Orionnnn/hub-stats huggingface dataset:
Ark: An Open-source Python-based Framework for Robot Learning
Authors: name: Magnus Dierking, name: Christopher E. Mower, name: Sarthak Das, name: Huang Helong, name: Jiacheng Qiu, name: Cody Reading, name: Wei Chen, name: Huidong Liang, name: Huang Guowei, name: Jan Peters, name: Quan Xingyue, name: Jun Wang, name: Haitham Bou-Ammar
Published: 2025-06-24 20:23:39

Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics
Challenges to the first humanoid-robot kickboxing tournament-yet commercial
autonomy still lags behind progress in machine learning. A major bottleneck is
software: current robot stacks demand steep learning curves, low-level C/C++
expertise, fragmented tooling, and intricate hardware integration, in stark
contrast to the Python-centric, well-documented ecosystems that propelled
modern AI. We introduce ARK, an open-source, Python-first robotics framework
designed to close that gap. ARK presents a Gym-style environment interface that
allows users to collect data, preprocess it, and train policies using
state-of-the-art imitation-learning algorithms (e.g., ACT, Diffusion Policy)
while seamlessly toggling between high-fidelity simulation and physical robots.
A lightweight client-server architecture provides networked
publisher-subscriber communication, and optional C/C++ bindings ensure
real-time performance when needed. ARK ships with reusable modules for control,
SLAM, motion planning, system identification, and visualization, along with
native ROS interoperability. Comprehensive documentation and case studies-from
manipulation to mobile navigation-demonstrate rapid prototyping, effortless
hardware swapping, and end-to-end pipelines that rival the convenience of
mainstream machine-learning workflows. By unifying robotics and AI practices
under a common Python umbrella, ARK lowers entry barriers and accelerates
research and commercial deployment of autonomous robots.
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation
  Models
Authors: name: Ronald Fecso, name: José Morano, name: Ursula Schmidt-Erfurth, name: Hrvoje Bogunović
Published: 2025-06-27 11:53:54

The rise of imaging techniques such as optical coherence tomography (OCT) and
advances in deep learning (DL) have enabled clinicians and researchers to
streamline retinal disease staging. A popular DL approach is self-supervised
learning (SSL), where models learn from vast amounts of unlabeled data,
avoiding costly annotation. SSL has allowed the development of foundation
models (FMs), large models that can be used for a variety of downstream tasks.
However, existing FMs for OCT, trained solely on image data, lack a
comprehensive and robust semantic understanding of images, as evidenced by
their downstream performance (especially for complex tasks), and thus require
supervised fine-tuning (which may be unfeasible) to better adapt to specific
applications and populations. To address this, we propose RetFiner, an SSL
vision-language refinement scheme that improves the representations of existing
FMs and enables their efficient and direct adaptation to specific populations
for improved downstream performance. Our method uses a diverse set of training
objectives which take advantage of the rich supervisory signal found in textual
data. We tested RetFiner on the retinal FMs RETFound, UrFound, and VisionFM,
showing significant improvements in linear probing performance on seven highly
diverse OCT classification tasks, with an average increase of 5.8, 3.9, and 2.1
percentage points over their baselines, respectively. Our code and model
weights are publicly available at https://github.com/ronnief1/RetFiner.
Gazal-R1: Achieving State-of-the-Art Medical Reasoning with
  Parameter-Efficient Two-Stage Training
Authors: name: Ahmed M. Adly, name: Mostafa Samy, name: Amr Fawzy
Published: 2025-06-18 09:44:21

