Orionnnn/hub-stats huggingface dataset:
AI for Service: Proactive Assistance with AI Glasses
Authors: name: Zichen Wen, name: Yiyu Wang, name: Chenfei Liao, name: Boxue Yang, name: Junxian Li, name: Weifeng Liu, name: Haocong He, name: Bolong Feng, name: Xuyang Liu, name: Yuanhuiyi Lyu, name: Xu Zheng, name: Xuming Hu, name: Linfeng Zhang
Published: 2025-10-16 06:55:28

In an era where AI is evolving from a passive tool into an active and
adaptive companion, we introduce AI for Service (AI4Service), a new paradigm
that enables proactive and real-time assistance in daily life. Existing AI
services remain largely reactive, responding only to explicit user commands. We
argue that a truly intelligent and helpful assistant should be capable of
anticipating user needs and taking actions proactively when appropriate. To
realize this vision, we propose Alpha-Service, a unified framework that
addresses two fundamental challenges: Know When to intervene by detecting
service opportunities from egocentric video streams, and Know How to provide
both generalized and personalized services. Inspired by the von Neumann
computer architecture and based on AI glasses, Alpha-Service consists of five
key components: an Input Unit for perception, a Central Processing Unit for
task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit
for long-term personalization, and an Output Unit for natural human
interaction. As an initial exploration, we implement Alpha-Service through a
multi-agent system deployed on AI glasses. Case studies, including a real-time
Blackjack advisor, a museum tour guide, and a shopping fit assistant,
demonstrate its ability to seamlessly perceive the environment, infer user
intent, and provide timely and useful assistance without explicit prompts.
Qwen3Guard Technical Report
Authors: name: Haiquan Zhao, name: Chenhan Yuan, name: Fei Huang, name: Xiaomeng Hu, name: Yichang Zhang, name: An Yang, name: Bowen Yu, name: Dayiheng Liu, name: Jingren Zhou, name: Junyang Lin, name: Baosong Yang, name: Chen Cheng, name: Jialong Tang, name: Jiandong Jiang, name: Jianwei Zhang, name: Jijie Xu, name: Ming Yan, name: Minmin Sun, name: Pei Zhang, name: Pengjun Xie, name: Qiaoyu Tang, name: Qin Zhu, name: Rong Zhang, name: Shibin Wu, name: Shuo Zhang, name: Tao He, name: Tianyi Tang, name: Tingyu Xia, name: Wei Liao, name: Weizhou Shen, name: Wenbiao Yin, name: Wenmeng Zhou, name: Wenyuan Yu, name: Xiaobin Wang, name: Xiaodong Deng, name: Xiaodong Xu, name: Xinyu Zhang, name: Yang Liu, name: Yeqiu Li, name: Yi Zhang, name: Yong Jiang, name: Yu Wan, name: Yuxin Zhou
Published: 2025-10-16 04:00:18

As large language models (LLMs) become more capable and widely used, ensuring
the safety of their outputs is increasingly critical. Existing guardrail
models, though useful in static evaluation settings, face two major limitations
in real-world applications: (1) they typically output only binary "safe/unsafe"
labels, which can be interpreted inconsistently across diverse safety policies,
rendering them incapable of accommodating varying safety tolerances across
domains; and (2) they require complete model outputs before performing safety
checks, making them fundamentally incompatible with streaming LLM inference,
thereby preventing timely intervention during generation and increasing
exposure to harmful partial outputs. To address these challenges, we present
Qwen3Guard, a series of multilingual safety guardrail models with two
specialized variants: Generative Qwen3Guard, which casts safety classification
as an instruction-following task to enable fine-grained tri-class judgments
(safe, controversial, unsafe); and Stream Qwen3Guard, which introduces a
token-level classification head for real-time safety monitoring during
incremental text generation. Both variants are available in three sizes (0.6B,
4B, and 8B parameters) and support up to 119 languages and dialects, providing
comprehensive, scalable, and low-latency safety moderation for global LLM
deployments. Evaluated across English, Chinese, and multilingual benchmarks,
Qwen3Guard achieves state-of-the-art performance in both prompt and response
safety classification. All models are released under the Apache 2.0 license for
public use.
MoM: Mixtures of Scenario-Aware Document Memories for
  Retrieval-Augmented Generation Systems
Authors: name: Jihao Zhao, name: Zhiyuan Ji, name: Simin Niu, name: Hanyu Wang, name: Feiyu Xiong, name: Zhiyu Li
Published: 2025-10-16 03:09:51

