Orionnnn/hub-stats huggingface dataset:
UloRL:An Ultra-Long Output Reinforcement Learning Approach for Advancing
  Large Language Models' Reasoning Abilities
Authors: name: Dong Du, name: Shulin Liu, name: Tao Yang, name: Shaohua Chen, name: Yang Li
Published: 2025-07-26 03:42:33

Recent advances in large language models (LLMs) have highlighted the
potential of reinforcement learning with verifiable rewards (RLVR) to enhance
reasoning capabilities through extended output sequences. However, traditional
RL frameworks face inefficiencies when handling ultra-long outputs due to
long-tail sequence distributions and entropy collapse during training. To
address these challenges, we propose an Ultra-Long Output Reinforcement
Learning (UloRL) approach for advancing large language models' reasoning
abilities. Specifically, we divide ultra long output decoding into short
segments, enabling efficient training by mitigating delays caused by long-tail
samples. Additionally, we introduce dynamic masking of well-Mastered Positive
Tokens (MPTs) to prevent entropy collapse. Experimental results demonstrate the
effectiveness of our approach. On the Qwen3-30B-A3B model, RL with segment
rollout achieved 2.06x increase in training speed, while RL training with
128k-token outputs improves the model's performance on AIME2025 from 70.9\% to
85.1\% and on BeyondAIME from 50.7\% to 61.9\%, even surpassing Qwen3-235B-A22B
with remarkable gains. These findings underscore the potential of our methods
to advance the reasoning capabilities of LLMs with ultra-long sequence
generation. We will release our code and model for further use by the
community.
ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with
  Concept Relation Alignment
Authors: name: Chong Xia, name: Shengjun Zhang, name: Fangfu Liu, name: Chang Liu, name: Khodchaphun Hirunyaratsameewong, name: Yueqi Duan
Published: 2025-07-25 08:21:12

Perpetual 3D scene generation aims to produce long-range and coherent 3D view
sequences, which is applicable for long-term video synthesis and 3D scene
reconstruction. Existing methods follow a "navigate-and-imagine" fashion and
rely on outpainting for successive view expansion. However, the generated view
sequences suffer from semantic drift issue derived from the accumulated
deviation of the outpainting module. To tackle this challenge, we propose
ScenePainter, a new framework for semantically consistent 3D scene generation,
which aligns the outpainter's scene-specific prior with the comprehension of
the current scene. To be specific, we introduce a hierarchical graph structure
dubbed SceneConceptGraph to construct relations among multi-level scene
concepts, which directs the outpainter for consistent novel views and can be
dynamically refined to enhance diversity. Extensive experiments demonstrate
that our framework overcomes the semantic drift issue and generates more
consistent and immersive 3D view sequences. Project Page:
https://xiac20.github.io/ScenePainter/.
GenoMAS: A Multi-Agent Framework for Scientific Discovery via
  Code-Driven Gene Expression Analysis
Authors: name: Haoyang Liu, name: Yijiang Li, name: Haohan Wang
Published: 2025-07-28 17:55:08

Gene expression analysis holds the key to many biomedical discoveries, yet
extracting insights from raw transcriptomic data remains formidable due to the
complexity of multiple large, semi-structured files and the need for extensive
domain expertise. Current automation approaches are often limited by either
inflexible workflows that break down in edge cases or by fully autonomous
agents that lack the necessary precision for rigorous scientific inquiry.
GenoMAS charts a different course by presenting a team of LLM-based scientists
that integrates the reliability of structured workflows with the adaptability
of autonomous agents. GenoMAS orchestrates six specialized LLM agents through
typed message-passing protocols, each contributing complementary strengths to a
shared analytic canvas. At the heart of GenoMAS lies a guided-planning
framework: programming agents unfold high-level task guidelines into Action
Units and, at each juncture, elect to advance, revise, bypass, or backtrack,
thereby maintaining logical coherence while bending gracefully to the
idiosyncrasies of genomic data.
  On the GenoTEX benchmark, GenoMAS reaches a Composite Similarity Correlation
of 89.13% for data preprocessing and an F_1 of 60.48% for gene
identification, surpassing the best prior art by 10.61% and 16.85%
respectively. Beyond metrics, GenoMAS surfaces biologically plausible
gene-phenotype associations corroborated by the literature, all while adjusting
for latent confounders. Code is available at https://github.com/Liu-Hy/GenoMAS.
A Survey of Self-Evolving Agents: On Path to Artificial Super
  Intelligence
Authors: name: Huan-ang Gao, name: Jiayi Geng, name: Wenyue Hua, name: Mengkang Hu, name: Xinzhe Juan, name: Hongzhang Liu, name: Shilong Liu, name: Jiahao Qiu, name: Xuan Qi, name: Yiran Wu, name: Hongru Wang, name: Han Xiao, name: Yuhang Zhou, name: Shaokun Zhang, name: Jiayi Zhang, name: Jinyu Xiang, name: Yixiong Fang, name: Qiwen Zhao, name: Dongrui Liu, name: Qihan Ren, name: Cheng Qian, name: Zhenghailong Wang, name: Minda Hu, name: Huazheng Wang, name: Qingyun Wu, name: Heng Ji, name: Mengdi Wang
Published: 2025-07-28 17:59:05

