Orionnnn/hub-stats huggingface dataset:
RLFR: Extending Reinforcement Learning for LLMs with Flow Environment
Authors: name: Jinghao Zhang, name: Naishan Zheng, name: Ruilin Li, name: Dongzhou Cheng, name: Zheming Liang, name: Feng Zhao, name: Jiaqi Wang
Published: 2025-10-11 13:00:25

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as
a promising framework for improving reasoning abilities in Large Language
Models (LLMs). However, policy optimized with binary verification prone to
overlook potential valuable exploration in reasoning trajectory. In view of
heavy annotation cost of golden Process Reward Models (PRMs), recent works
attempt using auxiliary signals for reward shaping of process tokens, involving
entropy and likelihood collected from logit space. In this work, we offer a
novel perspective on shaping RLVR with flow rewards derived from latent space,
and propose RLFR, where the flow fields of model latents are constructed from
either off-policy high-quality data and on-policy rejection sampling data, and
the velocity deviations of policy latents within it are quantified to serve as
a reward signal. RLFR first demonstrates that a well-established flow field can
be a sound environment for reward signal collection, highlighting the
expressive latent space is much underexplored. Moreover, RLFR is able to
compress any off-policy expert data as reference for constituting reward
signals, and we show that the efficient context dependence compressed within
the hidden states are utilized, rather than individual token-level denotation
for context comprehending. Experiments on both language and multimodal
reasoning benchmarks demonstrate the reliability of flow rewards, and
suggesting a promising paradigm for reward shaping with auxiliary signals.
On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in
  Large Vision-Language Models
Authors: name: Hoigi Seo, name: Dong Un Kang, name: Hyunjin Cho, name: Joohoon Lee, name: Se Young Chun
Published: 2025-10-10 05:12:52

Large vision-language models (LVLMs), which integrate a vision encoder (VE)
with a large language model, have achieved remarkable success across various
tasks. However, there are still crucial challenges in LVLMs such as object
hallucination, generating descriptions of objects that are not in the input
image. Here, we argue that uncertain visual tokens within the VE is a key
factor that contributes to object hallucination. Our statistical analysis found
that there are positive correlations between visual tokens with high epistemic
uncertainty and the occurrence of hallucinations. Furthermore, we show
theoretically and empirically that visual tokens in early VE layers that
exhibit large representation deviations under small adversarial perturbations
indicate high epistemic uncertainty. Based on these findings, we propose a
simple yet effective strategy to mitigate object hallucination by modifying the
VE only. Our method comprises a proxy method with adversarial perturbations for
identifying uncertain visual tokens efficiently and a method to mask these
uncertain visual tokens during the self-attention process in the middle layers
of the VE, suppressing their influence on visual encoding and thus alleviating
hallucinations. Extensive experiments show that our method significantly
reduces object hallucinations in LVLMs and can synergistically work with other
prior arts.
ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web
  Coding
Authors: name: Yuhang Li, name: Chenchen Zhang, name: Ruilin Lv, name: Ao Liu, name: Ken Deng, name: Yuanxing Zhang, name: Jiaheng Liu, name: Wiggin Zhou, name: Bo Zhou
Published: 2025-10-13 15:05:50

While Large Language Models (LLMs) excel at algorithmic code generation, they
struggle with front-end development, where correctness is judged on rendered
pixels and interaction. We present ReLook, an agentic, vision-grounded
reinforcement learning framework that empowers an agent to close a robust
generate--diagnose--refine loop by invoking a multimodal LLM (MLLM) as a tool.
During training, the agent uses the MLLM-in-the-loop both as a visual
critic--scoring code with screenshots--and as a source of actionable,
vision-grounded feedback; a strict zero-reward rule for invalid renders anchors
renderability and prevents reward hacking. To prevent behavioral collapse, we
introduce Forced Optimization, a strict acceptance rule that admits only
improving revisions, yielding monotonically better trajectories. At inference,
we decouple the critic and run a lightweight, critic-free self-edit cycle,
keeping latency comparable to base decoding while retaining most of the gains.
Across three widely used benchmarks, ReLook consistently outperforms strong
baselines in vision-grounded front-end code generation, highlighting the
benefits of agentic perception, visual rewards, and training-inference
decoupling.
GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
Authors: name: Hongxiang Li, name: Yaowei Li, name: Bin Lin, name: Yuwei Niu, name: Yuhang Yang, name: Xiaoshuang Huang, name: Jiayin Cai, name: Xiaolong Jiang, name: Yao Hu, name: Long Chen
Published: 2025-10-13 05:50:44

