Orionnnn/hub-stats huggingface dataset:
Genie Envisioner: A Unified World Foundation Platform for Robotic
  Manipulation
Authors: name: Yue Liao, name: Pengfei Zhou, name: Siyuan Huang, name: Donglin Yang, name: Shengcong Chen, name: Yuxin Jiang, name: Yue Hu, name: Jingbin Cai, name: Si Liu, name: Jianlan Luo, name: Liliang Chen, name: Shuicheng Yan, name: Maoqing Yao, name: Guanghui Ren
Published: 2025-08-07 17:59:44

We introduce Genie Envisioner (GE), a unified world foundation platform for
robotic manipulation that integrates policy learning, evaluation, and
simulation within a single video-generative framework. At its core, GE-Base is
a large-scale, instruction-conditioned video diffusion model that captures the
spatial, temporal, and semantic dynamics of real-world robotic interactions in
a structured latent space. Built upon this foundation, GE-Act maps latent
representations to executable action trajectories through a lightweight,
flow-matching decoder, enabling precise and generalizable policy inference
across diverse embodiments with minimal supervision. To support scalable
evaluation and training, GE-Sim serves as an action-conditioned neural
simulator, producing high-fidelity rollouts for closed-loop policy development.
The platform is further equipped with EWMBench, a standardized benchmark suite
measuring visual fidelity, physical consistency, and instruction-action
alignment. Together, these components establish Genie Envisioner as a scalable
and practical foundation for instruction-driven, general-purpose embodied
intelligence. All code, models, and benchmarks will be released publicly.
DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning
Authors: name: Xinrun Xu, name: Pi Bu, name: Ye Wang, name: Börje F. Karlsson, name: Ziming Wang, name: Tengtao Song, name: Qi Zhu, name: Jun Song, name: Zhiming Ding, name: Bo Zheng
Published: 2025-08-07 13:58:19

Although Vision Language Models (VLMs) exhibit strong perceptual abilities
and impressive visual reasoning, they struggle with attention to detail and
precise action planning in complex, dynamic environments, leading to subpar
performance. Real-world tasks typically require complex interactions, advanced
spatial reasoning, long-term planning, and continuous strategy refinement,
usually necessitating understanding the physics rules of the target scenario.
However, evaluating these capabilities in real-world scenarios is often
prohibitively expensive. To bridge this gap, we introduce DeepPHY, a novel
benchmark framework designed to systematically evaluate VLMs' understanding and
reasoning about fundamental physical principles through a series of challenging
simulated environments. DeepPHY integrates multiple physical reasoning
environments of varying difficulty levels and incorporates fine-grained
evaluation metrics. Our evaluation finds that even state-of-the-art VLMs
struggle to translate descriptive physical knowledge into precise, predictive
control.
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning
  Models
Authors: name: Linan Yue, name: Yichao Du, name: Yizhi Wang, name: Weibo Gao, name: Fangzhou Yao, name: Li Wang, name: Ye Liu, name: Ziyu Xu, name: Qi Liu, name: Shimin Di, name: Min-Ling Zhang
Published: 2025-08-04 06:54:31

Recently, Large Reasoning Models (LRMs) have gradually become a research
hotspot due to their outstanding performance in handling complex tasks. Among
them, DeepSeek R1 has garnered significant attention for its exceptional
performance and open-source nature, driving advancements in the research of
R1-style LRMs. Unlike traditional Large Language Models (LLMs), these models
enhance logical deduction and decision-making capabilities during reasoning by
incorporating mechanisms such as long chain-of-thought and self-reflection
through reinforcement learning. However, with the widespread application of
these models, the problem of overthinking has gradually emerged. Specifically,
when generating answers, these models often construct excessively long
reasoning chains with redundant or repetitive steps, which leads to reduced
reasoning efficiency and may affect the accuracy of the final answer. To this
end, various efficient reasoning methods have been proposed, aiming to reduce
the length of reasoning paths without compromising model performance and
reasoning capability. By reviewing the current research advancements in the
field of efficient reasoning methods systematically, we categorize existing
works into two main directions based on the lens of single-model optimization
versus model collaboration: (1) Efficient Reasoning with Single Model, which
focuses on improving the reasoning efficiency of individual models; and (2)
Efficient Reasoning with Model Collaboration, which explores optimizing
reasoning paths through collaboration among multiple models. Besides, we
maintain a public GitHub repository that tracks the latest progress in
efficient reasoning methods.
StrandDesigner: Towards Practical Strand Generation with Sketch Guidance
Authors: name: Na Zhang, name: Moran Li, name: Chengming Xu, name: Han Feng, name: Xiaobin Hu, name: Jiangning Zhang, name: Weijian Cao, name: Chengjie Wang, name: Yanwei Fu
Published: 2025-08-03 08:17:50

