Orionnnn/hub-stats huggingface dataset:
CharacterShot: Controllable and Consistent 4D Character Animation
Authors: name: Junyao Gao, name: Jiaxing Li, name: Wenran Liu, name: Yanhong Zeng, name: Fei Shen, name: Kai Chen, name: Yanan Sun, name: Cairong Zhao
Published: 2025-08-10 16:15:04

In this paper, we propose CharacterShot, a controllable and
consistent 4D character animation framework that enables any individual
designer to create dynamic 3D characters (i.e., 4D character animation) from a
single reference character image and a 2D pose sequence. We begin by
pretraining a powerful 2D character animation model based on a cutting-edge
DiT-based image-to-video model, which allows for any 2D pose sequnce as
controllable signal. We then lift the animation model from 2D to 3D through
introducing dual-attention module together with camera prior to generate
multi-view videos with spatial-temporal and spatial-view consistency. Finally,
we employ a novel neighbor-constrained 4D gaussian splatting optimization on
these multi-view videos, resulting in continuous and stable 4D character
representations. Moreover, to improve character-centric performance, we
construct a large-scale dataset Character4D, containing 13,115 unique
characters with diverse appearances and motions, rendered from multiple
viewpoints. Extensive experiments on our newly constructed benchmark,
CharacterBench, demonstrate that our approach outperforms current
state-of-the-art methods. Code, models, and datasets will be publicly available
at https://github.com/Jeoyal/CharacterShot.
Cut2Next: Generating Next Shot via In-Context Tuning
Authors: name: Jingwen He, name: Hongbo Liu, name: Jiajun Li, name: Ziqi Huang, name: Yu Qiao, name: Wanli Ouyang, name: Ziwei Liu
Published: 2025-08-11 17:56:59

Effective multi-shot generation demands purposeful, film-like transitions and
strict cinematic continuity. Current methods, however, often prioritize basic
visual consistency, neglecting crucial editing patterns (e.g., shot/reverse
shot, cutaways) that drive narrative flow for compelling storytelling. This
yields outputs that may be visually coherent but lack narrative sophistication
and true cinematic integrity. To bridge this, we introduce Next Shot Generation
(NSG): synthesizing a subsequent, high-quality shot that critically conforms to
professional editing patterns while upholding rigorous cinematic continuity.
Our framework, Cut2Next, leverages a Diffusion Transformer (DiT). It employs
in-context tuning guided by a novel Hierarchical Multi-Prompting strategy. This
strategy uses Relational Prompts to define overall context and inter-shot
editing styles. Individual Prompts then specify per-shot content and
cinematographic attributes. Together, these guide Cut2Next to generate
cinematically appropriate next shots. Architectural innovations, Context-Aware
Condition Injection (CACI) and Hierarchical Attention Mask (HAM), further
integrate these diverse signals without introducing new parameters. We
construct RawCuts (large-scale) and CuratedCuts (refined) datasets, both with
hierarchical prompts, and introduce CutBench for evaluation. Experiments show
Cut2Next excels in visual consistency and text fidelity. Crucially, user
studies reveal a strong preference for Cut2Next, particularly for its adherence
to intended editing patterns and overall cinematic continuity, validating its
ability to generate high-quality, narratively expressive, and cinematically
coherent subsequent shots.
Feedback-Driven Tool-Use Improvements in Large Language Models via
  Automated Build Environments
Authors: name: Junjie Ye, name: Changhao Jiang, name: Zhengyin Du, name: Yufei Xu, name: Xuesong Yao, name: Zhiheng Xi, name: Xiaoran Fan, name: Qi Zhang, name: Xuanjing Huang, name: Jiecao Chen
Published: 2025-08-12 09:45:19

