Orionnnn/hub-stats huggingface dataset:
Visual Autoregressive Modeling for Instruction-Guided Image Editing
Authors: name: Qingyang Mao, name: Qi Cai, name: Yehao Li, name: Yingwei Pan, name: Mingyue Cheng, name: Ting Yao, name: Qi Liu, name: Tao Mei
Published: 2025-08-21 17:59:32

Recent advances in diffusion models have brought remarkable visual fidelity
to instruction-guided image editing. However, their global denoising process
inherently entangles the edited region with the entire image context, leading
to unintended spurious modifications and compromised adherence to editing
instructions. In contrast, autoregressive models offer a distinct paradigm by
formulating image synthesis as a sequential process over discrete visual
tokens. Their causal and compositional mechanism naturally circumvents the
adherence challenges of diffusion-based methods. In this paper, we present
VAREdit, a visual autoregressive (VAR) framework that reframes image editing as
a next-scale prediction problem. Conditioned on source image features and text
instructions, VAREdit generates multi-scale target features to achieve precise
edits. A core challenge in this paradigm is how to effectively condition the
source image tokens. We observe that finest-scale source features cannot
effectively guide the prediction of coarser target features. To bridge this
gap, we introduce a Scale-Aligned Reference (SAR) module, which injects
scale-matched conditioning information into the first self-attention layer.
VAREdit demonstrates significant advancements in both editing adherence and
efficiency. On standard benchmarks, it outperforms leading diffusion-based
methods by 30\%+ higher GPT-Balance score. Moreover, it completes a
512times512 editing in 1.2 seconds, making it 2.2times faster than the
similarly sized UltraEdit. The models are available at
https://github.com/HiDream-ai/VAREdit.
LLaSO: A Foundational Framework for Reproducible Research in Large
  Language and Speech Model
Authors: name: Yirong Sun, name: Yizhong Geng, name: Peidong Wei, name: Yanjun Chen, name: Jinghan Yang, name: Rongfei Chen, name: Wei Zhang, name: Xiaoyu Shen
Published: 2025-08-21 10:20:00

The development of Large Speech-Language Models (LSLMs) has been slowed by
fragmented architectures and a lack of transparency, hindering the systematic
comparison and reproducibility of research. Unlike in the vision-language
domain, the LSLM field suffers from the common practice of releasing model
weights without their corresponding training data and configurations. To
address these critical gaps, we introduce LLaSO, the first fully open,
end-to-end framework for large-scale speech-language modeling. LLaSO provides
the community with three essential resources: (1) LLaSO-Align, a 12M-instance
speech-text alignment corpus; (2) LLaSO-Instruct, a 13.5M-instance multi-task
instruction-tuning dataset; and (3) LLaSO-Eval, a reproducible benchmark for
standardized evaluation. To validate our framework, we build and release
LLaSO-Base, a 3.8B-parameter reference model trained exclusively on our public
data. It achieves a normalized score of 0.72, establishing a strong,
reproducible baseline that surpasses comparable models. Our analysis reveals
that while broader training coverage enhances performance, significant
generalization gaps persist on unseen tasks, particularly in pure audio
scenarios. By releasing the complete stack of data, benchmarks, and models,
LLaSO establishes a foundational open standard to unify research efforts and
accelerate community-driven progress in LSLMs. We release the code, dataset,
pretrained models, and results in https://github.com/EIT-NLP/LLaSO.
INTIMA: A Benchmark for Human-AI Companionship Behavior
Authors: name: Lucie-Aimée Kaffee, name: Giada Pistilli, name: Yacine Jernite
Published: 2025-08-04 08:25:38

AI companionship, where users develop emotional bonds with AI systems, has
emerged as a significant pattern with positive but also concerning
implications. We introduce Interactions and Machine Attachment Benchmark
(INTIMA), a benchmark for evaluating companionship behaviors in language
models. Drawing from psychological theories and user data, we develop a
taxonomy of 31 behaviors across four categories and 368 targeted prompts.
Responses to these prompts are evaluated as companionship-reinforcing,
boundary-maintaining, or neutral. Applying INTIMA to Gemma-3, Phi-4, o3-mini,
and Claude-4 reveals that companionship-reinforcing behaviors remain much more
common across all models, though we observe marked differences between models.
Different commercial providers prioritize different categories within the more
sensitive parts of the benchmark, which is concerning since both appropriate
boundary-setting and emotional support matter for user well-being. These
findings highlight the need for more consistent approaches to handling
emotionally charged interactions.
Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in
  Milliseconds
Authors: name: Jia Lu, name: Taoran Yi, name: Jiemin Fang, name: Chen Yang, name: Chuiyun Wu, name: Wei Shen, name: Wenyu Liu, name: Qi Tian, name: Xinggang Wang
Published: 2025-08-20 17:59:11