We present Gazal-R1, a 32-billion-parameter language model that achieves
state-of-the-art performance in medical reasoning while providing transparent,
step-by-step explanations for clinical decision-making. Built upon Qwen3 32B,
our model demonstrates that strategic training can enable mid-sized models to
outperform significantly larger counterparts in specialized domains. We
developed a novel two-stage training pipeline: first, supervised fine-tuning on
a carefully curated dataset of 107,033 synthetic medical reasoning examples
that teaches structured clinical thinking, enhanced by advanced
parameter-efficient techniques including Weight-Decomposed Low-Rank Adaptation
(DoRA) and Rank-Stabilized LoRA (rsLoRA); second, reinforcement learning using
Group Relative Policy Optimization (GRPO) with a sophisticated multi-component
reward system that refines accuracy, format adherence, and reasoning quality.
Gazal-R1 achieves exceptional performance across medical benchmarks, scoring
87.1% on MedQA, 81.6% on MMLU Pro (Medical), and 79.6% on PubMedQA, surpassing
models up to 12x larger. Beyond its strong empirical results, this work
provides detailed insights into the challenges of training reasoning-capable
models in specialized domains, including issues with reward hacking, training
instability, and the fundamental tension between factual recall and detailed
reasoning. Our methodology offers a reproducible framework for developing
high-capability, domain-specific language models that balance performance,
efficiency, and explainability.
The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT
  Improvements
Authors: name: Bingchen Zhao, name: Despoina Magka, name: Minqi Jiang, name: Xian Li, name: Roberta Raileanu, name: Tatiana Shavrina, name: Jean-Christophe Gagnon-Audet, name: Kelvin Niu, name: Shagun Sodhani, name: Michael Shvartsman, name: Andrei Lupu, name: Alisia Lupidi, name: Edan Toledo, name: Karen Hambardzumyan, name: Martin Josifoski, name: Thomas Foster, name: Lucia Cipolina-Kun, name: Abhishek Charnalia, name: Derek Dunfield, name: Alexander H. Miller, name: Oisin Mac Aodha, name: Jakob Foerster, name: Yoram Bachrach
Published: 2025-06-27 17:44:32

Rapid advancements in large language models (LLMs) have the potential to
assist in scientific progress. A critical capability toward this endeavor is
the ability to reproduce existing work. To evaluate the ability of AI agents to
reproduce results in an active research area, we introduce the Automated LLM
Speedrunning Benchmark, leveraging the research community contributions on the
NanoGPT speedrun, a competition to train a GPT-2 model in the shortest time.
Each of the 19 speedrun tasks provides the agent with the previous records
training script, optionally paired with one of three hint formats, ranging from
pseudocode to paper-like descriptions of the new records improvements. Records
execute quickly by design and speedrun improvements encompass diverse
code-level changes, ranging from high-level algorithmic advancements to
hardware-aware optimizations. These features make the benchmark both accessible
and realistic for the frontier problem of improving LLM training. We find that
recent reasoning LLMs combined with SoTA scaffolds struggle to reimplement
already-known innovations in our benchmark, even when given detailed hints. Our
benchmark thus provides a simple, non-saturated measure of an LLMs ability to
automate scientific reproduction, a necessary (but not sufficient) skill for an
autonomous research agent.
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Authors: name: Yehui Tang, name: Xiaosong Li, name: Fangcheng Liu, name: Wei Guo, name: Hang Zhou, name: Yaoyuan Wang, name: Kai Han, name: Xianzhi Yu, name: Jinpeng Li, name: Hui Zang, name: Fei Mi, name: Xiaojun Meng, name: Zhicheng Liu, name: Hanting Chen, name: Binfan Zheng, name: Can Chen, name: Youliang Yan, name: Ruiming Tang, name: Peifeng Qin, name: Xinghao Chen, name: Dacheng Tao, name: Yunhe Wang
Published: 2025-05-27 16:40:21