Effective tool use is essential for large language models (LLMs) to interact
meaningfully with their environment. However, progress is limited by the lack
of efficient reinforcement learning (RL) frameworks specifically designed for
tool use, due to challenges in constructing stable training environments and
designing verifiable reward mechanisms. To address this, we propose an
automated environment construction pipeline, incorporating scenario
decomposition, document generation, function integration, complexity scaling,
and localized deployment. This enables the creation of high-quality training
environments that provide detailed and measurable feedback without relying on
external tools. Additionally, we introduce a verifiable reward mechanism that
evaluates both the precision of tool use and the completeness of task
execution. When combined with trajectory data collected from the constructed
environments, this mechanism integrates seamlessly with standard RL algorithms
to facilitate feedback-driven model training. Experiments on LLMs of varying
scales demonstrate that our approach significantly enhances the models'
tool-use performance without degrading their general capabilities, regardless
of inference modes or training algorithms. Our analysis suggests that these
gains result from improved context understanding and reasoning, driven by
updates to the lower-layer MLP parameters in models.
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Authors: name: Xinyu Geng, name: Peng Xia, name: Zhen Zhang, name: Xinyu Wang, name: Qiuchen Wang, name: Ruixue Ding, name: Chenxi Wang, name: Jialong Wu, name: Yida Zhao, name: Kuan Li, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-08-07 18:03:50

Web agents such as Deep Research have demonstrated superhuman cognitive
abilities, capable of solving highly challenging information-seeking problems.
However, most research remains primarily text-centric, overlooking visual
information in the real world. This makes multimodal Deep Research highly
challenging, as such agents require much stronger reasoning abilities in
perception, logic, knowledge, and the use of more sophisticated tools compared
to text-based agents. To address this limitation, we introduce WebWatcher, a
multi-modal Agent for Deep Research equipped with enhanced visual-language
reasoning capabilities. It leverages high-quality synthetic multimodal
trajectories for efficient cold start training, utilizes various tools for deep
reasoning, and further enhances generalization through reinforcement learning.
To better evaluate the capabilities of multimodal agents, we propose
BrowseComp-VL, a benchmark with BrowseComp-style that requires complex
information retrieval involving both visual and textual information.
Experimental results show that WebWatcher significantly outperforms proprietary
baseline, RAG workflow and open-source agents in four challenging VQA
benchmarks, which paves the way for solving complex multimodal
information-seeking tasks.
AutoCodeBench: Large Language Models are Automatic Code Benchmark
  Generators
Authors: name: Jason Chou, name: Ao Liu, name: Yuchi Deng, name: Zhiying Zeng, name: Tao Zhang, name: Haotian Zhu, name: Jianwei Cai, name: Yue Mao, name: Chenchen Zhang, name: Lingyun Tan, name: Ziyan Xu, name: Bohui Zhai, name: Hengyi Liu, name: Speed Zhu, name: Wiggin Zhou, name: Fengzong Lian
Published: 2025-08-12 17:29:20

Large Language Models (LLMs) have demonstrated remarkable capabilities across
various domains, with code generation emerging as a key area of focus. While
numerous benchmarks have been proposed to evaluate their code generation
abilities, these benchmarks face several critical limitations. First, they
often rely on manual annotations, which are time-consuming and difficult to
scale across different programming languages and problem complexities. Second,
most existing benchmarks focus primarily on Python, while the few multilingual
benchmarks suffer from limited difficulty and uneven language distribution. To
address these challenges, we propose AutoCodeGen, an automated method for
generating high-difficulty multilingual code generation datasets without manual
annotations. AutoCodeGen ensures the correctness and completeness of test cases
by generating test inputs with LLMs and obtaining test outputs through a
multilingual sandbox, while achieving high data quality through reverse-order
problem generation and multiple filtering steps. Using this novel method, we
introduce AutoCodeBench, a large-scale code generation benchmark comprising
3,920 problems evenly distributed across 20 programming languages. It is
specifically designed to evaluate LLMs on challenging, diverse, and practical
multilingual tasks. We evaluate over 30 leading open-source and proprietary
LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The
results show that even the most advanced LLMs struggle with the complexity,
diversity, and multilingual nature of these tasks. Besides, we introduce
AutoCodeBench-Complete, specifically designed for base models to assess their
few-shot code generation capabilities. We hope the AutoCodeBench series will
serve as a valuable resource and inspire the community to focus on more
challenging and practical multilingual code generation scenarios.
Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale
  Asynchronous RL
Authors: name: Jiaxuan Gao, name: Wei Fu, name: Minyang Xie, name: Shusheng Xu, name: Chuyi He, name: Zhiyu Mei, name: Banghua Zhu, name: Yi Wu
Published: 2025-08-11 13:36:57

