Orionnnn/hub-stats huggingface dataset:
Qwen3 Technical Report
Authors: name: An Yang, name: Anfeng Li, name: Baosong Yang, name: Beichen Zhang, name: Binyuan Hui, name: Bo Zheng, name: Bowen Yu, name: Chang Gao, name: Chengen Huang, name: Chenxu Lv, name: Chujie Zheng, name: Dayiheng Liu, name: Fan Zhou, name: Fei Huang, name: Feng Hu, name: Hao Ge, name: Haoran Wei, name: Huan Lin, name: Jialong Tang, name: Jian Yang, name: Jianhong Tu, name: Jianwei Zhang, name: Jianxin Yang, name: Jiaxi Yang, name: Jing Zhou, name: Jingren Zhou, name: Junyang Lin, name: Kai Dang, name: Keqin Bao, name: Kexin Yang, name: Le Yu, name: Lianghao Deng, name: Mei Li, name: Mingfeng Xue, name: Mingze Li, name: Pei Zhang, name: Peng Wang, name: Qin Zhu, name: Rui Men, name: Ruize Gao, name: Shixuan Liu, name: Shuang Luo, name: Tianhao Li, name: Tianyi Tang, name: Wenbiao Yin, name: Xingzhang Ren, name: Xinyu Wang, name: Xinyu Zhang, name: Xuancheng Ren, name: Yang Fan, name: Yang Su, name: Yichang Zhang, name: Yinger Zhang, name: Yu Wan, name: Yuqiong Liu, name: Zekun Wang, name: Zeyu Cui, name: Zhenru Zhang, name: Zhipeng Zhou, name: Zihan Qiu
Published: 2025-05-14 13:41:34

In this work, we present Qwen3, the latest version of the Qwen model family.
Qwen3 comprises a series of large language models (LLMs) designed to advance
performance, efficiency, and multilingual capabilities. The Qwen3 series
includes models of both dense and Mixture-of-Expert (MoE) architectures, with
parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is
the integration of thinking mode (for complex, multi-step reasoning) and
non-thinking mode (for rapid, context-driven responses) into a unified
framework. This eliminates the need to switch between different models--such as
chat-optimized models (e.g., GPT-4o) and dedicated reasoning models (e.g.,
QwQ-32B)--and enables dynamic mode switching based on user queries or chat
templates. Meanwhile, Qwen3 introduces a thinking budget mechanism, allowing
users to allocate computational resources adaptively during inference, thereby
balancing latency and performance based on task complexity. Moreover, by
leveraging the knowledge from the flagship models, we significantly reduce the
computational resources required to build smaller-scale models, while ensuring
their highly competitive performance. Empirical evaluations demonstrate that
Qwen3 achieves state-of-the-art results across diverse benchmarks, including
tasks in code generation, mathematical reasoning, agent tasks, etc.,
competitive against larger MoE models and proprietary models. Compared to its
predecessor Qwen2.5, Qwen3 expands multilingual support from 29 to 119
languages and dialects, enhancing global accessibility through improved
cross-lingual understanding and generation capabilities. To facilitate
reproducibility and community-driven research and development, all Qwen3 models
are publicly accessible under Apache 2.0.
Learning Dense Hand Contact Estimation from Imbalanced Data
Authors: name: Daniel Sungho Jung, name: Kyoung Mu Lee
Published: 2025-05-16 11:54:25

Hands are essential to human interaction, and understanding contact between
hands and the world can promote comprehensive understanding of their function.
Recently, there have been growing number of hand interaction datasets that
cover interaction with object, other hand, scene, and body. Despite the
significance of the task and increasing high-quality data, how to effectively
learn dense hand contact estimation remains largely underexplored. There are
two major challenges for learning dense hand contact estimation. First, there
exists class imbalance issue from hand contact datasets where majority of
samples are not in contact. Second, hand contact datasets contain spatial
imbalance issue with most of hand contact exhibited in finger tips, resulting
in challenges for generalization towards contacts in other hand regions. To
tackle these issues, we present a framework that learns dense HAnd COntact
estimation (HACO) from imbalanced data. To resolve the class imbalance issue,
we introduce balanced contact sampling, which builds and samples from multiple
sampling groups that fairly represent diverse contact statistics for both
contact and non-contact samples. Moreover, to address the spatial imbalance
issue, we propose vertex-level class-balanced (VCB) loss, which incorporates
spatially varying contact distribution by separately reweighting loss
contribution of each vertex based on its contact frequency across dataset. As a
result, we effectively learn to predict dense hand contact estimation with
large-scale hand contact data without suffering from class and spatial
imbalance issue. The codes will be released.
Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or
  Robot Hardware
Authors: name: Justin Yu, name: Letian Fu, name: Huang Huang, name: Karim El-Refai, name: Rares Andrei Ambrus, name: Richard Cheng, name: Muhammad Zubair Irshad, name: Ken Goldberg
Published: 2025-05-14 17:50:35