The surgence of Mixture of Experts (MoE) in Large Language Models promises a
small price of execution cost for a much larger model parameter count and
learning capacity, because only a small fraction of parameters are activated
for each input token. However, it is commonly observed that some experts are
activated far more often than others, leading to system inefficiency when
running the experts on different devices in parallel. Therefore, we introduce
Mixture of Grouped Experts (MoGE), which groups the experts during selection
and balances the expert workload better than MoE in nature. It constrains
tokens to activate an equal number of experts within each predefined expert
group. When a model execution is distributed on multiple devices, this
architectural design ensures a balanced computational load across devices,
significantly enhancing throughput, particularly for the inference phase.
Further, we build Pangu Pro MoE on Ascend NPUs, a sparse model based on MoGE
with 72 billion total parameters, 16 billion of which are activated for each
token. The configuration of Pangu Pro MoE is optimized for Ascend 300I Duo and
800I A2 through extensive system simulation studies. Our experiments indicate
that MoGE indeed leads to better expert load balancing and more efficient
execution for both model training and inference on Ascend NPUs. The inference
performance of Pangu Pro MoE achieves 1148 tokens/s per card and can be further
improved to 1528 tokens/s per card by speculative acceleration, outperforming
comparable 32B and 72B Dense models. Furthermore, we achieve an excellent
cost-to-performance ratio for model inference on Ascend 300I Duo. Our studies
show that Ascend NPUs are capable of training Pangu Pro MoE with massive
parallelization to make it a leading model within the sub-100B total parameter
class, outperforming prominent open-source models like GLM-Z1-32B and
Qwen3-32B.
XVerse: Consistent Multi-Subject Control of Identity and Semantic
  Attributes via DiT Modulation
Authors: name: Bowen Chen, name: Mengyi Zhao, name: Haomiao Sun, name: Li Chen, name: Xu Wang, name: Kang Du, name: Xinglong Wu
Published: 2025-06-26 16:04:16

Achieving fine-grained control over subject identity and semantic attributes
(pose, style, lighting) in text-to-image generation, particularly for multiple
subjects, often undermines the editability and coherence of Diffusion
Transformers (DiTs). Many approaches introduce artifacts or suffer from
attribute entanglement. To overcome these challenges, we propose a novel
multi-subject controlled generation model XVerse. By transforming reference
images into offsets for token-specific text-stream modulation, XVerse allows
for precise and independent control for specific subject without disrupting
image latents or features. Consequently, XVerse offers high-fidelity, editable
multi-subject image synthesis with robust control over individual subject
characteristics and semantic attributes. This advancement significantly
improves personalized and complex scene generation capabilities.
ShotBench: Expert-Level Cinematic Understanding in Vision-Language
  Models
Authors: name: Hongbo Liu, name: Jingwen He, name: Yi Jin, name: Dian Zheng, name: Yuhao Dong, name: Fan Zhang, name: Ziqi Huang, name: Yinan He, name: Yangguang Li, name: Weichao Chen, name: Yu Qiao, name: Wanli Ouyang, name: Shengjie Zhao, name: Ziwei Liu
Published: 2025-06-26 15:09:21

Cinematography, the fundamental visual language of film, is essential for
conveying narrative, emotion, and aesthetic quality. While recent
Vision-Language Models (VLMs) demonstrate strong general visual understanding,
their proficiency in comprehending the nuanced cinematic grammar embedded
within individual shots remains largely unexplored and lacks robust evaluation.
This critical gap limits both fine-grained visual comprehension and the
precision of AI-assisted video generation. To address this, we introduce
ShotBench, a comprehensive benchmark specifically designed for cinematic
language understanding. It features over 3.5k expert-annotated QA pairs from
images and video clips, meticulously curated from over 200 acclaimed
(predominantly Oscar-nominated) films and spanning eight key cinematography
dimensions. Our evaluation of 24 leading VLMs on ShotBench reveals their
substantial limitations: even the top-performing model achieves less than 60%
average accuracy, particularly struggling with fine-grained visual cues and
complex spatial reasoning. To catalyze advancement in this domain, we construct
ShotQA, a large-scale multimodal dataset comprising approximately 70k cinematic
QA pairs. Leveraging ShotQA, we develop ShotVL through supervised fine-tuning
and Group Relative Policy Optimization. ShotVL significantly outperforms all
existing open-source and proprietary models on ShotBench, establishing new
state-of-the-art performance. We open-source our models, data, and code to
foster rapid progress in this crucial area of AI-driven cinematic understanding
and generation.
From Ideal to Real: Unified and Data-Efficient Dense Prediction for
  Real-World Scenarios
Authors: name: Changliang Xia, name: Chengyou Jia, name: Zhuohang Dang, name: Minnan Luo
Published: 2025-06-25 09:40:50

