Orionnnn/hub-stats huggingface dataset:
Dynaword: From One-shot to Continuously Developed Datasets
Authors: name: Kenneth Enevoldsen, name: Kristian Nørgaard Jensen, name: Jan Kostkan, name: Balázs Szabó, name: Márton Kardos, name: Kirten Vad, name: Andrea Blasi Núñez, name: Gianluca Barmina, name: Jacob Nielsen, name: Rasmus Larsen, name: Peter Vahlstrup, name: Per Møldrup Dalum, name: Desmond Elliott, name: Lukas Galke, name: Peter Schneider-Kamp, name: Kristoffer Nielbo
Published: 2025-08-04 10:30:42

Large-scale datasets are foundational for research and development in natural
language processing. However, current approaches face three key challenges: (1)
reliance on ambiguously licensed sources restricting use, sharing, and
derivative works; (2) static dataset releases that prevent community
contributions and diminish longevity; and (3) quality assurance processes
restricted to publishing teams rather than leveraging community expertise.
  To address these limitations, we introduce two contributions: the Dynaword
approach and Danish Dynaword. The Dynaword approach is a framework for creating
large-scale, open datasets that can be continuously updated through community
collaboration. Danish Dynaword is a concrete implementation that validates this
approach and demonstrates its potential. Danish Dynaword contains over four
times as many tokens as comparable releases, is exclusively openly licensed,
and has received multiple contributions across industry and research. The
repository includes light-weight tests to ensure data formatting, quality, and
documentation, establishing a sustainable framework for ongoing community
contributions and dataset evolution.
Fitness aligned structural modeling enables scalable virtual screening
  with AuroBind
Authors: name: Zhongyue Zhang, name: Jiahua Rao, name: Jie Zhong, name: Weiqiang Bai, name: Dongxue Wang, name: Shaobo Ning, name: Lifeng Qiao, name: Sheng Xu, name: Runze Ma, name: Will Hua, name: Jack Xiaoyu Chen, name: Odin Zhang, name: Wei Lu, name: Hanyi Feng, name: He Yang, name: Xinchao Shi, name: Rui Li, name: Wanli Ouyang, name: Xinzhu Ma, name: Jiahao Wang, name: Jixian Zhang, name: Jia Duan, name: Siqi Sun, name: Jian Zhang, name: Shuangjia Zheng
Published: 2025-08-04 07:34:48

Most human proteins remain undrugged, over 96% of human proteins remain
unexploited by approved therapeutics. While structure-based virtual screening
promises to expand the druggable proteome, existing methods lack atomic-level
precision and fail to predict binding fitness, limiting translational impact.
We present AuroBind, a scalable virtual screening framework that fine-tunes a
custom atomic-level structural model on million-scale chemogenomic data.
AuroBind integrates direct preference optimization, self-distillation from
high-confidence complexes, and a teacher-student acceleration strategy to
jointly predict ligand-bound structures and binding fitness. The proposed
models outperform state-of-the-art models on structural and functional
benchmarks while enabling 100,000-fold faster screening across ultra-large
compound libraries. In a prospective screen across ten disease-relevant
targets, AuroBind achieved experimental hit rates of 7-69%, with top compounds
reaching sub-nanomolar to picomolar potency. For the orphan GPCRs GPR151 and
GPR160, AuroBind identified both agonists and antagonists with success rates of
16-30%, and functional assays confirmed GPR160 modulation in liver and prostate
cancer models. AuroBind offers a generalizable framework for structure-function
learning and high-throughput molecular screening, bridging the gap between
structure prediction and therapeutic discovery.
SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic
  Bidirectional Machine Translation System
Authors: name: Serry Sibaee, name: Omer Nacar, name: Yasser Al-Habashi, name: Adel Ammar, name: Wadii Boulila
Published: 2025-08-04 10:21:11