Scaling robot learning requires vast and diverse datasets. Yet the prevailing
data collection paradigm-human teleoperation-remains costly and constrained by
manual effort and physical robot access. We introduce Real2Render2Real (R2R2R),
a novel approach for generating robot training data without relying on object
dynamics simulation or teleoperation of robot hardware. The input is a
smartphone-captured scan of one or more objects and a single video of a human
demonstration. R2R2R renders thousands of high visual fidelity robot-agnostic
demonstrations by reconstructing detailed 3D object geometry and appearance,
and tracking 6-DoF object motion. R2R2R uses 3D Gaussian Splatting (3DGS) to
enable flexible asset generation and trajectory synthesis for both rigid and
articulated objects, converting these representations to meshes to maintain
compatibility with scalable rendering engines like IsaacLab but with collision
modeling off. Robot demonstration data generated by R2R2R integrates directly
with models that operate on robot proprioceptive states and image observations,
such as vision-language-action models (VLA) and imitation learning policies.
Physical experiments suggest that models trained on R2R2R data from a single
human demonstration can match the performance of models trained on 150 human
teleoperation demonstrations. Project page: https://real2render2real.com
3D-Fixup: Advancing Photo Editing with 3D Priors
Authors: name: Yen-Chi Cheng, name: Krishna Kumar Singh, name: Jae Shin Yoon, name: Alex Schwing, name: Liangyan Gui, name: Matheus Gadelha, name: Paul Guerrero, name: Nanxuan Zhao
Published: 2025-05-15 17:59:51

Despite significant advances in modeling image priors via diffusion models,
3D-aware image editing remains challenging, in part because the object is only
specified via a single image. To tackle this challenge, we propose 3D-Fixup, a
new framework for editing 2D images guided by learned 3D priors. The framework
supports difficult editing situations such as object translation and 3D
rotation. To achieve this, we leverage a training-based approach that harnesses
the generative power of diffusion models. As video data naturally encodes
real-world physical dynamics, we turn to video data for generating training
data pairs, i.e., a source and a target frame. Rather than relying solely on a
single trained model to infer transformations between source and target frames,
we incorporate 3D guidance from an Image-to-3D model, which bridges this
challenging task by explicitly projecting 2D information into 3D space. We
design a data generation pipeline to ensure high-quality 3D guidance throughout
training. Results show that by integrating these 3D priors, 3D-Fixup
effectively supports complex, identity coherent 3D-aware edits, achieving
high-quality results and advancing the application of diffusion models in
realistic image manipulation. The code is provided at
https://3dfixup.github.io/
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real
Authors: name: Prithwish Dan, name: Kushal Kedia, name: Angela Chao, name: Edward Weiyi Duan, name: Maximus Adrian Pace, name: Wei-Chiu Ma, name: Sanjiban Choudhury
Published: 2025-05-11 19:04:00