Large Language Models (LLMs) have demonstrated strong capabilities but remain
fundamentally static, unable to adapt their internal parameters to novel tasks,
evolving knowledge domains, or dynamic interaction contexts. As LLMs are
increasingly deployed in open-ended, interactive environments, this static
nature has become a critical bottleneck, necessitating agents that can
adaptively reason, act, and evolve in real time. This paradigm shift -- from
scaling static models to developing self-evolving agents -- has sparked growing
interest in architectures and methods enabling continual learning and
adaptation from data, interactions, and experiences. This survey provides the
first systematic and comprehensive review of self-evolving agents, organized
around three foundational dimensions -- what to evolve, when to evolve, and how
to evolve. We examine evolutionary mechanisms across agent components (e.g.,
models, memory, tools, architecture), categorize adaptation methods by stages
(e.g., intra-test-time, inter-test-time), and analyze the algorithmic and
architectural designs that guide evolutionary adaptation (e.g., scalar rewards,
textual feedback, single-agent and multi-agent systems). Additionally, we
analyze evaluation metrics and benchmarks tailored for self-evolving agents,
highlight applications in domains such as coding, education, and healthcare,
and identify critical challenges and research directions in safety,
scalability, and co-evolutionary dynamics. By providing a structured framework
for understanding and designing self-evolving agents, this survey establishes a
roadmap for advancing adaptive agentic systems in both research and real-world
deployments, ultimately shedding lights to pave the way for the realization of
Artificial Super Intelligence (ASI), where agents evolve autonomously,
performing at or beyond human-level intelligence across a wide array of tasks.
Rep-MTL: Unleashing the Power of Representation-level Task Saliency for
  Multi-Task Learning
Authors: name: Zedong Wang, name: Siyuan Li, name: Dan Xu
Published: 2025-07-28 17:59:28

Despite the promise of Multi-Task Learning in leveraging complementary
knowledge across tasks, existing multi-task optimization (MTO) techniques
remain fixated on resolving conflicts via optimizer-centric loss scaling and
gradient manipulation strategies, yet fail to deliver consistent gains. In this
paper, we argue that the shared representation space, where task interactions
naturally occur, offers rich information and potential for operations
complementary to existing optimizers, especially for facilitating the
inter-task complementarity, which is rarely explored in MTO. This intuition
leads to Rep-MTL, which exploits the representation-level task saliency to
quantify interactions between task-specific optimization and shared
representation learning. By steering these saliencies through entropy-based
penalization and sample-wise cross-task alignment, Rep-MTL aims to mitigate
negative transfer by maintaining the effective training of individual tasks
instead pure conflict-solving, while explicitly promoting complementary
information sharing. Experiments are conducted on four challenging MTL
benchmarks covering both task-shift and domain-shift scenarios. The results
show that Rep-MTL, even paired with the basic equal weighting policy, achieves
competitive performance gains with favorable efficiency. Beyond standard
performance metrics, Power Law exponent analysis demonstrates Rep-MTL's
efficacy in balancing task-specific learning and cross-task sharing. The
project page is available at HERE.
SmallThinker: A Family of Efficient Large Language Models Natively
  Trained for Local Deployment
Authors: name: Yixin Song, name: Zhenliang Xue, name: Dongliang Wei, name: Feiyang Chen, name: Jianxiang Gao, name: Junchen Liu, name: Hangyu Liang, name: Guangshuo Qin, name: Chengrong Tian, name: Bo Wen, name: Longyu Zhao, name: Xinrui Zheng, name: Zeyu Mi, name: Haibo Chen
Published: 2025-07-28 16:45:14