Realistic hair strand generation is crucial for applications like computer
graphics and virtual reality. While diffusion models can generate hairstyles
from text or images, these inputs lack precision and user-friendliness.
Instead, we propose the first sketch-based strand generation model, which
offers finer control while remaining user-friendly. Our framework tackles key
challenges, such as modeling complex strand interactions and diverse sketch
patterns, through two main innovations: a learnable strand upsampling strategy
that encodes 3D strands into multi-scale latent spaces, and a multi-scale
adaptive conditioning mechanism using a transformer with diffusion heads to
ensure consistency across granularity levels. Experiments on several benchmark
datasets show our method outperforms existing approaches in realism and
precision. Qualitative results further confirm its effectiveness. Code will be
released at [GitHub](https://github.com/fighting-Zhang/StrandDesigner).
Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during
  Multi-Hop Analysis
Authors: name: Anushka Yadav, name: Isha Nalawade, name: Srujana Pillarichety, name: Yashwanth Babu, name: Reshmi Ghosh, name: Samyadeep Basu, name: Wenlong Zhao, name: Ali Nasaeh, name: Sriram Balasubramanian, name: Soundararajan Srinivasan
Published: 2025-08-06 17:58:36

The emergence of reasoning models and their integration into practical AI
chat bots has led to breakthroughs in solving advanced math, deep search, and
extractive question answering problems that requires a complex and multi-step
thought process. Yet, a complete understanding of why these models hallucinate
more than general purpose language models is missing. In this investigative
study, we systematicallyexplore reasoning failures of contemporary language
models on multi-hop question answering tasks. We introduce a novel, nuanced
error categorization framework that examines failures across three critical
dimensions: the diversity and uniqueness of source documents involved ("hops"),
completeness in capturing relevant information ("coverage"), and cognitive
inefficiency ("overthinking"). Through rigorous hu-man annotation, supported by
complementary automated metrics, our exploration uncovers intricate error
patterns often hidden by accuracy-centric evaluations. This investigative
approach provides deeper insights into the cognitive limitations of current
models and offers actionable guidance toward enhancing reasoning fidelity,
transparency, and robustness in future language modeling efforts.
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Authors: name: Chengsong Huang, name: Wenhao Yu, name: Xiaoyang Wang, name: Hongming Zhang, name: Zongxia Li, name: Ruosen Li, name: Jiaxin Huang, name: Haitao Mi, name: Dong Yu
Published: 2025-08-07 03:38:16

Self-evolving Large Language Models (LLMs) offer a scalable path toward
super-intelligence by autonomously generating, refining, and learning from
their own experiences. However, existing methods for training such models still
rely heavily on vast human-curated tasks and labels, typically via fine-tuning
or reinforcement learning, which poses a fundamental bottleneck to advancing AI
systems toward capabilities beyond human intelligence. To overcome this
limitation, we introduce R-Zero, a fully autonomous framework that generates
its own training data from scratch. Starting from a single base LLM, R-Zero
initializes two independent models with distinct roles, a Challenger and a
Solver. These models are optimized separately and co-evolve through
interaction: the Challenger is rewarded for proposing tasks near the edge of
the Solver capability, and the Solver is rewarded for solving increasingly
challenging tasks posed by the Challenger. This process yields a targeted,
self-improving curriculum without any pre-existing tasks and labels.
Empirically, R-Zero substantially improves reasoning capability across
different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on
math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks.
Are Today's LLMs Ready to Explain Well-Being Concepts?
Authors: name: Bohan Jiang, name: Dawei Li, name: Zhen Tan, name: Chengshuai Zhao, name: Huan Liu
Published: 2025-08-06 00:45:02