The rich linguistic landscape of the Arab world is characterized by a
significant gap between Modern Standard Arabic (MSA), the language of formal
communication, and the diverse regional dialects used in everyday life. This
diglossia presents a formidable challenge for natural language processing,
particularly machine translation. This paper introduces SHAMI-MT, a
bidirectional machine translation system specifically engineered to bridge the
communication gap between MSA and the Syrian dialect. We present two
specialized models, one for MSA-to-Shami and another for Shami-to-MSA
translation, both built upon the state-of-the-art AraT5v2-base-1024
architecture. The models were fine-tuned on the comprehensive Nabra dataset and
rigorously evaluated on unseen data from the MADAR corpus. Our MSA-to-Shami
model achieved an outstanding average quality score of 4.01 out of 5.0
when judged by OPENAI model GPT-4.1, demonstrating its ability to produce
translations that are not only accurate but also dialectally authentic. This
work provides a crucial, high-fidelity tool for a previously underserved
language pair, advancing the field of dialectal Arabic translation and offering
significant applications in content localization, cultural heritage, and
intercultural communication.
Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction
Authors: name: Yuerong Song, name: Xiaoran Liu, name: Ruixiao Li, name: Zhigeng Liu, name: Zengfeng Huang, name: Qipeng Guo, name: Ziwei He, name: Xipeng Qiu
Published: 2025-08-04 16:14:03

Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and
parallel decoding but suffer from prohibitive quadratic computational
complexity and memory overhead during inference. Current caching techniques
accelerate decoding by storing full-layer states, yet impose substantial memory
usage that limit long-context applications. Our analysis of attention patterns
in dLLMs reveals persistent cross-layer sparsity, with pivotal tokens remaining
salient across decoding steps and low-relevance tokens staying unimportant,
motivating selective cache eviction. We propose Sparse-dLLM, the first
training-free framework integrating dynamic cache eviction with sparse
attention via delayed bidirectional sparse caching. By leveraging the stability
of token saliency over steps, it retains critical tokens and dynamically evicts
unimportant prefix/suffix entries using an attention-guided strategy. Extensive
experiments on LLaDA and Dream series demonstrate Sparse-dLLM achieves up to
10times higher throughput than vanilla dLLMs, with comparable performance
and similar peak memory costs, outperforming previous methods in efficiency and
effectiveness.
Dens3R: A Foundation Model for 3D Geometry Prediction
Authors: name: Xianze Fang, name: Jingnan Gao, name: Zhe Wang, name: Zhuo Chen, name: Xingyu Ren, name: Jiangjing Lyu, name: Qiaomu Ren, name: Zhonglei Yang, name: Xiaokang Yang, name: Yichao Yan, name: Chengfei Lyu
Published: 2025-07-22 07:22:30

Recent advances in dense 3D reconstruction have led to significant progress,
yet achieving accurate unified geometric prediction remains a major challenge.
Most existing methods are limited to predicting a single geometry quantity from
input images. However, geometric quantities such as depth, surface normals, and
point maps are inherently correlated, and estimating them in isolation often
fails to ensure consistency, thereby limiting both accuracy and practical
applicability. This motivates us to explore a unified framework that explicitly
models the structural coupling among different geometric properties to enable
joint regression. In this paper, we present Dens3R, a 3D foundation model
designed for joint geometric dense prediction and adaptable to a wide range of
downstream tasks. Dens3R adopts a two-stage training framework to progressively
build a pointmap representation that is both generalizable and intrinsically
invariant. Specifically, we design a lightweight shared encoder-decoder
backbone and introduce position-interpolated rotary positional encoding to
maintain expressive power while enhancing robustness to high-resolution inputs.
By integrating image-pair matching features with intrinsic invariance modeling,
Dens3R accurately regresses multiple geometric quantities such as surface
normals and depth, achieving consistent geometry perception from single-view to
multi-view inputs. Additionally, we propose a post-processing pipeline that
supports geometrically consistent multi-view inference. Extensive experiments
demonstrate the superior performance of Dens3R across various dense 3D
prediction tasks and highlight its potential for broader applications.
Platonic Representations for Poverty Mapping: Unified Vision-Language
  Codes or Agent-Induced Novelty?
Authors: name: Satiyabooshan Murugaboopathy, name: Connor T. Jerzak, name: Adel Daoud
Published: 2025-08-01 23:07:16