Recent advancements in LLM-based agents have demonstrated remarkable
capabilities in handling complex, knowledge-intensive tasks by integrating
external tools. Among diverse choices of tools, search tools play a pivotal
role in accessing vast external knowledge. However, open-source agents still
fall short of achieving expert-level Search Intelligence, the ability to
resolve ambiguous queries, generate precise searches, analyze results, and
conduct thorough exploration. Existing approaches fall short in scalability,
efficiency, and data quality. For example, small turn limits in existing online
RL methods, e.g. <=10, restrict complex strategy learning. This paper
introduces ASearcher, an open-source project for large-scale RL training of
search agents. Our key contributions include: (1) Scalable fully asynchronous
RL training that enables long-horizon search while maintaining high training
efficiency. (2) A prompt-based LLM agent that autonomously synthesizes
high-quality and challenging QAs, creating a large-scale QA dataset. Through RL
training, our prompt-based QwQ-32B agent achieves substantial improvements,
with 46.7% and 20.8% Avg@4 gains on xBench and GAIA, respectively. Notably, our
agent exhibits extreme long-horizon search, with tool calls exceeding 40 turns
and output tokens exceeding 150k during training time. With a simple agent
design and no external LLMs, ASearcher-Web-QwQ achieves Avg@4 scores of 42.1 on
xBench and 52.8 on GAIA, surpassing existing open-source 32B agents. We
open-source our models, training data, and codes in
https://github.com/inclusionAI/ASearcher.
HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating
  Local and Web Searches
Authors: name: Jiejun Tan, name: Zhicheng Dou, name: Yan Yu, name: Jiehan Cheng, name: Qiang Ju, name: Jian Xie, name: Ji-Rong Wen
Published: 2025-08-11 15:31:47

Recently, large reasoning models have demonstrated strong mathematical and
coding abilities, and deep search leverages their reasoning capabilities in
challenging information retrieval tasks. Existing deep search works are
generally limited to a single knowledge source, either local or the Web.
However, enterprises often require private deep search systems that can
leverage search tools over both local and the Web corpus. Simply training an
agent equipped with multiple search tools using flat reinforcement learning
(RL) is a straightforward idea, but it has problems such as low training data
efficiency and poor mastery of complex tools. To address the above issue, we
propose a hierarchical agentic deep search framework, HierSearch, trained with
hierarchical RL. At the low level, a local deep search agent and a Web deep
search agent are trained to retrieve evidence from their corresponding domains.
At the high level, a planner agent coordinates low-level agents and provides
the final answer. Moreover, to prevent direct answer copying and error
propagation, we design a knowledge refiner that filters out hallucinations and
irrelevant evidence returned by low-level agents. Experiments show that
HierSearch achieves better performance compared to flat RL, and outperforms
various deep search and multi-source retrieval-augmented generation baselines
in six benchmarks across general, finance, and medical domains.
Matrix-3D: Omnidirectional Explorable 3D World Generation
Authors: name: Zhongqi Yang, name: Wenhang Ge, name: Yuqi Li, name: Jiaqi Chen, name: Haoyuan Li, name: Mengyin An, name: Fei Kang, name: Hua Xue, name: Baixin Xu, name: Yuyang Yin, name: Eric Li, name: Yang Liu, name: Yikai Wang, name: Hao-Xiang Guo, name: Yahui Zhou
Published: 2025-08-11 15:29:57

