Orionnnn/hub-stats huggingface dataset:
OpenVision 2: A Family of Generative Pretrained Visual Encoders for
  Multimodal Learning
Authors: name: Yanqing Liu, name: Xianhang Li, name: Letian Zhang, name: Zirui Wang, name: Zeyu Zheng, name: Yuyin Zhou, name: Cihang Xie
Published: 2025-09-01 17:38:21

This paper provides a simplification on OpenVision's architecture and loss
design for enhancing its training efficiency. Following the prior
vision-language pretraining works CapPa and AIMv2, as well as modern multimodal
designs like LLaVA, our changes are straightforward: we remove the text encoder
(and therefore the contrastive loss), retaining only the captioning loss as a
purely generative training signal. We name this new version OpenVision 2. The
initial results are promising: despite this simplification, OpenVision 2
competitively matches the original model's performance on a broad set of
multimodal benchmarks while substantially cutting both training time and memory
consumption. For example, with ViT-L/14, it reduces training time by about 1.5x
(from 83h to 57h), and memory usage by about 1.8x (from 24.5GB to 13.8GB,
equivalently allowing the maximum batch size to grow from 2k to 8k). This
superior training efficiency also allows us to scale far beyond the largest
vision encoder used in OpenVision, reaching more than 1 billion parameters. We
hold a strong belief that this lightweight, generative-only paradigm is
compelling for future vision encoder development in multimodal foundation
models.
Kwai Keye-VL 1.5 Technical Report
Authors: name: Biao Yang, name: Bin Wen, name: Boyang Ding, name: Changyi Liu, name: Chenglong Chu, name: Chengru Song, name: Chongling Rao, name: Chuan Yi, name: Da Li, name: Dunju Zang, name: Fan Yang, name: Guorui Zhou, name: Guowang Zhang, name: Han Shen, name: Hao Peng, name: Haojie Ding, name: Hao Wang, name: Hengrui Ju, name: Jiaming Huang, name: Jiangxia Cao, name: Jiankang Chen, name: Jingyun Hua, name: Kaibing Chen, name: Kaiyu Jiang, name: Kaiyu Tang, name: Kun Gai, name: Muhao Wei, name: Qiang Wang, name: Ruitao Wang, name: Sen Na, name: Shengnan Zhang, name: Siyang Mao, name: Sui Huang, name: Tianke Zhang, name: Tingting Gao, name: Wei Chen, name: Wei Yuan, name: Xiangyu Wu, name: Xiao Hu, name: Xingyu Lu, name: Yi-Fan Zhang, name: Yiping Yang, name: Yulong Chen, name: Zeyi Lu, name: Zhenhua Wu, name: Zhixin Ling, name: Zhuoran Yang, name: Ziming Li, name: Di Xu, name: Haixuan Gao, name: Hang Li, name: Jing Wang, name: Lejian Ren, name: Qigen Hu, name: Qianqian Wang, name: Shiyao Wang, name: Xinchen Luo, name: Yan Li, name: Yuhang Hu, name: Zixing Zhang
Published: 2025-09-01 15:46:58

In recent years, the development of Large Language Models (LLMs) has
significantly advanced, extending their capabilities to multimodal tasks
through Multimodal Large Language Models (MLLMs). However, video understanding
remains a challenging area due to the dynamic and information-dense nature of
videos. Existing models struggle with the trade-off between spatial resolution
and temporal coverage when processing video content. We present Keye-VL-1.5,
which addresses fundamental challenges in video comprehension through three key
innovations. First, we introduce a novel Slow-Fast video encoding strategy that
dynamically allocates computational resources based on inter-frame similarity,
processing key frames with significant visual changes at higher resolution
(Slow pathway) while handling relatively static frames with increased temporal
coverage at lower resolution (Fast pathway). Second, we implement a progressive
four-stage pre-training methodology that systematically extends the model's
context length from 8K to 128K tokens, enabling processing of longer videos and
more complex visual content. Third, we develop a comprehensive post-training
pipeline focusing on reasoning enhancement and human preference alignment,
incorporating a 5-step chain-of-thought data construction process, iterative
GSPO-based reinforcement learning with progressive prompt hinting for difficult
cases, and alignment training. Through extensive evaluation on public
benchmarks and rigorous internal human assessment, Keye-VL-1.5 demonstrates
significant improvements over existing models, particularly excelling in video
understanding tasks while maintaining competitive performance on general
multimodal benchmarks.
Improving Large Vision and Language Models by Learning from a Panel of
  Peers
Authors: name: Jefferson Hernandez, name: Jing Shi, name: Simon Jenni, name: Vicente Ordonez, name: Kushal Kafle
Published: 2025-09-01 16:43:48