While frontier large language models (LLMs) continue to push capability
boundaries, their deployment remains confined to GPU-powered cloud
infrastructure. We challenge this paradigm with SmallThinker, a family of LLMs
natively designed - not adapted - for the unique constraints of local devices:
weak computational power, limited memory, and slow storage. Unlike traditional
approaches that mainly compress existing models built for clouds, we architect
SmallThinker from the ground up to thrive within these limitations. Our
innovation lies in a deployment-aware architecture that transforms constraints
into design principles. First, We introduce a two-level sparse structure
combining fine-grained Mixture-of-Experts (MoE) with sparse feed-forward
networks, drastically reducing computational demands without sacrificing model
capacity. Second, to conquer the I/O bottleneck of slow storage, we design a
pre-attention router that enables our co-designed inference engine to prefetch
expert parameters from storage while computing attention, effectively hiding
storage latency that would otherwise cripple on-device inference. Third, for
memory efficiency, we utilize NoPE-RoPE hybrid sparse attention mechanism to
slash KV cache requirements. We release SmallThinker-4B-A0.6B and
SmallThinker-21B-A3B, which achieve state-of-the-art performance scores and
even outperform larger LLMs. Remarkably, our co-designed system mostly
eliminates the need for expensive GPU hardware: with Q4_0 quantization, both
models exceed 20 tokens/s on ordinary consumer CPUs, while consuming only 1GB
and 8GB of memory respectively. SmallThinker is publicly available at
hf.co/PowerInfer/SmallThinker-4BA0.6B-Instruct and
hf.co/PowerInfer/SmallThinker-21BA3B-Instruct.
GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
Authors: name: Yuhan Wang, name: Siwei Yang, name: Bingchen Zhao, name: Letian Zhang, name: Qing Liu, name: Yuyin Zhou, name: Cihang Xie
Published: 2025-07-28 17:54:04

Recent advancements in large multimodal models like GPT-4o have set a new
standard for high-fidelity, instruction-guided image editing. However, the
proprietary nature of these models and their training data creates a
significant barrier for open-source research. To bridge this gap, we introduce
GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus
containing more than 1.5 million high-quality triplets (instruction, source
image, edited image). We systematically construct this dataset by leveraging
the versatile capabilities of GPT-4o to unify and refine three popular
image-editing datasets: OmniEdit, HQ-Edit, and UltraEdit. Specifically, our
methodology involves 1) regenerating output images to enhance visual quality
and instruction alignment, and 2) selectively rewriting prompts to improve
semantic clarity. To validate the efficacy of our dataset, we fine-tune
advanced open-source models on GPT-IMAGE-EDIT-1.5M. The empirical results are
exciting, e.g., the fine-tuned FluxKontext achieves highly competitive
performance across a comprehensive suite of benchmarks, including 7.24 on
GEdit-EN, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, showing stronger
instruction following and higher perceptual quality while maintaining identity.
These scores markedly exceed all previously published open-source methods and
substantially narrow the gap to leading proprietary models. We hope the full
release of GPT-IMAGE-EDIT-1.5M can help to catalyze further open research in
instruction-guided image editing.
Agentic Reinforced Policy Optimization
Authors: name: Guanting Dong, name: Hangyu Mao, name: Kai Ma, name: Licheng Bao, name: Yifei Chen, name: Zhongyuan Wang, name: Zhongxia Chen, name: Jiazhen Du, name: Huiyang Wang, name: Fuzheng Zhang, name: Guorui Zhou, name: Yutao Zhu, name: Ji-Rong Wen, name: Zhicheng Dou
Published: 2025-07-26 07:53:11