Explorable 3D world generation from a single image or text prompt forms a
cornerstone of spatial intelligence. Recent works utilize video model to
achieve wide-scope and generalizable 3D world generation. However, existing
approaches often suffer from a limited scope in the generated scenes. In this
work, we propose Matrix-3D, a framework that utilize panoramic representation
for wide-coverage omnidirectional explorable 3D world generation that combines
conditional video generation and panoramic 3D reconstruction. We first train a
trajectory-guided panoramic video diffusion model that employs scene mesh
renders as condition, to enable high-quality and geometrically consistent scene
video generation. To lift the panorama scene video to 3D world, we propose two
separate methods: (1) a feed-forward large panorama reconstruction model for
rapid 3D scene reconstruction and (2) an optimization-based pipeline for
accurate and detailed 3D scene reconstruction. To facilitate effective
training, we also introduce the Matrix-Pano dataset, the first large-scale
synthetic collection comprising 116K high-quality static panoramic video
sequences with depth and trajectory annotations. Extensive experiments
demonstrate that our proposed framework achieves state-of-the-art performance
in panoramic video generation and 3D world generation. See more in
https://matrix-3d.github.io.
UNCAGE: Contrastive Attention Guidance for Masked Generative
  Transformers in Text-to-Image Generation
Authors: name: Wonjun Kang, name: Byeongkeun Ahn, name: Minjae Lee, name: Kevin Galim, name: Seunghyuk Oh, name: Hyung Il Koo, name: Nam Ik Cho
Published: 2025-08-07 13:51:17

Text-to-image (T2I) generation has been actively studied using Diffusion
Models and Autoregressive Models. Recently, Masked Generative Transformers have
gained attention as an alternative to Autoregressive Models to overcome the
inherent limitations of causal attention and autoregressive decoding through
bidirectional attention and parallel decoding, enabling efficient and
high-quality image generation. However, compositional T2I generation remains
challenging, as even state-of-the-art Diffusion Models often fail to accurately
bind attributes and achieve proper text-image alignment. While Diffusion Models
have been extensively studied for this issue, Masked Generative Transformers
exhibit similar limitations but have not been explored in this context. To
address this, we propose Unmasking with Contrastive Attention Guidance
(UNCAGE), a novel training-free method that improves compositional fidelity by
leveraging attention maps to prioritize the unmasking of tokens that clearly
represent individual objects. UNCAGE consistently improves performance in both
quantitative and qualitative evaluations across multiple benchmarks and
metrics, with negligible inference overhead. Our code is available at
https://github.com/furiosa-ai/uncage.
Test-Time Reinforcement Learning for GUI Grounding via Region
  Consistency
Authors: name: Yong Du, name: Yuchen Yan, name: Fei Tang, name: Zhengxi Lu, name: Chang Zong, name: Weiming Lu, name: Shengpei Jiang, name: Yongliang Shen
Published: 2025-08-07 17:54:27

Graphical User Interface (GUI) grounding, the task of mapping natural
language instructions to precise screen coordinates, is fundamental to
autonomous GUI agents. While existing methods achieve strong performance
through extensive supervised training or reinforcement learning with labeled
rewards, they remain constrained by the cost and availability of pixel-level
annotations. We observe that when models generate multiple predictions for the
same GUI element, the spatial overlap patterns reveal implicit confidence
signals that can guide more accurate localization. Leveraging this insight, we
propose GUI-RC (Region Consistency), a test-time scaling method that constructs
spatial voting grids from multiple sampled predictions to identify consensus
regions where models show highest agreement. Without any training, GUI-RC
improves accuracy by 2-3% across various architectures on ScreenSpot
benchmarks. We further introduce GUI-RCPO (Region Consistency Policy
Optimization), which transforms these consistency patterns into rewards for
test-time reinforcement learning. By computing how well each prediction aligns
with the collective consensus, GUI-RCPO enables models to iteratively refine
their outputs on unlabeled data during inference. Extensive experiments
demonstrate the generality of our approach: GUI-RC boosts
Qwen2.5-VL-3B-Instruct from 80.11% to 83.57% on ScreenSpot-v2, while GUI-RCPO
further improves it to 85.14% through self-supervised optimization. Our
approach reveals the untapped potential of test-time scaling and test-time
reinforcement learning for GUI grounding, offering a promising path toward more
robust and data-efficient GUI agents.
Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language
  Models
Authors: name: Wen Wang, name: Bozhen Fang, name: Chenchen Jing, name: Yongliang Shen, name: Yangyi Shen, name: Qiuyu Wang, name: Hao Ouyang, name: Hao Chen, name: Chunhua Shen
Published: 2025-08-12 17:59:57