Dense prediction tasks hold significant importance of computer vision, aiming
to learn pixel-wise annotated label for an input image. Despite advances in
this field, existing methods primarily focus on idealized conditions, with
limited generalization to real-world scenarios and facing the challenging
scarcity of real-world data. To systematically study this problem, we first
introduce DenseWorld, a benchmark spanning a broad set of 25 dense prediction
tasks that correspond to urgent real-world applications, featuring unified
evaluation across tasks. Then, we propose DenseDiT, which maximally exploits
generative models' visual priors to perform diverse real-world dense prediction
tasks through a unified strategy. DenseDiT combines a parameter-reuse mechanism
and two lightweight branches that adaptively integrate multi-scale context,
working with less than 0.1% additional parameters. Evaluations on DenseWorld
reveal significant performance drops in existing general and specialized
baselines, highlighting their limited real-world generalization. In contrast,
DenseDiT achieves superior results using less than 0.01% training data of
baselines, underscoring its practical value for real-world deployment. Our
data, and checkpoints and codes are available at
https://xcltql666.github.io/DenseDiTProj
BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
Authors: name: Jiacheng Chen, name: Ramin Mehran, name: Xuhui Jia, name: Saining Xie, name: Sanghyun Woo
Published: 2025-06-20 19:38:34

We present BlenderFusion, a generative visual compositing framework that
synthesizes new scenes by recomposing objects, camera, and background. It
follows a layering-editing-compositing pipeline: (i) segmenting and converting
visual inputs into editable 3D entities (layering), (ii) editing them in
Blender with 3D-grounded control (editing), and (iii) fusing them into a
coherent scene using a generative compositor (compositing). Our generative
compositor extends a pre-trained diffusion model to process both the original
(source) and edited (target) scenes in parallel. It is fine-tuned on video
frames with two key training strategies: (i) source masking, enabling flexible
modifications like background replacement; (ii) simulated object jittering,
facilitating disentangled control over objects and camera. BlenderFusion
significantly outperforms prior methods in complex compositional scene editing
tasks.
Noise Consistency Training: A Native Approach for One-Step Generator in
  Learning Additional Controls
Authors: name: Yihong Luo, name: Shuchen Xue, name: Tianyang Hu, name: Jing Tang
Published: 2025-06-24 15:58:55

The pursuit of efficient and controllable high-quality content generation
remains a central challenge in artificial intelligence-generated content
(AIGC). While one-step generators, enabled by diffusion distillation
techniques, offer excellent generation quality and computational efficiency,
adapting them to new control conditions--such as structural constraints,
semantic guidelines, or external inputs--poses a significant challenge.
Conventional approaches often necessitate computationally expensive
modifications to the base model and subsequent diffusion distillation. This
paper introduces Noise Consistency Training (NCT), a novel and lightweight
approach to directly integrate new control signals into pre-trained one-step
generators without requiring access to original training images or retraining
the base diffusion model. NCT operates by introducing an adapter module and
employs a noise consistency loss in the noise space of the generator. This loss
aligns the adapted model's generation behavior across noises that are
conditionally dependent to varying degrees, implicitly guiding it to adhere to
the new control. Theoretically, this training objective can be understood as
minimizing the distributional distance between the adapted generator and the
conditional distribution induced by the new conditions. NCT is modular,
data-efficient, and easily deployable, relying only on the pre-trained one-step
generator and a control signal model. Extensive experiments demonstrate that
NCT achieves state-of-the-art controllable generation in a single forward pass,
surpassing existing multi-step and distillation-based methods in both
generation quality and computational efficiency. Code is available at
https://github.com/Luo-Yihong/NCT
MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
Authors: name: Xi Chen, name: Mingkang Zhu, name: Shaoteng Liu, name: Xiaoyang Wu, name: Xiaogang Xu, name: Yu Liu, name: Xiang Bai, name: Hengshuang Zhao
Published: 2025-06-27 17:59:27

