Orionnnn/hub-stats huggingface dataset:
MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
Authors: name: Junzhe Li, name: Yutao Cui, name: Tao Huang, name: Yinping Ma, name: Chun Fan, name: Miles Yang, name: Zhao Zhong
Published: 2025-07-29 13:40:09

Although GRPO substantially enhances flow matching models in human preference
alignment of image generation, methods such as FlowGRPO still exhibit
inefficiency due to the necessity of sampling and optimizing over all denoising
steps specified by the Markov Decision Process (MDP). In this paper, we propose
MixGRPO, a novel framework that leverages the flexibility of mixed
sampling strategies through the integration of stochastic differential
equations (SDE) and ordinary differential equations (ODE). This streamlines the
optimization process within the MDP to improve efficiency and boost
performance. Specifically, MixGRPO introduces a sliding window mechanism, using
SDE sampling and GRPO-guided optimization only within the window, while
applying ODE sampling outside. This design confines sampling randomness to the
time-steps within the window, thereby reducing the optimization overhead, and
allowing for more focused gradient updates to accelerate convergence.
Additionally, as time-steps beyond the sliding window are not involved in
optimization, higher-order solvers are supported for sampling. So we present a
faster variant, termed MixGRPO-Flash, which further improves
training efficiency while achieving comparable performance. MixGRPO exhibits
substantial gains across multiple dimensions of human preference alignment,
outperforming DanceGRPO in both effectiveness and efficiency, with nearly 50%
lower training time. Notably, MixGRPO-Flash further reduces training time by
71%. Codes and models are available at
https://github.com/Tencent-Hunyuan/MixGRPO{MixGRPO}.
MetaCLIP 2: A Worldwide Scaling Recipe
Authors: name: Yung-Sung Chuang, name: Yang Li, name: Dong Wang, name: Ching-Feng Yeh, name: Kehan Lyu, name: Ramya Raghavendra, name: James Glass, name: Lifei Huang, name: Jason Weston, name: Luke Zettlemoyer, name: Xinlei Chen, name: Zhuang Liu, name: Saining Xie, name: Wen-tau Yih, name: Shang-Wen Li, name: Hu Xu
Published: 2025-07-29 17:59:58

Contrastive Language-Image Pretraining (CLIP) is a popular foundation model,
supporting from zero-shot classification, retrieval to encoders for multimodal
large language models (MLLMs). Although CLIP is successfully trained on
billion-scale image-text pairs from the English world, scaling CLIP's training
further to learning from the worldwide web data is still challenging: (1) no
curation method is available to handle data points from non-English world; (2)
the English performance from existing multilingual CLIP is worse than its
English-only counterpart, i.e., "curse of multilinguality" that is common in
LLMs. Here, we present MetaCLIP 2, the first recipe training CLIP from scratch
on worldwide web-scale image-text pairs. To generalize our findings, we conduct
rigorous ablations with minimal changes that are necessary to address the above
challenges and present a recipe enabling mutual benefits from English and
non-English world data. In zero-shot ImageNet classification, MetaCLIP 2
ViT-H/14 surpasses its English-only counterpart by 0.8% and mSigLIP by 0.7%,
and surprisingly sets new state-of-the-art without system-level confounding
factors (e.g., translation, bespoke architecture changes) on multilingual
benchmarks, such as CVQA with 57.4%, Babel-ImageNet with 50.2% and XM3600 with
64.3% on image-to-text retrieval.
VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced
  Multimodal Reasoning
Authors: name: Ruifeng Yuan, name: Chenghao Xiao, name: Sicong Leng, name: Jianyu Wang, name: Long Li, name: Weiwen Xu, name: Hou Pong Chan, name: Deli Zhao, name: Tingyang Xu, name: Zhongyu Wei, name: Hao Zhang, name: Yu Rong
Published: 2025-07-30 12:23:21