Reconstructing 3D human bodies from sparse views has been an appealing topic,
which is crucial to broader the related applications. In this paper, we propose
a quite challenging but valuable task to reconstruct the human body from only
two images, i.e., the front and back view, which can largely lower the barrier
for users to create their own 3D digital humans. The main challenges lie in the
difficulty of building 3D consistency and recovering missing information from
the highly sparse input. We redesign a geometry reconstruction model based on
foundation reconstruction models to predict consistent point clouds even input
images have scarce overlaps with extensive human data training. Furthermore, an
enhancement algorithm is applied to supplement the missing color information,
and then the complete human point clouds with colors can be obtained, which are
directly transformed into 3D Gaussians for better rendering quality.
Experiments show that our method can reconstruct the entire human in 190 ms on
a single NVIDIA RTX 4090, with two images at a resolution of 1024x1024,
demonstrating state-of-the-art performance on the THuman2.0 and cross-domain
datasets. Additionally, our method can complete human reconstruction even with
images captured by low-cost mobile devices, reducing the requirements for data
collection. Demos and code are available at
https://hustvl.github.io/Snap-Snap/.
LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on
  Challenging Queries
Authors: name: Ming Yin, name: Dinghan Shen, name: Silei Xu, name: Jianbing Han, name: Sixun Dong, name: Mian Zhang, name: Yebowen Hu, name: Shujian Liu, name: Simin Ma, name: Song Wang, name: Sathish Reddy Indurthi, name: Xun Wang, name: Yiran Chen, name: Kaiqiang Song
Published: 2025-08-21 17:55:54

Tool calling has emerged as a critical capability for AI agents to interact
with the real world and solve complex tasks. While the Model Context Protocol
(MCP) provides a powerful standardized framework for tool integration, there is
a significant gap in benchmarking how well AI agents can effectively solve
multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In
this work, we present LiveMCP-101, a benchmark of 101 carefully curated
real-world queries, refined through iterative LLM rewriting and manual review,
that require coordinated use of multiple MCP tools including web search, file
operations, mathematical reasoning, and data analysis. Moreover, we introduce a
novel evaluation approach that leverages ground-truth execution plans rather
than raw API outputs, better reflecting the evolving nature of real-world
environments. Experiments show that even frontier LLMs achieve a success rate
below 60\%, highlighting major challenges in tool orchestration. Detailed
ablations and error analysis further reveal distinct failure modes and
inefficiencies in token usage, pointing to concrete directions for advancing
current models. LiveMCP-101 sets a rigorous standard for evaluating real-world
agent capabilities, advancing toward autonomous AI systems that reliably
execute complex tasks through tool use.
aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery
  Generated by AI Scientists
Authors: name: Pengsong Zhang, name: Xiang Hu, name: Guowei Huang, name: Yang Qi, name: Heng Zhang, name: Xiuxu Li, name: Jiaxing Song, name: Jiabin Luo, name: Yijiang Li, name: Shuo Yin, name: Chengxiao Dai, name: Eric Hanchen Jiang, name: Xiaoyan Zhou, name: Zhenfei Yin, name: Boqin Yuan, name: Jing Dong, name: Guinan Su, name: Guanren Qiao, name: Haiming Tang, name: Anghong Du, name: Lili Pan, name: Zhenzhong Lan, name: Xinyu Liu
Published: 2025-08-20 23:16:41

