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2,502.04519 | GenVC: Self-Supervised Zero-Shot Voice Conversion | ['Zexin Cai', 'Henry Li Xinyuan', 'Ashi Garg', 'Leibny Paola García-Perera', 'Kevin Duh', 'Sanjeev Khudanpur', 'Matthew Wiesner', 'Nicholas Andrews'] | ['eess.AS', 'cs.LG'] | Zero-shot voice conversion has recently made substantial progress, but many
models still depend on external supervised systems to disentangle speaker
identity and linguistic content. Furthermore, current methods often use
parallel conversion, where the converted speech inherits the source utterance's
temporal structure... | 2025-02-06T21:40:09Z | null | null | null | null | null | null | null | null | null | null |
2,502.05003 | QuEST: Stable Training of LLMs with 1-Bit Weights and Activations | ['Andrei Panferov', 'Jiale Chen', 'Soroush Tabesh', 'Roberto L. Castro', 'Mahdi Nikdan', 'Dan Alistarh'] | ['cs.LG'] | One approach to reducing the massive costs of large language models (LLMs) is
the use of quantized or sparse representations for training or deployment.
While post-training compression methods are very popular, the question of
obtaining even more accurate compressed models by directly training over such
representations... | 2025-02-07T15:23:34Z | null | null | null | null | null | null | null | null | null | null |
2,502.05139 | Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for
Speech, Music, and Sound | ['Andros Tjandra', 'Yi-Chiao Wu', 'Baishan Guo', 'John Hoffman', 'Brian Ellis', 'Apoorv Vyas', 'Bowen Shi', 'Sanyuan Chen', 'Matt Le', 'Nick Zacharov', 'Carleigh Wood', 'Ann Lee', 'Wei-Ning Hsu'] | ['cs.SD', 'cs.LG', 'eess.AS'] | The quantification of audio aesthetics remains a complex challenge in audio
processing, primarily due to its subjective nature, which is influenced by
human perception and cultural context. Traditional methods often depend on
human listeners for evaluation, leading to inconsistencies and high resource
demands. This pap... | 2025-02-07T18:15:57Z | Repository: https://github.com/facebookresearch/audiobox-aesthetics
Website:
https://ai.meta.com/research/publications/meta-audiobox-aesthetics-unified-automatic-quality-assessment-for-speech-music-and-sound/ | null | null | null | null | null | null | null | null | null |
2,502.05153 | Hummingbird: High Fidelity Image Generation via Multimodal Context
Alignment | ['Minh-Quan Le', 'Gaurav Mittal', 'Tianjian Meng', 'A S M Iftekhar', 'Vishwas Suryanarayanan', 'Barun Patra', 'Dimitris Samaras', 'Mei Chen'] | ['cs.CV'] | While diffusion models are powerful in generating high-quality, diverse
synthetic data for object-centric tasks, existing methods struggle with
scene-aware tasks such as Visual Question Answering (VQA) and Human-Object
Interaction (HOI) Reasoning, where it is critical to preserve scene attributes
in generated images co... | 2025-02-07T18:32:51Z | Accepted to ICLR 2025. Project page with code release:
https://roar-ai.github.io/hummingbird | null | null | Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment | ['Minh-Quan Le', 'Gaurav Mittal', 'Tianjian Meng', 'A. S. M. Iftekhar', 'Vishwas Suryanarayanan', 'Barun Patra', 'Dimitris Samaras', 'Mei Chen'] | 2,025 | International Conference on Learning Representations | 0 | 43 | ['Computer Science'] |
2,502.05163 | DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM
Guardrails | ['Yihe Deng', 'Yu Yang', 'Junkai Zhang', 'Wei Wang', 'Bo Li'] | ['cs.CL', 'cs.LG'] | The rapid advancement of large language models (LLMs) has increased the need
for guardrail models to ensure responsible use, particularly in detecting
unsafe and illegal content. While substantial safety data exist in English,
multilingual guardrail modeling remains underexplored due to the scarcity of
open-source safe... | 2025-02-07T18:45:03Z | 24 pages, 9 figures, 5 tables | null | null | null | null | null | null | null | null | null |
2,502.05171 | Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth
Approach | ['Jonas Geiping', 'Sean McLeish', 'Neel Jain', 'John Kirchenbauer', 'Siddharth Singh', 'Brian R. Bartoldson', 'Bhavya Kailkhura', 'Abhinav Bhatele', 'Tom Goldstein'] | ['cs.LG', 'cs.CL'] | We study a novel language model architecture that is capable of scaling
test-time computation by implicitly reasoning in latent space. Our model works
by iterating a recurrent block, thereby unrolling to arbitrary depth at
test-time. This stands in contrast to mainstream reasoning models that scale up
compute by produc... | 2025-02-07T18:55:02Z | The model is available at
https://huggingface.co/tomg-group-umd/huginn-0125. Code and data recipe can
be found at https://github.com/seal-rg/recurrent-pretraining | null | null | null | null | null | null | null | null | null |
2,502.05173 | VideoRoPE: What Makes for Good Video Rotary Position Embedding? | ['Xilin Wei', 'Xiaoran Liu', 'Yuhang Zang', 'Xiaoyi Dong', 'Pan Zhang', 'Yuhang Cao', 'Jian Tong', 'Haodong Duan', 'Qipeng Guo', 'Jiaqi Wang', 'Xipeng Qiu', 'Dahua Lin'] | ['cs.CV'] | While Rotary Position Embedding (RoPE) and its variants are widely adopted
for their long-context capabilities, the extension of the 1D RoPE to video,
with its complex spatio-temporal structure, remains an open challenge. This
work first introduces a comprehensive analysis that identifies four key
characteristics essen... | 2025-02-07T18:56:04Z | null | null | null | VideoRoPE: What Makes for Good Video Rotary Position Embedding? | ['Xilin Wei', 'Xiaoran Liu', 'Yuhang Zang', 'Xiao-wen Dong', 'Pan Zhang', 'Yuhang Cao', 'Jian Tong', 'Haodong Duan', 'Qipeng Guo', 'Jiaqi Wang', 'Xipeng Qiu', 'Dahua Lin'] | 2,025 | arXiv.org | 6 | 80 | ['Computer Science'] |
2,502.05178 | QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive
Multimodal Understanding and Generation | ['Yue Zhao', 'Fuzhao Xue', 'Scott Reed', 'Linxi Fan', 'Yuke Zhu', 'Jan Kautz', 'Zhiding Yu', 'Philipp Krähenbühl', 'De-An Huang'] | ['cs.CV'] | We introduce Quantized Language-Image Pretraining (QLIP), a visual
tokenization method that combines state-of-the-art reconstruction quality with
state-of-the-art zero-shot image understanding. QLIP trains a
binary-spherical-quantization-based autoencoder with reconstruction and
language-image alignment objectives. We ... | 2025-02-07T18:59:57Z | Tech report. Project page: https://nvlabs.github.io/QLIP/ | null | null | null | null | null | null | null | null | null |
2,502.05179 | FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution
Video Generation | ['Shilong Zhang', 'Wenbo Li', 'Shoufa Chen', 'Chongjian Ge', 'Peize Sun', 'Yida Zhang', 'Yi Jiang', 'Zehuan Yuan', 'Binyue Peng', 'Ping Luo'] | ['cs.CV'] | DiT diffusion models have achieved great success in text-to-video generation,
leveraging their scalability in model capacity and data scale. High content and
motion fidelity aligned with text prompts, however, often require large model
parameters and a substantial number of function evaluations (NFEs). Realistic
and vi... | 2025-02-07T18:59:59Z | Model and Weight: https://github.com/FoundationVision/FlashVideo | null | null | FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation | ['Shilong Zhang', 'Wenbo Li', 'Shoufa Chen', 'Chongjian Ge', 'Peize Sun', 'Yida Zhang', 'Yi Jiang', 'Zehuan Yuan', 'Binyue Peng', 'Ping Luo'] | 2,025 | arXiv.org | 6 | 69 | ['Computer Science'] |
2,502.05364 | Hypencoder: Hypernetworks for Information Retrieval | ['Julian Killingback', 'Hansi Zeng', 'Hamed Zamani'] | ['cs.IR', 'cs.LG'] | Existing information retrieval systems are largely constrained by their
reliance on vector inner products to assess query-document relevance, which
naturally limits the expressiveness of the relevance score they can produce. We
propose a new paradigm; instead of representing a query as a vector, we use a
small neural n... | 2025-02-07T22:31:38Z | null | null | null | Hypencoder: Hypernetworks for Information Retrieval | ['Julian Killingback', 'Hansi Zeng', 'Hamed Zamani'] | 2,025 | arXiv.org | 1 | 81 | ['Computer Science'] |
2,502.05374 | Towards LLM Unlearning Resilient to Relearning Attacks: A
Sharpness-Aware Minimization Perspective and Beyond | ['Chongyu Fan', 'Jinghan Jia', 'Yihua Zhang', 'Anil Ramakrishna', 'Mingyi Hong', 'Sijia Liu'] | ['cs.LG', 'cs.CL'] | The LLM unlearning technique has recently been introduced to comply with data
regulations and address the safety and ethical concerns of LLMs by removing the
undesired data-model influence. However, state-of-the-art unlearning methods
face a critical vulnerability: they are susceptible to ``relearning'' the
removed inf... | 2025-02-07T23:03:55Z | Accepted by ICML 2025 | null | null | null | null | null | null | null | null | null |
2,502.05478 | OntoTune: Ontology-Driven Self-training for Aligning Large Language
Models | ['Zhiqiang Liu', 'Chengtao Gan', 'Junjie Wang', 'Yichi Zhang', 'Zhongpu Bo', 'Mengshu Sun', 'Huajun Chen', 'Wen Zhang'] | ['cs.CL'] | Existing domain-specific Large Language Models (LLMs) are typically developed
by fine-tuning general-purposed LLMs with large-scale domain-specific corpora.