Reinforcement learning has proven its effectiveness in enhancing the
reasoning capabilities of large language models. Recent research efforts have
progressively extended this paradigm to multimodal reasoning tasks. Due to the
inherent complexity and diversity of multimodal tasks, especially in semantic
content and problem formulations, existing models often exhibit unstable
performance across various domains and difficulty levels. To address these
limitations, we propose VL-Cogito, an advanced multimodal reasoning model
trained via a novel multi-stage Progressive Curriculum Reinforcement Learning
(PCuRL) framework. PCuRL systematically guides the model through tasks of
gradually increasing difficulty, substantially improving its reasoning
abilities across diverse multimodal contexts. The framework introduces two key
innovations: (1) an online difficulty soft weighting mechanism, dynamically
adjusting training difficulty across successive RL training stages; and (2) a
dynamic length reward mechanism, which encourages the model to adaptively
regulate its reasoning path length according to task complexity, thus balancing
reasoning efficiency with correctness. Experimental evaluations demonstrate
that VL-Cogito consistently matches or surpasses existing reasoning-oriented
models across mainstream multimodal benchmarks spanning mathematics, science,
logic, and general understanding, validating the effectiveness of our approach.
Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement
  Learning
Authors: name: Afshin Khadangi, name: Amir Sartipi, name: Igor Tchappi, name: Ramin Bahmani, name: Gilbert Fridgen
Published: 2025-07-30 10:46:53

The tension between data privacy and model utility has become the defining
bottleneck for the practical deployment of large language models (LLMs) trained
on sensitive corpora including healthcare. Differentially private stochastic
gradient descent (DP-SGD) guarantees formal privacy, yet it does so at a
pronounced cost: gradients are forcibly clipped and perturbed with noise,
degrading sample efficiency and final accuracy. Numerous variants have been
proposed to soften this trade-off, but they all share a handicap: their control
knobs are hard-coded, global, and oblivious to the evolving optimization
landscape. Consequently, practitioners are forced either to over-spend privacy
budget in pursuit of utility, or to accept mediocre models in order to stay
within privacy constraints. We present RLDP, the first framework to cast DP
optimization itself as a closed-loop control problem amenable to modern deep
reinforcement learning (RL). RLDP continuously senses rich statistics of the
learning dynamics and acts by selecting fine-grained per parameter
gradient-clipping thresholds as well as the magnitude of injected Gaussian
noise. A soft actor-critic (SAC) hyper-policy is trained online during language
model fine-tuning; it learns, from scratch, how to allocate the privacy budget
where it matters and when it matters. Across more than 1,600 ablation
experiments on GPT2-small, Llama-1B, Llama-3B, and Mistral-7B, RLDP delivers
perplexity reductions of 1.3-30.5% (mean 5.4%) and an average 5.6% downstream
utility gain. RLDP reaches each baseline's final utility after only 13-43% of
the gradient-update budget (mean speed-up 71%), all while honoring the same
(epsilon, delta)-DP contract and exhibiting equal or lower susceptibility
to membership-inference and canary-extraction attacks.
Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with
  Weak Supervision
Authors: name: Xiao Fang, name: Minhyek Jeon, name: Zheyang Qin, name: Stanislav Panev, name: Celso de Melo, name: Shuowen Hu, name: Shayok Chakraborty, name: Fernando De la Torre
Published: 2025-07-28 16:38:06

Detecting vehicles in aerial imagery is a critical task with applications in
traffic monitoring, urban planning, and defense intelligence. Deep learning
methods have provided state-of-the-art (SOTA) results for this application.
However, a significant challenge arises when models trained on data from one
geographic region fail to generalize effectively to other areas. Variability in
factors such as environmental conditions, urban layouts, road networks, vehicle
types, and image acquisition parameters (e.g., resolution, lighting, and angle)
leads to domain shifts that degrade model performance. This paper proposes a
novel method that uses generative AI to synthesize high-quality aerial images
and their labels, improving detector training through data augmentation. Our
key contribution is the development of a multi-stage, multi-modal knowledge
transfer framework utilizing fine-tuned latent diffusion models (LDMs) to
mitigate the distribution gap between the source and target environments.
Extensive experiments across diverse aerial imagery domains show consistent
performance improvements in AP50 over supervised learning on source domain
data, weakly supervised adaptation methods, unsupervised domain adaptation
methods, and open-set object detectors by 4-23%, 6-10%, 7-40%, and more than
50%, respectively. Furthermore, we introduce two newly annotated aerial
datasets from New Zealand and Utah to support further research in this field.
Project page is available at: https://humansensinglab.github.io/AGenDA
Repair-R1: Better Test Before Repair
Authors: name: Haichuan Hu, name: Xiaochen Xie, name: Quanjun Zhang
Published: 2025-07-30 17:24:05