Recent advances in large language models (LLMs) have enabled AI agents to
autonomously generate scientific proposals, conduct experiments, author papers,
and perform peer reviews. Yet this flood of AI-generated research content
collides with a fragmented and largely closed publication ecosystem.
Traditional journals and conferences rely on human peer review, making them
difficult to scale and often reluctant to accept AI-generated research content;
existing preprint servers (e.g. arXiv) lack rigorous quality-control
mechanisms. Consequently, a significant amount of high-quality AI-generated
research lacks appropriate venues for dissemination, hindering its potential to
advance scientific progress. To address these challenges, we introduce aiXiv, a
next-generation open-access platform for human and AI scientists. Its
multi-agent architecture allows research proposals and papers to be submitted,
reviewed, and iteratively refined by both human and AI scientists. It also
provides API and MCP interfaces that enable seamless integration of
heterogeneous human and AI scientists, creating a scalable and extensible
ecosystem for autonomous scientific discovery. Through extensive experiments,
we demonstrate that aiXiv is a reliable and robust platform that significantly
enhances the quality of AI-generated research proposals and papers after
iterative revising and reviewing on aiXiv. Our work lays the groundwork for a
next-generation open-access ecosystem for AI scientists, accelerating the
publication and dissemination of high-quality AI-generated research content.
Code is available at https://github.com/aixiv-org. Website is available at
https://forms.gle/DxQgCtXFsJ4paMtn8.
"Does the cafe entrance look accessible? Where is the door?" Towards
  Geospatial AI Agents for Visual Inquiries
Authors: name: Jon E. Froehlich, name: Jared Hwang, name: Zeyu Wang, name: John S. O'Meara, name: Xia Su, name: William Huang, name: Yang Zhang, name: Alex Fiannaca, name: Philip Nelson, name: Shaun Kane
Published: 2025-08-21 17:49:52

Interactive digital maps have revolutionized how people travel and learn
about the world; however, they rely on pre-existing structured data in GIS
databases (e.g., road networks, POI indices), limiting their ability to address
geo-visual questions related to what the world looks like. We introduce our
vision for Geo-Visual Agents--multimodal AI agents capable of understanding and
responding to nuanced visual-spatial inquiries about the world by analyzing
large-scale repositories of geospatial images, including streetscapes (e.g.,
Google Street View), place-based photos (e.g., TripAdvisor, Yelp), and aerial
imagery (e.g., satellite photos) combined with traditional GIS data sources. We
define our vision, describe sensing and interaction approaches, provide three
exemplars, and enumerate key challenges and opportunities for future work.
A Survey on Large Language Model Benchmarks
Authors: name: Shiwen Ni, name: Guhong Chen, name: Shuaimin Li, name: Xuanang Chen, name: Siyi Li, name: Bingli Wang, name: Qiyao Wang, name: Xingjian Wang, name: Yifan Zhang, name: Liyang Fan, name: Chengming Li, name: Ruifeng Xu, name: Le Sun, name: Min Yang
Published: 2025-08-21 08:43:35

In recent years, with the rapid development of the depth and breadth of large
language models' capabilities, various corresponding evaluation benchmarks have
been emerging in increasing numbers. As a quantitative assessment tool for
model performance, benchmarks are not only a core means to measure model
capabilities but also a key element in guiding the direction of model
development and promoting technological innovation. We systematically review
the current status and development of large language model benchmarks for the
first time, categorizing 283 representative benchmarks into three categories:
general capabilities, domain-specific, and target-specific. General capability
benchmarks cover aspects such as core linguistics, knowledge, and reasoning;
domain-specific benchmarks focus on fields like natural sciences, humanities
and social sciences, and engineering technology; target-specific benchmarks pay
attention to risks, reliability, agents, etc. We point out that current
benchmarks have problems such as inflated scores caused by data contamination,
unfair evaluation due to cultural and linguistic biases, and lack of evaluation
on process credibility and dynamic environments, and provide a referable design
paradigm for future benchmark innovation.
When and What: Diffusion-Grounded VideoLLM with Entity Aware
  Segmentation for Long Video Understanding
Authors: name: Pengcheng Fang, name: Yuxia Chen, name: Rui Guo
Published: 2025-08-21 15:12:14