However, training on large-scale corpora often fails to effectively organize
domain knowledge of LLMs, leading to fragmented understanding. Inspired by how
humans ... | 2025-02-08T07:38:45Z | Accepted by WWW25 | null | null | null | null | null | null | null | null | null |
2,502.05512 | IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot
Text-To-Speech System | ['Wei Deng', 'Siyi Zhou', 'Jingchen Shu', 'Jinchao Wang', 'Lu Wang'] | ['cs.SD', 'cs.AI', 'eess.AS'] | Recently, large language model (LLM) based text-to-speech (TTS) systems have
gradually become the mainstream in the industry due to their high naturalness
and powerful zero-shot voice cloning capabilities.Here, we introduce the
IndexTTS system, which is mainly based on the XTTS and Tortoise model. We add
some novel imp... | 2025-02-08T10:23:20Z | null | null | null | null | null | null | null | null | null | null |
2,502.05564 | TabICL: A Tabular Foundation Model for In-Context Learning on Large Data | ['Jingang Qu', 'David Holzmüller', 'Gaël Varoquaux', 'Marine Le Morvan'] | ['cs.LG', 'cs.AI'] | The long-standing dominance of gradient-boosted decision trees on tabular
data is currently challenged by tabular foundation models using In-Context
Learning (ICL): setting the training data as context for the test data and
predicting in a single forward pass without parameter updates. While TabPFNv2
foundation model e... | 2025-02-08T13:25:04Z | Published at ICML 2025 | null | null | null | null | null | null | null | null | null |
2,502.05633 | Mol-MoE: Training Preference-Guided Routers for Molecule Generation | ['Diego Calanzone', "Pierluca D'Oro", 'Pierre-Luc Bacon'] | ['cs.LG'] | Recent advances in language models have enabled framing molecule generation
as sequence modeling. However, existing approaches often rely on
single-objective reinforcement learning, limiting their applicability to
real-world drug design, where multiple competing properties must be optimized.
Traditional multi-objective... | 2025-02-08T16:28:33Z | We release our code and data at: https://github.com/ddidacus/mol-moe | null | null | Mol-MoE: Training Preference-Guided Routers for Molecule Generation | ['Diego Calanzone', "P. D'Oro", 'Pierre-Luc Bacon'] | 2,025 | arXiv.org | 1 | 48 | ['Computer Science'] |
2,502.05664 | CODESIM: Multi-Agent Code Generation and Problem Solving through
Simulation-Driven Planning and Debugging | ['Md. Ashraful Islam', 'Mohammed Eunus Ali', 'Md Rizwan Parvez'] | ['cs.CL', 'cs.AI'] | Large Language Models (LLMs) have made significant strides in code generation
and problem solving. Current approaches employ external tool-based iterative
debuggers that use compiler or other tool-based runtime feedback to refine
coarse programs generated by various methods. However, the effectiveness of
these approach... | 2025-02-08T18:43:59Z | Accepted in NAACL 2025 Findings | null | null | null | null | null | null | null | null | null |
2,502.05674 | ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution
Shifts | ['Ashi Garg', 'Zexin Cai', 'Lin Zhang', 'Henry Li Xinyuan', 'Leibny Paola García-Perera', 'Kevin Duh', 'Sanjeev Khudanpur', 'Matthew Wiesner', 'Nicholas Andrews'] | ['eess.AS', 'cs.SD'] | The problem of synthetic speech detection has enjoyed considerable attention,
with recent methods achieving low error rates across several established
benchmarks. However, to what extent can low error rates on academic benchmarks
translate to more realistic conditions? In practice, while the training set is
fixed at on... | 2025-02-08T19:49:09Z | null | null | null | null | null | null | null | null | null | null |
2,502.05795 | The Curse of Depth in Large Language Models | ['Wenfang Sun', 'Xinyuan Song', 'Pengxiang Li', 'Lu Yin', 'Yefeng Zheng', 'Shiwei Liu'] | ['cs.LG', 'cs.AI'] | In this paper, we introduce the Curse of Depth, a concept that highlights,
explains, and addresses the recent observation in modern Large Language Models
(LLMs) where nearly half of the layers are less effective than expected. We
first confirm the wide existence of this phenomenon across the most popular
families of LL... | 2025-02-09T07:03:36Z | null | null | null | null | null | null | null | null | null | null |
2,502.05878 | Retrieval-augmented Large Language Models for Financial Time Series
Forecasting | ['Mengxi Xiao', 'Zihao Jiang', 'Lingfei Qian', 'Zhengyu Chen', 'Yueru He', 'Yijing Xu', 'Yuecheng Jiang', 'Dong Li', 'Ruey-Ling Weng', 'Min Peng', 'Jimin Huang', 'Sophia Ananiadou', 'Qianqian Xie'] | ['cs.CL'] | Accurately forecasting stock price movements is critical for informed
financial decision-making, supporting applications ranging from algorithmic
trading to risk management. However, this task remains challenging due to the
difficulty of retrieving subtle yet high-impact patterns from noisy financial
time-series data, ... | 2025-02-09T12:26:05Z | 11 pages, 4 figures | null | null | null | null | null | null | null | null | null |
2,502.05932 | Skill Expansion and Composition in Parameter Space | ['Tenglong Liu', 'Jianxiong Li', 'Yinan Zheng', 'Haoyi Niu', 'Yixing Lan', 'Xin Xu', 'Xianyuan Zhan'] | ['cs.LG', 'cs.AI', 'cs.RO'] | Humans excel at reusing prior knowledge to address new challenges and
developing skills while solving problems. This paradigm becomes increasingly
popular in the development of autonomous agents, as it develops systems that
can self-evolve in response to new challenges like human beings. However,
previous methods suffe... | 2025-02-09T15:22:38Z | ICLR 2025, 37 pages | null | null | null | null | null | null | null | null | null |
2,502.06253 | Find Central Dogma Again: Leveraging Multilingual Transfer in Large
Language Models | ['Wang Liang'] | ['q-bio.GN', '92-10', 'J.3'] | In recent years, large language models (LLMs) have achieved state-of-the-art
results in various biological sequence analysis tasks, such as sequence
classification, structure prediction, and function prediction. Similar to
advancements in AI for other scientific fields, deeper research into biological
LLMs has begun to... | 2025-02-10T08:37:21Z | 31 pages,8 figures | null | null | null | null | null | null | null | null | null |
2,502.06352 | LANTERN++: Enhancing Relaxed Speculative Decoding with Static Tree
Drafting for Visual Auto-regressive Models | ['Sihwan Park', 'Doohyuk Jang', 'Sungyub Kim', 'Souvik Kundu', 'Eunho Yang'] | ['cs.CV'] | Speculative decoding has been widely used to accelerate auto-regressive (AR)
text generation. However, its effectiveness for visual AR models remains
limited due to token selection ambiguity, where multiple tokens share similarly
low probabilities and thus reduce acceptance rates. Recently, relaxed
speculative decoding... | 2025-02-10T11:05:18Z | ICLR 2025 Workshop at SCOPE (Oral), 16 pages, 5 figures, short paper
(6 pages exclude reference and appendix) | null | null | LANTERN++: Enhanced Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models | ['Sihwan Park', 'Doohyuk Jang', 'Sung-Yub Kim', 'Souvik Kundu', 'Eunho Yang'] | 2,025 | arXiv.org | 0 | 20 | ['Computer Science'] |
2,502.06367 | FOCUS -- Multi-View Foot Reconstruction From Synthetically Trained Dense
Correspondences | ['Oliver Boyne', 'Roberto Cipolla'] | ['cs.CV'] | Surface reconstruction from multiple, calibrated images is a challenging task
- often requiring a large number of collected images with significant overlap.
