requires authors to
coordinate many pieces of multiform knowledge. To fully understand writers'
cognitive thought... | 2025-02-05T05:57:37Z | Equal contribution: Linghe Wang, Minhwa Lee | project page:
https://minnesotanlp.github.io/scholawrite/ | null | null | ScholaWrite: A Dataset of End-to-End Scholarly Writing Process | ['Linghe Wang', 'Minhwa Lee', 'Ross Volkov', 'Luan Tuyen Chau', 'Dongyeop Kang'] | 2,025 | arXiv.org | 4 | 46 | ['Computer Science', 'Biology'] |
2,502.03128 | Metis: A Foundation Speech Generation Model with Masked Generative
Pre-training | ['Yuancheng Wang', 'Jiachen Zheng', 'Junan Zhang', 'Xueyao Zhang', 'Huan Liao', 'Zhizheng Wu'] | ['cs.SD', 'cs.AI', 'cs.LG', 'eess.AS', 'eess.SP'] | We introduce Metis, a foundation model for unified speech generation. Unlike
previous task-specific or multi-task models, Metis follows a pre-training and
fine-tuning paradigm. It is pre-trained on large-scale unlabeled speech data
using masked generative modeling and then fine-tuned to adapt to diverse speech
generati... | 2025-02-05T12:36:21Z | null | null | null | Metis: A Foundation Speech Generation Model with Masked Generative Pre-training | ['Yuancheng Wang', 'Jiachen Zheng', 'Junan Zhang', 'Xueyao Zhang', 'Huan Liao', 'Zhizheng Wu'] | 2,025 | arXiv.org | 3 | 0 | ['Computer Science', 'Engineering'] |
2,502.03212 | Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech
Recognition and Subtitling | ['Jakob Poncelet', 'Hugo Van hamme'] | ['eess.AS', 'cs.SD'] | The recent advancement of speech recognition technology has been driven by
large-scale datasets and attention-based architectures, but many challenges
still remain, especially for low-resource languages and dialects. This paper
explores the integration of weakly supervised transcripts from TV subtitles
into automatic s... | 2025-02-05T14:26:58Z | Preprint | null | null | null | null | null | null | null | null | null |
2,502.03333 | RadVLM: A Multitask Conversational Vision-Language Model for Radiology | ['Nicolas Deperrois', 'Hidetoshi Matsuo', 'Samuel Ruipérez-Campillo', 'Moritz Vandenhirtz', 'Sonia Laguna', 'Alain Ryser', 'Koji Fujimoto', 'Mizuho Nishio', 'Thomas M. Sutter', 'Julia E. Vogt', 'Jonas Kluckert', 'Thomas Frauenfelder', 'Christian Blüthgen', 'Farhad Nooralahzadeh', 'Michael Krauthammer'] | ['cs.CV', 'cs.AI'] | The widespread use of chest X-rays (CXRs), coupled with a shortage of
radiologists, has driven growing interest in automated CXR analysis and
AI-assisted reporting. While existing vision-language models (VLMs) show
promise in specific tasks such as report generation or abnormality detection,
they often lack support for... | 2025-02-05T16:27:02Z | 21 pages, 15 figures | null | null | null | null | null | null | null | null | null |
2,502.03382 | High-Fidelity Simultaneous Speech-To-Speech Translation | ['Tom Labiausse', 'Laurent Mazaré', 'Edouard Grave', 'Patrick Pérez', 'Alexandre Défossez', 'Neil Zeghidour'] | ['cs.CL', 'cs.SD', 'eess.AS'] | We introduce Hibiki, a decoder-only model for simultaneous speech
translation. Hibiki leverages a multistream language model to synchronously
process source and target speech, and jointly produces text and audio tokens to
perform speech-to-text and speech-to-speech translation. We furthermore address
the fundamental ch... | 2025-02-05T17:18:55Z | null | null | null | High-Fidelity Simultaneous Speech-To-Speech Translation | ['Tom Labiausse', "Laurent Mazar'e", 'Edouard Grave', "Patrick P'erez", "Alexandre D'efossez", 'Neil Zeghidour'] | 2,025 | arXiv.org | 1 | 42 | ['Computer Science', 'Engineering'] |