Unified multimodal models integrate the reasoning capacity of large language
models with both image understanding and generation, showing great promise for
advanced multimodal intelligence. However, the community still lacks a rigorous
reasoning-centric benchmark to systematically evaluate the alignment between
understanding and generation, and their generalization potential in complex
visual tasks. To this end, we introduce GIR-Bench, a comprehensive
benchmark that evaluates unified models across three complementary
perspectives. Firstly, we investigate understanding-generation consistency
(GIR-Bench-UGC), asking whether models can consistently leverage the same
knowledge in both understanding and generation tasks. Secondly, we investigate
whether models can perform reasoning-centric text-to-image generation that
requires applying logical constraints and implicit knowledge to generate
faithful visual content (GIR-Bench-T2I). Thirdly, we evaluate whether models
can handle multi-step reasoning in editing (GIR-Bench-Edit). For each subset,
we carefully design different task-specific evaluation pipelines tailored for
each task. This enables fine-grained and interpretable evaluation while
mitigating biases from the prevalent MLLM-as-a-Judge paradigm. Extensive
ablations over various unified models and generation-only systems have shown
that: Although unified models are more capable of reasoning-driven visual
tasks, they still exhibit a persistent gap between understanding and
generation. The data and code for GIR-Bench are available at
https://hkust-longgroup.github.io/GIR-Bench{https://hkust-longgroup.github.io/GIR-Bench}.
OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni
  MLLMs
Authors: name: Caorui Li, name: Yu Chen, name: Yiyan Ji, name: Jin Xu, name: Zhenyu Cui, name: Shihao Li, name: Yuanxing Zhang, name: Jiafu Tang, name: Zhenghao Song, name: Dingling Zhang, name: Ying He, name: Haoxiang Liu, name: Yuxuan Wang, name: Qiufeng Wang, name: Zhenhe Wu, name: Jiehui Luo, name: Zhiyu Pan, name: Weihao Xie, name: Chenchen Zhang, name: Zhaohui Wang, name: Jiayi Tian, name: Yanghai Wang, name: Zhe Cao, name: Minxin Dai, name: Ke Wang, name: Runzhe Wen, name: Yinghao Ma, name: Yaning Pan, name: Sungkyun Chang, name: Termeh Taheri, name: Haiwen Xia, name: Christos Plachouras, name: Emmanouil Benetos, name: Yizhi Li, name: Ge Zhang, name: Jian Yang, name: Tianhao Peng, name: Zili Wang, name: Minghao Liu, name: Junran Peng, name: Zhaoxiang Zhang, name: Jiaheng Liu
Published: 2025-10-12 16:34:00

Recent advances in multimodal large language models (MLLMs) have demonstrated
substantial potential in video understanding. However, existing benchmarks fail
to comprehensively evaluate synergistic reasoning capabilities across audio and
visual modalities, often neglecting either one of the modalities or integrating
them in a logically inconsistent manner. To bridge this gap, we introduce
OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to
assessing synergistic audio-visual understanding, with a strong emphasis on
modality complementarity and logical consistency. Specifically, OmniVideoBench
comprises 1000 high-quality question-answer(QA) pairs, each annotated with
step-by-step reasoning traces, derived from 628 diverse videos ranging from
several seconds to 30 minutes, and manually verified to guarantee complete
correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully
designed question types, covering temporal reasoning, spatial localization,
counting, causal inference, summarization, and beyond, thereby capturing the
essential challenges of video understanding. Evaluation of multiple MLLMs on
OmniVideoBench reveals a pronounced gap between model performance and human
reasoning, with open-source models lagging significantly behind their
closed-source counterparts, underscoring the inherent difficulty of genuine
audio-visual reasoning. We will release OmniVideoBench to foster the
development of MLLMs with stronger and more generalizable reasoning
capabilities.
Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Authors: name: Ganlin Yang, name: Tianyi Zhang, name: Haoran Hao, name: Weiyun Wang, name: Yibin Liu, name: Dehui Wang, name: Guanzhou Chen, name: Zijian Cai, name: Junting Chen, name: Weijie Su, name: Wengang Zhou, name: Yu Qiao, name: Jifeng Dai, name: Jiangmiao Pang, name: Gen Luo, name: Wenhai Wang, name: Yao Mu, name: Zhi Hou
Published: 2025-10-13 05:51:22

