Orionnnn/hub-stats huggingface dataset:
InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long
  Adaptation
Authors: name: Weilin Zhao, name: Zihan Zhou, name: Zhou Su, name: Chaojun Xiao, name: Yuxuan Li, name: Yanghao Li, name: Yudi Zhang, name: Weilun Zhao, name: Zhen Li, name: Yuxiang Huang, name: Ao Sun, name: Xu Han, name: Zhiyuan Liu
Published: 2025-09-29 12:08:33

Long-sequence processing is a critical capability for modern large language
models. However, the self-attention mechanism in the standard Transformer
architecture faces severe computational and memory bottlenecks when processing
long sequences. While trainable sparse attention methods offer a promising
solution, existing approaches such as NSA introduce excessive extra parameters
and disrupt the conventional pretrain-on-short, finetune-on-long
workflow, resulting in slow convergence and difficulty in acceleration. To
overcome these limitations, we introduce dense-sparse switchable attention
framework, termed as InfLLM-V2. InfLLM-V2 is a trainable sparse attention that
seamlessly adapts models from short to long sequences. Specifically, InfLLM-V2
reuses dense attention parameters through parameter-free architecture
modification, maintaining consistency between short and long sequence
processing. Additionally, InfLLM-V2 ensures computational efficiency across all
sequence lengths, by using dense attention for short inputs and smoothly
transitioning to sparse attention for long sequences. To achieve practical
acceleration, we further introduce an efficient implementation of InfLLM-V2
that significantly reduces the computational overhead. Our experiments on
long-context understanding and chain-of-thought reasoning demonstrate that
InfLLM-V2 is 4times faster than dense attention while retaining 98.1% and
99.7% of the performance, respectively. Based on the InfLLM-V2 framework, we
have trained and open-sourced MiniCPM4.1
(https://huggingface.co/openbmb/MiniCPM4.1-8B), a hybrid reasoning model,
providing a reproducible implementation for the research community.
Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and
  Planning
Authors: name: Sai Wang, name: Yu Wu, name: Zhongwen Xu
Published: 2025-09-29 17:02:31

The pursuit of artificial agents that can learn to master complex
environments has led to remarkable successes, yet prevailing deep reinforcement
learning methods often rely on immense experience, encoding their knowledge
opaquely within neural network weights. We propose a different paradigm, one in
which an agent learns to play by reasoning and planning. We introduce Cogito,
ergo ludo (CEL), a novel agent architecture that leverages a Large Language
Model (LLM) to build an explicit, language-based understanding of its
environment's mechanics and its own strategy. Starting from a tabula rasa state
with no prior knowledge (except action set), CEL operates on a cycle of
interaction and reflection. After each episode, the agent analyzes its complete
trajectory to perform two concurrent learning processes: Rule Induction, where
it refines its explicit model of the environment's dynamics, and Strategy and
Playbook Summarization, where it distills experiences into an actionable
strategic playbook. We evaluate CEL on diverse grid-world tasks (i.e.,
Minesweeper, Frozen Lake, and Sokoban), and show that the CEL agent
successfully learns to master these games by autonomously discovering their
rules and developing effective policies from sparse rewards. Ablation studies
confirm that the iterative process is critical for sustained learning. Our work
demonstrates a path toward more general and interpretable agents that not only
act effectively but also build a transparent and improving model of their world
through explicit reasoning on raw experience.
VideoScore2: Think before You Score in Generative Video Evaluation
Authors: name: Xuan He, name: Dongfu Jiang, name: Ping Nie, name: Minghao Liu, name: Zhengxuan Jiang, name: Mingyi Su, name: Wentao Ma, name: Junru Lin, name: Chun Ye, name: Yi Lu, name: Keming Wu, name: Benjamin Schneider, name: Quy Duc Do, name: Zhuofeng Li, name: Yiming Jia, name: Yuxuan Zhang, name: Guo Cheng, name: Haozhe Wang, name: Wangchunshu Zhou, name: Qunshu Lin, name: Yuanxing Zhang, name: Ge Zhang, name: Wenhao Huang, name: Wenhu Chen
Published: 2025-09-26 18:09:03