Human videos offer a scalable way to train robot manipulation policies, but
lack the action labels needed by standard imitation learning algorithms.
Existing cross-embodiment approaches try to map human motion to robot actions,
but often fail when the embodiments differ significantly. We propose X-Sim, a
real-to-sim-to-real framework that uses object motion as a dense and
transferable signal for learning robot policies. X-Sim starts by reconstructing
a photorealistic simulation from an RGBD human video and tracking object
trajectories to define object-centric rewards. These rewards are used to train
a reinforcement learning (RL) policy in simulation. The learned policy is then
distilled into an image-conditioned diffusion policy using synthetic rollouts
rendered with varied viewpoints and lighting. To transfer to the real world,
X-Sim introduces an online domain adaptation technique that aligns real and
simulated observations during deployment. Importantly, X-Sim does not require
any robot teleoperation data. We evaluate it across 5 manipulation tasks in 2
environments and show that it: (1) improves task progress by 30% on average
over hand-tracking and sim-to-real baselines, (2) matches behavior cloning with
10x less data collection time, and (3) generalizes to new camera viewpoints and
test-time changes. Code and videos are available at
https://portal-cornell.github.io/X-Sim/.
Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target
  Self-Distillation
Authors: name: Stefan Vasilev, name: Christian Herold, name: Baohao Liao, name: Seyyed Hadi Hashemi, name: Shahram Khadivi, name: Christof Monz
Published: 2025-05-09 13:19:09

This paper introduces Unilogit, a novel self-distillation method for machine
unlearning in Large Language Models. Unilogit addresses the challenge of
selectively forgetting specific information while maintaining overall model
utility, a critical task in compliance with data privacy regulations like GDPR.
Unlike prior methods that rely on static hyperparameters or starting model
outputs, Unilogit dynamically adjusts target logits to achieve a uniform
probability for the target token, leveraging the current model's outputs for
more accurate self-distillation targets. This approach not only eliminates the
need for additional hyperparameters but also enhances the model's ability to
approximate the golden targets. Extensive experiments on public benchmarks and
an in-house e-commerce dataset demonstrate Unilogit's superior performance in
balancing forget and retain objectives, outperforming state-of-the-art methods
such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness
across various scenarios, highlighting its practical applicability and
effectiveness in achieving efficacious machine unlearning.
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and
  Challenge
Authors: name: Ranjan Sapkota, name: Konstantinos I. Roumeliotis, name: Manoj Karkee
Published: 2025-05-15 16:21:33

This study critically distinguishes between AI Agents and Agentic AI,
offering a structured conceptual taxonomy, application mapping, and challenge
analysis to clarify their divergent design philosophies and capabilities. We
begin by outlining the search strategy and foundational definitions,
characterizing AI Agents as modular systems driven by Large Language Models
(LLMs) and Large Image Models (LIMs) for narrow, task-specific automation.
Generative AI is positioned as a precursor, with AI Agents advancing through
tool integration, prompt engineering, and reasoning enhancements. In contrast,
Agentic AI systems represent a paradigmatic shift marked by multi-agent
collaboration, dynamic task decomposition, persistent memory, and orchestrated
autonomy. Through a sequential evaluation of architectural evolution,
operational mechanisms, interaction styles, and autonomy levels, we present a
comparative analysis across both paradigms. Application domains such as
customer support, scheduling, and data summarization are contrasted with
Agentic AI deployments in research automation, robotic coordination, and
medical decision support. We further examine unique challenges in each paradigm
including hallucination, brittleness, emergent behavior, and coordination
failure and propose targeted solutions such as ReAct loops, RAG, orchestration
layers, and causal modeling. This work aims to provide a definitive roadmap for
developing robust, scalable, and explainable AI agent and Agentic AI-driven
systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision
Support System, Agentic-AI Applications
QuXAI: Explainers for Hybrid Quantum Machine Learning Models
Authors: name: Saikat Barua, name: Mostafizur Rahman, name: Shehenaz Khaled, name: Md Jafor Sadek, name: Rafiul Islam, name: Shahnewaz Siddique
Published: 2025-05-15 10:51:34

The emergence of hybrid quantum-classical machine learning (HQML) models
opens new horizons of computational intelligence but their fundamental
complexity frequently leads to black box behavior that undermines transparency
and reliability in their application. Although XAI for quantum systems still in
its infancy, a major research gap is evident in robust global and local
explainability approaches that are designed for HQML architectures that employ
quantized feature encoding followed by classical learning. The gap is the focus
of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an
explainer for explaining feature importance in these hybrid systems. Our model
entails the creation of HQML models incorporating quantum feature maps, the use
of Q-MEDLEY, which combines feature based inferences, preserving the quantum
transformation stage and visualizing the resulting attributions. Our result
shows that Q-MEDLEY delineates influential classical aspects in HQML models, as
well as separates their noise, and competes well against established XAI
techniques in classical validation settings. Ablation studies more
significantly expose the virtues of the composite structure used in Q-MEDLEY.
The implications of this work are critically important, as it provides a route
to improve the interpretability and reliability of HQML models, thus promoting
greater confidence and being able to engage in safer and more responsible use
of quantum-enhanced AI technology.
Parallel Scaling Law for Language Models
Authors: name: Mouxiang Chen, name: Binyuan Hui, name: Zeyu Cui, name: Jiaxi Yang, name: Dayiheng Liu, name: Jianling Sun, name: Junyang Lin, name: Zhongxin Liu
Published: 2025-05-15 16:24:45