Well-being encompasses mental, physical, and social dimensions essential to
personal growth and informed life decisions. As individuals increasingly
consult Large Language Models (LLMs) to understand well-being, a key challenge
emerges: Can LLMs generate explanations that are not only accurate but also
tailored to diverse audiences? High-quality explanations require both factual
correctness and the ability to meet the expectations of users with varying
expertise. In this work, we construct a large-scale dataset comprising 43,880
explanations of 2,194 well-being concepts, generated by ten diverse LLMs. We
introduce a principle-guided LLM-as-a-judge evaluation framework, employing
dual judges to assess explanation quality. Furthermore, we show that
fine-tuning an open-source LLM using Supervised Fine-Tuning (SFT) and Direct
Preference Optimization (DPO) can significantly enhance the quality of
generated explanations. Our results reveal: (1) The proposed LLM judges align
well with human evaluations; (2) explanation quality varies significantly
across models, audiences, and categories; and (3) DPO- and SFT-finetuned models
outperform their larger counterparts, demonstrating the effectiveness of
preference-based learning for specialized explanation tasks.
Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
Authors: name: Yuhan Zhang, name: Long Zhuo, name: Ziyang Chu, name: Tong Wu, name: Zhibing Li, name: Liang Pan, name: Dahua Lin, name: Ziwei Liu
Published: 2025-08-07 17:50:13

Despite rapid advances in 3D content generation, quality assessment for the
generated 3D assets remains challenging. Existing methods mainly rely on
image-based metrics and operate solely at the object level, limiting their
ability to capture spatial coherence, material authenticity, and high-fidelity
local details. 1) To address these challenges, we introduce Hi3DEval, a
hierarchical evaluation framework tailored for 3D generative content. It
combines both object-level and part-level evaluation, enabling holistic
assessments across multiple dimensions as well as fine-grained quality
analysis. Additionally, we extend texture evaluation beyond aesthetic
appearance by explicitly assessing material realism, focusing on attributes
such as albedo, saturation, and metallicness. 2) To support this framework, we
construct Hi3DBench, a large-scale dataset comprising diverse 3D assets and
high-quality annotations, accompanied by a reliable multi-agent annotation
pipeline. We further propose a 3D-aware automated scoring system based on
hybrid 3D representations. Specifically, we leverage video-based
representations for object-level and material-subject evaluations to enhance
modeling of spatio-temporal consistency and employ pretrained 3D features for
part-level perception. Extensive experiments demonstrate that our approach
outperforms existing image-based metrics in modeling 3D characteristics and
achieves superior alignment with human preference, providing a scalable
alternative to manual evaluations. The project page is available at
https://zyh482.github.io/Hi3DEval/.
Evaluating, Synthesizing, and Enhancing for Customer Support
  Conversation
Authors: name: Jie Zhu, name: Huaixia Dou, name: Junhui Li, name: Lifan Guo, name: Feng Chen, name: Chi Zhang, name: Fang Kong
Published: 2025-08-06 13:11:17

Effective customer support requires not only accurate problem solving but
also structured and empathetic communication aligned with professional
standards. However, existing dialogue datasets often lack strategic guidance,
and real-world service data is difficult to access and annotate. To address
this, we introduce the task of Customer Support Conversation (CSC), aimed at
training customer service agents to respond using well-defined support
strategies. We propose a structured CSC framework grounded in COPC guidelines,
defining five conversational stages and twelve strategies to guide high-quality
interactions. Based on this, we construct CSConv, an evaluation dataset of
1,855 real-world customer-agent conversations rewritten using LLMs to reflect
deliberate strategy use, and annotated accordingly. Additionally, we develop a
role-playing approach that simulates strategy-rich conversations using
LLM-powered roles aligned with the CSC framework, resulting in the training
dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS
significantly improves their ability to generate high-quality, strategy-aligned
responses on CSConv. Human evaluations further confirm gains in problem
resolution. All code and data will be made publicly available at
https://github.com/aliyun/qwen-dianjin.
On the Generalization of SFT: A Reinforcement Learning Perspective with
  Reward Rectification
Authors: name: Yongliang Wu, name: Yizhou Zhou, name: Zhou Ziheng, name: Yingzhe Peng, name: Xinyu Ye, name: Xinting Hu, name: Wenbo Zhu, name: Lu Qi, name: Ming-Hsuan Yang, name: Xu Yang
Published: 2025-08-07 17:59:04