2,502.03387 | LIMO: Less is More for Reasoning | ['Yixin Ye', 'Zhen Huang', 'Yang Xiao', 'Ethan Chern', 'Shijie Xia', 'Pengfei Liu'] | ['cs.CL', 'cs.AI'] | We present a fundamental discovery that challenges our understanding of how
complex reasoning emerges in large language models. While conventional wisdom
suggests that sophisticated reasoning tasks demand extensive training data
(>100,000 examples), we demonstrate that complex mathematical reasoning
abilities can be ef... | 2025-02-05T17:23:45Z | 17 pages | null | null | LIMO: Less is More for Reasoning | ['Yixin Ye', 'Zhen Huang', 'Yang Xiao', 'Ethan Chern', 'Shijie Xia', 'Pengfei Liu'] | 2,025 | arXiv.org | 166 | 0 | ['Computer Science'] |
2,502.03438 | BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic
Theorem Proving | ['Ran Xin', 'Chenguang Xi', 'Jie Yang', 'Feng Chen', 'Hang Wu', 'Xia Xiao', 'Yifan Sun', 'Shen Zheng', 'Kai Shen'] | ['cs.AI'] | Recent advancements in large language models (LLMs) have spurred growing
interest in automatic theorem proving using Lean4, where effective tree search
methods are crucial for navigating the underlying large proof search spaces.
While the existing approaches primarily rely on value functions and/or Monte
Carlo Tree Sea... | 2025-02-05T18:33:36Z | null | null | null | BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving | ['Ran Xin', 'Chenguang Xi', 'Jie Yang', 'Feng Chen', 'Hang Wu', 'Xia Xiao', 'Yifan Sun', 'Shen Zheng', 'Kai Shen'] | 2,025 | arXiv.org | 16 | 32 | ['Computer Science'] |
2,502.03492 | Teaching Language Models to Critique via Reinforcement Learning | ['Zhihui Xie', 'Jie chen', 'Liyu Chen', 'Weichao Mao', 'Jingjing Xu', 'Lingpeng Kong'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Teaching large language models (LLMs) to critique and refine their outputs is
crucial for building systems that can iteratively improve, yet it is
fundamentally limited by the ability to provide accurate judgments and
actionable suggestions. In this work, we study LLM critics for code generation
and propose $\texttt{CT... | 2025-02-05T02:18:46Z | null | null | null | Teaching Language Models to Critique via Reinforcement Learning | ['Zhihui Xie', 'Jie chen', 'Liyu Chen', 'Weichao Mao', 'Jingjing Xu', 'Lingpeng Kong'] | 2,025 | arXiv.org | 13 | 0 | ['Computer Science'] |
2,502.03499 | Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and
Multi-Task Learning | ['Zehui Li', 'Vallijah Subasri', 'Yifei Shen', 'Dongsheng Li', 'Yiren Zhao', 'Guy-Bart Stan', 'Caihua Shan'] | ['q-bio.GN', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) demonstrate remarkable generalizability across
diverse tasks, yet genomic foundation models (GFMs) still require separate
finetuning for each downstream application, creating significant overhead as
model sizes grow. Moreover, existing GFMs are constrained by rigid output
formats, limiting ... | 2025-02-05T09:20:52Z | null | null | null | Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning | ['Zehui Li', 'Vallijah Subasri', 'Yifei Shen', 'Dongsheng Li', 'Yiren Zhao', 'Guy-Bart Stan', 'Caihua Shan'] | 2,025 | arXiv.org | 0 | 0 | ['Computer Science', 'Biology'] |
2,502.03629 | REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image
Transformations | ['Peter Sushko', 'Ayana Bharadwaj', 'Zhi Yang Lim', 'Vasily Ilin', 'Ben Caffee', 'Dongping Chen', 'Mohammadreza Salehi', 'Cheng-Yu Hsieh', 'Ranjay Krishna'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Existing image editing models struggle to meet real-world demands. Despite
excelling in academic benchmarks, they have yet to be widely adopted for real
user needs. Datasets that power these models use artificial edits, lacking the