Large-scale reinforcement learning with verifiable rewards (RLVR) has
demonstrated its effectiveness in harnessing the potential of large language
models (LLMs) for single-turn reasoning tasks. In realistic reasoning
scenarios, LLMs can often utilize external tools to assist in task-solving
processes. However, current RL algorithms inadequately balance the models'
intrinsic long-horizon reasoning capabilities and their proficiency in
multi-turn tool interactions. To bridge this gap, we propose Agentic Reinforced
Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training
multi-turn LLM-based agents. Through preliminary experiments, we observe that
LLMs tend to exhibit highly uncertain behavior, characterized by an increase in
the entropy distribution of generated tokens, immediately following
interactions with external tools. Motivated by this observation, ARPO
incorporates an entropy-based adaptive rollout mechanism, dynamically balancing
global trajectory sampling and step-level sampling, thereby promoting
exploration at steps with high uncertainty after tool usage. By integrating an
advantage attribution estimation, ARPO enables LLMs to internalize advantage
differences in stepwise tool-use interactions. Our experiments across 13
challenging benchmarks in computational reasoning, knowledge reasoning, and
deep search domains demonstrate ARPO's superiority over trajectory-level RL
algorithms. Remarkably, ARPO achieves improved performance using only half of
the tool-use budget required by existing methods, offering a scalable solution
for aligning LLM-based agents with real-time dynamic environments. Our code and
datasets are released at https://github.com/dongguanting/ARPO
Geometric-Mean Policy Optimization
Authors: name: Yuzhong Zhao, name: Yue Liu, name: Junpeng Liu, name: Jingye Chen, name: Xun Wu, name: Yaru Hao, name: Tengchao Lv, name: Shaohan Huang, name: Lei Cui, name: Qixiang Ye, name: Fang Wan, name: Furu Wei
Published: 2025-07-28 09:54:05

Recent advancements, such as Group Relative Policy Optimization (GRPO), have
enhanced the reasoning capabilities of large language models by optimizing the
arithmetic mean of token-level rewards. However, GRPO suffers from unstable
policy updates when processing tokens with outlier importance-weighted rewards,
which manifests as extreme importance sampling ratios during training, i.e.,
the ratio between the sampling probabilities assigned to a token by the current
and old policies. In this work, we propose Geometric-Mean Policy Optimization
(GMPO), a stabilized variant of GRPO. Instead of optimizing the arithmetic
mean, GMPO maximizes the geometric mean of token-level rewards, which is
inherently less sensitive to outliers and maintains a more stable range of
importance sampling ratio. In addition, we provide comprehensive theoretical
and experimental analysis to justify the design and stability benefits of GMPO.
Beyond improved stability, GMPO-7B outperforms GRPO by an average of 4.1% on
multiple mathematical benchmarks and 1.4% on multimodal reasoning benchmark,
including AIME24, AMC, MATH500, OlympiadBench, Minerva, and Geometry3K. Code is
available at https://github.com/callsys/GMPO.
ForCenNet: Foreground-Centric Network for Document Image Rectification
Authors: name: Peng Cai, name: Qiang Li, name: Kaicheng Yang, name: Dong Guo, name: Jia Li, name: Nan Zhou, name: Xiang An, name: Ninghua Yang, name: Jiankang Deng
Published: 2025-07-26 05:36:48

Document image rectification aims to eliminate geometric deformation in
photographed documents to facilitate text recognition. However, existing
methods often neglect the significance of foreground elements, which provide
essential geometric references and layout information for document image
correction. In this paper, we introduce Foreground-Centric Network (ForCenNet)
to eliminate geometric distortions in document images. Specifically, we
initially propose a foreground-centric label generation method, which extracts
detailed foreground elements from an undistorted image. Then we introduce a
foreground-centric mask mechanism to enhance the distinction between readable
and background regions. Furthermore, we design a curvature consistency loss to
leverage the detailed foreground labels to help the model understand the
distorted geometric distribution. Extensive experiments demonstrate that
ForCenNet achieves new state-of-the-art on four real-world benchmarks, such as
DocUNet, DIR300, WarpDoc, and DocReal. Quantitative analysis shows that the
proposed method effectively undistorts layout elements, such as text lines and
table borders. The resources for further comparison are provided at
https://github.com/caipeng328/ForCenNet.
Music Arena: Live Evaluation for Text-to-Music
Authors: name: Yonghyun Kim, name: Wayne Chi, name: Anastasios N. Angelopoulos, name: Wei-Lin Chiang, name: Koichi Saito, name: Shinji Watanabe, name: Yuki Mitsufuji, name: Chris Donahue
Published: 2025-07-28 14:52:57