Recent advances in text-to-video generation have produced increasingly
realistic and diverse content, yet evaluating such videos remains a fundamental
challenge due to their multi-faceted nature encompassing visual quality,
semantic alignment, and physical consistency. Existing evaluators and reward
models are limited to single opaque scores, lack interpretability, or provide
only coarse analysis, making them insufficient for capturing the comprehensive
nature of video quality assessment. We present VideoScore2, a
multi-dimensional, interpretable, and human-aligned framework that explicitly
evaluates visual quality, text-to-video alignment, and physical/common-sense
consistency while producing detailed chain-of-thought rationales. Our model is
trained on a large-scale dataset VideoFeedback2 containing 27,168
human-annotated videos with both scores and reasoning traces across three
dimensions, using a two-stage pipeline of supervised fine-tuning followed by
reinforcement learning with Group Relative Policy Optimization (GRPO) to
enhance analytical robustness. Extensive experiments demonstrate that
VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our
in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance
across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc),
while providing interpretable assessments that bridge the gap between
evaluation and controllable generation through effective reward modeling for
Best-of-N sampling. Project Page: https://tiger-ai-lab.github.io/VideoScore2/
Critique-Coder: Enhancing Coder Models by Critique Reinforcement
  Learning
Authors: name: Chi Ruan, name: Dongfu Jiang, name: Yubo Wang, name: Wenhu Chen
Published: 2025-09-26 18:30:49

Reinforcement Learning (RL) has emerged as a popular training paradigm,
particularly when paired with reasoning models. While effective, it primarily
focuses on generating responses and lacks mechanisms to explicitly foster
critique or reflection. Several recent studies, like Critique-Fine-Tuning (CFT)
and Critique-Guided-Distillation (CGD) have shown the benefits of explicitly
teaching LLMs how to critique. Motivated by them, we propose Critique
Reinforcement Learning (CRL), where the model is tasked with generating a
critique for a given (question, solution) pair. The reward is determined solely
by whether the final judgment label c in {True, False}
of the generated critique aligns with the ground-truth judgment c^*. Building
on this point, we introduce Critique-Coder, which is trained on a
hybrid of RL and CRL by substituting 20\% of the standard RL data with CRL
data. We fine-tune multiple models (Critique-Coder) and evaluate them
on different benchmarks to show their advantages over RL-only models. We show
that Critique-Coder consistently outperforms RL-only baselines on all
the evaluated benchmarks. Notably, our Critique-Coder-8B can reach
over 60\% on LiveCodeBench (v5), outperforming other reasoning models like
DeepCoder-14B and GPT-o1. Beyond code generation, Critique-Coder also
demonstrates enhanced general reasoning abilities, as evidenced by its better
performance on logic reasoning tasks from the BBEH dataset. This indicates that
the application of CRL on coding datasets enhances general reasoning and
critique abilities, which are transferable across a broad range of tasks.
Hence, we believe that CRL works as a great complement to standard RL for LLM
reasoning.
RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human
  Mobility
Authors: name: Haoyu He, name: Haozheng Luo, name: Yan Chen, name: Qi R. Wang
Published: 2025-09-27 04:55:56

Predicting human mobility is inherently challenging due to complex long-range
dependencies and multi-scale periodic behaviors. To address this, we introduce
RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility),
a unified framework that leverages large language models (LLMs) as
general-purpose spatio-temporal predictors and trajectory reasoners.
Methodologically, RHYTHM employs temporal tokenization to partition each
trajectory into daily segments and encode them as discrete tokens with
hierarchical attention that captures both daily and weekly dependencies,
thereby significantly reducing the sequence length while preserving cyclical
information. Additionally, we enrich token representations by adding
pre-computed prompt embeddings for trajectory segments and prediction targets
via a frozen LLM, and feeding these combined embeddings back into the LLM
backbone to capture complex interdependencies. Computationally, RHYTHM freezes
the pretrained LLM's backbone to reduce attention complexity and memory cost.
We evaluate our model against state-of-the-art methods using three real-world
datasets. Notably, RHYTHM achieves a 2.4% improvement in overall accuracy, a
5.0% increase on weekends, and a 24.6% reduction in training time. Code is
publicly available at https://github.com/he-h/rhythm.
HunyuanImage 3.0 Technical Report
Authors: name: Siyu Cao, name: Hangting Chen, name: Peng Chen, name: Yiji Cheng, name: Yutao Cui, name: Xinchi Deng, name: Ying Dong, name: Kipper Gong, name: Tianpeng Gu, name: Xiusen Gu, name: Tiankai Hang, name: Duojun Huang, name: Jie Jiang, name: Zhengkai Jiang, name: Weijie Kong, name: Changlin Li, name: Donghao Li, name: Junzhe Li, name: Xin Li, name: Yang Li, name: Zhenxi Li, name: Zhimin Li, name: Jiaxin Lin, name: Linus, name: Lucaz Liu, name: Shu Liu, name: Songtao Liu, name: Yu Liu, name: Yuhong Liu, name: Yanxin Long, name: Fanbin Lu, name: Qinglin Lu, name: Yuyang Peng, name: Yuanbo Peng, name: Xiangwei Shen, name: Yixuan Shi, name: Jiale Tao, name: Yangyu Tao, name: Qi Tian, name: Pengfei Wan, name: Chunyu Wang, name: Kai Wang, name: Lei Wang, name: Linqing Wang, name: Lucas Wang, name: Qixun Wang, name: Weiyan Wang, name: Hao Wen, name: Bing Wu, name: Jianbing Wu, name: Yue Wu, name: Senhao Xie, name: Fang Yang, name: Miles Yang, name: Xiaofeng Yang, name: Xuan Yang, name: Zhantao Yang, name: Jingmiao Yu, name: Zheng Yuan, name: Chao Zhang, name: Jian-Wei Zhang, name: Peizhen Zhang, name: Shi-Xue Zhang, name: Tao Zhang, name: Weigang Zhang, name: Yepeng Zhang, name: Yingfang Zhang, name: Zihao Zhang, name: Zijian Zhang, name: Penghao Zhao, name: Zhiyuan Zhao, name: Xuefei Zhe, name: Jianchen Zhu, name: Zhao Zhong
Published: 2025-09-28 16:14:10