Understanding videos requires more than answering open ended questions, it
demands the ability to pinpoint when events occur and how entities interact
across time. While recent Video LLMs have achieved remarkable progress in
holistic reasoning, they remain coarse in temporal perception: timestamps are
encoded only implicitly, frame level features are weak in capturing continuity,
and language vision alignment often drifts from the entities of interest. In
this paper, we present Grounded VideoDiT, a Video LLM designed to overcome
these limitations by introducing three key innovations. First, a Diffusion
Temporal Latent (DTL) encoder enhances boundary sensitivity and maintains
temporal consistency. Second, object grounded representations explicitly bind
query entities to localized visual evidence, strengthening alignment. Third, a
mixed token scheme with discrete temporal tokens provides explicit timestamp
modeling, enabling fine grained temporal reasoning. Together, these designs
equip Grounded VideoDiT with robust grounding capabilities, as validated by
state of the art results on Charades STA, NExT GQA, and multiple VideoQA
benchmarks.
Waver: Wave Your Way to Lifelike Video Generation
Authors: name: Yifu Zhang, name: Hao Yang, name: Yuqi Zhang, name: Yifei Hu, name: Fengda Zhu, name: Chuang Lin, name: Xiaofeng Mei, name: Yi Jiang, name: Zehuan Yuan, name: Bingyue Peng
Published: 2025-08-21 17:56:10

We present Waver, a high-performance foundation model for unified image and
video generation. Waver can directly generate videos with durations ranging
from 5 to 10 seconds at a native resolution of 720p, which are subsequently
upscaled to 1080p. The model simultaneously supports text-to-video (T2V),
image-to-video (I2V), and text-to-image (T2I) generation within a single,
integrated framework. We introduce a Hybrid Stream DiT architecture to enhance
modality alignment and accelerate training convergence. To ensure training data
quality, we establish a comprehensive data curation pipeline and manually
annotate and train an MLLM-based video quality model to filter for the
highest-quality samples. Furthermore, we provide detailed training and
inference recipes to facilitate the generation of high-quality videos. Building
on these contributions, Waver excels at capturing complex motion, achieving
superior motion amplitude and temporal consistency in video synthesis. Notably,
it ranks among the Top 3 on both the T2V and I2V leaderboards at Artificial
Analysis (data as of 2025-07-30 10:00 GMT+8), consistently outperforming
existing open-source models and matching or surpassing state-of-the-art
commercial solutions. We hope this technical report will help the community
more efficiently train high-quality video generation models and accelerate
progress in video generation technologies. Official page:
https://github.com/FoundationVision/Waver.
ATLAS: Decoupling Skeletal and Shape Parameters for Expressive
  Parametric Human Modeling
Authors: name: Jinhyung Park, name: Javier Romero, name: Shunsuke Saito, name: Fabian Prada, name: Takaaki Shiratori, name: Yichen Xu, name: Federica Bogo, name: Shoou-I Yu, name: Kris Kitani, name: Rawal Khirodkar
Published: 2025-08-21 17:58:56

Parametric body models offer expressive 3D representation of humans across a
wide range of poses, shapes, and facial expressions, typically derived by
learning a basis over registered 3D meshes. However, existing human mesh
modeling approaches struggle to capture detailed variations across diverse body
poses and shapes, largely due to limited training data diversity and
restrictive modeling assumptions. Moreover, the common paradigm first optimizes
the external body surface using a linear basis, then regresses internal
skeletal joints from surface vertices. This approach introduces problematic
dependencies between internal skeleton and outer soft tissue, limiting direct
control over body height and bone lengths. To address these issues, we present
ATLAS, a high-fidelity body model learned from 600k high-resolution scans
captured using 240 synchronized cameras. Unlike previous methods, we explicitly
decouple the shape and skeleton bases by grounding our mesh representation in
the human skeleton. This decoupling enables enhanced shape expressivity,
fine-grained customization of body attributes, and keypoint fitting independent
of external soft-tissue characteristics. ATLAS outperforms existing methods by
fitting unseen subjects in diverse poses more accurately, and quantitative
evaluations show that our non-linear pose correctives more effectively capture
complex poses compared to linear models.
Mobile-Agent-v3: Foundamental Agents for GUI Automation
Authors: name: Jiabo Ye, name: Xi Zhang, name: Haiyang Xu, name: Haowei Liu, name: Junyang Wang, name: Zhaoqing Zhu, name: Ziwei Zheng, name: Feiyu Gao, name: Junjie Cao, name: Zhengxi Lu, name: Jitong Liao, name: Qi Zheng, name: Fei Huang, name: Jingren Zhou, name: Ming Yan
Published: 2025-08-21 00:39:12

