Orionnnn/hub-stats huggingface dataset:
True Multimodal In-Context Learning Needs Attention to the Visual
  Context
Authors: name: Shuo Chen, name: Jianzhe Liu, name: Zhen Han, name: Yan Xia, name: Daniel Cremers, name: Philip Torr, name: Volker Tresp, name: Jindong Gu
Published: 2025-07-21 17:08:18

Multimodal Large Language Models (MLLMs), built on powerful language
backbones, have enabled Multimodal In-Context Learning (MICL)-adapting to new
tasks from a few multimodal demonstrations consisting of images, questions, and
answers. Despite showing noticeable improvement on standard vision-language
datasets, current MLLMs struggle to leverage visual information in the
demonstrations. Specifically, they tend to neglect visual cues and over-rely on
textual patterns, leading to mere text imitation rather than genuine multimodal
adaptation. This behavior makes MICL still unimodal and largely restricts its
practical utility. More importantly, this limitation is often concealed by the
improved performance on tasks that do not require understanding the visual
context. As a result, how to effectively enhance MICL ability and reliably
evaluate the MICL performance remains underexplored. To address these issues,
we first introduce Dynamic Attention Reallocation (DARA), an efficient
fine-tuning strategy that encourages models to attend to the visual context by
rebalancing attention across visual and textual tokens. In addition, we present
TrueMICL, an MICL-dedicated dataset with both support and test sets that
explicitly requires the integration of multimodal information-particularly
visual content-for correct task completion. Extensive experiments demonstrate
the effectiveness of our holistic solution, showcasing substantial improvements
in the true multimodal in-context learning capabilities. Code and datasets are
available at https://chenxshuo.github.io/true-micl-colm .
HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic
  Learning
Authors: name: Li Jun, name: Wang Jinpeng, name: Tan Chaolei, name: Lian Niu, name: Chen Long, name: Zhang Min, name: Wang Yaowei, name: Xia Shu-Tao, name: Chen Bin
Published: 2025-07-23 10:59:46

Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of
matching untrimmed videos with text queries describing only partial content.
Existing methods suffer from geometric distortion in Euclidean space that
sometimes misrepresents the intrinsic hierarchical structure of videos and
overlooks certain hierarchical semantics, ultimately leading to suboptimal
temporal modeling. To address this issue, we propose the first hyperbolic
modeling framework for PRVR, namely HLFormer, which leverages hyperbolic space
learning to compensate for the suboptimal hierarchical modeling capabilities of
Euclidean space. Specifically, HLFormer integrates the Lorentz Attention Block
and Euclidean Attention Block to encode video embeddings in hybrid spaces,
using the Mean-Guided Adaptive Interaction Module to dynamically fuse features.
Additionally, we introduce a Partial Order Preservation Loss to enforce "text <
video" hierarchy through Lorentzian cone constraints. This approach further
enhances cross-modal matching by reinforcing partial relevance between video
content and text queries. Extensive experiments show that HLFormer outperforms
state-of-the-art methods. Code is released at
https://github.com/lijun2005/ICCV25-HLFormer.
Iwin Transformer: Hierarchical Vision Transformer using Interleaved
  Windows
Authors: name: Simin Huo, name: Ning Li
Published: 2025-07-24 13:45:48

We introduce Iwin Transformer, a novel position-embedding-free hierarchical
vision transformer, which can be fine-tuned directly from low to high
resolution, through the collaboration of innovative interleaved window
attention and depthwise separable convolution. This approach uses attention to
connect distant tokens and applies convolution to link neighboring tokens,
enabling global information exchange within a single module, overcoming Swin
Transformer's limitation of requiring two consecutive blocks to approximate
global attention. Extensive experiments on visual benchmarks demonstrate that
Iwin Transformer exhibits strong competitiveness in tasks such as image
classification (87.4 top-1 accuracy on ImageNet-1K), semantic segmentation and
video action recognition. We also validate the effectiveness of the core
component in Iwin as a standalone module that can seamlessly replace the
self-attention module in class-conditional image generation. The concepts and
methods introduced by the Iwin Transformer have the potential to inspire future
research, like Iwin 3D Attention in video generation. The code and models are
available at https://github.com/cominder/Iwin-Transformer.
nablaNABLA: Neighborhood Adaptive Block-Level Attention
Authors: name: Dmitrii Mikhailov, name: Aleksey Letunovskiy, name: Maria Kovaleva, name: Vladimir Arkhipkin, name: Vladimir Korviakov, name: Vladimir Polovnikov, name: Viacheslav Vasilev, name: Evelina Sidorova, name: Denis Dimitrov
Published: 2025-07-17 21:36:36