We look at the specific case of human foot reconstruction. As with previous
successful foot reconstruction work, we seek to extract rich per-pixel geometry
cues fr... | 2025-02-10T11:36:45Z | 13 pages, 11 figures | null | null | null | null | null | null | null | null | null |
2,502.06394 | SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
Annotators | ['Daniil Moskovskiy', 'Nikita Sushko', 'Sergey Pletenev', 'Elena Tutubalina', 'Alexander Panchenko'] | ['cs.CL'] | Existing approaches to multilingual text detoxification are hampered by the
scarcity of parallel multilingual datasets. In this work, we introduce a
pipeline for the generation of multilingual parallel detoxification data. We
also introduce SynthDetoxM, a manually collected and synthetically generated
multilingual para... | 2025-02-10T12:30:25Z | Accepted to NAACL 2025 Main Conference | null | null | SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators | ['Daniil Moskovskiy', 'Nikita Sushko', 'Sergey Pletenev', 'Elena Tutubalina', 'Alexander Panchenko'] | 2,025 | North American Chapter of the Association for Computational Linguistics | 0 | 0 | ['Computer Science'] |
2,502.06608 | TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified
Flow Models | ['Yangguang Li', 'Zi-Xin Zou', 'Zexiang Liu', 'Dehu Wang', 'Yuan Liang', 'Zhipeng Yu', 'Xingchao Liu', 'Yuan-Chen Guo', 'Ding Liang', 'Wanli Ouyang', 'Yan-Pei Cao'] | ['cs.CV', 'cs.AI'] | Recent advancements in diffusion techniques have propelled image and video
generation to unprecedented levels of quality, significantly accelerating the
deployment and application of generative AI. However, 3D shape generation
technology has so far lagged behind, constrained by limitations in 3D data
scale, complexity ... | 2025-02-10T16:07:54Z | null | null | null | null | null | null | null | null | null | null |
2,502.06692 | Multi-label Scandinavian Language Identification (SLIDE) | ['Mariia Fedorova', 'Jonas Sebulon Frydenberg', 'Victoria Handford', 'Victoria Ovedie Chruickshank Langø', 'Solveig Helene Willoch', 'Marthe Løken Midtgaard', 'Yves Scherrer', 'Petter Mæhlum', 'David Samuel'] | ['cs.CL', 'cs.AI'] | Identifying closely related languages at sentence level is difficult, in
particular because it is often impossible to assign a sentence to a single
language. In this paper, we focus on multi-label sentence-level Scandinavian
language identification (LID) for Danish, Norwegian Bokm\r{a}l, Norwegian
Nynorsk, and Swedish.... | 2025-02-10T17:16:55Z | null | null | null | Multi-label Scandinavian Language Identification (SLIDE) | ['Mariia Fedorova', 'Jonas Sebulon Frydenberg', 'Victoria Handford', 'Victoria Ovedie Chruickshank Lango', 'Solveig Helene Willoch', 'M. Midtgaard', 'Yves Scherrer', 'Petter Maehlum', 'David Samuel'] | 2,025 | arXiv.org | 0 | 32 | ['Computer Science'] |
2,502.06703 | Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
Scaling | ['Runze Liu', 'Junqi Gao', 'Jian Zhao', 'Kaiyan Zhang', 'Xiu Li', 'Biqing Qi', 'Wanli Ouyang', 'Bowen Zhou'] | ['cs.CL'] | Test-Time Scaling (TTS) is an important method for improving the performance
of Large Language Models (LLMs) by using additional computation during the
inference phase. However, current studies do not systematically analyze how
policy models, Process Reward Models (PRMs), and problem difficulty influence
TTS. This lack... | 2025-02-10T17:30:23Z | null | null | null | null | null | null | null | null | null | null |
2,502.06734 | Señorita-2M: A High-Quality Instruction-based Dataset for General
Video Editing by Video Specialists | ['Bojia Zi', 'Penghui Ruan', 'Marco Chen', 'Xianbiao Qi', 'Shaozhe Hao', 'Shihao Zhao', 'Youze Huang', 'Bin Liang', 'Rong Xiao', 'Kam-Fai Wong'] | ['cs.CV'] | Recent advancements in video generation have spurred the development of video
editing techniques, which can be divided into inversion-based and end-to-end
methods. However, current video editing methods still suffer from several
challenges. Inversion-based methods, though training-free and flexible, are
time-consuming ... | 2025-02-10T17:58:22Z | null | null | null | null | null | null | null | null | null | null |
2,502.06755 | Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
Models | ['Samuel Stevens', 'Wei-Lun Chao', 'Tanya Berger-Wolf', 'Yu Su'] | ['cs.CV'] | To truly understand vision models, we must not only interpret their learned
features but also validate these interpretations through controlled
experiments. Current approaches either provide interpretable features without
the ability to test their causal influence, or enable model editing without
interpretable controls... | 2025-02-10T18:32:41Z | Main text is 11 pages with 7 figures | null | null | Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision Models | ['Samuel Stevens', 'Wei-Lun Chao', 'Tanya Y. Berger-Wolf', 'Yu Su'] | 2,025 | arXiv.org | 6 | 61 | ['Computer Science'] |
2,502.06764 | History-Guided Video Diffusion | ['Kiwhan Song', 'Boyuan Chen', 'Max Simchowitz', 'Yilun Du', 'Russ Tedrake', 'Vincent Sitzmann'] | ['cs.LG', 'cs.CV'] | Classifier-free guidance (CFG) is a key technique for improving conditional
generation in diffusion models, enabling more accurate control while enhancing
sample quality. It is natural to extend this technique to video diffusion,
which generates video conditioned on a variable number of context frames,
collectively ref... | 2025-02-10T18:44:25Z | Project Website: https://boyuan.space/history-guidance | null | null | null | null | null | null | null | null | null |
2,502.06772 | ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates | ['Ling Yang', 'Zhaochen Yu', 'Bin Cui', 'Mengdi Wang'] | ['cs.CL', 'cs.AI', 'cs.LG'] | We present that hierarchical LLM reasoning via scaling thought templates can
effectively optimize the reasoning search space and outperform the mathematical
reasoning capabilities of powerful LLMs like OpenAI o1-preview and DeepSeek V3.
We train our ReasonFlux-32B model with only 8 GPUs and introduces three
innovations... | 2025-02-10T18:51:47Z | Code: https://github.com/Gen-Verse/ReasonFlux | null | null | ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates | ['Ling Yang', 'Zhaochen Yu', 'Bin Cui', 'Mengdi Wang'] | 2,025 | arXiv.org | 18 | 58 | ['Computer Science'] |
2,502.06781 | Exploring the Limit of Outcome Reward for Learning Mathematical
Reasoning | ['Chengqi Lyu', 'Songyang Gao', 'Yuzhe Gu', 'Wenwei Zhang', 'Jianfei Gao', 'Kuikun Liu', 'Ziyi Wang', 'Shuaibin Li', 'Qian Zhao', 'Haian Huang', 'Weihan Cao', 'Jiangning Liu', 'Hongwei Liu', 'Junnan Liu', 'Songyang Zhang', 'Dahua Lin', 'Kai Chen'] | ['cs.CL', 'cs.LG'] | Reasoning abilities, especially those for solving complex math problems, are
crucial components of general intelligence. Recent advances by proprietary
companies, such as o-series models of OpenAI, have made remarkable progress on
reasoning tasks. However, the complete technical details remain unrevealed, and
the techn... | 2025-02-10T18:57:29Z | We released our code, data, and model on
https://github.com/InternLM/OREAL | null | null | Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning | ['Chengqi Lyu', 'Songyang Gao', 'Yuzhe Gu', 'Wenwei Zhang', 'Jianfei Gao', 'Kuikun Liu', 'Ziyi Wang', 'Shuaibin Li', 'Qian Zhao', 'Haian Huang', 'Weihan Cao', 'Jiangning Liu', 'Hong-wei Liu', 'Junnan Liu', 'Songyang Zhang', 'Dahua Lin', 'Kai Chen'] | 2,025 | arXiv.org | 19 | 0 | ['Computer Science'] |
2,502.06782 | Lumina-Video: Efficient and Flexible Video Generation with Multi-scale
Next-DiT | ['Dongyang Liu', 'Shicheng Li', 'Yutong Liu', 'Zhen Li', 'Kai Wang', 'Xinyue Li', 'Qi Qin', 'Yufei Liu', 'Yi Xin', 'Zhongyu Li', 'Bin Fu', 'Chenyang Si', 'Yuewen Cao', 'Conghui He', 'Ziwei Liu', 'Yu Qiao', 'Qibin Hou', 'Hongsheng Li', 'Peng Gao'] | ['cs.CV'] | Recent advancements have established Diffusion Transformers (DiTs) as a
dominant framework in generative modeling. Building on this success,
Lumina-Next achieves exceptional performance in the generation of
photorealistic images with Next-DiT. However, its potential for video
generation remains largely untapped, with s... | 2025-02-10T18:58:11Z | null | null | null | null | null | null | null | null | null | null |
2,502.06788 | EVEv2: Improved Baselines for Encoder-Free Vision-Language Models | ['Haiwen Diao', 'Xiaotong Li', 'Yufeng Cui', 'Yueze Wang', 'Haoge Deng', 'Ting Pan', 'Wenxuan Wang', 'Huchuan Lu', 'Xinlong Wang'] | ['cs.CV', 'cs.AI'] | Existing encoder-free vision-language models (VLMs) are rapidly narrowing the
performance gap with their encoder-based counterparts, highlighting the
promising potential for unified multimodal systems with structural simplicity
and efficient deployment. We systematically clarify the performance gap between
VLMs using p... | 2025-02-10T18:59:58Z | 19 pages, 9 figures | null | null | EVEv2: Improved Baselines for Encoder-Free Vision-Language Models | ['Haiwen Diao', 'Xiaotong Li', 'Yufeng Cui', 'Yueze Wang', 'Haoge Deng', 'Ting Pan', 'Wenxuan Wang', 'Huchuan Lu', 'Xinlong Wang'] | 2,025 | arXiv.org | 8 | 0 | ['Computer Science'] |
2,502.06814 | Diffusion Instruction Tuning | ['Chen Jin', 'Ryutaro Tanno', 'Amrutha Saseendran', 'Tom Diethe', 'Philip Teare'] | ['cs.LG', 'cs.AI', 'cs.GR'] | We introduce Lavender, a simple supervised fine-tuning (SFT) method that
boosts the performance of advanced vision-language models (VLMs) by leveraging
state-of-the-art image generation models such as Stable Diffusion.