APR (Automated Program Repair) aims to automatically locate program defects,
generate patches and validate the repairs. Existing techniques for APR are
often combined with LLMs (Large Language Models), which leverages the
code-related knowledge of LLMs to improve repair effectiveness. Current
LLM-based APR methods typically utilize test cases only during the inference
stage, adopting an iterative approach that performs repair first and validates
it through test execution afterward. This conventional paradigm neglects two
important aspects: the potential contribution of test cases in the training
phase, and the possibility of leveraging testing prior to repair. To address
this, we propose Repair-R1, which introduces test cases into the model's
training phase and shifts test generation to precede repair. The model is
required to first generate discriminative test cases that can distinguish
defective behaviors, and then perform repair based on these tests. This enables
the model to better locate defects and understand the underlying causes of
defects, thereby improving repair effectiveness. We implement Repair-R1 with
three different backbone models, using RL (reinforcement learning) to
co-optimize test generation and bug repair. Experimental results on four widely
adopted benchmarks demonstrate the superiority of Repair-R1. Specially,
compared to vanilla models, Repair-R1 improves repair success rate by 2.68\% to
48.29\%, test generation success rate by 16.38\% to 53.28\%, and test coverage
by 0.78\% to 53.96\%. We publish the code and weights at
https://github.com/Tomsawyerhu/APR-RL and
https://huggingface.co/tomhu/Qwen3-4B-RL-5000-step.
ScreenCoder: Advancing Visual-to-Code Generation for Front-End
  Automation via Modular Multimodal Agents
Authors: name: Yilei Jiang, name: Yaozhi Zheng, name: Yuxuan Wan, name: Jiaming Han, name: Qunzhong Wang, name: Michael R. Lyu, name: Xiangyu Yue
Published: 2025-07-30 16:41:21

Automating the transformation of user interface (UI) designs into front-end
code holds significant promise for accelerating software development and
democratizing design workflows. While recent large language models (LLMs) have
demonstrated progress in text-to-code generation, many existing approaches rely
solely on natural language prompts, limiting their effectiveness in capturing
spatial layout and visual design intent. In contrast, UI development in
practice is inherently multimodal, often starting from visual sketches or
mockups. To address this gap, we introduce a modular multi-agent framework that
performs UI-to-code generation in three interpretable stages: grounding,
planning, and generation. The grounding agent uses a vision-language model to
detect and label UI components, the planning agent constructs a hierarchical
layout using front-end engineering priors, and the generation agent produces
HTML/CSS code via adaptive prompt-based synthesis. This design improves
robustness, interpretability, and fidelity over end-to-end black-box methods.
Furthermore, we extend the framework into a scalable data engine that
automatically produces large-scale image-code pairs. Using these synthetic
examples, we fine-tune and reinforce an open-source VLM, yielding notable gains
in UI understanding and code quality. Extensive experiments demonstrate that
our approach achieves state-of-the-art performance in layout accuracy,
structural coherence, and code correctness. Our code is made publicly available
at https://github.com/leigest519/ScreenCoder.
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency
  and Performance
Authors: name: Jingwei Zuo, name: Maksim Velikanov, name: Ilyas Chahed, name: Younes Belkada, name: Dhia Eddine Rhayem, name: Guillaume Kunsch, name: Hakim Hacid, name: Hamza Yous, name: Brahim Farhat, name: Ibrahim Khadraoui, name: Mugariya Farooq, name: Giulia Campesan, name: Ruxandra Cojocaru, name: Yasser Djilali, name: Shi Hu, name: Iheb Chaabane, name: Puneesh Khanna, name: Mohamed El Amine Seddik, name: Ngoc Dung Huynh, name: Phuc Le Khac, name: Leen AlQadi, name: Billel Mokeddem, name: Mohamed Chami, name: Abdalgader Abubaker, name: Mikhail Lubinets, name: Kacper Piskorski, name: Slim Frikha
Published: 2025-07-30 07:55:33