We present HunyuanImage 3.0, a native multimodal model that unifies
multimodal understanding and generation within an autoregressive framework,
with its image generation module publicly available. The achievement of
HunyuanImage 3.0 relies on several key components, including meticulous data
curation, advanced architecture design, a native Chain-of-Thoughts schema,
progressive model pre-training, aggressive model post-training, and an
efficient infrastructure that enables large-scale training and inference. With
these advancements, we successfully trained a Mixture-of-Experts (MoE) model
comprising over 80 billion parameters in total, with 13 billion parameters
activated per token during inference, making it the largest and most powerful
open-source image generative model to date. We conducted extensive experiments
and the results of automatic and human evaluation of text-image alignment and
visual quality demonstrate that HunyuanImage 3.0 rivals previous
state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0,
we aim to enable the community to explore new ideas with a state-of-the-art
foundation model, fostering a dynamic and vibrant multimodal ecosystem. All
open source assets are publicly available at
https://github.com/Tencent-Hunyuan/HunyuanImage-3.0
REMA: A Unified Reasoning Manifold Framework for Interpreting Large
  Language Model
Authors: name: Bo Li, name: Guanzhi Deng, name: Ronghao Chen, name: Junrong Yue, name: Shuo Zhang, name: Qinghua Zhao, name: Linqi Song, name: Lijie Wen
Published: 2025-09-26 16:02:27

Understanding how Large Language Models (LLMs) perform complex reasoning and
their failure mechanisms is a challenge in interpretability research. To
provide a measurable geometric analysis perspective, we define the concept of
the Reasoning Manifold, a latent low-dimensional geometric structure formed by
the internal representations corresponding to all correctly reasoned
generations. This structure can be conceptualized as the embodiment of the
effective thinking paths that the model has learned to successfully solve a
given task. Based on this concept, we build REMA, a framework that explains the
origins of failures by quantitatively comparing the spatial relationships of
internal model representations corresponding to both erroneous and correct
reasoning samples. Specifically, REMA first quantifies the geometric deviation
of each erroneous representation by calculating its k-nearest neighbors
distance to the approximated manifold formed by correct representations,
thereby providing a unified failure signal. It then localizes the divergence
points where these deviations first become significant by tracking this
deviation metric across the model's layers and comparing it against a baseline
of internal fluctuations from correct representations, thus identifying where
the reasoning chain begins to go off-track. Our extensive experiments on
diverse language and multimodal models and tasks demonstrate the
low-dimensional nature of the reasoning manifold and the high separability
between erroneous and correct reasoning representations. The results also
validate the effectiveness of the REMA framework in analyzing the origins of
reasoning failures. This research connects abstract reasoning failures to
measurable geometric deviations in representations, providing new avenues for
in-depth understanding and diagnosis of the internal computational processes of
black-box models.
IWR-Bench: Can LVLMs reconstruct interactive webpage from a user
  interaction video?
Authors: name: Yang Chen, name: Minghao Liu, name: Yufan Shen, name: Yunwen Li, name: Tianyuan Huang, name: Xinyu Fang, name: Tianyu Zheng, name: Wenxuan Huang, name: Cheng Yang, name: Daocheng Fu, name: Jianbiao Mei, name: Rong Wu, name: Licheng Wen, name: Xuemeng Yang, name: Song Mao, name: Qunshu Lin, name: Zhi Yu, name: Yongliang Shen, name: Yu Qiao, name: Botian Shi
Published: 2025-09-29 12:38:06