Specifically, Lavender aligns the text-vision attention in the VLM transformer
with the equivalent us... | 2025-02-04T22:20:20Z | Project page at https://astrazeneca.github.io/vlm/ | null | null | Diffusion Instruction Tuning | ['Chen Jin', 'Ryutaro Tanno', 'Amrutha Saseendran', 'Tom Diethe', 'Philip Teare'] | 2,025 | arXiv.org | 0 | 89 | ['Computer Science'] |
2,502.06858 | LLM-Supported Natural Language to Bash Translation | ['Finnian Westenfelder', 'Erik Hemberg', 'Miguel Tulla', 'Stephen Moskal', "Una-May O'Reilly", 'Silviu Chiricescu'] | ['cs.CL', 'cs.AI'] | The Bourne-Again Shell (Bash) command-line interface for Linux systems has
complex syntax and requires extensive specialized knowledge. Using the natural
language to Bash command (NL2SH) translation capabilities of large language
models (LLMs) for command composition circumvents these issues. However, the
NL2SH perform... | 2025-02-07T19:35:55Z | 13 pages, NAACL 2025 | null | null | null | null | null | null | null | null | null |
2,502.06876 | Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and
Harmlessness of Large Language Model via Model Merging | ['Jinluan Yang', 'Dingnan Jin', 'Anke Tang', 'Li Shen', 'Didi Zhu', 'Zhengyu Chen', 'Ziyu Zhao', 'Daixin Wang', 'Qing Cui', 'Zhiqiang Zhang', 'Jun Zhou', 'Fei Wu', 'Kun Kuang'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Achieving balanced alignment of large language models (LLMs) in terms of
Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a
cornerstone of responsible AI. Existing methods like data mixture strategies
face limitations, including heavy reliance on expert knowledge and conflicting
optimization signals... | 2025-02-08T11:56:58Z | null | null | null | Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging | ['Jinluan Yang', 'Dingnan Jin', 'A. Tang', 'Li Shen', 'Didi Zhu', 'Zhengyu Chen', 'Daixin Wang', 'Qing Cui', 'Zhiqiang Zhang', 'Jun Zhou', 'Fei Wu', 'Kun Kuang'] | 2,025 | arXiv.org | 6 | 0 | ['Computer Science'] |
2,502.06997 | Conditional diffusion model with spatial attention and latent embedding
for medical image segmentation | ['Behzad Hejrati', 'Soumyanil Banerjee', 'Carri Glide-Hurst', 'Ming Dong'] | ['eess.IV', 'cs.CV'] | Diffusion models have been used extensively for high quality image and video
generation tasks. In this paper, we propose a novel conditional diffusion model
with spatial attention and latent embedding (cDAL) for medical image
segmentation. In cDAL, a convolutional neural network (CNN) based discriminator
is used at eve... | 2025-02-10T19:47:28Z | 13 pages, 5 figures, 3 tables, Accepted in MICCAI 2024 | null | null | Conditional Diffusion Model with Spatial Attention and Latent Embedding for Medical Image Segmentation | ['Behzad Hejrati', 'Soumyanil Banerjee', 'C. Glide-Hurst', 'Ming Dong'] | 2,024 | International Conference on Medical Image Computing and Computer-Assisted Intervention | 0 | 31 | ['Medicine', 'Computer Science', 'Engineering'] |
2,502.07272 | GENERator: A Long-Context Generative Genomic Foundation Model | ['Wei Wu', 'Qiuyi Li', 'Mingyang Li', 'Kun Fu', 'Fuli Feng', 'Jieping Ye', 'Hui Xiong', 'Zheng Wang'] | ['cs.CL', 'q-bio.GN'] | Advancements in DNA sequencing technologies have significantly improved our
ability to decode genomic sequences. However, the prediction and interpretation
of these sequences remain challenging due to the intricate nature of genetic
material. Large language models (LLMs) have introduced new opportunities for
biological... | 2025-02-11T05:39:49Z | null | null | null | null | null | null | null | null | null | null |
2,502.07316 | CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction | ['Junlong Li', 'Daya Guo', 'Dejian Yang', 'Runxin Xu', 'Yu Wu', 'Junxian He'] | ['cs.CL', 'cs.AI'] | Reasoning is a fundamental capability of Large Language Models. While prior
research predominantly focuses on enhancing narrow skills like math or code
generation, improving performance on many other reasoning tasks remains
challenging due to sparse and fragmented training data. To address this issue,
we propose CodeI/... | 2025-02-11T07:26:50Z | ICML 2025 | null | null | CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction | ['Junlong Li', 'Daya Guo', 'Dejian Yang', 'Runxin Xu', 'Yu Wu', 'Junxian He'] | 2,025 | arXiv.org | 13 | 50 | ['Computer Science'] |
2,502.07374 | LLMs Can Easily Learn to Reason from Demonstrations Structure, not
content, is what matters! | ['Dacheng Li', 'Shiyi Cao', 'Tyler Griggs', 'Shu Liu', 'Xiangxi Mo', 'Eric Tang', 'Sumanth Hegde', 'Kourosh Hakhamaneshi', 'Shishir G. Patil', 'Matei Zaharia', 'Joseph E. Gonzalez', 'Ion Stoica'] | ['cs.AI'] | Large reasoning models (LRMs) tackle complex reasoning problems by following
long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
and self-validation. However, the training techniques and data requirements to
elicit Long CoT remain poorly understood. In this work, we find that a Large
Language m... | 2025-02-11T08:48:48Z | null | null | null | null | null | null | null | null | null | null |
2,502.07527 | Nature Language Model: Deciphering the Language of Nature for Scientific
Discovery | ['Yingce Xia', 'Peiran Jin', 'Shufang Xie', 'Liang He', 'Chuan Cao', 'Renqian Luo', 'Guoqing Liu', 'Yue Wang', 'Zequn Liu', 'Yuan-Jyue Chen', 'Zekun Guo', 'Yeqi Bai', 'Pan Deng', 'Yaosen Min', 'Ziheng Lu', 'Hongxia Hao', 'Han Yang', 'Jielan Li', 'Chang Liu', 'Jia Zhang', 'Jianwei Zhu', 'Ran Bi', 'Kehan Wu', 'Wei Zhang'... | ['cs.AI', 'cs.LG'] | Foundation models have revolutionized natural language processing and
artificial intelligence, significantly enhancing how machines comprehend and
generate human languages. Inspired by the success of these foundation models,
researchers have developed foundation models for individual scientific domains,
including small... | 2025-02-11T13:08:03Z | 95 pages | null | null | null | null | null | null | null | null | null |
2,502.07599 | DPO-Shift: Shifting the Distribution of Direct Preference Optimization | ['Xiliang Yang', 'Feng Jiang', 'Qianen Zhang', 'Lei Zhao', 'Xiao Li'] | ['cs.CL'] | Direct Preference Optimization (DPO) and its variants have become
increasingly popular for aligning language models with human preferences. These
methods aim to teach models to better distinguish between chosen (or preferred)
and rejected (or dispreferred) responses. However, prior research has
identified that the prob... | 2025-02-11T14:49:44Z | null | null | null | null | null | null | null | null | null | null |
2,502.0764 | Goedel-Prover: A Frontier Model for Open-Source Automated Theorem
Proving | ['Yong Lin', 'Shange Tang', 'Bohan Lyu', 'Jiayun Wu', 'Hongzhou Lin', 'Kaiyu Yang', 'Jia Li', 'Mengzhou Xia', 'Danqi Chen', 'Sanjeev Arora', 'Chi Jin'] | ['cs.LG', 'cs.AI'] | We introduce Goedel-Prover, an open-source language model that achieves
state-of-the-art (as of April 5 2025) performance in automated formal proof
generation for mathematical problems. A key challenge in this field is the
scarcity of formalized mathematical statements and proofs, which we address
through the following... | 2025-02-11T15:27:35Z | null | null | null | Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving | ['Yong Lin', 'Shange Tang', 'Bohan Lyu', 'Jiayun Wu', 'Hongzhou Lin', 'Kaiyu Yang', 'Jia Li', 'Mengzhou Xia', 'Danqi Chen', 'Sanjeev Arora', 'Chi Jin'] | 2,025 | arXiv.org | 26 | 51 | ['Computer Science'] |
2,502.07671 | Steering Protein Family Design through Profile Bayesian Flow | ['Jingjing Gong', 'Yu Pei', 'Siyu Long', 'Yuxuan Song', 'Zhe Zhang', 'Wenhao Huang', 'Ziyao Cao', 'Shuyi Zhang', 'Hao Zhou', 'Wei-Ying Ma'] | ['q-bio.BM'] | Protein family design emerges as a promising alternative by combining the
advantages of de novo protein design and mutation-based directed evolution.In
this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for
specifically generative modeling of protein families. ProfileBFN extends the
discrete Bayesia... | 2025-02-11T16:15:59Z | null | null | null | null | null | null | null | null | null | null |
2,502.07737 | Next Block Prediction: Video Generation via Semi-Autoregressive Modeling | ['Shuhuai Ren', 'Shuming Ma', 'Xu Sun', 'Furu Wei'] | ['cs.CV', 'cs.AI'] | Next-Token Prediction (NTP) is a de facto approach for autoregressive (AR)
video generation, but it suffers from suboptimal unidirectional dependencies
and slow inference speed. In this work, we propose a semi-autoregressive
(semi-AR) framework, called Next-Block Prediction (NBP), for video generation.
By uniformly dec... | 2025-02-11T17:57:53Z | project page: https://renshuhuai-andy.github.io/NBP-project/ | null | null | null | null | null | null | null | null | null |
2,502.0776 | Scalable Fingerprinting of Large Language Models | ['Anshul Nasery', 'Jonathan Hayase', 'Creston Brooks', 'Peiyao Sheng', 'Himanshu Tyagi', 'Pramod Viswanath', 'Sewoong Oh'] | ['cs.CR', 'cs.LG'] | Model fingerprinting has emerged as a powerful tool for model owners to
identify their shared model given API access. However, to lower false discovery
rate, fight fingerprint leakage, and defend against coalitions of model users
attempting to bypass detection, we argue that {\em scalability} is critical,
i.e., scaling... | 2025-02-11T18:43:07Z | 23 pages 15 figures | null | null | null | null | null | null | null | null | null |
2,502.0778 | DarwinLM: Evolutionary Structured Pruning of Large Language Models | ['Shengkun Tang', 'Oliver Sieberling', 'Eldar Kurtic', 'Zhiqiang Shen', 'Dan Alistarh'] | ['cs.LG', 'cs.CL'] | Large Language Models (LLMs) have achieved significant success across various
NLP tasks. However, their massive computational costs limit their widespread
use, particularly in real-time applications. Structured pruning offers an
effective solution by compressing models and directly providing end-to-end
speed improvemen... | 2025-02-11T18:59:35Z | Code: https://github.com/IST-DASLab/DarwinLM | null | null | null | null | null | null | null | null | null |
2,502.07864 | TransMLA: Multi-Head Latent Attention Is All You Need | ['Fanxu Meng', 'Pingzhi Tang', 'Xiaojuan Tang', 'Zengwei Yao', 'Xing Sun', 'Muhan Zhang'] | ['cs.LG', 'cs.AI'] | In this paper, we present TransMLA, a framework that seamlessly converts any
GQA-based pre-trained model into an MLA-based model. Our approach enables
direct compatibility with DeepSeek's codebase, allowing these models to fully
leverage DeepSeek-specific optimizations such as vLLM and SGlang. By
compressing 93% of the... | 2025-02-11T18:20:18Z | https://github.com/fxmeng/TransMLA | null | null | null | null | null | null | null | null | null |
2,502.07938 | Adapting Multilingual Embedding Models to Historical Luxembourgish | ['Andrianos Michail', 'Corina Julia Raclé', 'Juri Opitz', 'Simon Clematide'] | ['cs.CL'] | The growing volume of digitized historical texts requires effective semantic
search using text embeddings. However, pre-trained multilingual models face
challenges with historical content due to OCR noise and outdated spellings.