The traditional RAG paradigm, which typically engages in the comprehension of
relevant text chunks in response to received queries, inherently restricts both
the depth of knowledge internalization and reasoning capabilities. To address
this limitation, our research transforms the text processing in RAG from
passive chunking to proactive understanding, defining this process as document
memory extraction with the objective of simulating human cognitive processes
during reading. Building upon this, we propose the Mixtures of scenario-aware
document Memories (MoM) framework, engineered to efficiently handle documents
from multiple domains and train small language models (SLMs) to acquire the
ability to proactively explore and construct document memories. The MoM
initially instructs large language models (LLMs) to simulate domain experts in
generating document logical outlines, thereby directing structured chunking and
core content extraction. It employs a multi-path sampling and multi-perspective
evaluation mechanism, specifically designing comprehensive metrics that
represent chunk clarity and extraction completeness to select the optimal
document memories. Additionally, to infuse deeper human-like reading abilities
during the training of SLMs, we incorporate a reverse reasoning strategy, which
deduces refined expert thinking paths from high-quality outcomes. Finally,
leveraging diverse forms of content generated by MoM, we develop a three-layer
document memory retrieval mechanism, which is grounded in our theoretical proof
from the perspective of probabilistic modeling. Extensive experimental results
across three distinct domains demonstrate that the MoM framework not only
resolves text chunking challenges in existing RAG systems, providing LLMs with
semantically complete document memories, but also paves the way for SLMs to
achieve human-centric intelligent text processing.
Agentic Entropy-Balanced Policy Optimization
Authors: name: Guanting Dong, name: Licheng Bao, name: Zhongyuan Wang, name: Kangzhi Zhao, name: Xiaoxi Li, name: Jiajie Jin, name: Jinghan Yang, name: Hangyu Mao, name: Fuzheng Zhang, name: Kun Gai, name: Guorui Zhou, name: Yutao Zhu, name: Ji-Rong Wen, name: Zhicheng Dou
Published: 2025-10-16 10:40:52

Recently, Agentic Reinforcement Learning (Agentic RL) has made significant
progress in incentivizing the multi-turn, long-horizon tool-use capabilities of
web agents. While mainstream agentic RL algorithms autonomously explore
high-uncertainty tool-call steps under the guidance of entropy, excessive
reliance on entropy signals can impose further constraints, leading to the
training collapse. In this paper, we delve into the challenges caused by
entropy and propose the Agentic Entropy-Balanced Policy Optimization (AEPO), an
agentic RL algorithm designed to balance entropy in both the rollout and policy
update phases. AEPO comprises two core components: (1) a dynamic
entropy-balanced rollout mechanism that adaptively allocate global and branch
sampling budget through entropy pre-monitoring, while imposing a branch penalty
on consecutive high-entropy tool-call steps to prevent over-branching issues;
and (2) Entropy-Balanced Policy Optimization that inserts a stop-gradient
operation into the high-entropy clipping term to preserve and properly rescale
gradients on high-entropy tokens, while incorporating entropy-aware advantage
estimation to prioritize learning on high-uncertainty tokens. Results across 14
challenging datasets show that AEPO consistently outperforms 7 mainstream RL
algorithms. With just 1K RL samples, Qwen3-14B with AEPO achieves impressive
results: 47.6% on GAIA, 11.2% on Humanity's Last Exam, and 43.0% on WebWalker
for Pass@1; 65.0% on GAIA, 26.0% on Humanity's Last Exam, and 70.0% on
WebWalker for Pass@5. Further analysis reveals that AEPO improves rollout
sampling diversity while maintaining stable policy entropy, facilitating
scalable web agent training.
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
Authors: name: Cheng Cui, name: Ting Sun, name: Suyin Liang, name: Tingquan Gao, name: Zelun Zhang, name: Jiaxuan Liu, name: Xueqing Wang, name: Changda Zhou, name: Hongen Liu, name: Manhui Lin, name: Yue Zhang, name: Yubo Zhang, name: Handong Zheng, name: Jing Zhang, name: Jun Zhang, name: Yi Liu, name: Dianhai Yu, name: Yanjun Ma
Published: 2025-10-16 10:18:48