We investigate whether socio-economic indicators like household wealth leave
recoverable imprints in satellite imagery (capturing physical features) and
Internet-sourced text (reflecting historical/economic narratives). Using
Demographic and Health Survey (DHS) data from African neighborhoods, we pair
Landsat images with LLM-generated textual descriptions conditioned on
location/year and text retrieved by an AI search agent from web sources. We
develop a multimodal framework predicting household wealth (International
Wealth Index) through five pipelines: (i) vision model on satellite images,
(ii) LLM using only location/year, (iii) AI agent searching/synthesizing web
text, (iv) joint image-text encoder, (v) ensemble of all signals. Our framework
yields three contributions. First, fusing vision and agent/LLM text outperforms
vision-only baselines in wealth prediction (e.g., R-squared of 0.77 vs. 0.63 on
out-of-sample splits), with LLM-internal knowledge proving more effective than
agent-retrieved text, improving robustness to out-of-country and out-of-time
generalization. Second, we find partial representational convergence: fused
embeddings from vision/language modalities correlate moderately (median cosine
similarity of 0.60 after alignment), suggesting a shared latent code of
material well-being while retaining complementary details, consistent with the
Platonic Representation Hypothesis. Although LLM-only text outperforms
agent-retrieved data, challenging our Agent-Induced Novelty Hypothesis, modest
gains from combining agent data in some splits weakly support the notion that
agent-gathered information introduces unique representational structures not
fully captured by static LLM knowledge. Third, we release a large-scale
multimodal dataset comprising more than 60,000 DHS clusters linked to satellite
images, LLM-generated descriptions, and agent-retrieved texts.
Uncertainty-Based Methods for Automated Process Reward Data Construction
  and Output Aggregation in Mathematical Reasoning
Authors: name: Jiuzhou Han, name: Wray Buntine, name: Ehsan Shareghi
Published: 2025-08-03 14:14:13

Large language models have demonstrated remarkable capabilities in complex
mathematical reasoning tasks, but they inevitably generate errors throughout
multi-step solutions. Process-level Reward Models (PRMs) have shown great
promise by providing supervision and evaluation at each intermediate step,
thereby effectively improving the models' reasoning abilities. However,
training effective PRMs requires high-quality process reward data, yet existing
methods for constructing such data are often labour-intensive or inefficient.
In this paper, we propose an uncertainty-driven framework for automated process
reward data construction, encompassing both data generation and annotation
processes for PRMs. Additionally, we identify the limitations of both majority
vote and PRMs, and introduce two generic uncertainty-aware output aggregation
methods: Hybrid Majority Reward Vote and Weighted Reward Frequency Vote, which
combine the strengths of majority vote with PRMs. Extensive experiments on
ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the
proposed PRM data construction framework, and demonstrate that the two output
aggregation methods further improve the mathematical reasoning abilities across
diverse PRMs. The code and data will be publicly available at
https://github.com/Jiuzhouh/UnPRM.
Artificial Intelligence and Misinformation in Art: Can Vision Language
  Models Judge the Hand or the Machine Behind the Canvas?
Authors: name: Tarian Fu, name: Javier Conde, name: Gonzalo Martínez, name: Pedro Reviriego, name: Elena Merino-Gómez, name: Fernando Moral
Published: 2025-08-02 15:27:31

The attribution of artworks in general and of paintings in particular has
always been an issue in art. The advent of powerful artificial intelligence
models that can generate and analyze images creates new challenges for painting
attribution. On the one hand, AI models can create images that mimic the style
of a painter, which can be incorrectly attributed, for example, by other AI
models. On the other hand, AI models may not be able to correctly identify the
artist for real paintings, inducing users to incorrectly attribute paintings.
In this paper, both problems are experimentally studied using state-of-the-art
AI models for image generation and analysis on a large dataset with close to
40,000 paintings from 128 artists. The results show that vision language models
have limited capabilities to: 1) perform canvas attribution and 2) to identify
AI generated images. As users increasingly rely on queries to AI models to get
information, these results show the need to improve the capabilities of VLMs to
reliably perform artist attribution and detection of AI generated images to
prevent the spread of incorrect information.
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine
  Learning
Authors: name: Sebastián Andrés Cajas Ordóñez, name: Luis Fernando Torres Torres, name: Mario Bifulco, name: Carlos Andrés Durán, name: Cristian Bosch, name: Ricardo Simón Carbajo
Published: 2025-07-28 21:23:51