This study examines multilingual embeddings for cross-lingual semantic search
in historical... | 2025-02-11T20:35:29Z | To appear in LaTeCH-CLfL 2025 | null | null | null | null | null | null | null | null | null |
2,502.07945 | SurGrID: Controllable Surgical Simulation via Scene Graph to Image
Diffusion | ['Yannik Frisch', 'Ssharvien Kumar Sivakumar', 'Çağhan Köksal', 'Elsa Böhm', 'Felix Wagner', 'Adrian Gericke', 'Ghazal Ghazaei', 'Anirban Mukhopadhyay'] | ['cs.CV', 'cs.LG'] | Surgical simulation offers a promising addition to conventional surgical
training. However, available simulation tools lack photorealism and rely on
hardcoded behaviour. Denoising Diffusion Models are a promising alternative for
high-fidelity image synthesis, but existing state-of-the-art conditioning
methods fall shor... | 2025-02-11T20:49:13Z | null | null | 10.1007/s11548-025-03397-y | null | null | null | null | null | null | null |
2,502.07972 | Training Sparse Mixture Of Experts Text Embedding Models | ['Zach Nussbaum', 'Brandon Duderstadt'] | ['cs.CL', 'cs.AI', 'cs.IR'] | Transformer-based text embedding models have improved their performance on
benchmarks like MIRACL and BEIR by increasing their parameter counts. However,
this scaling approach introduces significant deployment challenges, including
increased inference latency and memory usage. These challenges are particularly
severe i... | 2025-02-11T21:36:31Z | null | null | null | Training Sparse Mixture Of Experts Text Embedding Models | ['Zach Nussbaum', 'Brandon Duderstadt'] | 2,025 | arXiv.org | 2 | 38 | ['Computer Science'] |
2,502.08127 | Fino1: On the Transferability of Reasoning-Enhanced LLMs and
Reinforcement Learning to Finance | ['Lingfei Qian', 'Weipeng Zhou', 'Yan Wang', 'Xueqing Peng', 'Han Yi', 'Yilun Zhao', 'Jimin Huang', 'Qianqian Xie', 'Jian-yun Nie'] | ['cs.CL'] | As the fundamental capability behind decision-making in finance, financial
reasoning poses distinct challenges for LLMs. Although reinforcement learning
(RL) have boosted generic reasoning, the progress in finance is hindered by the
absence of empirical study of building effective financial chain-of-thought
(CoT) corpu... | 2025-02-12T05:13:04Z | 13 pages, 2 figures, 3 Tables | null | null | Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance | ['Lingfei Qian', 'Weipeng Zhou', 'Yan Wang', 'Xueqing Peng', 'Han Yi', 'Yilun Zhao', 'Jimin Huang', 'Qianqian Xie', 'Jian-yun Nie'] | 2,025 | null | 0 | 28 | ['Computer Science'] |
2,502.08153 | Stable rationality of hypersurfaces in schön affine varieties | ['Taro Yoshino'] | ['math.AG', '14E08, 14M25'] | In recent years, there has been a development in approaching rationality
problems through the motivic methods (cf. [Kontsevich--Tschinkel'19],
[Nicaise--Shinder'19], [Nicaise--Ottem'21]). This method requires the explicit
construction of degeneration families of curves with favorable properties.
While the specific cons... | 2025-02-12T06:41:13Z | 50 pages. arXiv admin note: text overlap with arXiv:2312.15605 | null | null | Stable rationality of hypersurfaces in sch\"{o}n affine varieties | ['Taro Yoshino'] | 2,025 | null | 0 | 0 | ['Mathematics'] |
2,502.08213 | LLM Modules: Knowledge Transfer from a Large to a Small Model using
Enhanced Cross-Attention | ['Konstantin Kolomeitsev'] | ['cs.CL', 'cs.LG', 'I.2.7; D.2.11'] | In this work, we propose an architecture of LLM Modules that enables the
transfer of knowledge from a large pre-trained model to a smaller model using
an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B
model is frozen and its representations are passed through specially designed
attention lay... | 2025-02-12T08:48:55Z | Code and pre-trained weights available at
https://huggingface.co/kkolomeitsev/llm-modules | null | null | null | null | null | null | null | null | null |
2,502.08226 | TRISHUL: Towards Region Identification and Screen Hierarchy
Understanding for Large VLM based GUI Agents | ['Kunal Singh', 'Shreyas Singh', 'Mukund Khanna'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Recent advancements in Large Vision Language Models (LVLMs) have enabled the
development of LVLM-based Graphical User Interface (GUI) agents under various
paradigms. Training-based approaches, such as CogAgent and SeeClick, struggle
with cross-dataset and cross-platform generalization due to their reliance on
dataset-s... | 2025-02-12T09:12:30Z | 8 pages 5 figures | null | null | null | null | null | null | null | null | null |
2,502.08468 | mmE5: Improving Multimodal Multilingual Embeddings via High-quality
Synthetic Data | ['Haonan Chen', 'Liang Wang', 'Nan Yang', 'Yutao Zhu', 'Ziliang Zhao', 'Furu Wei', 'Zhicheng Dou'] | ['cs.CV', 'cs.AI', 'cs.CL'] | Multimodal embedding models have gained significant attention for their
ability to map data from different modalities, such as text and images, into a
unified representation space. However, the limited labeled multimodal data
often hinders embedding performance. Recent approaches have leveraged data
synthesis to addres... | 2025-02-12T15:03:33Z | null | null | null | null | null | null | null | null | null | null |
2,502.08489 | Salamandra Technical Report | ['Aitor Gonzalez-Agirre', 'Marc Pàmies', 'Joan Llop', 'Irene Baucells', 'Severino Da Dalt', 'Daniel Tamayo', 'José Javier Saiz', 'Ferran Espuña', 'Jaume Prats', 'Javier Aula-Blasco', 'Mario Mina', 'Iñigo Pikabea', 'Adrián Rubio', 'Alexander Shvets', 'Anna Sallés', 'Iñaki Lacunza', 'Jorge Palomar', 'Júlia Falcão', 'Lucí... | ['cs.CL'] | This work introduces Salamandra, a suite of open-source decoder-only large
language models available in three different sizes: 2, 7, and 40 billion
parameters. The models were trained from scratch on highly multilingual data
that comprises text in 35 European languages and code. Our carefully curated
corpus is made exc... | 2025-02-12T15:26:08Z | null | null | null | null | null | null | null | null | null | null |
2,502.08769 | Cluster and Predict Latent Patches for Improved Masked Image Modeling | ['Timothée Darcet', 'Federico Baldassarre', 'Maxime Oquab', 'Julien Mairal', 'Piotr Bojanowski'] | ['cs.CV', 'cs.AI'] | Masked Image Modeling (MIM) offers a promising approach to self-supervised
representation learning, however existing MIM models still lag behind the
state-of-the-art. In this paper, we systematically analyze target
representations, loss functions, and architectures, to introduce CAPI - a novel
pure-MIM framework that r... | 2025-02-12T20:17:10Z | 26 pages, 14 figures, accepted in TMLR 2025 | null | null | Cluster and Predict Latent Patches for Improved Masked Image Modeling | ['Timothée Darcet', 'Federico Baldassarre', 'Maxime Oquab', 'J. Mairal', 'Piotr Bojanowski'] | 2,025 | arXiv.org | 6 | 0 | ['Computer Science'] |
2,502.08807 | InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data
Volume Variation in DNNs | ['Zifan He', 'Anderson Truong', 'Yingqi Cao', 'Jason Cong'] | ['cs.AR', 'cs.LG'] | The rise of deep neural networks (DNNs) has driven an increased demand for
computing power and memory. Modern DNNs exhibit high data volume variation
(HDV) across tasks, which poses challenges for FPGA acceleration: conventional
accelerators rely on fixed execution patterns (dataflow or sequential) that can
lead to pip... | 2025-02-12T21:43:51Z | FCCM 2025 | null | null | null | null | null | null | null | null | null |
2,502.0882 | Can a Single Model Master Both Multi-turn Conversations and Tool Use?
CoALM: A Unified Conversational Agentic Language Model | ['Emre Can Acikgoz', 'Jeremiah Greer', 'Akul Datta', 'Ze Yang', 'William Zeng', 'Oussama Elachqar', 'Emmanouil Koukoumidis', 'Dilek Hakkani-Tür', 'Gokhan Tur'] | ['cs.AI', 'cs.CL'] | Large Language Models (LLMs) with API-calling capabilities enabled building
effective Language Agents (LA), while also revolutionizing the conventional
task-oriented dialogue (TOD) paradigm. However, current approaches face a
critical dilemma: TOD systems are often trained on a limited set of target
APIs, requiring new... | 2025-02-12T22:18:34Z | null | null | null | null | null | null | null | null | null | null |
2,502.09042 | Typhoon T1: An Open Thai Reasoning Model | ['Pittawat Taveekitworachai', 'Potsawee Manakul', 'Kasima Tharnpipitchai', 'Kunat Pipatanakul'] | ['cs.CL', 'cs.AI'] | This paper introduces Typhoon T1, an open effort to develop an open Thai
reasoning model. A reasoning model is a relatively new type of generative model
built on top of large language models (LLMs). A reasoning model generates a
long chain of thought before arriving at a final answer, an approach found to
improve perfo... | 2025-02-13T07:55:54Z | 25 pages, 6 figures | null | null | null | null | null | null | null | null | null |
2,502.09056 | Adapting Language-Specific LLMs to a Reasoning Model in One Day via
Model Merging -- An Open Recipe | ['Kunat Pipatanakul', 'Pittawat Taveekitworachai', 'Potsawee Manakul', 'Kasima Tharnpipitchai'] | ['cs.CL', 'cs.AI'] | This paper investigates data selection and model merging methodologies aimed
at incorporating advanced reasoning capabilities such as those of DeepSeek R1
into language-specific large language models (LLMs), with a particular focus on
the Thai LLM. Our goal is to enhance the reasoning capabilities of
language-specific ... | 2025-02-13T08:10:45Z | 9 pages | null | null | null | null | null | null | null | null | null |
2,502.09082 | CoSER: Coordinating LLM-Based Persona Simulation of Established Roles | ['Xintao Wang', 'Heng Wang', 'Yifei Zhang', 'Xinfeng Yuan', 'Rui Xu', 'Jen-tse Huang', 'Siyu Yuan', 'Haoran Guo', 'Jiangjie Chen', 'Shuchang Zhou', 'Wei Wang', 'Yanghua Xiao'] | ['cs.CL', 'cs.AI'] | Role-playing language agents (RPLAs) have emerged as promising applications
of large language models (LLMs). However, simulating established characters
presents a challenging task for RPLAs, due to the lack of authentic character
datasets and nuanced evaluation methods using such data. In this paper, we
present CoSER, ... | 2025-02-13T08:55:24Z | Accepted by ICML 2025 | null | null | null | null | null | null | null | null | null |
2,502.09135 | Interpreting and Steering Protein Language Models through Sparse
Autoencoders | ['Edith Natalia Villegas Garcia', 'Alessio Ansuini'] | ['cs.LG', 'q-bio.BM'] | The rapid advancements in transformer-based language models have
revolutionized natural language processing, yet understanding the internal
mechanisms of these models remains a significant challenge. This paper explores
the application of sparse autoencoders (SAE) to interpret the internal
representations of protein la... | 2025-02-13T10:11:36Z | 11 pages, 6 figures | null | null | null | null | null | null | null | null | null |
2,502.09284 | SparQLe: Speech Queries to Text Translation Through LLMs | ['Amirbek Djanibekov', 'Hanan Aldarmaki'] | ['cs.CL', 'cs.AI'] | With the growing influence of Large Language Models (LLMs), there is
increasing interest in integrating speech representations with them to enable
more seamless multi-modal processing and speech understanding. This study
introduces a novel approach that combines self-supervised speech
representations with instruction-t... | 2025-02-13T12:57:15Z | null | null | null | null | null | null | null | null | null | null |
2,502.09387 | Truth Knows No Language: Evaluating Truthfulness Beyond English | ['Blanca Calvo Figueras', 'Eneko Sagarzazu', 'Julen Etxaniz', 'Jeremy Barnes', 'Pablo Gamallo', 'Iria De Dios Flores', 'Rodrigo Agerri'] | ['cs.CL', 'cs.AI', 'cs.CY'] | We introduce a professionally translated extension of the TruthfulQA
benchmark designed to evaluate truthfulness in Basque, Catalan, Galician, and
Spanish. Truthfulness evaluations of large language models (LLMs) have
primarily been conducted in English. However, the ability of LLMs to maintain
truthfulness across lang... | 2025-02-13T15:04:53Z | 14 pages, 6 figures, 8 tables | null | null | null | null | null | null | null | null | null |
2,502.09509 | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
Image Modeling | ['Theodoros Kouzelis', 'Ioannis Kakogeorgiou', 'Spyros Gidaris', 'Nikos Komodakis'] | ['cs.LG'] | Latent generative models have emerged as a leading approach for high-quality
image synthesis. These models rely on an autoencoder to compress images into a
latent space, followed by a generative model to learn the latent distribution.