In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. Code is available at https://github.com/PaddlePaddle/PaddleOCR .
MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal
  Mathematical Reasoning
Authors: name: Weikang Shi, name: Aldrich Yu, name: Rongyao Fang, name: Houxing Ren, name: Ke Wang, name: Aojun Zhou, name: Changyao Tian, name: Xinyu Fu, name: Yuxuan Hu, name: Zimu Lu, name: Linjiang Huang, name: Si Liu, name: Rui Liu, name: Hongsheng Li
Published: 2025-10-16 17:58:58

While Large Language Models (LLMs) have excelled in textual reasoning, they
struggle with mathematical domains like geometry that intrinsically rely on
visual aids. Existing approaches to Visual Chain-of-Thought (VCoT) are often
limited by rigid external tools or fail to generate the high-fidelity,
strategically-timed diagrams necessary for complex problem-solving. To bridge
this gap, we introduce MathCanvas, a comprehensive framework designed to endow
unified Large Multimodal Models (LMMs) with intrinsic VCoT capabilities for
mathematics. Our approach consists of two phases. First, a Visual Manipulation
stage pre-trains the model on a novel 15.2M-pair corpus, comprising 10M
caption-to-diagram pairs (MathCanvas-Imagen) and 5.2M step-by-step editing
trajectories (MathCanvas-Edit), to master diagram generation and editing.
Second, a Strategic Visual-Aided Reasoning stage fine-tunes the model on
MathCanvas-Instruct, a new 219K-example dataset of interleaved visual-textual
reasoning paths, teaching it when and how to leverage visual aids. To
facilitate rigorous evaluation, we introduce MathCanvas-Bench, a challenging
benchmark with 3K problems that require models to produce interleaved
visual-textual solutions. Our model, BAGEL-Canvas, trained under this
framework, achieves an 86% relative improvement over strong LMM baselines on
MathCanvas-Bench, demonstrating excellent generalization to other public math
benchmarks. Our work provides a complete toolkit-framework, datasets, and
benchmark-to unlock complex, human-like visual-aided reasoning in LMMs. Project
Page: https://mathcanvas.github.io/
WithAnyone: Towards Controllable and ID Consistent Image Generation
Authors: name: Hengyuan Xu, name: Wei Cheng, name: Peng Xing, name: Yixiao Fang, name: Shuhan Wu, name: Rui Wang, name: Xianfang Zeng, name: Daxin Jiang, name: Gang Yu, name: Xingjun Ma, name: Yu-Gang Jiang
Published: 2025-10-16 17:59:54

Identity-consistent generation has become an important focus in text-to-image
research, with recent models achieving notable success in producing images
aligned with a reference identity. Yet, the scarcity of large-scale paired
datasets containing multiple images of the same individual forces most
approaches to adopt reconstruction-based training. This reliance often leads to
a failure mode we term copy-paste, where the model directly replicates the
reference face rather than preserving identity across natural variations in
pose, expression, or lighting. Such over-similarity undermines controllability
and limits the expressive power of generation. To address these limitations, we
(1) construct a large-scale paired dataset MultiID-2M, tailored for
multi-person scenarios, providing diverse references for each identity; (2)
introduce a benchmark that quantifies both copy-paste artifacts and the
trade-off between identity fidelity and variation; and (3) propose a novel
training paradigm with a contrastive identity loss that leverages paired data
to balance fidelity with diversity. These contributions culminate in
WithAnyone, a diffusion-based model that effectively mitigates copy-paste while
preserving high identity similarity. Extensive qualitative and quantitative
experiments demonstrate that WithAnyone significantly reduces copy-paste
artifacts, improves controllability over pose and expression, and maintains
strong perceptual quality. User studies further validate that our method
achieves high identity fidelity while enabling expressive controllable
generation.
Learning an Image Editing Model without Image Editing Pairs
Authors: name: Nupur Kumari, name: Sheng-Yu Wang, name: Nanxuan Zhao, name: Yotam Nitzan, name: Yuheng Li, name: Krishna Kumar Singh, name: Richard Zhang, name: Eli Shechtman, name: Jun-Yan Zhu, name: Xun Huang
Published: 2025-10-16 17:59:57