Quantum Support Vector Machines face scalability challenges due to
high-dimensional quantum states and hardware limitations. We propose an
embedding-aware quantum-classical pipeline combining class-balanced k-means
distillation with pretrained Vision Transformer embeddings. Our key finding:
ViT embeddings uniquely enable quantum advantage, achieving up to 8.02%
accuracy improvements over classical SVMs on Fashion-MNIST and 4.42% on MNIST,
while CNN features show performance degradation. Using 16-qubit tensor network
simulation via cuTensorNet, we provide the first systematic evidence that
quantum kernel advantage depends critically on embedding choice, revealing
fundamental synergy between transformer attention and quantum feature spaces.
This provides a practical pathway for scalable quantum machine learning that
leverages modern neural architectures.
ReMoMask: Retrieval-Augmented Masked Motion Generation
Authors: name: Zhengdao Li, name: Siheng Wang, name: Zeyu Zhang, name: Hao Tang
Published: 2025-08-04 16:56:35

Text-to-Motion (T2M) generation aims to synthesize realistic and semantically
aligned human motion sequences from natural language descriptions. However,
current approaches face dual challenges: Generative models (e.g., diffusion
models) suffer from limited diversity, error accumulation, and physical
implausibility, while Retrieval-Augmented Generation (RAG) methods exhibit
diffusion inertia, partial-mode collapse, and asynchronous artifacts. To
address these limitations, we propose ReMoMask, a unified framework integrating
three key innovations: 1) A Bidirectional Momentum Text-Motion Model decouples
negative sample scale from batch size via momentum queues, substantially
improving cross-modal retrieval precision; 2) A Semantic Spatio-temporal
Attention mechanism enforces biomechanical constraints during part-level fusion
to eliminate asynchronous artifacts; 3) RAG-Classier-Free Guidance incorporates
minor unconditional generation to enhance generalization. Built upon MoMask's
RVQ-VAE, ReMoMask efficiently generates temporally coherent motions in minimal
steps. Extensive experiments on standard benchmarks demonstrate the
state-of-the-art performance of ReMoMask, achieving a 3.88% and 10.97%
improvement in FID scores on HumanML3D and KIT-ML, respectively, compared to
the previous SOTA method RAG-T2M. Code:
https://github.com/AIGeeksGroup/ReMoMask. Website:
https://aigeeksgroup.github.io/ReMoMask.
Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and
  Regional Languages Around the Globe
Authors: name: Tiantian Feng, name: Kevin Huang, name: Anfeng Xu, name: Xuan Shi, name: Thanathai Lertpetchpun, name: Jihwan Lee, name: Yoonjeong Lee, name: Dani Byrd, name: Shrikanth Narayanan
Published: 2025-08-03 09:51:28

We present Voxlect, a novel benchmark for modeling dialects and regional
languages worldwide using speech foundation models. Specifically, we report
comprehensive benchmark evaluations on dialects and regional language varieties
in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai,
Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over
2 million training utterances from 30 publicly available speech corpora that
are provided with dialectal information. We evaluate the performance of several
widely used speech foundation models in classifying speech dialects. We assess
the robustness of the dialectal models under noisy conditions and present an
error analysis that highlights modeling results aligned with geographic
continuity. In addition to benchmarking dialect classification, we demonstrate
several downstream applications enabled by Voxlect. Specifically, we show that
Voxlect can be applied to augment existing speech recognition datasets with
dialect information, enabling a more detailed analysis of ASR performance
across dialectal variations. Voxlect is also used as a tool to evaluate the
performance of speech generation systems. Voxlect is publicly available with
the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect.
A Glimpse to Compress: Dynamic Visual Token Pruning for Large
  Vision-Language Models
Authors: name: Quan-Sheng Zeng, name: Yunheng Li, name: Qilong Wang, name: Peng-Tao Jiang, name: Zuxuan Wu, name: Ming-Ming Cheng, name: Qibin Hou
Published: 2025-08-03 02:15:43