It is commonly believed that scaling language models should commit a
significant space or time cost, by increasing the parameters (parameter
scaling) or output tokens (inference-time scaling). We introduce the third and
more inference-efficient scaling paradigm: increasing the model's parallel
computation during both training and inference time. We apply P diverse and
learnable transformations to the input, execute forward passes of the model in
parallel, and dynamically aggregate the P outputs. This method, namely
parallel scaling (ParScale), scales parallel computation by reusing existing
parameters and can be applied to any model structure, optimization procedure,
data, or task. We theoretically propose a new scaling law and validate it
through large-scale pre-training, which shows that a model with P parallel
streams is similar to scaling the parameters by O(log P) while showing
superior inference efficiency. For example, ParScale can use up to 22times
less memory increase and 6times less latency increase compared to parameter
scaling that achieves the same performance improvement. It can also recycle an
off-the-shelf pre-trained model into a parallelly scaled one by post-training
on a small amount of tokens, further reducing the training budget. The new
scaling law we discovered potentially facilitates the deployment of more
powerful models in low-resource scenarios, and provides an alternative
perspective for the role of computation in machine learning.
PointArena: Probing Multimodal Grounding Through Language-Guided
  Pointing
Authors: name: Long Cheng, name: Jiafei Duan, name: Yi Ru Wang, name: Haoquan Fang, name: Boyang Li, name: Yushan Huang, name: Elvis Wang, name: Ainaz Eftekhar, name: Jason Lee, name: Wentao Yuan, name: Rose Hendrix, name: Noah A. Smith, name: Fei Xia, name: Dieter Fox, name: Ranjay Krishna
Published: 2025-05-15 06:04:42

Pointing serves as a fundamental and intuitive mechanism for grounding
language within visual contexts, with applications spanning robotics, assistive
technologies, and interactive AI systems. While recent multimodal models have
started to support pointing capabilities, existing benchmarks typically focus
only on referential object localization tasks. We introduce PointArena, a
comprehensive platform for evaluating multimodal pointing across diverse
reasoning scenarios. PointArena comprises three components: (1) Point-Bench, a
curated dataset containing approximately 1,000 pointing tasks across five
reasoning categories; (2) Point-Battle, an interactive, web-based arena
facilitating blind, pairwise model comparisons, which has already gathered over
4,500 anonymized votes; and (3) Point-Act, a real-world robotic manipulation
system allowing users to directly evaluate multimodal model pointing
capabilities in practical settings. We conducted extensive evaluations of both
state-of-the-art open-source and proprietary multimodal models. Results
indicate that Molmo-72B consistently outperforms other models, though
proprietary models increasingly demonstrate comparable performance.
Additionally, we find that supervised training specifically targeting pointing
tasks significantly enhances model performance. Across our multi-stage
evaluation pipeline, we also observe strong correlations, underscoring the
critical role of precise pointing capabilities in enabling multimodal models to
effectively bridge abstract reasoning with concrete, real-world actions.
Project page: https://pointarena.github.io/
Achieving Tokenizer Flexibility in Language Models through Heuristic
  Adaptation and Supertoken Learning
Authors: name: Shaurya Sharthak, name: Vinayak Pahalwan, name: Adithya Kamath, name: Adarsh Shirawalmath
Published: 2025-05-14 19:00:27