We identify that existing autoencoders lack equivariance to semantic-preserving
trans... | 2025-02-13T17:21:51Z | Preprint | null | null | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling | ['Theodoros Kouzelis', 'Ioannis Kakogeorgiou', 'Spyros Gidaris', 'Nikos Komodakis'] | 2,025 | arXiv.org | 8 | 71 | ['Computer Science'] |
2,502.09604 | SelfCite: Self-Supervised Alignment for Context Attribution in Large
Language Models | ['Yung-Sung Chuang', 'Benjamin Cohen-Wang', 'Shannon Zejiang Shen', 'Zhaofeng Wu', 'Hu Xu', 'Xi Victoria Lin', 'James Glass', 'Shang-Wen Li', 'Wen-tau Yih'] | ['cs.CL', 'cs.AI', 'cs.LG'] | We introduce SelfCite, a novel self-supervised approach that aligns LLMs to
generate high-quality, fine-grained, sentence-level citations for the
statements in their generated responses. Instead of only relying on costly and
labor-intensive annotations, SelfCite leverages a reward signal provided by the
LLM itself thro... | 2025-02-13T18:55:13Z | ICML 2025 main conference paper. The source code is available at
https://github.com/facebookresearch/SelfCite | null | null | null | null | null | null | null | null | null |
2,502.09613 | Latent Radiance Fields with 3D-aware 2D Representations | ['Chaoyi Zhou', 'Xi Liu', 'Feng Luo', 'Siyu Huang'] | ['cs.CV'] | Latent 3D reconstruction has shown great promise in empowering 3D semantic
understanding and 3D generation by distilling 2D features into the 3D space.
However, existing approaches struggle with the domain gap between 2D feature
space and 3D representations, resulting in degraded rendering performance. To
address this ... | 2025-02-13T18:59:09Z | Accepted to ICLR 2025; Project page:
https://latent-radiance-field.github.io/LRF | null | null | null | null | null | null | null | null | null |
2,502.0962 | Exploring the Potential of Encoder-free Architectures in 3D LMMs | ['Yiwen Tang', 'Zoey Guo', 'Zhuhao Wang', 'Ray Zhang', 'Qizhi Chen', 'Junli Liu', 'Delin Qu', 'Zhigang Wang', 'Dong Wang', 'Xuelong Li', 'Bin Zhao'] | ['cs.CV', 'cs.AI', 'cs.CL'] | Encoder-free architectures have been preliminarily explored in the 2D visual
domain, yet it remains an open question whether they can be effectively applied
to 3D understanding scenarios. In this paper, we present the first
comprehensive investigation into the potential of encoder-free architectures to
alleviate the ch... | 2025-02-13T18:59:45Z | During the review process, we discovered that a portion of the test
dataset used in our submission contained content that may have infringed upon
the commercial copyrights of others. Due to the conflict regarding these
commercial copyrights, we have unfortunately had to retract the submission | null | null | Exploring the Potential of Encoder-free Architectures in 3D LMMs | ['Yiwen Tang', 'Zoey Guo', 'Zhuhao Wang', 'Ray Zhang', 'Qizhi Chen', 'Junli Liu', 'Delin Qu', 'Zhigang Wang', 'Dong Wang', 'Xuelong Li', 'Bin Zhao'] | 2,025 | arXiv.org | 11 | 46 | ['Computer Science'] |
2,502.0965 | Principled Data Selection for Alignment: The Hidden Risks of Difficult
Examples | ['Chengqian Gao', 'Haonan Li', 'Liu Liu', 'Zeke Xie', 'Peilin Zhao', 'Zhiqiang Xu'] | ['cs.CL', 'cs.AI', 'cs.LG'] | The alignment of large language models (LLMs) often assumes that using more
clean data yields better outcomes, overlooking the match between model capacity
and example difficulty. Challenging this, we propose a new principle:
Preference data vary in difficulty, and overly difficult examples hinder
alignment, by exceedi... | 2025-02-11T17:01:11Z | Accepted at ICML 2025 | null | null | Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples | ['Chengqian Gao', 'Haonan Li', 'Liu Liu', 'Zeke Xie', 'Peilin Zhao', 'Zhiqiang Xu'] | 2,025 | arXiv.org | 4 | 90 | ['Computer Science'] |
2,502.09653 | SASVi -- Segment Any Surgical Video | ['Ssharvien Kumar Sivakumar', 'Yannik Frisch', 'Amin Ranem', 'Anirban Mukhopadhyay'] | ['eess.IV', 'cs.CV'] | Purpose: Foundation models, trained on multitudes of public datasets, often
require additional fine-tuning or re-prompting mechanisms to be applied to
visually distinct target domains such as surgical videos. Further, without
domain knowledge, they cannot model the specific semantics of the target
domain. Hence, when a... | 2025-02-12T00:29:41Z | null | null | 10.1007/s11548-025-03408-y | null | null | null | null | null | null | null |
2,502.09692 | AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive
Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers | ['Benedikt Alkin', 'Maurits Bleeker', 'Richard Kurle', 'Tobias Kronlachner', 'Reinhard Sonnleitner', 'Matthias Dorfer', 'Johannes Brandstetter'] | ['cs.LG', 'cs.AI'] | Recent advances in neural surrogate modeling offer the potential for
transformative innovations in applications such as automotive aerodynamics.