Traditional alignment methods for Large Vision and Language Models (LVLMs)
primarily rely on human-curated preference data. Human-generated preference
data is costly; machine-generated preference data is limited in quality; and
self-supervised preference data often introduces hallucinations. To overcome
these limitations, we propose a novel Panel-of-Peers learning framework
inspired by collaborative learning among humans. This approach leverages a
panel of LVLMs, each evaluating and learning from their collective outputs
through an iterative self-improvement process. By simulating a peer review
system, our models generate, assess, and refine outputs in response to a
curated set of prompts, mimicking a classroom learning environment. We
demonstrate that this methodology enhances model performance without requiring
extensive human-labeled datasets. Our experiments show significant improvement
across multiple benchmarks, demonstrating the potential of peer evaluations as
a scalable alternative to self-supervised alignment. Notably, we show that
Panel-of-Peers increases the average score on fifteen benchmarks from 48% to
57%
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Authors: name: Guibin Zhang, name: Hejia Geng, name: Xiaohang Yu, name: Zhenfei Yin, name: Zaibin Zhang, name: Zelin Tan, name: Heng Zhou, name: Zhongzhi Li, name: Xiangyuan Xue, name: Yijiang Li, name: Yifan Zhou, name: Yang Chen, name: Chen Zhang, name: Yutao Fan, name: Zihu Wang, name: Songtao Huang, name: Yue Liao, name: Hongru Wang, name: Mengyue Yang, name: Heng Ji, name: Michael Littman, name: Jun Wang, name: Shuicheng Yan, name: Philip Torr, name: Lei Bai
Published: 2025-09-02 17:46:26

The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm
shift from conventional reinforcement learning applied to large language models
(LLM RL), reframing LLMs from passive sequence generators into autonomous,
decision-making agents embedded in complex, dynamic worlds. This survey
formalizes this conceptual shift by contrasting the degenerate single-step
Markov Decision Processes (MDPs) of LLM-RL with the temporally extended,
partially observable Markov decision processes (POMDPs) that define Agentic RL.
Building on this foundation, we propose a comprehensive twofold taxonomy: one
organized around core agentic capabilities, including planning, tool use,
memory, reasoning, self-improvement, and perception, and the other around their
applications across diverse task domains. Central to our thesis is that
reinforcement learning serves as the critical mechanism for transforming these
capabilities from static, heuristic modules into adaptive, robust agentic
behavior. To support and accelerate future research, we consolidate the
landscape of open-source environments, benchmarks, and frameworks into a
practical compendium. By synthesizing over five hundred recent works, this
survey charts the contours of this rapidly evolving field and highlights the
opportunities and challenges that will shape the development of scalable,
general-purpose AI agents.
POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models
  for Document Conversion
Authors: name: Yuan Liu, name: Zhongyin Zhao, name: Le Tian, name: Haicheng Wang, name: Xubing Ye, name: Yangxiu You, name: Zilin Yu, name: Chuhan Wu, name: Xiao Zhou, name: Yang Yu, name: Jie Zhou
Published: 2025-09-01 07:54:18

High-quality labeled data is essential for training accurate document
conversion models, particularly in domains with complex formats such as tables,
formulas, and multi-column text. However, manual annotation is both costly and
time-consuming, while automatic labeling using existing models often lacks
accuracy in handling such challenging scenarios. Consequently, training student
models by distilling outputs from teacher models can significantly limit their
performance in real-world applications. In this paper, we propose a fully
automated, distillation-free framework comprising two stages for constructing
high-quality document extraction datasets and models capable of handling
diverse document formats and layouts. In the first stage, we introduce a method
for generating large-scale, diverse synthetic data, which enables a model to
extract key elements in a unified format with strong initial performance. In
the second stage, we present a self-improvement approach that further adapts
the model, initially trained on synthetic data, to real-world documents.
Specifically, we first use the fine-tuned model to annotate real documents,
then apply a suite of filtering strategies to verify annotation quality, and
finally retrain the model on the verified dataset. By iteratively repeating
this process, we progressively enhance both the model's conversion capabilities
and the quality of the generated data. We train a public POINTS-1.5 model to
obtain POINTS-Reader, which surpasses many existing public and proprietary
models of comparable or larger size. Our model is available at
https://github.com/Tencent/POINTS-Reader.
M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via
  Self-Supervision
Authors: name: Che Liu, name: Zheng Jiang, name: Chengyu Fang, name: Heng Guo, name: Yan-Jie Zhou, name: Jiaqi Qu, name: Le Lu, name: Minfeng Xu
Published: 2025-09-01 10:59:39