We present Music Arena, an open platform for scalable human preference
evaluation of text-to-music (TTM) models. Soliciting human preferences via
listening studies is the gold standard for evaluation in TTM, but these studies
are expensive to conduct and difficult to compare, as study protocols may
differ across systems. Moreover, human preferences might help researchers align
their TTM systems or improve automatic evaluation metrics, but an open and
renewable source of preferences does not currently exist. We aim to fill these
gaps by offering *live* evaluation for TTM. In Music Arena, real-world users
input text prompts of their choosing and compare outputs from two TTM systems,
and their preferences are used to compile a leaderboard. While Music Arena
follows recent evaluation trends in other AI domains, we also design it with
key features tailored to music: an LLM-based routing system to navigate the
heterogeneous type signatures of TTM systems, and the collection of *detailed*
preferences including listening data and natural language feedback. We also
propose a rolling data release policy with user privacy guarantees, providing a
renewable source of preference data and increasing platform transparency.
Through its standardized evaluation protocol, transparent data access policies,
and music-specific features, Music Arena not only addresses key challenges in
the TTM ecosystem but also demonstrates how live evaluation can be thoughtfully
adapted to unique characteristics of specific AI domains.
  Music Arena is available at: https://music-arena.org
Region-based Cluster Discrimination for Visual Representation Learning
Authors: name: Yin Xie, name: Kaicheng Yang, name: Xiang An, name: Kun Wu, name: Yongle Zhao, name: Weimo Deng, name: Zimin Ran, name: Yumeng Wang, name: Ziyong Feng, name: Roy Miles, name: Ismail Elezi, name: Jiankang Deng
Published: 2025-07-26 17:47:09

Learning visual representations is foundational for a broad spectrum of
downstream tasks. Although recent vision-language contrastive models, such as
CLIP and SigLIP, have achieved impressive zero-shot performance via large-scale
vision-language alignment, their reliance on global representations constrains
their effectiveness for dense prediction tasks, such as grounding, OCR, and
segmentation. To address this gap, we introduce Region-Aware Cluster
Discrimination (RICE), a novel method that enhances region-level visual and OCR
capabilities. We first construct a billion-scale candidate region dataset and
propose a Region Transformer layer to extract rich regional semantics. We
further design a unified region cluster discrimination loss that jointly
supports object and OCR learning within a single classification framework,
enabling efficient and scalable distributed training on large-scale data.
Extensive experiments show that RICE consistently outperforms previous methods
on tasks, including segmentation, dense detection, and visual perception for
Multimodal Large Language Models (MLLMs). The pre-trained models have been
released at https://github.com/deepglint/MVT.
Reconstructing 4D Spatial Intelligence: A Survey
Authors: name: Yukang Cao, name: Jiahao Lu, name: Zhisheng Huang, name: Zhuowei Shen, name: Chengfeng Zhao, name: Fangzhou Hong, name: Zhaoxi Chen, name: Xin Li, name: Wenping Wang, name: Yuan Liu, name: Ziwei Liu
Published: 2025-07-28 17:59:02

Reconstructing 4D spatial intelligence from visual observations has long been
a central yet challenging task in computer vision, with broad real-world
applications. These range from entertainment domains like movies, where the
focus is often on reconstructing fundamental visual elements, to embodied AI,
which emphasizes interaction modeling and physical realism. Fueled by rapid
advances in 3D representations and deep learning architectures, the field has
evolved quickly, outpacing the scope of previous surveys. Additionally,
existing surveys rarely offer a comprehensive analysis of the hierarchical
structure of 4D scene reconstruction. To address this gap, we present a new
perspective that organizes existing methods into five progressive levels of 4D
spatial intelligence: (1) Level 1 -- reconstruction of low-level 3D attributes
(e.g., depth, pose, and point maps); (2) Level 2 -- reconstruction of 3D scene
components (e.g., objects, humans, structures); (3) Level 3 -- reconstruction
of 4D dynamic scenes; (4) Level 4 -- modeling of interactions among scene
components; and (5) Level 5 -- incorporation of physical laws and constraints.
We conclude the survey by discussing the key challenges at each level and
highlighting promising directions for advancing toward even richer levels of 4D
spatial intelligence. To track ongoing developments, we maintain an up-to-date
project page: https://github.com/yukangcao/Awesome-4D-Spatial-Intelligence.