The webpage-to-code task requires models to understand visual representations
of webpages and generate corresponding code. However, existing benchmarks
primarily focus on static screenshot-to-code tasks, thereby overlooking the
dynamic interactions fundamental to real-world web applications. To address
this limitation, this paper introduces IWR-Bench, a novel benchmark for
evaluating the capabilities of Large Vision-Language Models (LVLMs) in
interactive webpage reconstruction from video. IWR-Bench comprises 113
meticulously curated tasks from 100 real-world websites, with 1,001 actions and
featuring diverse interaction complexities (e.g., web games), visual styles,
and domains. Aligning with standard web development practices, each task
includes not only user interaction videos but also all crawled static assets
(e.g., images, videos). This benchmark evaluates models on two fundamental
challenges: comprehensive multi-modal reasoning to infer interaction logic from
video and assets, and advanced code generation to translate this logic into
functional code. An agent-as-a-judge framework with a comprehensive metric
system automatically assesses the functional correctness and visual fidelity of
generated webpages. Extensive experiments on 28 LVLMs reveal a significant
challenge: the best model achieves an overall score of only 36.35%, as
functional correctness (24.39% IFS) lags significantly behind visual fidelity
(64.25% VFS). These results highlight critical limitations in current models'
ability to reason about temporal dynamics and synthesize event-driven logic,
establishing IWR-Bench as a challenging frontier for vision-language research.
The benchmark and evaluation code will be made publicly available. Code is
available at https://github.com/L-O-I/IWR-Bench.
Efficient Multi-turn RL for GUI Agents via Decoupled Training and
  Adaptive Data Curation
Authors: name: Pengxiang Li, name: Zechen Hu, name: Zirui Shang, name: Jingrong Wu, name: Yang Liu, name: Hui Liu, name: Zhi Gao, name: Chenrui Shi, name: Bofei Zhang, name: Zihao Zhang, name: Xiaochuan Shi, name: Zedong YU, name: Yuwei Wu, name: Xinxiao Wu, name: Yunde Jia, name: Liuyu Xiang, name: Zhaofeng He, name: Qing Li
Published: 2025-09-28 13:19:20

Vision-language model (VLM) based GUI agents show promise for automating
complex desktop and mobile tasks, but face significant challenges in applying
reinforcement learning (RL): (1) slow multi-turn interactions with GUI
environments for policy rollout, and (2) insufficient high-quality
agent-environment interactions for policy learning. To address these
challenges, we propose DART, a Decoupled Agentic RL Training framework for GUI
agents, which coordinates heterogeneous modules in a highly decoupled manner.
DART separates the training system into four asynchronous modules: environment
cluster, rollout service, data manager, and trainer. This design enables
non-blocking communication, asynchronous training, rollout-wise trajectory
sampling, and per-worker model synchronization, significantly improving the
system efficiency: 1.6*GPU utilization for rollout, 1.9* training throughput,
and 5.5* environment utilization. To facilitate effective learning from
abundant samples, we introduce an adaptive data curation scheme: (1)
pre-collecting successful trajectories for challenging tasks to supplement
sparse success in online sampling; (2) dynamically adjusting rollout numbers
and trajectory lengths based on task difficulty; (3) training selectively on
high-entropy steps to prioritize critical decisions; (4) stabilizing learning
via truncated importance sampling for policy mismatch between policy rollout
and updating. On the OSWorld benchmark, DART-GUI-7B achieves a 42.13% task
success rate, a 14.61% absolute gain over the base model, and 7.34% higher than
open-source SOTA. We will fully open-source our training framework, data, and
model checkpoints via computer-use-agents.github.io/dart-gui, which we believe
is a timely contribution to the open-source community of agentic RL training.
PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on
  Structured Images
Authors: name: Shuoshuo Zhang, name: Zijian Li, name: Yizhen Zhang, name: Jingjing Fu, name: Lei Song, name: Jiang Bian, name: Jun Zhang, name: Yujiu Yang, name: Rui Wang
Published: 2025-09-29 17:59:49