We present a simple yet theoretically motivated improvement to Supervised
Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited
generalization compared to reinforcement learning (RL). Through mathematical
analysis, we reveal that standard SFT gradients implicitly encode a problematic
reward structure that may severely restrict the generalization capabilities of
model. To rectify this, we propose Dynamic Fine-Tuning (DFT), stabilizing
gradient updates for each token by dynamically rescaling the objective function
with the probability of this token. Remarkably, this single-line code change
significantly outperforms standard SFT across multiple challenging benchmarks
and base models, demonstrating greatly improved generalization. Additionally,
our approach shows competitive results in offline RL settings, offering an
effective yet simpler alternative. This work bridges theoretical insight and
practical solutions, substantially advancing SFT performance. The code will be
available at https://github.com/yongliang-wu/DFT.
MOSEv2: A More Challenging Dataset for Video Object Segmentation in
  Complex Scenes
Authors: name: Henghui Ding, name: Kaining Ying, name: Chang Liu, name: Shuting He, name: Xudong Jiang, name: Yu-Gang Jiang, name: Philip H. S. Torr, name: Song Bai
Published: 2025-08-07 17:59:27

Video object segmentation (VOS) aims to segment specified target objects
throughout a video. Although state-of-the-art methods have achieved impressive
performance (e.g., 90+% J&F) on existing benchmarks such as DAVIS and
YouTube-VOS, these datasets primarily contain salient, dominant, and isolated
objects, limiting their generalization to real-world scenarios. To advance VOS
toward more realistic environments, coMplex video Object SEgmentation (MOSEv1)
was introduced to facilitate VOS research in complex scenes. Building on the
strengths and limitations of MOSEv1, we present MOSEv2, a significantly more
challenging dataset designed to further advance VOS methods under real-world
conditions. MOSEv2 consists of 5,024 videos and over 701,976 high-quality masks
for 10,074 objects across 200 categories. Compared to its predecessor, MOSEv2
introduces significantly greater scene complexity, including more frequent
object disappearance and reappearance, severe occlusions and crowding, smaller
objects, as well as a range of new challenges such as adverse weather (e.g.,
rain, snow, fog), low-light scenes (e.g., nighttime, underwater), multi-shot
sequences, camouflaged objects, non-physical targets (e.g., shadows,
reflections), scenarios requiring external knowledge, etc. We benchmark 20
representative VOS methods under 5 different settings and observe consistent
performance drops. For example, SAM2 drops from 76.4% on MOSEv1 to only 50.9%
on MOSEv2. We further evaluate 9 video object tracking methods and find similar
declines, demonstrating that MOSEv2 presents challenges across tasks. These
results highlight that despite high accuracy on existing datasets, current VOS
methods still struggle under real-world complexities. MOSEv2 is publicly
available at https://MOSE.video.
MedBLINK: Probing Basic Perception in Multimodal Language Models for
  Medicine
Authors: name: Mahtab Bigverdi, name: Wisdom Ikezogwo, name: Kevin Zhang, name: Hyewon Jeong, name: Mingyu Lu, name: Sungjae Cho, name: Linda Shapiro, name: Ranjay Krishna
Published: 2025-08-04 23:19:18

Multimodal language models (MLMs) show promise for clinical decision support
and diagnostic reasoning, raising the prospect of end-to-end automated medical
image interpretation. However, clinicians are highly selective in adopting AI
tools; a model that makes errors on seemingly simple perception tasks such as
determining image orientation or identifying whether a CT scan is
contrast-enhance are unlikely to be adopted for clinical tasks. We introduce
Medblink, a benchmark designed to probe these models for such perceptual
abilities. Medblink spans eight clinically meaningful tasks across multiple
imaging modalities and anatomical regions, totaling 1,429 multiple-choice
questions over 1,605 images. We evaluate 19 state-of-the-art MLMs, including
general purpose (GPT4o, Claude 3.5 Sonnet) and domain specific (Med Flamingo,
LLaVA Med, RadFM) models. While human annotators achieve 96.4% accuracy, the
best-performing model reaches only 65%. These results show that current MLMs
frequently fail at routine perceptual checks, suggesting the need to strengthen
their visual grounding to support clinical adoption. Data is available on our
project page.
CM^3: Calibrating Multimodal Recommendation
Authors: name: Xin Zhou, name: Yongjie Wang, name: Zhiqi Shen
Published: 2025-08-02 06:44:59