Yet, industrial-scale problems often involve volumetric meshes with cell counts
reaching 100 million, presenting major scalability challenges. Complex
geometries further compl... | 2025-02-13T17:58:07Z | Preprint. Github: https://github.com/Emmi-AI/AB-UPT | null | null | AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers | ['Maurits J. R. Bleeker', 'Matthias Dorfer', 'T. Kronlachner', 'Reinhard Sonnleitner', 'Benedikt Alkin', 'Johannes Brandstetter'] | 2,025 | null | 3 | 76 | ['Computer Science'] |
2,502.09814 | INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for
16 African Languages | ['Hao Yu', 'Jesujoba O. Alabi', 'Andiswa Bukula', 'Jian Yun Zhuang', 'En-Shiun Annie Lee', 'Tadesse Kebede Guge', 'Israel Abebe Azime', 'Happy Buzaaba', 'Blessing Kudzaishe Sibanda', 'Godson K. Kalipe', 'Jonathan Mukiibi', 'Salomon Kabongo Kabenamualu', 'Mmasibidi Setaka', 'Lolwethu Ndolela', 'Nkiruka Odu', 'Rooweither... | ['cs.CL'] | Slot-filling and intent detection are well-established tasks in
Conversational AI. However, current large-scale benchmarks for these tasks
often exclude evaluations of low-resource languages and rely on translations
from English benchmarks, thereby predominantly reflecting Western-centric
concepts. In this paper, we in... | 2025-02-13T23:17:10Z | null | null | null | null | null | null | null | null | null | null |
2,502.09927 | Granite Vision: a lightweight, open-source multimodal model for
enterprise Intelligence | ['Granite Vision Team', 'Leonid Karlinsky', 'Assaf Arbelle', 'Abraham Daniels', 'Ahmed Nassar', 'Amit Alfassi', 'Bo Wu', 'Eli Schwartz', 'Dhiraj Joshi', 'Jovana Kondic', 'Nimrod Shabtay', 'Pengyuan Li', 'Roei Herzig', 'Shafiq Abedin', 'Shaked Perek', 'Sivan Harary', 'Udi Barzelay', 'Adi Raz Goldfarb', 'Aude Oliva', 'Be... | ['cs.CV', 'cs.AI'] | We introduce Granite Vision, a lightweight large language model with vision
capabilities, specifically designed to excel in enterprise use cases,
particularly in visual document understanding. Our model is trained on a
comprehensive instruction-following dataset, including document-related tasks,
such as content extrac... | 2025-02-14T05:36:32Z | null | null | null | null | null | null | null | null | null | null |
2,502.09992 | Large Language Diffusion Models | ['Shen Nie', 'Fengqi Zhu', 'Zebin You', 'Xiaolu Zhang', 'Jingyang Ou', 'Jun Hu', 'Jun Zhou', 'Yankai Lin', 'Ji-Rong Wen', 'Chongxuan Li'] | ['cs.CL', 'cs.LG'] | Autoregressive models (ARMs) are widely regarded as the cornerstone of large
language models (LLMs). We challenge this notion by introducing LLaDA, a
diffusion model trained from scratch under the pre-training and supervised
fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data
masking process a... | 2025-02-14T08:23:51Z | null | null | null | null | null | null | null | null | null | null |
2,502.10059 | RealCam-I2V: Real-World Image-to-Video Generation with Interactive
Complex Camera Control | ['Teng Li', 'Guangcong Zheng', 'Rui Jiang', 'Shuigen Zhan', 'Tao Wu', 'Yehao Lu', 'Yining Lin', 'Chuanyun Deng', 'Yepan Xiong', 'Min Chen', 'Lin Cheng', 'Xi Li'] | ['cs.CV'] | Recent advancements in camera-trajectory-guided image-to-video generation
offer higher precision and better support for complex camera control compared
to text-based approaches. However, they also introduce significant usability
challenges, as users often struggle to provide precise camera parameters when
working with ... | 2025-02-14T10:21:49Z | Accepted by ICCV 2025 | null | null | RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control | ['Teng Li', 'Guangcong Zheng', 'Rui Jiang', 'Shuigenzhan', 'Tao Wu', 'Yehao Lu', 'Yining Lin', 'Xi Li'] | 2,025 | arXiv.org | 9 | 0 | ['Computer Science'] |
2,502.1014 | Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of
Small Multilingual Language Models for Low-Resource Languages | ['Daniil Gurgurov', 'Ivan Vykopal', 'Josef van Genabith', 'Simon Ostermann'] | ['cs.CL'] | Low-resource languages (LRLs) face significant challenges in natural language
processing (NLP) due to limited data. While current state-of-the-art large
language models (LLMs) still struggle with LRLs, smaller multilingual models
(mLMs) such as mBERT and XLM-R offer greater promise due to a better fit of
their capacity... | 2025-02-14T13:10:39Z | Pre-print | null | null | null | null | null | null | null | null | null |
2,502.10173 | Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
Language Diffusion Model | ['Bo Ni', 'Markus J. Buehler'] | ['q-bio.BM', 'cond-mat.mes-hall', 'cond-mat.mtrl-sci', 'cs.LG'] | Proteins are dynamic molecular machines whose biological functions, spanning
enzymatic catalysis, signal transduction, and structural adaptation, are
intrinsically linked to their motions. Designing proteins with targeted dynamic
properties, however, remains a challenge due to the complex, degenerate
relationships betw... | 2025-02-14T14:07:54Z | null | null | null | null | null | null | null | null | null | null |
2,502.10248 | Step-Video-T2V Technical Report: The Practice, Challenges, and Future of
Video Foundation Model | ['Guoqing Ma', 'Haoyang Huang', 'Kun Yan', 'Liangyu Chen', 'Nan Duan', 'Shengming Yin', 'Changyi Wan', 'Ranchen Ming', 'Xiaoniu Song', 'Xing Chen', 'Yu Zhou', 'Deshan Sun', 'Deyu Zhou', 'Jian Zhou', 'Kaijun Tan', 'Kang An', 'Mei Chen', 'Wei Ji', 'Qiling Wu', 'Wen Sun', 'Xin Han', 'Yanan Wei', 'Zheng Ge', 'Aojie Li', 'B... | ['cs.CV', 'cs.CL'] | We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model
with 30B parameters and the ability to generate videos up to 204 frames in
length. A deep compression Variational Autoencoder, Video-VAE, is designed for
video generation tasks, achieving 16x16 spatial and 8x temporal compression
ratios, whil... | 2025-02-14T15:58:10Z | 36 pages, 14 figures | null | null | Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model | ['Guoqing Ma', 'Haoyang Huang', 'Kun Yan', 'Liangyu Chen', 'Nan Duan', 'Sheng-Siang Yin', 'Changyi Wan', 'Ranchen Ming', 'Xiaoniu Song', 'Xing Chen', 'Yu Zhou', 'Deshan Sun', 'Deyu Zhou', 'Jian Zhou', 'Kaijun Tan', 'Kang An', 'Mei Chen', 'Wei Ji', 'Qiling Wu', 'Wenzheng Sun', 'Xin Han', 'Yana Wei', 'Zheng Ge', 'Aojie L... | 2,025 | arXiv.org | 41 | 47 | ['Computer Science'] |
2,502.10341 | Organize the Web: Constructing Domains Enhances Pre-Training Data
Curation | ['Alexander Wettig', 'Kyle Lo', 'Sewon Min', 'Hannaneh Hajishirzi', 'Danqi Chen', 'Luca Soldaini'] | ['cs.CL'] | Modern language models are trained on large, unstructured datasets consisting
of trillions of tokens and obtained by crawling the web. The unstructured
nature makes it difficult to reason about their contents and develop systematic
approaches to data curation. In this paper, we unpack monolithic web corpora by
developi... | 2025-02-14T18:02:37Z | Accepted at ICML 2025. Project page: https://weborganizer.allen.ai | null | null | null | null | null | null | null | null | null |
2,502.10362 | CLaMP 3: Universal Music Information Retrieval Across Unaligned
Modalities and Unseen Languages | ['Shangda Wu', 'Zhancheng Guo', 'Ruibin Yuan', 'Junyan Jiang', 'Seungheon Doh', 'Gus Xia', 'Juhan Nam', 'Xiaobing Li', 'Feng Yu', 'Maosong Sun'] | ['cs.SD', 'eess.AS'] | CLaMP 3 is a unified framework developed to address challenges of cross-modal
and cross-lingual generalization in music information retrieval. Using
contrastive learning, it aligns all major music modalities--including sheet
music, performance signals, and audio recordings--with multilingual text in a
shared representa... | 2025-02-14T18:42:25Z | 20 pages, 8 figures, 12 tables, accepted by ACL 2025 | null | null | null | null | null | null | null | null | null |
2,502.10373 | OWLS: Scaling Laws for Multilingual Speech Recognition and Translation
Models | ['William Chen', 'Jinchuan Tian', 'Yifan Peng', 'Brian Yan', 'Chao-Han Huck Yang', 'Shinji Watanabe'] | ['cs.CL', 'cs.AI', 'cs.LG', 'eess.AS'] | Neural scaling laws offer valuable insights for designing robust sequence
processing architectures. While these laws have been extensively characterized
in other modalities, their behavior in speech remains comparatively
underexplored. In this work, we introduce OWLS, an open-access, reproducible
suite of multilingual ... | 2025-02-14T18:51:40Z | 23 pages, 13 figures | null | null | null | null | null | null | null | null | null |
2,502.10385 | Simplifying DINO via Coding Rate Regularization | ['Ziyang Wu', 'Jingyuan Zhang', 'Druv Pai', 'XuDong Wang', 'Chandan Singh', 'Jianwei Yang', 'Jianfeng Gao', 'Yi Ma'] | ['cs.CV', 'cs.AI'] | DINO and DINOv2 are two model families being widely used to learn
representations from unlabeled imagery data at large scales. Their learned
representations often enable state-of-the-art performance for downstream tasks,
such as image classification and segmentation. However, they employ many
empirically motivated desi... | 2025-02-14T18:58:04Z | 17 pages, 5 figures | null | null | Simplifying DINO via Coding Rate Regularization | ['Ziyang Wu', 'Jingyuan Zhang', 'Druv Pai', 'XuDong Wang', 'Chandan Singh', 'Jianwei Yang', 'Jianfeng Gao', 'Yi Ma'] | 2,025 | arXiv.org | 1 | 45 | ['Computer Science'] |
2,502.10391 | MM-RLHF: The Next Step Forward in Multimodal LLM Alignment | ['Yi-Fan Zhang', 'Tao Yu', 'Haochen Tian', 'Chaoyou Fu', 'Peiyan Li', 'Jianshu Zeng', 'Wulin Xie', 'Yang Shi', 'Huanyu Zhang', 'Junkang Wu', 'Xue Wang', 'Yibo Hu', 'Bin Wen', 'Fan Yang', 'Zhang Zhang', 'Tingting Gao', 'Di Zhang', 'Liang Wang', 'Rong Jin', 'Tieniu Tan'] | ['cs.CL', 'cs.CV'] | Despite notable advancements in Multimodal Large Language Models (MLLMs),
most state-of-the-art models have not undergone thorough alignment with human
preferences. This gap exists because current alignment research has primarily
achieved progress in specific areas (e.g., hallucination reduction), while the
broader que... | 2025-02-14T18:59:51Z | Project Page: https://mm-rlhf.github.io/ | null | null | MM-RLHF: The Next Step Forward in Multimodal LLM Alignment | ['Yi-Fan Zhang', 'Tao Yu', 'Haochen Tian', 'Chaoyou Fu', 'Peiyan Li', 'Jianshu Zeng', 'Wulin Xie', 'Yang Shi', 'Huanyu Zhang', 'Junkang Wu', 'Xue Wang', 'Yibo Hu', 'Bin Wen', 'Fan Yang', 'Zhang Zhang', 'Tingting Gao', 'Di Zhang', 'Liang Wang', 'Rong Jin', 'Tien-Ping Tan'] | 2,025 | arXiv.org | 21 | 0 | ['Computer Science'] |
2,502.10392 | TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual
Grounding | ['Wenxuan Guo', 'Xiuwei Xu', 'Ziwei Wang', 'Jianjiang Feng', 'Jie Zhou', 'Jiwen Lu'] | ['cs.CV', 'cs.LG'] | In this paper, we propose an efficient multi-level convolution architecture
for 3D visual grounding. Conventional methods are difficult to meet the
requirements of real-time inference due to the two-stage or point-based
architecture. Inspired by the success of multi-level fully sparse convolutional
architecture in 3D o... | 2025-02-14T18:59:59Z | Accepted at CVPR2025 with a top score | null | null | null | null | null | null | null | null | null |
2,502.10582 | Named entity recognition for Serbian legal documents: Design,
methodology and dataset development | ['Vladimir Kalušev', 'Branko Brkljač'] | ['cs.CL', '68T10, 68T30, 68T35, 68T50, 91F20', 'I.5.2; I.5.4; I.5.5; I.2.1; I.2.7; I.2; H.4.1'] | Recent advancements in the field of natural language processing (NLP) and
especially large language models (LLMs) and their numerous applications have
brought research attention to design of different document processing tools and
enhancements in the process of document archiving, search and retrieval. Domain
of offici... | 2025-02-14T22:23:39Z | 9 pages, 6 figures, 1 table, associated NER4Legal_SRB model and
dataset are available at https://huggingface.co/kalusev/NER4Legal_SRB , paper
submitted to 15th International Conference on Information Society and
Technology (ICIST), Kopaonik, Serbia, 9-12 March 2025, conference track:
Generative AI and Large Lan... | null | null | null | null | null | null | null | null | null |
2,502.10645 | BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop | ['Lucas Charpentier', 'Leshem Choshen', 'Ryan Cotterell', 'Mustafa Omer Gul', 'Michael Hu', 'Jaap Jumelet', 'Tal Linzen', 'Jing Liu', 'Aaron Mueller', 'Candace Ross', 'Raj Sanjay Shah', 'Alex Warstadt', 'Ethan Wilcox', 'Adina Williams'] | ['cs.CL'] | BabyLM aims to dissolve the boundaries between cognitive modeling and
language modeling. We call for both workshop papers and for researchers to join
the 3rd BabyLM competition. As in previous years, we call for participants in
the data-efficient pretraining challenge in the general track. This year, we
also offer a ne... | 2025-02-15T02:46:43Z | EMNLP 2025 BabyLM Workshop. arXiv admin note: text overlap with
arXiv:2404.06214 | null | null | null | null | null | null | null | null | null |
2,502.1081 | SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming
Video Understanding | ['Zhenyu Yang', 'Yuhang Hu', 'Zemin Du', 'Dizhan Xue', 'Shengsheng Qian', 'Jiahong Wu', 'Fan Yang', 'Weiming Dong', 'Changsheng Xu'] | ['cs.CV'] | Despite the significant advancements of Large Vision-Language Models (LVLMs)
on established benchmarks, there remains a notable gap in suitable evaluation
regarding their applicability in the emerging domain of long-context streaming
video understanding. Current benchmarks for video understanding typically
emphasize is... | 2025-02-15T14:29:44Z | ICLR 2025 Accept (Spotlight) | null | null | null | null | null | null | null | null | null |
2,502.10841 | SkyReels-A1: Expressive Portrait Animation in Video Diffusion
Transformers | ['Di Qiu', 'Zhengcong Fei', 'Rui Wang', 'Jialin Bai', 'Changqian Yu', 'Mingyuan Fan', 'Guibin Chen', 'Xiang Wen'] | ['cs.CV'] | We present SkyReels-A1, a simple yet effective framework built upon video
diffusion Transformer to facilitate portrait image animation. Existing
methodologies still encounter issues, including identity distortion, background
instability, and unrealistic facial dynamics, particularly in head-only
animation scenarios. Be... | 2025-02-15T16:08:40Z | null | null | null | SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers | ['Di Qiu', 'Zhengcong Fei', 'Rui Wang', 'Jialin Bai', 'Changqian Yu', 'Mingyuan Fan', 'Guibin Chen', 'Xiang Wen'] | 2,025 | arXiv.org | 11 | 0 | ['Computer Science'] |
2,502.10852 | Multilingual Encoder Knows more than You Realize: Shared Weights
Pretraining for Extremely Low-Resource Languages | ['Zeli Su', 'Ziyin Zhang', 'Guixian Xu', 'Jianing Liu', 'XU Han', 'Ting Zhang', 'Yushuang Dong'] | ['cs.CL', 'cs.AI'] | While multilingual language models like XLM-R have advanced multilingualism
in NLP, they still perform poorly in extremely low-resource languages. This
situation is exacerbated by the fact that modern LLMs such as LLaMA and Qwen
support far fewer languages than XLM-R, making text generation models
non-existent for many... | 2025-02-15T16:53:10Z | ACL 2025 camera-ready | null | null | Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages | ['Zeli Su', 'Ziyin Zhang', 'Guixian Xu', 'Jianing Liu', 'XU Han', 'Ting Zhang', 'Yushuang Dong'] | 2,025 | arXiv.org | 1 | 27 | ['Computer Science'] |
2,502.10868 | NitiBench: A Comprehensive Study of LLM Framework Capabilities for Thai
Legal Question Answering | ['Pawitsapak Akarajaradwong', 'Pirat Pothavorn', 'Chompakorn Chaksangchaichot', 'Panuthep Tasawong', 'Thitiwat Nopparatbundit', 'Sarana Nutanong'] | ['cs.CL'] | The application of large language models (LLMs) in the legal domain holds
significant potential for information retrieval and question answering, yet
Thai legal QA systems face challenges due to a lack of standardized evaluation
benchmarks and the complexity of Thai legal structures. This paper introduces
NitiBench, a ... | 2025-02-15T17:52:14Z | null | null | null | NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering | ['Pawitsapak Akarajaradwong', 'Pirat Pothavorn', 'Chompakorn Chaksangchaichot', 'Panuthep Tasawong', 'Thitiwat Nopparatbundit', 'Sarana Nutanong'] | 2,025 | arXiv.org | 1 | 0 | ['Computer Science'] |
2,502.1099 | FinMTEB: Finance Massive Text Embedding Benchmark | ['Yixuan Tang', 'Yi Yang'] | ['cs.CL', 'cs.IR'] | Embedding models play a crucial role in representing and retrieving
information across various NLP applications. Recent advances in large language
models (LLMs) have further enhanced the performance of embedding models. While
these models are often benchmarked on general-purpose datasets, real-world
applications demand... | 2025-02-16T04:23:52Z | https://github.com/yixuantt/FinMTEB | null | null | FinMTEB: Finance Massive Text Embedding Benchmark | ['Yixuan Tang', 'Yi Yang'] | 2,025 | arXiv.org | 2 | 75 | ['Computer Science'] |
2,502.10996 | RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation | ['Pengcheng Jiang', 'Lang Cao', 'Ruike Zhu', 'Minhao Jiang', 'Yunyi Zhang', 'Jimeng Sun', 'Jiawei Han'] | ['cs.CL'] | Large language models (LLMs) have achieved impressive performance on
knowledge-intensive tasks, yet they often struggle with multi-step reasoning
due to the unstructured nature of retrieved context. While retrieval-augmented
generation (RAG) methods provide external information, the lack of explicit
organization among ... | 2025-02-16T05:01:49Z | under review | null | null | RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation | ['Pengcheng Jiang', 'Lang Cao', 'Ruike Zhu', 'Minhao Jiang', 'Yunyi Zhang', 'Jimeng Sun', 'Jiawei Han'] | 2,025 | arXiv.org | 4 | 77 | ['Computer Science'] |
2,502.11079 | Phantom: Subject-consistent video generation via cross-modal alignment | ['Lijie Liu', 'Tianxiang Ma', 'Bingchuan Li', 'Zhuowei Chen', 'Jiawei Liu', 'Gen Li', 'Siyu Zhou', 'Qian He', 'Xinglong Wu'] | ['cs.CV', 'cs.AI'] | The continuous development of foundational models for video generation is
evolving into various applications, with subject-consistent video generation
still in the exploratory stage. We refer to this as Subject-to-Video, which
extracts subject elements from reference images and generates
subject-consistent videos follo... | 2025-02-16T11:02:50Z | null | null | null | null | null | null | null | null | null | null |
2,502.11084 | Rewrite to Jailbreak: Discover Learnable and Transferable Implicit
Harmfulness Instruction | ['Yuting Huang', 'Chengyuan Liu', 'Yifeng Feng', 'Yiquan Wu', 'Chao Wu', 'Fei Wu', 'Kun Kuang'] | ['cs.CL'] | As Large Language Models (LLMs) are widely applied in various domains, the
safety of LLMs is increasingly attracting attention to avoid their powerful
capabilities being misused. Existing jailbreak methods create a forced
instruction-following scenario, or search adversarial prompts with prefix or
suffix tokens to achi... | 2025-02-16T11:43:39Z | 22 pages, 10 figures, accepted to ACL 2025 findings | null | null | null | null | null | null | null | null | null |
2,502.11102 | OptMATH: A Scalable Bidirectional Data Synthesis Framework for
Optimization Modeling | ['Hongliang Lu', 'Zhonglin Xie', 'Yaoyu Wu', 'Can Ren', 'Yuxuan Chen', 'Zaiwen Wen'] | ['cs.AI', 'cs.LG'] | Despite the rapid development of large language models (LLMs), a fundamental
challenge persists: the lack of high-quality optimization modeling datasets
hampers LLMs' robust modeling of practical optimization problems from natural
language descriptions (NL). This data scarcity also contributes to the
generalization dif... | 2025-02-16T12:38:37Z | This paper has 36 pages, 18 figures, and two co-first authors:
Hongliang Lu and Zhonglin Xie | null | null | null | null | null | null | null | null | null |
2,502.11157 | Dyve: Thinking Fast and Slow for Dynamic Process Verification | ['Jianyuan Zhong', 'Zeju Li', 'Zhijian Xu', 'Xiangyu Wen', 'Qiang Xu'] | ['cs.AI'] | We present Dyve, a dynamic process verifier that enhances reasoning error
detection in large language models by integrating fast and slow thinking,
inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate
token-level confirmation System 1 for straightforward steps and comprehensive
analysis System 2 for... | 2025-02-16T15:11:19Z | 8 pages, 4 figures | null | null | Dyve: Thinking Fast and Slow for Dynamic Process Verification | ['Jianyuan Zhong', 'Zeju Li', 'Zhijian Xu', 'Xiangyu Wen', 'Qiang Xu'] | 2,025 | arXiv.org | 4 | 23 | ['Computer Science'] |
2,502.11183 | Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming
Tree Search Exploration Pitfalls | ['Ante Wang', 'Linfeng Song', 'Ye Tian', 'Dian Yu', 'Haitao Mi', 'Xiangyu Duan', 'Zhaopeng Tu', 'Jinsong Su', 'Dong Yu'] | ['cs.CL'] | Recent advancements in tree search algorithms guided by verifiers have
significantly enhanced the reasoning capabilities of large language models
(LLMs), but at the cost of increased computational resources. In this work, we
identify two key challenges contributing to this inefficiency:
$\textit{over-exploration}$ due ... | 2025-02-16T16:12:01Z | null | null | null | null | null | null | null | null | null | null |
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