Medical image retrieval is essential for clinical decision-making and
translational research, relying on discriminative visual representations. Yet,
current methods remain fragmented, relying on separate architectures and
training strategies for 2D, 3D, and video-based medical data. This
modality-specific design hampers scalability and inhibits the development of
unified representations. To enable unified learning, we curate a large-scale
hybrid-modality dataset comprising 867,653 medical imaging samples, including
2D X-rays and ultrasounds, RGB endoscopy videos, and 3D CT scans. Leveraging
this dataset, we train M3Ret, a unified visual encoder without any
modality-specific customization. It successfully learns transferable
representations using both generative (MAE) and contrastive (SimDINO)
self-supervised learning (SSL) paradigms. Our approach sets a new
state-of-the-art in zero-shot image-to-image retrieval across all individual
modalities, surpassing strong baselines such as DINOv3 and the text-supervised
BMC-CLIP. More remarkably, strong cross-modal alignment emerges without paired
data, and the model generalizes to unseen MRI tasks, despite never observing
MRI during pretraining, demonstrating the generalizability of purely visual
self-supervision to unseen modalities. Comprehensive analyses further validate
the scalability of our framework across model and data sizes. These findings
deliver a promising signal to the medical imaging community, positioning M3Ret
as a step toward foundation models for visual SSL in multimodal medical image
understanding.
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn
  Tool-Integrated Reasoning
Authors: name: Zhenghai Xue, name: Longtao Zheng, name: Qian Liu, name: Yingru Li, name: Xiaosen Zheng, name: Zejun Ma, name: Bo An
Published: 2025-09-02 16:30:19

Large Language Models (LLMs) can significantly improve their reasoning
capabilities by interacting with external tools, a paradigm known as
Tool-Integrated Reasoning (TIR). However, extending TIR to multi-turn scenarios
using Reinforcement Learning (RL) is often hindered by training instability and
performance collapse. We identify that such instability is primarily caused by
a distributional drift from external tool feedback, leading to the generation
of low-probability tokens. This issue compounds over successive turns, causing
catastrophic gradient norm explosions that derail the training process. To
address this challenge, we introduce SimpleTIR , a plug-and-play algorithm that
stabilizes multi-turn TIR training. Its core strategy is to identify and filter
out trajectories containing void turns, i.e., turns that yield neither a code
block nor a final answer. By removing these problematic trajectories from the
policy update, SimpleTIR effectively blocks the harmful, high-magnitude
gradients, thus stabilizing the learning dynamics. Extensive experiments show
that SimpleTIR achieves state-of-the-art performance on challenging math
reasoning benchmarks, notably elevating the AIME24 score from a text-only
baseline of 22.1 to 50.5 when starting from the Qwen2.5-7B base model.
Furthermore, by avoiding the constraints of supervised fine-tuning, SimpleTIR
encourages the model to discover diverse and sophisticated reasoning patterns,
such as self-correction and cross-validation.
Towards More Diverse and Challenging Pre-training for Point Cloud
  Learning: Self-Supervised Cross Reconstruction with Decoupled Views
Authors: name: Xiangdong Zhang, name: Shaofeng Zhang, name: Junchi Yan
Published: 2025-09-01 08:42:17