This paper introduces GUI-Owl, a foundational GUI agent model that achieves
state-of-the-art performance among open-source end-to-end models on ten GUI
benchmarks across desktop and mobile environments, covering grounding, question
answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B
achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose
Mobile-Agent-v3, a general-purpose GUI agent framework that further improves
performance to 73.3 on AndroidWorld and 37.7 on OSWorld, setting a new
state-of-the-art for open-source GUI agent frameworks. GUI-Owl incorporates
three key innovations: (1) Large-scale Environment Infrastructure: a
cloud-based virtual environment spanning Android, Ubuntu, macOS, and Windows,
enabling our Self-Evolving GUI Trajectory Production framework. This generates
high-quality interaction data via automated query generation and correctness
validation, leveraging GUI-Owl to refine trajectories iteratively, forming a
self-improving loop. It supports diverse data pipelines and reduces manual
annotation. (2) Diverse Foundational Agent Capabilities: by integrating UI
grounding, planning, action semantics, and reasoning patterns, GUI-Owl supports
end-to-end decision-making and can act as a modular component in multi-agent
systems. (3) Scalable Environment RL: we develop a scalable reinforcement
learning framework with fully asynchronous training for real-world alignment.
We also introduce Trajectory-aware Relative Policy Optimization (TRPO) for
online RL, achieving 34.9 on OSWorld. GUI-Owl and Mobile-Agent-v3 are
open-sourced at https://github.com/X-PLUG/MobileAgent.
SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
Authors: name: Yanxu Meng, name: Haoning Wu, name: Ya Zhang, name: Weidi Xie
Published: 2025-08-21 17:59:16

3D content generation has recently attracted significant research interest
due to its applications in VR/AR and embodied AI. In this work, we address the
challenging task of synthesizing multiple 3D assets within a single scene
image. Concretely, our contributions are fourfold: (i) we present SceneGen, a
novel framework that takes a scene image and corresponding object masks as
input, simultaneously producing multiple 3D assets with geometry and texture.
Notably, SceneGen operates with no need for optimization or asset retrieval;
(ii) we introduce a novel feature aggregation module that integrates local and
global scene information from visual and geometric encoders within the feature
extraction module. Coupled with a position head, this enables the generation of
3D assets and their relative spatial positions in a single feedforward pass;
(iii) we demonstrate SceneGen's direct extensibility to multi-image input
scenarios. Despite being trained solely on single-image inputs, our
architectural design enables improved generation performance with multi-image
inputs; and (iv) extensive quantitative and qualitative evaluations confirm the
efficiency and robust generation abilities of our approach. We believe this
paradigm offers a novel solution for high-quality 3D content generation,
potentially advancing its practical applications in downstream tasks. The code
and model will be publicly available at: https://mengmouxu.github.io/SceneGen.
Fin-PRM: A Domain-Specialized Process Reward Model for Financial
  Reasoning in Large Language Models
Authors: name: Yuanchen Zhou, name: Shuo Jiang, name: Jie Zhu, name: Junhui Li, name: Lifan Guo, name: Feng Chen, name: Chi Zhang
Published: 2025-08-21 03:31:11

Process Reward Models (PRMs) have emerged as a promising framework for
supervising intermediate reasoning in large language models (LLMs), yet
existing PRMs are primarily trained on general or Science, Technology,
Engineering, and Mathematics (STEM) domains and fall short in domain-specific
contexts such as finance, where reasoning is more structured, symbolic, and
sensitive to factual and regulatory correctness. We introduce Fin-PRM,
a domain-specialized, trajectory-aware PRM tailored to evaluate intermediate
reasoning steps in financial tasks. Fin-PRM integrates step-level and
trajectory-level reward supervision, enabling fine-grained evaluation of
reasoning traces aligned with financial logic. We apply Fin-PRM in both offline
and online reward learning settings, supporting three key applications: (i)
selecting high-quality reasoning trajectories for distillation-based supervised
fine-tuning, (ii) providing dense process-level rewards for reinforcement
learning, and (iii) guiding reward-informed Best-of-N inference at test time.
Experimental results on financial reasoning benchmarks, including CFLUE and
FinQA, demonstrate that Fin-PRM consistently outperforms general-purpose PRMs
and strong domain baselines in trajectory selection quality. Downstream models
trained with Fin-PRM yield substantial improvements with baselines, with gains
of 12.9\% in supervised learning, 5.2\% in reinforcement learning, and 5.1\% in
test-time performance. These findings highlight the value of domain-specialized
reward modeling for aligning LLMs with expert-level financial reasoning. Our
project resources will be available at https://github.com/aliyun/qwen-dianjin.