Recent image editing models have achieved impressive results while following
natural language editing instructions, but they rely on supervised fine-tuning
with large datasets of input-target pairs. This is a critical bottleneck, as
such naturally occurring pairs are hard to curate at scale. Current workarounds
use synthetic training pairs that leverage the zero-shot capabilities of
existing models. However, this can propagate and magnify the artifacts of the
pretrained model into the final trained model. In this work, we present a new
training paradigm that eliminates the need for paired data entirely. Our
approach directly optimizes a few-step diffusion model by unrolling it during
training and leveraging feedback from vision-language models (VLMs). For each
input and editing instruction, the VLM evaluates if an edit follows the
instruction and preserves unchanged content, providing direct gradients for
end-to-end optimization. To ensure visual fidelity, we incorporate distribution
matching loss (DMD), which constrains generated images to remain within the
image manifold learned by pretrained models. We evaluate our method on standard
benchmarks and include an extensive ablation study. Without any paired data,
our method performs on par with various image editing diffusion models trained
on extensive supervised paired data, under the few-step setting. Given the same
VLM as the reward model, we also outperform RL-based techniques like Flow-GRPO.
LaSeR: Reinforcement Learning with Last-Token Self-Rewarding
Authors: name: Wenkai Yang, name: Weijie Liu, name: Ruobing Xie, name: Yiju Guo, name: Lulu Wu, name: Saiyong Yang, name: Yankai Lin
Published: 2025-10-16 17:55:11

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as
a core paradigm for enhancing the reasoning capabilities of Large Language
Models (LLMs). To address the lack of verification signals at test time, prior
studies incorporate the training of model's self-verification capability into
the standard RLVR process, thereby unifying reasoning and verification
capabilities within a single LLM. However, previous practice requires the LLM
to sequentially generate solutions and self-verifications using two separate
prompt templates, which significantly reduces efficiency. In this work, we
theoretically reveal that the closed-form solution to the RL objective of
self-verification can be reduced to a remarkably simple form: the true
reasoning reward of a solution is equal to its last-token self-rewarding score,
which is computed as the difference between the policy model's next-token
log-probability assigned to any pre-specified token at the solution's last
token and a pre-calculated constant, scaled by the KL coefficient. Based on
this insight, we propose LaSeR (Reinforcement Learning with Last-Token
Self-Rewarding), an algorithm that simply augments the original RLVR loss with
a MSE loss that aligns the last-token self-rewarding scores with verifier-based
reasoning rewards, jointly optimizing the reasoning and self-rewarding
capabilities of LLMs. The optimized self-rewarding scores can be utilized in
both training and testing to enhance model performance. Notably, our algorithm
derives these scores from the predicted next-token probability distribution of
the last token immediately after generation, incurring only the minimal extra
cost of one additional token inference. Experiments show that our method not
only improves the model's reasoning performance but also equips it with
remarkable self-rewarding capability, thereby boosting its inference-time
scaling performance.
LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent
  Training
Authors: name: Yiming Wang, name: Da Yin, name: Yuedong Cui, name: Ruichen Zheng, name: Zhiqian Li, name: Zongyu Lin, name: Di Wu, name: Xueqing Wu, name: Chenchen Ye, name: Yu Zhou, name: Kai-Wei Chang
Published: 2025-10-16 17:59:38