Visual token compression is critical for Large Vision-Language Models (LVLMs)
to efficiently process high-resolution inputs. Existing methods that typically
adopt fixed compression ratios cannot adapt to scenes of varying complexity,
often causing imprecise pruning that discards informative visual tokens and
results in degraded model performance. To address this issue, we introduce a
dynamic pruning framework, GlimpsePrune, inspired by human cognition. It takes
a data-driven ''glimpse'' and prunes irrelevant visual tokens in a single
forward pass before answer generation. This approach prunes 92.6% of visual
tokens while on average fully retaining the baseline performance on free-form
VQA tasks. The reduced computational cost also enables more effective
fine-tuning: an enhanced GlimpsePrune+ achieves 110% of the baseline
performance while maintaining a similarly high pruning rate. Our work paves a
new way for building more powerful and efficient LVLMs.
Beyond the Trade-off: Self-Supervised Reinforcement Learning for
  Reasoning Models' Instruction Following
Authors: name: Qingyu Ren, name: Qianyu He, name: Bowei Zhang, name: Jie Zeng, name: Jiaqing Liang, name: Yanghua Xiao, name: Weikang Zhou, name: Zeye Sun, name: Fei Yu
Published: 2025-08-04 07:48:59

Reasoning models excel in complex problem solving but exhibit a concerning
trade off between reasoning capabilities and instruction following abilities.
Existing approaches for improving instruction following rely on stronger
external models, creating methodological bottlenecks and practical limitations
including increased costs and accessibility constraints. We propose a
self-supervised RL framework that leverages reasoning models' own internal
signals to improve instruction following capabilities without external
supervision. Extensive experiments demonstrate that our framework significantly
improves instruction following capabilities while maintaining reasoning
performance, offering a scalable and cost-effective approach to enhance
instruction following in reasoning models. The data and code are publicly
available at https://github.com/Rainier-rq/verl-if.
Qwen-Image Technical Report
Authors: name: Chenfei Wu, name: Jiahao Li, name: Jingren Zhou, name: Junyang Lin, name: Kaiyuan Gao, name: Kun Yan, name: Sheng-ming Yin, name: Shuai Bai, name: Xiao Xu, name: Yilei Chen, name: Yuxiang Chen, name: Zecheng Tang, name: Zekai Zhang, name: Zhengyi Wang, name: An Yang, name: Bowen Yu, name: Chen Cheng, name: Dayiheng Liu, name: Deqing Li, name: Hang Zhang, name: Hao Meng, name: Hu Wei, name: Jingyuan Ni, name: Kai Chen, name: Kuan Cao, name: Liang Peng, name: Lin Qu, name: Minggang Wu, name: Peng Wang, name: Shuting Yu, name: Tingkun Wen, name: Wensen Feng, name: Xiaoxiao Xu, name: Yi Wang, name: Yichang Zhang, name: Yongqiang Zhu, name: Yujia Wu, name: Yuxuan Cai, name: Zenan Liu
Published: 2025-08-04 11:49:20

We present Qwen-Image, an image generation foundation model in the Qwen
series that achieves significant advances in complex text rendering and precise
image editing. To address the challenges of complex text rendering, we design a
comprehensive data pipeline that includes large-scale data collection,
filtering, annotation, synthesis, and balancing. Moreover, we adopt a
progressive training strategy that starts with non-text-to-text rendering,
evolves from simple to complex textual inputs, and gradually scales up to
paragraph-level descriptions. This curriculum learning approach substantially
enhances the model's native text rendering capabilities. As a result,
Qwen-Image not only performs exceptionally well in alphabetic languages such as
English, but also achieves remarkable progress on more challenging logographic
languages like Chinese. To enhance image editing consistency, we introduce an
improved multi-task training paradigm that incorporates not only traditional
text-to-image (T2I) and text-image-to-image (TI2I) tasks but also
image-to-image (I2I) reconstruction, effectively aligning the latent
representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed
the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and
reconstructive representations, respectively. This dual-encoding mechanism
enables the editing module to strike a balance between preserving semantic
consistency and maintaining visual fidelity. Qwen-Image achieves
state-of-the-art performance, demonstrating its strong capabilities in both
image generation and editing across multiple benchmarks.