Pretrained language models (LLMs) are often constrained by their fixed
tokenization schemes, leading to inefficiencies and performance limitations,
particularly for multilingual or specialized applications. This tokenizer
lock-in presents significant challenges. standard methods to overcome this
often require prohibitive computational resources. Although tokenizer
replacement with heuristic initialization aims to reduce this burden, existing
methods often require exhaustive residual fine-tuning and still may not fully
preserve semantic nuances or adequately address the underlying compression
inefficiencies. Our framework introduces two innovations: first, Tokenadapt, a
model-agnostic tokenizer transplantation method, and second, novel
pre-tokenization learning for multi-word Supertokens to enhance compression and
reduce fragmentation. Tokenadapt initializes new unique token embeddings via a
hybrid heuristic that combines two methods: a local estimate based on subword
decomposition using the old tokenizer, and a global estimate utilizing the
top-k semantically similar tokens from the original vocabulary. This
methodology aims to preserve semantics while significantly minimizing
retraining requirements. Empirical investigations validate both contributions:
the transplantation heuristic successfully initializes unique tokens, markedly
outperforming conventional baselines and sophisticated methods including
Transtokenizer and ReTok, while our Supertokens achieve notable compression
gains. Our zero-shot perplexity results demonstrate that the TokenAdapt hybrid
initialization consistently yields lower perplexity ratios compared to both
ReTok and TransTokenizer baselines across different base models and newly
trained target tokenizers. TokenAdapt typically reduced the overall perplexity
ratio significantly compared to ReTok, yielding at least a 2-fold improvement
in these aggregate scores.
Depth Anything with Any Prior
Authors: name: Zehan Wang, name: Siyu Chen, name: Lihe Yang, name: Jialei Wang, name: Ziang Zhang, name: Hengshuang Zhao, name: Zhou Zhao
Published: 2025-05-15 17:59:50

This work presents Prior Depth Anything, a framework that combines incomplete
but precise metric information in depth measurement with relative but complete
geometric structures in depth prediction, generating accurate, dense, and
detailed metric depth maps for any scene. To this end, we design a
coarse-to-fine pipeline to progressively integrate the two complementary depth
sources. First, we introduce pixel-level metric alignment and distance-aware
weighting to pre-fill diverse metric priors by explicitly using depth
prediction. It effectively narrows the domain gap between prior patterns,
enhancing generalization across varying scenarios. Second, we develop a
conditioned monocular depth estimation (MDE) model to refine the inherent noise
of depth priors. By conditioning on the normalized pre-filled prior and
prediction, the model further implicitly merges the two complementary depth
sources. Our model showcases impressive zero-shot generalization across depth
completion, super-resolution, and inpainting over 7 real-world datasets,
matching or even surpassing previous task-specific methods. More importantly,
it performs well on challenging, unseen mixed priors and enables test-time
improvements by switching prediction models, providing a flexible
accuracy-efficiency trade-off while evolving with advancements in MDE models.
Style Customization of Text-to-Vector Generation with Image Diffusion
  Priors
Authors: name: Peiying Zhang, name: Nanxuan Zhao, name: Jing Liao
Published: 2025-05-15 17:59:21

Scalable Vector Graphics (SVGs) are highly favored by designers due to their
resolution independence and well-organized layer structure. Although existing
text-to-vector (T2V) generation methods can create SVGs from text prompts, they
often overlook an important need in practical applications: style
customization, which is vital for producing a collection of vector graphics
with consistent visual appearance and coherent aesthetics. Extending existing
T2V methods for style customization poses certain challenges.
Optimization-based T2V models can utilize the priors of text-to-image (T2I)
models for customization, but struggle with maintaining structural regularity.
On the other hand, feed-forward T2V models can ensure structural regularity,
yet they encounter difficulties in disentangling content and style due to
limited SVG training data.
  To address these challenges, we propose a novel two-stage style customization
pipeline for SVG generation, making use of the advantages of both feed-forward
T2V models and T2I image priors. In the first stage, we train a T2V diffusion
model with a path-level representation to ensure the structural regularity of
SVGs while preserving diverse expressive capabilities. In the second stage, we
customize the T2V diffusion model to different styles by distilling customized
T2I models. By integrating these techniques, our pipeline can generate
high-quality and diverse SVGs in custom styles based on text prompts in an
efficient feed-forward manner. The effectiveness of our method has been
validated through extensive experiments. The project page is
https://customsvg.github.io.