Digital agents require diverse, large-scale UI trajectories to generalize
across real-world tasks, yet collecting such data is prohibitively expensive in
both human annotation, infra and engineering perspectives. To this end, we
introduce UI-Simulator, a scalable paradigm that generates
structured UI states and transitions to synthesize training trajectories at
scale. Our paradigm integrates a digital world simulator for diverse UI states,
a guided rollout process for coherent exploration, and a trajectory wrapper
that produces high-quality and diverse trajectories for agent training. We
further propose UI-Simulator-Grow, a targeted scaling strategy that
enables more rapid and data-efficient scaling by prioritizing high-impact tasks
and synthesizes informative trajectory variants. Experiments on WebArena and
AndroidWorld show that UI-Simulator rivals or surpasses open-source agents
trained on real UIs with significantly better robustness, despite using weaker
teacher models. Moreover, UI-Simulator-Grow matches the performance of
Llama-3-70B-Instruct using only Llama-3-8B-Instruct as the base model,
highlighting the potential of targeted synthesis scaling paradigm to
continuously and efficiently enhance the digital agents.
Information Gain-based Policy Optimization: A Simple and Effective
  Approach for Multi-Turn LLM Agents
Authors: name: Guoqing Wang, name: Sunhao Dai, name: Guangze Ye, name: Zeyu Gan, name: Wei Yao, name: Yong Deng, name: Xiaofeng Wu, name: Zhenzhe Ying
Published: 2025-10-16 17:59:32

Large language model (LLM)-based agents are increasingly trained with
reinforcement learning (RL) to enhance their ability to interact with external
environments through tool use, particularly in search-based settings that
require multi-turn reasoning and knowledge acquisition. However, existing
approaches typically rely on outcome-based rewards that are only provided at
the final answer. This reward sparsity becomes particularly problematic in
multi-turn settings, where long trajectories exacerbate two critical issues:
(i) advantage collapse, where all rollouts receive identical rewards and
provide no useful learning signals, and (ii) lack of fine-grained credit
assignment, where dependencies between turns are obscured, especially in
long-horizon tasks. In this paper, we propose Information Gain-based Policy
Optimization (IGPO), a simple yet effective RL framework that provides dense
and intrinsic supervision for multi-turn agent training. IGPO models each
interaction turn as an incremental process of acquiring information about the
ground truth, and defines turn-level rewards as the marginal increase in the
policy's probability of producing the correct answer. Unlike prior
process-level reward approaches that depend on external reward models or costly
Monte Carlo estimation, IGPO derives intrinsic rewards directly from the
model's own belief updates. These intrinsic turn-level rewards are combined
with outcome-level supervision to form dense reward trajectories. Extensive
experiments on both in-domain and out-of-domain benchmarks demonstrate that
IGPO consistently outperforms strong baselines in multi-turn scenarios,
achieving higher accuracy and improved sample efficiency.
From Pixels to Words -- Towards Native Vision-Language Primitives at
  Scale
Authors: name: Haiwen Diao, name: Mingxuan Li, name: Silei Wu, name: Linjun Dai, name: Xiaohua Wang, name: Hanming Deng, name: Lewei Lu, name: Dahua Lin, name: Ziwei Liu
Published: 2025-10-16 17:59:58

The edifice of native Vision-Language Models (VLMs) has emerged as a rising
contender to typical modular VLMs, shaped by evolving model architectures and
training paradigms. Yet, two lingering clouds cast shadows over its widespread
exploration and promotion: (-) What fundamental constraints set native VLMs
apart from modular ones, and to what extent can these barriers be overcome? (-)
How to make research in native VLMs more accessible and democratized, thereby
accelerating progress in the field. In this paper, we clarify these challenges
and outline guiding principles for constructing native VLMs. Specifically, one
native VLM primitive should: (i) effectively align pixel and word
representations within a shared semantic space; (ii) seamlessly integrate the
strengths of formerly separate vision and language modules; (iii) inherently
embody various cross-modal properties that support unified vision-language
encoding, aligning, and reasoning. Hence, we launch NEO, a novel family of
native VLMs built from first principles, capable of rivaling top-tier modular
counterparts across diverse real-world scenarios. With only 390M image-text
examples, NEO efficiently develops visual perception from scratch while
mitigating vision-language conflicts inside a dense and monolithic model
crafted from our elaborate primitives. We position NEO as a cornerstone for
scalable and powerful native VLMs, paired with a rich set of reusable
components that foster a cost-effective and extensible ecosystem. Our code and
models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.