Point cloud learning, especially in a self-supervised way without manual
labels, has gained growing attention in both vision and learning communities
due to its potential utility in a wide range of applications. Most existing
generative approaches for point cloud self-supervised learning focus on
recovering masked points from visible ones within a single view. Recognizing
that a two-view pre-training paradigm inherently introduces greater diversity
and variance, it may thus enable more challenging and informative pre-training.
Inspired by this, we explore the potential of two-view learning in this domain.
In this paper, we propose Point-PQAE, a cross-reconstruction generative
paradigm that first generates two decoupled point clouds/views and then
reconstructs one from the other. To achieve this goal, we develop a crop
mechanism for point cloud view generation for the first time and further
propose a novel positional encoding to represent the 3D relative position
between the two decoupled views. The cross-reconstruction significantly
increases the difficulty of pre-training compared to self-reconstruction, which
enables our method to surpass previous single-modal self-reconstruction methods
in 3D self-supervised learning. Specifically, it outperforms the
self-reconstruction baseline (Point-MAE) by 6.5%, 7.0%, and 6.7% in three
variants of ScanObjectNN with the Mlp-Linear evaluation protocol. The code is
available at https://github.com/aHapBean/Point-PQAE.
Universal Deep Research: Bring Your Own Model and Strategy
Authors: name: Peter Belcak, name: Pavlo Molchanov
Published: 2025-08-29 21:22:19

Deep research tools are among the most impactful and most commonly
encountered agentic systems today. We observe, however, that each deep research
agent introduced so far is hard-coded to carry out a particular research
strategy using a fixed choice of tools. We introduce Universal Deep Research
(UDR), a generalist agentic system that wraps around any language model and
enables the user to create, edit, and refine their own entirely custom deep
research strategies without any need for additional training or finetuning. To
showcase the generality of our system, we equip UDR with example minimal,
expansive, and intensive research strategies, and provide a user interface to
facilitate experimentation with the system.
MedDINOv3: How to adapt vision foundation models for medical image
  segmentation?
Authors: name: Yuheng Li, name: Yizhou Wu, name: Yuxiang Lai, name: Mingzhe Hu, name: Xiaofeng Yang
Published: 2025-09-02 14:44:43

Accurate segmentation of organs and tumors in CT and MRI scans is essential
for diagnosis, treatment planning, and disease monitoring. While deep learning
has advanced automated segmentation, most models remain task-specific, lacking
generalizability across modalities and institutions. Vision foundation models
(FMs) pretrained on billion-scale natural images offer powerful and
transferable representations. However, adapting them to medical imaging faces
two key challenges: (1) the ViT backbone of most foundation models still
underperform specialized CNNs on medical image segmentation, and (2) the large
domain gap between natural and medical images limits transferability. We
introduce MedDINOv3, a simple and effective framework for adapting
DINOv3 to medical segmentation. We first revisit plain ViTs and design a simple
and effective architecture with multi-scale token aggregation. Then, we perform
domain-adaptive pretraining on CT-3M, a curated collection of 3.87M
axial CT slices, using a multi-stage DINOv3 recipe to learn robust dense
features. MedDINOv3 matches or exceeds state-of-the-art performance across four
segmentation benchmarks, demonstrating the potential of vision foundation
models as unified backbones for medical image segmentation. The code is
available at https://github.com/ricklisz/MedDINOv3.
Jointly Reinforcing Diversity and Quality in Language Model Generations
Authors: name: Tianjian Li, name: Yiming Zhang, name: Ping Yu, name: Swarnadeep Saha, name: Daniel Khashabi, name: Jason Weston, name: Jack Lanchantin, name: Tianlu Wang
Published: 2025-09-02 17:38:47

Post-training of Large Language Models (LMs) often prioritizes accuracy and
helpfulness at the expense of diversity. This creates a tension: while
post-training improves response quality, it also sharpens output distributions
and reduces the range of ideas, limiting the usefulness of LMs in creative and
exploratory tasks such as brainstorming, storytelling, or problem solving. We
address this challenge with Diversity-Aware Reinforcement Learning (DARLING), a
framework that jointly optimizes for response quality and semantic diversity.
At its core, DARLING introduces a learned partition function to measure
diversity beyond surface-level lexical variations. This diversity signal is
then combined with a quality reward during online reinforcement learning,
encouraging models to generate outputs that are both high-quality and distinct.
Experiments across multiple model families and sizes show that DARLING
generalizes to two regimes: non-verifiable tasks (instruction following and
creative writing) and verifiable tasks (competition math). On five benchmarks
in the first setting, DARLING consistently outperforms quality-only RL
baselines, producing outputs that are simultaneously of higher quality and
novelty. In the second setting, DARLING achieves higher pass@1 (solution
quality) and pass@k (solution variety). Most strikingly, explicitly optimizing
for diversity catalyzes exploration in online RL, which manifests itself as
higher-quality responses.
Implicit Actor Critic Coupling via a Supervised Learning Framework for
  RLVR
Authors: name: Jiaming Li, name: Longze Chen, name: Ze Gong, name: Yukun Chen, name: Lu Wang, name: Wanwei He, name: Run Luo, name: Min Yang
Published: 2025-09-02 17:22:46

Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have
empowered large language models (LLMs) to tackle challenging reasoning tasks
such as mathematics and programming. RLVR leverages verifiable outcome rewards
to guide policy optimization, enabling LLMs to progressively improve output
quality in a grounded and reliable manner. Despite its promise, the RLVR
paradigm poses significant challenges, as existing methods often suffer from
sparse reward signals and unstable policy gradient updates, particularly in
RL-based approaches. To address the challenges, we propose PACS, a
novel RLVR framework that achieves imPlicit Actor
Critic coupling via a Supervised learning framework. By
treating the outcome reward as a predictable label, we reformulate the RLVR
problem into a supervised learning task over a score function parameterized by
the policy model and optimized using cross-entropy loss. A detailed gradient
analysis shows that this supervised formulation inherently recovers the
classical policy gradient update while implicitly coupling actor and critic
roles, yielding more stable and efficient training. Benchmarking on challenging
mathematical reasoning tasks, PACS outperforms strong RLVR baselines, such as
PPO and GRPO, achieving superior reasoning performance. For instance, PACS
achieves 59.78\% at pass@256 on AIME 2025, representing improvements of 13.32
and 14.36 points over PPO and GRPO. This simple yet powerful framework offers a
promising avenue for LLMs post-training with verifiable rewards. Our code and
data are available as open source at https://github.com/ritzz-ai/PACS.
Democracy-in-Silico: Institutional Design as Alignment in AI-Governed
  Polities
Authors: name: Trisanth Srinivasan, name: Santosh Patapati
Published: 2025-08-27 04:44:41

This paper introduces Democracy-in-Silico, an agent-based simulation where
societies of advanced AI agents, imbued with complex psychological personas,
govern themselves under different institutional frameworks. We explore what it
means to be human in an age of AI by tasking Large Language Models (LLMs) to
embody agents with traumatic memories, hidden agendas, and psychological
triggers. These agents engage in deliberation, legislation, and elections under
various stressors, such as budget crises and resource scarcity. We present a
novel metric, the Power-Preservation Index (PPI), to quantify misaligned
behavior where agents prioritize their own power over public welfare. Our
findings demonstrate that institutional design, specifically the combination of
a Constitutional AI (CAI) charter and a mediated deliberation protocol, serves
as a potent alignment mechanism. These structures significantly reduce corrupt
power-seeking behavior, improve policy stability, and enhance citizen welfare
compared to less constrained democratic models. The simulation reveals that an
institutional design may offer a framework for aligning the complex, emergent
behaviors of future artificial agent societies, forcing us to reconsider what
human rituals and responsibilities are essential in an age of shared authorship
with non-human entities.
From reactive to cognitive: brain-inspired spatial intelligence for
  embodied agents
Authors: name: Shouwei Ruan, name: Liyuan Wang, name: Caixin Kang, name: Qihui Zhu, name: Songming Liu, name: Xingxing Wei, name: Hang Su
Published: 2025-08-24 03:20:48

Spatial cognition enables adaptive goal-directed behavior by constructing
internal models of space. Robust biological systems consolidate spatial
knowledge into three interconnected forms: landmarks for salient cues,
route knowledge for movement trajectories, and survey
knowledge for map-like representations. While recent advances in multi-modal
large language models (MLLMs) have enabled visual-language reasoning in
embodied agents, these efforts lack structured spatial memory and instead
operate reactively, limiting their generalization and adaptability in complex
real-world environments. Here we present Brain-inspired Spatial Cognition for
Navigation (BSC-Nav), a unified framework for constructing and leveraging
structured spatial memory in embodied agents. BSC-Nav builds allocentric
cognitive maps from egocentric trajectories and contextual cues, and
dynamically retrieves spatial knowledge aligned with semantic goals. Integrated
with powerful MLLMs, BSC-Nav achieves state-of-the-art efficacy and efficiency
across diverse navigation tasks, demonstrates strong zero-shot generalization,
and supports versatile embodied behaviors in the real physical world, offering
a scalable and biologically grounded path toward general-purpose spatial
intelligence.