Structured images (e.g., charts and geometric diagrams) remain challenging
for multimodal large language models (MLLMs), as perceptual slips can cascade
into erroneous conclusions. Intermediate visual cues can steer reasoning;
however, existing cue-based methods are constrained with low-fidelity image
processing and linear, rigid reasoning patterns, limiting their effectiveness
on complex structured-image tasks. In this paper, we propose PixelCraft, a
novel multi-agent system for high-fidelity image processing and flexible visual
reasoning on structured images. The system comprises a dispatcher, a planner, a
reasoner, critics, and a set of visual tool agents. To achieve high-fidelity
processing, we construct a high-quality corpus and fine-tune an MLLM into a
grounding model, whose pixel-level localizations are integrated with
traditional computer vision (CV) algorithms in tool agents. Building on this
foundation, PixelCraft facilitates flexible visual reasoning through a dynamic
three-stage workflow of tool selection, agent discussion, and self-criticism.
Moreover, unlike prior linear reasoning patterns that simply append historical
images, PixelCraft maintains an image memory to allow the planner to adaptively
revisit earlier visual steps, explore alternative reasoning branches, and
dynamically adjust the reasoning trajectory during discussion. Extensive
experiments on challenging chart and geometry benchmarks demonstrate that
PixelCraft significantly improves visual reasoning performance for advanced
MLLMs, setting a new standard for structured image reasoning. Our code will be
available at https://github.com/microsoft/PixelCraft.
GSM8K-V: Can Vision Language Models Solve Grade School Math Word
  Problems in Visual Contexts
Authors: name: Fan Yuan, name: Yuchen Yan, name: Yifan Jiang, name: Haoran Zhao, name: Tao Feng, name: Jinyan Chen, name: Yanwei Lou, name: Wenqi Zhang, name: Yongliang Shen, name: Weiming Lu, name: Jun Xiao, name: Yueting Zhuang
Published: 2025-09-29 17:57:05

Vision language models (VLMs) achieve unified modeling of images and text,
enabling them to accomplish complex real-world tasks through perception,
planning, and reasoning. Among these tasks, reasoning is particularly
representative, with mathematical reasoning serving as a prominent example. It
highlights the high-level capability of VLMs to comprehend mathematical
information in images and to perform sophisticated reasoning. Recently,
numerous visual mathematical reasoning benchmarks have been proposed, but they
are often restricted to geometry, lack coverage of math word problems, and
rarely assess reasoning across multiple images. To address these gaps, we
introduce GSM8K-V, a purely visual multi-image mathematical reasoning
benchmark. GSM8K-V is built by systematically mapping each sample from the
widely used text-based GSM8K into visual form. Through a carefully designed
automated image-generation pipeline combined with meticulous human annotation,
we curate 1,319 high-quality samples. We evaluate a wide range of open-source
and closed-source models on GSM8K-V. Results show that although existing VLMs
have nearly saturated performance on text-based GSM8K, there remains
substantial room for improvement on GSM8K-V. For example, the best-performing
model, Gemini-2.5-Pro, achieves 95.22% accuracy on GSM8K but only 46.93% on
GSM8K-V. We conduct a comprehensive analysis of GSM8K-V, examining the
limitations of current models as well as potential directions for improvement.
GSM8K-V offers a new perspective on visual mathematical reasoning and
establishes a benchmark to guide the development of more robust and
generalizable VLMs.
Rolling Forcing: Autoregressive Long Video Diffusion in Real Time
Authors: name: Kunhao Liu, name: Wenbo Hu, name: Jiale Xu, name: Ying Shan, name: Shijian Lu
Published: 2025-09-29 17:57:14

Streaming video generation, as one fundamental component in interactive world
models and neural game engines, aims to generate high-quality, low-latency, and
temporally coherent long video streams. However, most existing work suffers
from severe error accumulation that often significantly degrades the generated
stream videos over long horizons. We design Rolling Forcing, a novel video
generation technique that enables streaming long videos with minimal error
accumulation. Rolling Forcing comes with three novel designs. First, instead of
iteratively sampling individual frames, which accelerates error propagation, we
design a joint denoising scheme that simultaneously denoises multiple frames
with progressively increasing noise levels. This design relaxes the strict
causality across adjacent frames, effectively suppressing error growth. Second,
we introduce the attention sink mechanism into the long-horizon stream video
generation task, which allows the model to keep key value states of initial
frames as a global context anchor and thereby enhances long-term global
consistency. Third, we design an efficient training algorithm that enables
few-step distillation over largely extended denoising windows. This algorithm
operates on non-overlapping windows and mitigates exposure bias conditioned on
self-generated histories. Extensive experiments show that Rolling Forcing
enables real-time streaming generation of multi-minute videos on a single GPU,
with substantially reduced error accumulation.