Recent progress in transformer-based architectures has demonstrated
remarkable success in video generation tasks. However, the quadratic complexity
of full attention mechanisms remains a critical bottleneck, particularly for
high-resolution and long-duration video sequences. In this paper, we propose
NABLA, a novel Neighborhood Adaptive Block-Level Attention mechanism that
dynamically adapts to sparsity patterns in video diffusion transformers (DiTs).
By leveraging block-wise attention with adaptive sparsity-driven threshold,
NABLA reduces computational overhead while preserving generative quality. Our
method does not require custom low-level operator design and can be seamlessly
integrated with PyTorch's Flex Attention operator. Experiments demonstrate that
NABLA achieves up to 2.7x faster training and inference compared to baseline
almost without compromising quantitative metrics (CLIP score, VBench score,
human evaluation score) and visual quality drop. The code and model weights are
available here: https://github.com/gen-ai-team/Wan2.1-NABLA
Agentar-Fin-R1: Enhancing Financial Intelligence through Domain
  Expertise, Training Efficiency, and Advanced Reasoning
Authors: name: Yanjun Zheng, name: Xiyang Du, name: Longfei Liao, name: Xiaoke Zhao, name: Zhaowen Zhou, name: Jingze Song, name: Bo Zhang, name: Jiawei Liu, name: Xiang Qi, name: Zhe Li, name: Zhiqiang Zhang, name: Wei Wang, name: Peng Zhang
Published: 2025-07-22 17:52:16

Large Language Models (LLMs) exhibit considerable promise in financial
applications; however, prevailing models frequently demonstrate limitations
when confronted with scenarios that necessitate sophisticated reasoning
capabilities, stringent trustworthiness criteria, and efficient adaptation to
domain-specific requirements. We introduce the Agentar-Fin-R1 series of
financial large language models (8B and 32B parameters), specifically
engineered based on the Qwen3 foundation model to enhance reasoning
capabilities, reliability, and domain specialization for financial
applications. Our optimization approach integrates a high-quality, systematic
financial task label system with a comprehensive multi-layered trustworthiness
assurance framework. This framework encompasses high-quality trustworthy
knowledge engineering, multi-agent trustworthy data synthesis, and rigorous
data validation governance. Through label-guided automated difficulty-aware
optimization, tow-stage training pipeline, and dynamic attribution systems, we
achieve substantial improvements in training efficiency. Our models undergo
comprehensive evaluation on mainstream financial benchmarks including Fineva,
FinEval, and FinanceIQ, as well as general reasoning datasets such as MATH-500
and GPQA-diamond. To thoroughly assess real-world deployment capabilities, we
innovatively propose the Finova evaluation benchmark, which focuses on
agent-level financial reasoning and compliance verification. Experimental
results demonstrate that Agentar-Fin-R1 not only achieves state-of-the-art
performance on financial tasks but also exhibits exceptional general reasoning
capabilities, validating its effectiveness as a trustworthy solution for
high-stakes financial applications. The Finova bench is available at
https://github.com/antgroup/Finova.
Group Sequence Policy Optimization
Authors: name: Chujie Zheng, name: Shixuan Liu, name: Mingze Li, name: Xiong-Hui Chen, name: Bowen Yu, name: Chang Gao, name: Kai Dang, name: Yuqiong Liu, name: Rui Men, name: An Yang, name: Jingren Zhou, name: Junyang Lin
Published: 2025-07-24 03:50:32