scale and ecological validity necessary to address the true diversity of user
requests. ... | 2025-02-05T21:35:48Z | Published at CVPR 2025 | null | null | REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations | ['Peter Sushko', 'Ayana Bharadwaj', 'Zhi Yang Lim', 'Vasily Ilin', 'Ben Caffee', 'Dongping Chen', 'Mohammadreza Salehi', 'Cheng-Yu Hsieh', 'Ranjay Krishna'] | 2,025 | arXiv.org | 1 | 71 | ['Computer Science'] |
2,502.03738 | Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More | ['Feng Wang', 'Yaodong Yu', 'Guoyizhe Wei', 'Wei Shao', 'Yuyin Zhou', 'Alan Yuille', 'Cihang Xie'] | ['cs.CV'] | Since the introduction of Vision Transformer (ViT), patchification has long
been regarded as a de facto image tokenization approach for plain visual
architectures. By compressing the spatial size of images, this approach can
effectively shorten the token sequence and reduce the computational cost of
ViT-like plain arch... | 2025-02-06T03:01:38Z | null | null | null | null | null | null | null | null | null | null |
2,502.03793 | It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like
Masked Language Models As Generative Classifiers | ['Benjamin Clavié', 'Nathan Cooper', 'Benjamin Warner'] | ['cs.CL', 'cs.AI'] | While encoder-only models such as BERT and ModernBERT are ubiquitous in
real-world NLP applications, their conventional reliance on task-specific
classification heads can limit their applicability compared to decoder-based
large language models (LLMs). In this work, we introduce
ModernBERT-Large-Instruct, a 0.4B-parame... | 2025-02-06T05:47:37Z | null | null | null | null | null | null | null | null | null | null |
2,502.03979 | Towards Unified Music Emotion Recognition across Dimensional and
Categorical Models | ['Jaeyong Kang', 'Dorien Herremans'] | ['cs.SD', 'cs.AI', 'eess.AS'] | One of the most significant challenges in Music Emotion Recognition (MER)
comes from the fact that emotion labels can be heterogeneous across datasets
with regard to the emotion representation, including categorical (e.g., happy,
sad) versus dimensional labels (e.g., valence-arousal). In this paper, we
present a unifie... | 2025-02-06T11:20:22Z | null | null | null | Towards Unified Music Emotion Recognition across Dimensional and Categorical Models | ['Jaeyong Kang', 'Dorien Herremans'] | 2,025 | arXiv.org | 0 | 56 | ['Computer Science', 'Engineering'] |
2,502.04128 | Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based
Speech Synthesis | ['Zhen Ye', 'Xinfa Zhu', 'Chi-Min Chan', 'Xinsheng Wang', 'Xu Tan', 'Jiahe Lei', 'Yi Peng', 'Haohe Liu', 'Yizhu Jin', 'Zheqi Dai', 'Hongzhan Lin', 'Jianyi Chen', 'Xingjian Du', 'Liumeng Xue', 'Yunlin Chen', 'Zhifei Li', 'Lei Xie', 'Qiuqiang Kong', 'Yike Guo', 'Wei Xue'] | ['eess.AS', 'cs.AI', 'cs.CL', 'cs.MM', 'cs.SD'] | Recent advances in text-based large language models (LLMs), particularly in
the GPT series and the o1 model, have demonstrated the effectiveness of scaling
both training-time and inference-time compute. However, current
state-of-the-art TTS systems leveraging LLMs are often multi-stage, requiring
separate models (e.g.,... | 2025-02-06T15:04:00Z | null | null | null | Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis | ['Zhen Ye', 'Xinfa Zhu', 'Chi-min Chan', 'Xinsheng Wang', 'Xu Tan', 'Jiahe Lei', 'Yi Peng', 'Haohe Liu', 'Yizhu Jin', 'Zheqi Dai', 'Hongzhan Lin', 'Jianyi Chen', 'Xingjian Du', 'Liumeng Xue', 'Yunlin Chen', 'Zhifei Li', 'Lei Xie', 'Qiuqiang Kong', 'Yi-Ting Guo', 'Wei Xue'] | 2,025 | arXiv.org | 9 | 68 | ['Engineering', 'Computer Science'] |