Alignment and uniformity are fundamental principles within the domain of
contrastive learning. In recommender systems, prior work has established that
optimizing the Bayesian Personalized Ranking (BPR) loss contributes to the
objectives of alignment and uniformity. Specifically, alignment aims to draw
together the representations of interacting users and items, while uniformity
mandates a uniform distribution of user and item embeddings across a unit
hypersphere. This study revisits the alignment and uniformity properties within
the context of multimodal recommender systems, revealing a proclivity among
extant models to prioritize uniformity to the detriment of alignment. Our
hypothesis challenges the conventional assumption of equitable item treatment
through a uniformity loss, proposing a more nuanced approach wherein items with
similar multimodal attributes converge toward proximal representations within
the hyperspheric manifold. Specifically, we leverage the inherent similarity
between items' multimodal data to calibrate their uniformity distribution,
thereby inducing a more pronounced repulsive force between dissimilar entities
within the embedding space. A theoretical analysis elucidates the relationship
between this calibrated uniformity loss and the conventional uniformity
function. Moreover, to enhance the fusion of multimodal features, we introduce
a Spherical B\'ezier method designed to integrate an arbitrary number of
modalities while ensuring that the resulting fused features are constrained to
the same hyperspherical manifold. Empirical evaluations conducted on five
real-world datasets substantiate the superiority of our approach over competing
baselines. We also shown that the proposed methods can achieve up to a 5.4%
increase in NDCG@20 performance via the integration of MLLM-extracted features.
Source code is available at: https://github.com/enoche/CM3.
Data and AI governance: Promoting equity, ethics, and fairness in large
  language models
Authors: name: Alok Abhishek, name: Lisa Erickson, name: Tushar Bandopadhyay
Published: 2025-08-05 23:15:31

In this paper, we cover approaches to systematically govern, assess and
quantify bias across the complete life cycle of machine learning models, from
initial development and validation to ongoing production monitoring and
guardrail implementation. Building upon our foundational work on the Bias
Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the
authors share prevalent bias and fairness related gaps in Large Language Models
(LLMs) and discuss data and AI governance framework to address Bias, Ethics,
Fairness, and Factuality within LLMs. The data and AI governance approach
discussed in this paper is suitable for practical, real-world applications,
enabling rigorous benchmarking of LLMs prior to production deployment,
facilitating continuous real-time evaluation, and proactively governing LLM
generated responses. By implementing the data and AI governance across the life
cycle of AI development, organizations can significantly enhance the safety and
responsibility of their GenAI systems, effectively mitigating risks of
discrimination and protecting against potential reputational or brand-related
harm. Ultimately, through this article, we aim to contribute to advancement of
the creation and deployment of socially responsible and ethically aligned
generative artificial intelligence powered applications.
DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior
Authors: name: Junzhe Lu, name: Jing Lin, name: Hongkun Dou, name: Ailing Zeng, name: Yue Deng, name: Xian Liu, name: Zhongang Cai, name: Lei Yang, name: Yulun Zhang, name: Haoqian Wang, name: Ziwei Liu
Published: 2025-08-01 12:56:39

We present DPoser-X, a diffusion-based prior model for 3D whole-body human
poses. Building a versatile and robust full-body human pose prior remains
challenging due to the inherent complexity of articulated human poses and the
scarcity of high-quality whole-body pose datasets. To address these
limitations, we introduce a Diffusion model as body Pose prior (DPoser) and
extend it to DPoser-X for expressive whole-body human pose modeling. Our
approach unifies various pose-centric tasks as inverse problems, solving them
through variational diffusion sampling. To enhance performance on downstream
applications, we introduce a novel truncated timestep scheduling method
specifically designed for pose data characteristics. We also propose a masked
training mechanism that effectively combines whole-body and part-specific
datasets, enabling our model to capture interdependencies between body parts
while avoiding overfitting to specific actions. Extensive experiments
demonstrate DPoser-X's robustness and versatility across multiple benchmarks
for body, hand, face, and full-body pose modeling. Our model consistently
outperforms state-of-the-art alternatives, establishing a new benchmark for
whole-body human pose prior modeling.