This paper introduces Group Sequence Policy Optimization (GSPO), our stable,
efficient, and performant reinforcement learning algorithm for training large
language models. Unlike previous algorithms that adopt token-level importance
ratios, GSPO defines the importance ratio based on sequence likelihood and
performs sequence-level clipping, rewarding, and optimization. We demonstrate
that GSPO achieves superior training efficiency and performance compared to the
GRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, and
has the potential for simplifying the design of RL infrastructure. These merits
of GSPO have contributed to the remarkable improvements in the latest Qwen3
models.
Technical Report of TeleChat2, TeleChat2.5 and T1
Authors: name: Zihan Wang, name: Xinzhang Liu, name: Yitong Yao, name: Chao Wang, name: Yu Zhao, name: Zhihao Yang, name: Wenmin Deng, name: Kaipeng Jia, name: Jiaxin Peng, name: Yuyao Huang, name: Sishi Xiong, name: Zhuo Jiang, name: Kaidong Yu, name: Xiaohui Hu, name: Fubei Yao, name: Ruiyu Fang, name: Zhuoru Jiang, name: Ruiting Song, name: Qiyi Xie, name: Rui Xue, name: Xuewei He, name: Yanlei Xue, name: Zhu Yuan, name: Zhaoxi Zhang, name: Zilu Huang, name: Shiquan Wang, name: Xin Wang, name: Hanming Wu, name: Mingyuan Wang, name: Xufeng Zhan, name: Yuhan Sun, name: Zhaohu Xing, name: Yuhao Jiang, name: Bingkai Yang, name: Shuangyong Song, name: Yongxiang Li, name: Zhongjiang He, name: Xuelong Li
Published: 2025-07-24 01:00:48

We introduce the latest series of TeleChat models: TeleChat2,
TeleChat2.5, and T1, offering a significant upgrade over
their predecessor, TeleChat. Despite minimal changes to the model architecture,
the new series achieves substantial performance gains through enhanced training
strategies in both pre-training and post-training stages. The series begins
with TeleChat2, which undergoes pretraining on 10 trillion
high-quality and diverse tokens. This is followed by Supervised Fine-Tuning
(SFT) and Direct Preference Optimization (DPO) to further enhance its
capabilities. TeleChat2.5 and T1 expand the pipeline by
incorporating a continual pretraining phase with domain-specific datasets,
combined with reinforcement learning (RL) to improve performance in code
generation and mathematical reasoning tasks. The T1 variant is
designed for complex reasoning, supporting long Chain-of-Thought (CoT)
reasoning and demonstrating substantial improvements in mathematics and coding.
In contrast, TeleChat2.5 prioritizes speed, delivering rapid
inference. Both flagship models of T1 and TeleChat2.5 are
dense Transformer-based architectures with 115B parameters, showcasing
significant advancements in reasoning and general task performance compared to
the original TeleChat. Notably, T1-115B outperform proprietary models
such as OpenAI's o1-mini and GPT-4o. We publicly release TeleChat2,
TeleChat2.5 and T1, including post-trained versions with 35B
and 115B parameters, to empower developers and researchers with
state-of-the-art language models tailored for diverse applications.
Discovering and using Spelke segments
Authors: name: Rahul Venkatesh, name: Klemen Kotar, name: Lilian Naing Chen, name: Seungwoo Kim, name: Luca Thomas Wheeler, name: Jared Watrous, name: Ashley Xu, name: Gia Ancone, name: Wanhee Lee, name: Honglin Chen, name: Daniel Bear, name: Stefan Stojanov, name: Daniel Yamins
Published: 2025-07-21 20:11:57

Segments in computer vision are often defined by semantic considerations and
are highly dependent on category-specific conventions. In contrast,
developmental psychology suggests that humans perceive the world in terms of
Spelke objects--groupings of physical things that reliably move together when
acted on by physical forces. Spelke objects thus operate on category-agnostic
causal motion relationships which potentially better support tasks like
manipulation and planning. In this paper, we first benchmark the Spelke object
concept, introducing the SpelkeBench dataset that contains a wide variety of
well-defined Spelke segments in natural images. Next, to extract Spelke
segments from images algorithmically, we build SpelkeNet, a class of visual
world models trained to predict distributions over future motions. SpelkeNet
supports estimation of two key concepts for Spelke object discovery: (1) the
motion affordance map, identifying regions likely to move under a poke, and (2)
the expected-displacement map, capturing how the rest of the scene will move.
These concepts are used for "statistical counterfactual probing", where diverse
"virtual pokes" are applied on regions of high motion-affordance, and the
resultant expected displacement maps are used define Spelke segments as
statistical aggregates of correlated motion statistics. We find that SpelkeNet
outperforms supervised baselines like SegmentAnything (SAM) on SpelkeBench.
Finally, we show that the Spelke concept is practically useful for downstream
applications, yielding superior performance on the 3DEditBench benchmark for
physical object manipulation when used in a variety of off-the-shelf object
manipulation models.
Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age
  Estimation and Gender Classification for Targeted Advertisement
Authors: name: Muhammad Imran Zaman, name: Nisar Ahmed
Published: 2025-07-24 16:41:26