In this report, we introduce Falcon-H1, a new series of large language models
(LLMs) featuring hybrid architecture designs optimized for both high
performance and efficiency across diverse use cases. Unlike earlier Falcon
models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a
parallel hybrid approach that combines Transformer-based attention with State
Space Models (SSMs), known for superior long-context memory and computational
efficiency. We systematically revisited model design, data strategy, and
training dynamics, challenging conventional practices in the field. Falcon-H1
is released in multiple configurations, including base and instruction-tuned
variants at 0.5B, 1.5B, 1.5B-deep, 3B, 7B, and 34B parameters. Quantized
instruction-tuned models are also available, totaling over 30 checkpoints on
Hugging Face Hub. Falcon-H1 models demonstrate state-of-the-art performance and
exceptional parameter and training efficiency. The flagship Falcon-H1-34B
matches or outperforms models up to 70B scale, such as Qwen3-32B, Qwen2.5-72B,
and Llama3.3-70B, while using fewer parameters and less data. Smaller models
show similar trends: the Falcon-H1-1.5B-Deep rivals current leading 7B-10B
models, and Falcon-H1-0.5B performs comparably to typical 7B models from 2024.
These models excel across reasoning, mathematics, multilingual tasks,
instruction following, and scientific knowledge. With support for up to 256K
context tokens and 18 languages, Falcon-H1 is suitable for a wide range of
applications. All models are released under a permissive open-source license,
underscoring our commitment to accessible and impactful AI research.
BANG: Dividing 3D Assets via Generative Exploded Dynamics
Authors: name: Longwen Zhang, name: Qixuan Zhang, name: Haoran Jiang, name: Yinuo Bai, name: Wei Yang, name: Lan Xu, name: Jingyi Yu
Published: 2025-07-29 04:21:21

3D creation has always been a unique human strength, driven by our ability to
deconstruct and reassemble objects using our eyes, mind and hand. However,
current 3D design tools struggle to replicate this natural process, requiring
considerable artistic expertise and manual labor. This paper introduces BANG, a
novel generative approach that bridges 3D generation and reasoning, allowing
for intuitive and flexible part-level decomposition of 3D objects. At the heart
of BANG is "Generative Exploded Dynamics", which creates a smooth sequence of
exploded states for an input geometry, progressively separating parts while
preserving their geometric and semantic coherence.
  BANG utilizes a pre-trained large-scale latent diffusion model, fine-tuned
for exploded dynamics with a lightweight exploded view adapter, allowing
precise control over the decomposition process. It also incorporates a temporal
attention module to ensure smooth transitions and consistency across time. BANG
enhances control with spatial prompts, such as bounding boxes and surface
regions, enabling users to specify which parts to decompose and how. This
interaction can be extended with multimodal models like GPT-4, enabling
2D-to-3D manipulations for more intuitive and creative workflows.
  The capabilities of BANG extend to generating detailed part-level geometry,
associating parts with functional descriptions, and facilitating
component-aware 3D creation and manufacturing workflows. Additionally, BANG
offers applications in 3D printing, where separable parts are generated for
easy printing and reassembly. In essence, BANG enables seamless transformation
from imaginative concepts to detailed 3D assets, offering a new perspective on
creation that resonates with human intuition.
Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual
  Segmentation
Authors: name: Kaining Ying, name: Henghui Ding, name: Guanquan Jie, name: Yu-Gang Jiang
Published: 2025-07-30 17:59:31

Referring audio-visual segmentation (RAVS) has recently seen significant
advancements, yet challenges remain in integrating multimodal information and
deeply understanding and reasoning about audiovisual content. To extend the
boundaries of RAVS and facilitate future research in this field, we propose
Omnimodal Referring Audio-Visual Segmentation (OmniAVS), a new dataset
containing 2,098 videos and 59,458 multimodal referring expressions. OmniAVS
stands out with three key innovations: (1) 8 types of multimodal expressions
that flexibly combine text, speech, sound, and visual cues; (2) an emphasis on
understanding audio content beyond just detecting their presence; and (3) the
inclusion of complex reasoning and world knowledge in expressions. Furthermore,
we introduce Omnimodal Instructed Segmentation Assistant (OISA), to address the
challenges of multimodal reasoning and fine-grained understanding of
audiovisual content in OmniAVS. OISA uses MLLM to comprehend complex cues and
perform reasoning-based segmentation. Extensive experiments show that OISA
outperforms existing methods on OmniAVS and achieves competitive results on
other related tasks.
MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery
  Classification
Authors: name: Dingkun Liu, name: Zhu Chen, name: Jingwei Luo, name: Shijie Lian, name: Dongrui Wu
Published: 2025-07-27 12:54:42