Diffusion large language models (dLLMs) generate text through iterative
denoising, yet current decoding strategies discard rich intermediate
predictions in favor of the final output. Our work here reveals a critical
phenomenon, temporal oscillation, where correct answers often emerge in the
middle process, but are overwritten in later denoising steps. To address this
issue, we introduce two complementary methods that exploit temporal
consistency: 1) Temporal Self-Consistency Voting, a training-free, test-time
decoding strategy that aggregates predictions across denoising steps to select
the most consistent output; and 2) a post-training method termed Temporal
Consistency Reinforcement, which uses Temporal Semantic Entropy (TSE), a
measure of semantic stability across intermediate predictions, as a reward
signal to encourage stable generations. Empirical results across multiple
benchmarks demonstrate the effectiveness of our approach. Using the negative
TSE reward alone, we observe a remarkable average improvement of 24.7% on the
Countdown dataset over an existing dLLM. Combined with the accuracy reward, we
achieve absolute gains of 2.0% on GSM8K, 4.3% on MATH500, 6.6% on SVAMP, and
25.3% on Countdown, respectively. Our findings underscore the untapped
potential of temporal dynamics in dLLMs and offer two simple yet effective
tools to harness them.
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging
  Face
Authors: name: Benjamin Laufer, name: Hamidah Oderinwale, name: Jon Kleinberg
Published: 2025-08-09 04:08:49

Many have observed that the development and deployment of generative machine
learning (ML) and artificial intelligence (AI) models follow a distinctive
pattern in which pre-trained models are adapted and fine-tuned for specific
downstream tasks. However, there is limited empirical work that examines the
structure of these interactions. This paper analyzes 1.86 million models on
Hugging Face, a leading peer production platform for model development. Our
study of model family trees -- networks that connect fine-tuned models to their
base or parent -- reveals sprawling fine-tuning lineages that vary widely in
size and structure. Using an evolutionary biology lens to study ML models, we
use model metadata and model cards to measure the genetic similarity and
mutation of traits over model families. We find that models tend to exhibit a
family resemblance, meaning their genetic markers and traits exhibit more
overlap when they belong to the same model family. However, these similarities
depart in certain ways from standard models of asexual reproduction, because
mutations are fast and directed, such that two `sibling' models tend to exhibit
more similarity than parent/child pairs. Further analysis of the directional
drifts of these mutations reveals qualitative insights about the open machine
learning ecosystem: Licenses counter-intuitively drift from restrictive,
commercial licenses towards permissive or copyleft licenses, often in violation
of upstream license's terms; models evolve from multi-lingual compatibility
towards english-only compatibility; and model cards reduce in length and
standardize by turning, more often, to templates and automatically generated
text. Overall, this work takes a step toward an empirically grounded
understanding of model fine-tuning and suggests that ecological models and
methods can yield novel scientific insights.
Spectrum Projection Score: Aligning Retrieved Summaries with Reader
  Models in Retrieval-Augmented Generation
Authors: name: Zhanghao Hu, name: Qinglin Zhu, name: Siya Qi, name: Yulan He, name: Hanqi Yan, name: Lin Gui
Published: 2025-08-08 00:13:48

Large Language Models (LLMs) have shown improved generation performance
through retrieval-augmented generation (RAG) following the retriever-reader
paradigm, which supplements model inputs with externally retrieved knowledge.
However, prior work often evaluates RAG holistically, assessing the retriever
and reader jointly, making it difficult to isolate the true contribution of
retrieval, particularly given the prompt sensitivity of LLMs used as readers.
We introduce Spectrum Projection Score (SPS), a lightweight, supervision-free
metric that allows the reader to gauge the semantic alignment of a retrieved
summary with its hidden representation by comparing the area formed by
generated tokens from the summary, and the principal directions of subspace in
the reader and to measure the relevance. Building on SPS we present xCompress,
an inference time controller framework that dynamically samples, ranks, and
compresses retrieval summary candidates. Extensive experiments on five QA
benchmarks with four open source LLMs show that SPS not only enhances
performance across a range of tasks but also provides a principled perspective
on the interaction between retrieval and generation.