This paper presents a novel deep learning-based approach for simultaneous age
and gender classification from facial images, designed to enhance the
effectiveness of targeted advertising campaigns. We propose a custom
Convolutional Neural Network (CNN) architecture, optimized for both tasks,
which leverages the inherent correlation between age and gender information
present in facial features. Unlike existing methods that often treat these
tasks independently, our model learns shared representations, leading to
improved performance. The network is trained on a large, diverse dataset of
facial images, carefully pre-processed to ensure robustness against variations
in lighting, pose, and image quality. Our experimental results demonstrate a
significant improvement in gender classification accuracy, achieving 95%, and a
competitive mean absolute error of 5.77 years for age estimation. Critically,
we analyze the performance across different age groups, identifying specific
challenges in accurately estimating the age of younger individuals. This
analysis reveals the need for targeted data augmentation and model refinement
to address these biases. Furthermore, we explore the impact of different CNN
architectures and hyperparameter settings on the overall performance, providing
valuable insights for future research.
DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Authors: name: Miguel Aspis, name: Sebastián A. Cajas Ordónez, name: Andrés L. Suárez-Cetrulo, name: Ricardo Simón Carbajo
Published: 2025-07-24 14:39:20

Learning from non-stationary data streams subject to concept drift requires
models that can adapt on-the-fly while remaining resource-efficient. Existing
adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or
simple voting schemes that fail to optimally leverage specialized knowledge.
This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture
that addresses these limitations through a novel co-training framework.
DriftMoE features a compact neural router that is co-trained alongside a pool
of incremental Hoeffding tree experts. The key innovation lies in a symbiotic
learning loop that enables expert specialization: the router selects the most
suitable expert for prediction, the relevant experts update incrementally with
the true label, and the router refines its parameters using a multi-hot
correctness mask that reinforces every accurate expert. This feedback loop
provides the router with a clear training signal while accelerating expert
specialization. We evaluate DriftMoE's performance across nine state-of-the-art
data stream learning benchmarks spanning abrupt, gradual, and real-world drifts
testing two distinct configurations: one where experts specialize on data
regimes (multi-class variant), and another where they focus on single-class
specialization (task-based variant). Our results demonstrate that DriftMoE
achieves competitive results with state-of-the-art stream learning adaptive
ensembles, offering a principled and efficient approach to concept drift
adaptation. All code, data pipelines, and reproducibility scripts are available
in our public GitHub repository: https://github.com/miguel-ceadar/drift-moe.
GLiNER2: An Efficient Multi-Task Information Extraction System with
  Schema-Driven Interface
Authors: name: Urchade Zaratiana, name: Gil Pasternak, name: Oliver Boyd, name: George Hurn-Maloney, name: Ash Lewis
Published: 2025-07-24 16:11:14

Information extraction (IE) is fundamental to numerous NLP applications, yet
existing solutions often require specialized models for different tasks or rely
on computationally expensive large language models. We present GLiNER2, a
unified framework that enhances the original GLiNER architecture to support
named entity recognition, text classification, and hierarchical structured data
extraction within a single efficient model. Built pretrained transformer
encoder architecture, GLiNER2 maintains CPU efficiency and compact size while
introducing multi-task composition through an intuitive schema-based interface.
Our experiments demonstrate competitive performance across extraction and
classification tasks with substantial improvements in deployment accessibility
compared to LLM-based alternatives. We release GLiNER2 as an open-source
pip-installable library with pre-trained models and documentation at
https://github.com/fastino-ai/GLiNER2.
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical
  Imaging
Authors: name: Salah Eddine Bekhouche, name: Gaby Maroun, name: Fadi Dornaika, name: Abdenour Hadid
Published: 2025-07-21 13:18:05