2,502.04153 | UltraIF: Advancing Instruction Following from the Wild | ['Kaikai An', 'Li Sheng', 'Ganqu Cui', 'Shuzheng Si', 'Ning Ding', 'Yu Cheng', 'Baobao Chang'] | ['cs.CL', 'cs.AI'] | Instruction-following made modern large language models (LLMs) helpful
assistants. However, the key to taming LLMs on complex instructions remains
mysterious, for that there are huge gaps between models trained by open-source
community and those trained by leading companies. To bridge the gap, we propose
a simple and s... | 2025-02-06T15:39:16Z | null | null | null | null | null | null | null | null | null | null |
2,502.04328 | Ola: Pushing the Frontiers of Omni-Modal Language Model | ['Zuyan Liu', 'Yuhao Dong', 'Jiahui Wang', 'Ziwei Liu', 'Winston Hu', 'Jiwen Lu', 'Yongming Rao'] | ['cs.CV', 'cs.CL', 'cs.MM', 'cs.SD', 'eess.AS', 'eess.IV'] | Recent advances in large language models, particularly following GPT-4o, have
sparked increasing interest in developing omni-modal models capable of
understanding more modalities. While some open-source alternatives have
emerged, there is still a notable lag behind specialized single-modality models
in performance. In ... | 2025-02-06T18:59:55Z | null | null | null | Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive Modality Alignment | ['Zuyan Liu', 'Yuhao Dong', 'Jiahui Wang', 'Ziwei Liu', 'Winston Hu', 'Jiwen Lu', 'Yongming Rao'] | 2,025 | arXiv.org | 17 | 78 | ['Computer Science', 'Engineering'] |
2,502.0435 | CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance | ['Yongchao Chen', 'Yilun Hao', 'Yueying Liu', 'Yang Zhang', 'Chuchu Fan'] | ['cs.CL', 'cs.AI', 'cs.LG', 'cs.SC', 'cs.SE'] | Existing methods fail to effectively steer Large Language Models (LLMs)
between textual reasoning and code generation, leaving symbolic computing
capabilities underutilized. We introduce CodeSteer, an effective method for
guiding LLM code/text generation. We construct a comprehensive benchmark
SymBench comprising 37 sy... | 2025-02-04T15:53:59Z | 28 pages, 12 figures | International Conference on Machine Learning (ICML'2025) | null | null | null | null | null | null | null | null |
2,502.04404 | Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
Language Models | ['Xiao-Wen Yang', 'Xuan-Yi Zhu', 'Wen-Da Wei', 'Ding-Chu Zhang', 'Jie-Jing Shao', 'Zhi Zhou', 'Lan-Zhe Guo', 'Yu-Feng Li'] | ['cs.CL', 'cs.AI'] | The integration of slow-thinking mechanisms into large language models (LLMs)
offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified
by systems like OpenAI's o1. However, several significant challenges remain,
including inefficient overthinking and an overreliance on auxiliary reward
models. We p... | 2025-02-06T08:52:43Z | This is a preprint under review, 15 pages, 13 figures | null | null | null | null | null | null | null | null | null |
2,502.04465 | FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks | ['Luca Della Libera', 'Francesco Paissan', 'Cem Subakan', 'Mirco Ravanelli'] | ['cs.LG', 'cs.AI', 'cs.SD', 'eess.AS'] | Large language models have revolutionized natural language processing through
self-supervised pretraining on massive datasets. Inspired by this success,
researchers have explored adapting these methods to speech by discretizing
continuous audio into tokens using neural audio codecs. However, existing
approaches face li... | 2025-02-06T19:24:50Z | 18 pages | null | null | FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks | ['Luca Della Libera', 'F. Paissan', 'Cem Subakan', 'M. Ravanelli'] | 2,025 | arXiv.org | 1 | 0 | ['Computer Science', 'Engineering'] |
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