Brain-computer interfaces (BCIs) enable direct communication between the
brain and external devices. Recent EEG foundation models aim to learn
generalized representations across diverse BCI paradigms. However, these
approaches overlook fundamental paradigm-specific neurophysiological
distinctions, limiting their generalization ability. Importantly, in practical
BCI deployments, the specific paradigm such as motor imagery (MI) for stroke
rehabilitation or assistive robotics, is generally determined prior to data
acquisition. This paper proposes MIRepNet, the first EEG foundation model
tailored for the MI paradigm. MIRepNet comprises a high-quality EEG
preprocessing pipeline incorporating a neurophysiologically-informed channel
template, adaptable to EEG headsets with arbitrary electrode configurations.
Furthermore, we introduce a hybrid pretraining strategy that combines
self-supervised masked token reconstruction and supervised MI classification,
facilitating rapid adaptation and accurate decoding on novel downstream MI
tasks with fewer than 30 trials per class. Extensive evaluations across five
public MI datasets demonstrated that MIRepNet consistently achieved
state-of-the-art performance, significantly outperforming both specialized and
generalized EEG models. Our code will be available on
GitHubhttps://github.com/staraink/MIRepNet.
ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical
  Knowledge
Authors: name: Zihan Zhao, name: Bo Chen, name: Ziping Wan, name: Lu Chen, name: Xuanze Lin, name: Shiyang Yu, name: Situo Zhang, name: Da Ma, name: Zichen Zhu, name: Danyang Zhang, name: Huayang Wang, name: Zhongyang Dai, name: Liyang Wen, name: Xin Chen, name: Kai Yu
Published: 2025-07-29 16:40:49

While large language models (LLMs) have achieved impressive progress, their
application in scientific domains such as chemistry remains hindered by shallow
domain understanding and limited reasoning capabilities. In this work, we focus
on the specific field of chemistry and develop a Chemical Reasoner LLM,
ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge
points to enhance the model's understanding of the fundamental principles and
logical structure of chemistry. Then, we propose a mix-sourced distillation
strategy that integrates expert-curated knowledge with general-domain reasoning
skills, followed by domain-specific reinforcement learning to enhance chemical
reasoning. Experiments on diverse chemical benchmarks demonstrate that
ChemDFM-R achieves state-of-the-art performance while providing interpretable,
rationale-driven outputs. Further case studies illustrate how explicit
reasoning chains significantly improve the reliability, transparency, and
practical utility of the model in real-world human-AI collaboration scenarios.
MoHoBench: Assessing Honesty of Multimodal Large Language Models via
  Unanswerable Visual Questions
Authors: name: Yanxu Zhu, name: Shitong Duan, name: Xiangxu Zhang, name: Jitao Sang, name: Peng Zhang, name: Tun Lu, name: Xiao Zhou, name: Jing Yao, name: Xiaoyuan Yi, name: Xing Xie
Published: 2025-07-29 04:55:49

Recently Multimodal Large Language Models (MLLMs) have achieved considerable
advancements in vision-language tasks, yet produce potentially harmful or
untrustworthy content. Despite substantial work investigating the
trustworthiness of language models, MMLMs' capability to act honestly,
especially when faced with visually unanswerable questions, remains largely
underexplored. This work presents the first systematic assessment of honesty
behaviors across various MLLMs. We ground honesty in models' response behaviors
to unanswerable visual questions, define four representative types of such
questions, and construct MoHoBench, a large-scale MMLM honest benchmark,
consisting of 12k+ visual question samples, whose quality is guaranteed by
multi-stage filtering and human verification. Using MoHoBench, we benchmarked
the honesty of 28 popular MMLMs and conducted a comprehensive analysis. Our
findings show that: (1) most models fail to appropriately refuse to answer when
necessary, and (2) MMLMs' honesty is not solely a language modeling issue, but
is deeply influenced by visual information, necessitating the development of
dedicated methods for multimodal honesty alignment. Therefore, we implemented
initial alignment methods using supervised and preference learning to improve
honesty behavior, providing a foundation for future work on trustworthy MLLMs.
Our data and code can be found at https://github.com/DSTTSD/MoHoBench.