Medical image segmentation is crucial for many healthcare tasks, including
disease diagnosis and treatment planning. One key area is the segmentation of
skin lesions, which is vital for diagnosing skin cancer and monitoring
patients. In this context, this paper introduces SegDT, a new segmentation
model based on diffusion transformer (DiT). SegDT is designed to work on
low-cost hardware and incorporates Rectified Flow, which improves the
generation quality at reduced inference steps and maintains the flexibility of
standard diffusion models. Our method is evaluated on three benchmarking
datasets and compared against several existing works, achieving
state-of-the-art results while maintaining fast inference speeds. This makes
the proposed model appealing for real-world medical applications. This work
advances the performance and capabilities of deep learning models in medical
image analysis, enabling faster, more accurate diagnostic tools for healthcare
professionals. The code is made publicly available at
https://github.com/Bekhouche/SegDT{GitHub}.
MUR: Momentum Uncertainty guided Reasoning for Large Language Models
Authors: name: Hang Yan, name: Fangzhi Xu, name: Rongman Xu, name: Yifei Li, name: Jian Zhang, name: Haoran Luo, name: Xiaobao Wu, name: Luu Anh Tuan, name: Haiteng Zhao, name: Qika Lin, name: Jun Liu
Published: 2025-07-20 13:36:19

Large Language Models (LLMs) have achieved impressive performance on
reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an
open challenge. While Test-Time Scaling (TTS) improves reasoning quality, it
often leads to overthinking, wasting tokens on redundant computations. This
work investigates how to efficiently and adaptively guide LLM test-time scaling
without additional training. Inspired by the concept of momentum in physics, we
propose Momentum Uncertainty-guided Reasoning (MUR), which dynamically
allocates thinking budgets to critical reasoning steps by tracking and
aggregating stepwise uncertainty over time. To support flexible inference-time
control, we introduce gamma-control, a simple mechanism that tunes the
reasoning budget via a single hyperparameter. We provide in-depth theoretical
proof to support the superiority of MUR in terms of stability and biases. MUR
is comprehensively evaluated against various TTS methods across four
challenging benchmarks (MATH-500, AIME24, AIME25, and GPQA-diamond) using
different sizes of recent Qwen3 models (1.7B, 4B, and 8B). Results demonstrate
that MUR reduces computation by over 50% on average while improving accuracy by
0.62-3.37%.
A New Pair of GloVes
Authors: name: Riley Carlson, name: John Bauer, name: Christopher D. Manning
Published: 2025-07-24 05:29:18

This report documents, describes, and evaluates new 2024 English GloVe
(Global Vectors for Word Representation) models. While the original GloVe
models built in 2014 have been widely used and found useful, languages and the
world continue to evolve and we thought that current usage could benefit from
updated models. Moreover, the 2014 models were not carefully documented as to
the exact data versions and preprocessing that were used, and we rectify this
by documenting these new models. We trained two sets of word embeddings using
Wikipedia, Gigaword, and a subset of Dolma. Evaluation through vocabulary
comparison, direct testing, and NER tasks shows that the 2024 vectors
incorporate new culturally and linguistically relevant words, perform
comparably on structural tasks like analogy and similarity, and demonstrate
improved performance on recent, temporally dependent NER datasets such as
non-Western newswire data.
Hierarchical Budget Policy Optimization for Adaptive Reasoning
Authors: name: Shangke Lyu, name: Linjuan Wu, name: Yuchen Yan, name: Xingyu Wu, name: Hao Li, name: Yongliang Shen, name: Peisheng Jiang, name: Weiming Lu, name: Jun Xiao, name: Yueting Zhuang
Published: 2025-07-21 17:52:34

Large reasoning models achieve remarkable performance through extensive
chain-of-thought generation, yet exhibit significant computational inefficiency
by applying uniform reasoning strategies regardless of problem complexity. We
present Hierarchical Budget Policy Optimization (HBPO), a reinforcement
learning framework that enables models to learn problem-specific reasoning
depths without sacrificing capability. HBPO addresses the fundamental challenge
of exploration space collapse in efficiency-oriented training, where penalties
on long output length systematically bias models away from necessary long
reasoning paths. Through hierarchical budget exploration, our approach
partitions rollout samples into multiple subgroups with distinct token budgets,
aiming to enable efficient resource allocation while preventing degradation of
capability. We introduce differentiated reward mechanisms that create
budget-aware incentives aligned with the complexity of the problem, allowing
models to discover natural correspondences between task requirements and
computational effort. Extensive experiments demonstrate that HBPO reduces
average token usage by up to 60.6% while improving accuracy by 3.14% across
four reasoning benchmarks. Unlike existing methods that impose external
constraints or rely on discrete mode selection, HBPO exhibits emergent adaptive
behavior where models automatically adjust reasoning depth based on problem
complexity. Our results suggest that reasoning efficiency and capability are
not inherently conflicting, and can be simultaneously optimized through
appropriately structured hierarchical training that preserves exploration
diversity.