This work explores enabling Chain-of-Thought (CoT) reasoning to link visual
cues across multiple images. A straightforward solution is to adapt rule-based
reinforcement learning for Vision-Language Models (VLMs). However, such methods
typically rely on manually curated question-answer pairs, which can be
particularly challenging when dealing with fine grained visual details and
complex logic across images. Inspired by self-supervised visual representation
learning, we observe that images contain inherent constraints that can serve as
supervision. Based on this insight, we construct image triplets comprising two
augmented views of the same image and a third, similar but distinct image.
During training, the model is prompted to generate a reasoning process to
compare these images (i.e., determine same or different). Then we optimize the
model with rule-based reinforcement learning. Due to the high visual similarity
and the presence of augmentations, the model must attend to subtle visual
changes and perform logical reasoning to succeed. Experiments show that,
although trained solely on visual comparison tasks, the learned reasoning
ability generalizes effectively to a wide range of questions. Without relying
on any human-annotated question-answer pairs, our method achieves significant
improvements on multi-image reasoning benchmarks and shows strong performance
on general vision tasks.
Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
Authors: name: Yifan Shen, name: Yuanzhe Liu, name: Jingyuan Zhu, name: Xu Cao, name: Xiaofeng Zhang, name: Yixiao He, name: Wenming Ye, name: James Matthew Rehg, name: Ismini Lourentzou
Published: 2025-06-26 18:00:00

Current Vision-Language Models (VLMs) struggle with fine-grained spatial
reasoning, particularly when multi-step logic and precise spatial alignment are
required. In this work, we introduce SpatialReasoner-R1, a vision-language
reasoning model designed to address these limitations. To construct
high-quality supervision for spatial reasoning, we design a Multi-Model Monte
Carlo Tree Search (M3CTS) method that generates diverse, logically consistent
Long Chain-of-Thought (LongCoT) reasoning trajectories. In addition, we propose
fine-grained Direct Preference Optimization (fDPO), which introduces
segment-specific preference granularity for descriptive grounding and logical
reasoning, guided by a spatial reward mechanism that evaluates candidate
responses based on visual consistency, spatial grounding, and logical
coherence. Experimental results demonstrate that fDPO achieves an average
improvement of 4.1% over standard DPO across spatial quality tasks, and a 9.0%
gain in spatial quantity tasks. SpatialReasoner-R1, trained with fDPO, sets a
new SoTA on SPATIALRGPT-Bench, outperforming the strongest baseline by 9.8% in
average accuracy, while maintaining competitive performance on general
vision-language tasks.
LLaVA-Scissor: Token Compression with Semantic Connected Components for
  Video LLMs
Authors: name: Boyuan Sun, name: Jiaxing Zhao, name: Xihan Wei, name: Qibin Hou
Published: 2025-06-27 02:29:58

In this paper, we present LLaVA-Scissor, a training-free token compression
strategy designed for video multimodal large language models. Previous methods
mostly attempt to compress tokens based on attention scores, but fail to
effectively capture all semantic regions and often lead to token redundancy.
Differently, we propose to leverage the Semantic Connected Components (SCC)
approach that assigns tokens to distinct semantic regions within the token set,
ensuring comprehensive semantic coverage. The outcome is a two-step
spatio-temporal token compression strategy that utilizes SCC in both spatial
and temporal domains. This strategy can effectively compress tokens by
representing the entire video with a set of non-overlapping semantic tokens. We
conduct extensive evaluations of the token compression capabilities of
LLaVA-Scissor across diverse video understanding benchmarks, including video
question answering, long video understanding, and comprehensive multi-choices
benchmarks. Experimental results show that the proposed LLaVA-Scissor
outperforms other token compression methods, achieving superior performance in
various video understanding benchmarks, particularly at low token retention
ratios. Project page: https://github.com/HumanMLLM/LLaVA-Scissor.
Generative Blocks World: Moving Things Around in Pictures
Authors: name: Vaibhav Vavilala, name: Seemandhar Jain, name: Rahul Vasanth, name: D. A. Forsyth, name: Anand Bhattad
Published: 2025-06-25 17:59:55

We describe Generative Blocks World to interact with the scene of a generated
image by manipulating simple geometric abstractions. Our method represents
scenes as assemblies of convex 3D primitives, and the same scene can be
represented by different numbers of primitives, allowing an editor to move
either whole structures or small details. Once the scene geometry has been
edited, the image is generated by a flow-based method which is conditioned on
depth and a texture hint. Our texture hint takes into account the modified 3D
primitives, exceeding texture-consistency provided by existing key-value
caching techniques. These texture hints (a) allow accurate object and camera
moves and (b) largely preserve the identity of objects depicted. Quantitative
and qualitative experiments demonstrate that our approach outperforms prior
works in visual fidelity, editability, and compositional generalization.