While significant research has focused on developing embodied reasoning
capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs
into Vision-Language-Action (VLA) models for end-to-end robot control, few
studies directly address the critical gap between upstream VLM-based reasoning
and downstream VLA policy learning. In this work, we take an initial step
toward bridging embodied reasoning with VLA policy learning by introducing
Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning
capability, which is a foundational vision-language model designed to integrate
high-level reasoning with low-level control for embodied agents. Built upon the
high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance
across a range of embodied reasoning benchmarks - including spatial reasoning,
embodied grounding, embodied QA, and task planning. Furthermore, we
systematically examine how different VLM initializations affect supervised VLA
fine-tuning, offering novel insights into mitigating the domain shift between
internet-scale pre-training data and embodied-specific policy learning data.
Based on these insights, our approach achieves state-of-the-art results on the
WidowX benchmark and competitive performance on the Google Robot benchmark.
DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
Authors: name: Haoran Feng, name: Dizhe Zhang, name: Xiangtai Li, name: Bo Du, name: Lu Qi
Published: 2025-10-13 17:59:15

In this work, we propose DiT360, a DiT-based framework that performs hybrid
training on perspective and panoramic data for panoramic image generation. For
the issues of maintaining geometric fidelity and photorealism in generation
quality, we attribute the main reason to the lack of large-scale, high-quality,
real-world panoramic data, where such a data-centric view differs from prior
methods that focus on model design. Basically, DiT360 has several key modules
for inter-domain transformation and intra-domain augmentation, applied at both
the pre-VAE image level and the post-VAE token level. At the image level, we
incorporate cross-domain knowledge through perspective image guidance and
panoramic refinement, which enhance perceptual quality while regularizing
diversity and photorealism. At the token level, hybrid supervision is applied
across multiple modules, which include circular padding for boundary
continuity, yaw loss for rotational robustness, and cube loss for distortion
awareness. Extensive experiments on text-to-panorama, inpainting, and
outpainting tasks demonstrate that our method achieves better boundary
consistency and image fidelity across eleven quantitative metrics. Our code is
available at https://github.com/Insta360-Research-Team/DiT360.
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion
  Models via Likelihood Preference
Authors: name: Jianhao Yuan, name: Fabio Pizzati, name: Francesco Pinto, name: Lars Kunze, name: Ivan Laptev, name: Paul Newman, name: Philip Torr, name: Daniele De Martini
Published: 2025-10-13 15:19:07

Intuitive physics understanding in video diffusion models plays an essential
role in building general-purpose physically plausible world simulators, yet
accurately evaluating such capacity remains a challenging task due to the
difficulty in disentangling physics correctness from visual appearance in
generation. To the end, we introduce LikePhys, a training-free method that
evaluates intuitive physics in video diffusion models by distinguishing
physically valid and impossible videos using the denoising objective as an
ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By
testing on our constructed benchmark of twelve scenarios spanning over four
physics domains, we show that our evaluation metric, Plausibility Preference
Error (PPE), demonstrates strong alignment with human preference, outperforming
state-of-the-art evaluator baselines. We then systematically benchmark
intuitive physics understanding in current video diffusion models. Our study
further analyses how model design and inference settings affect intuitive
physics understanding and highlights domain-specific capacity variations across
physical laws. Empirical results show that, despite current models struggling
with complex and chaotic dynamics, there is a clear trend of improvement in
physics understanding as model capacity and inference settings scale.
AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration
Authors: name: Xinlong Chen, name: Yue Ding, name: Weihong Lin, name: Jingyun Hua, name: Linli Yao, name: Yang Shi, name: Bozhou Li, name: Yuanxing Zhang, name: Qiang Liu, name: Pengfei Wan, name: Liang Wang, name: Tieniu Tan
Published: 2025-10-12 01:20:22

Audiovisual video captioning aims to generate semantically rich descriptions
with temporal alignment between visual and auditory events, thereby benefiting
both video understanding and generation. In this paper, we present AVoCaDO, a
powerful audiovisual video captioner driven by the temporal orchestration
between audio and visual modalities. We propose a two-stage post-training
pipeline: (1) AVoCaDO SFT, which fine-tunes the model on a newly curated
dataset of 107K high-quality, temporally-aligned audiovisual captions; and (2)
AVoCaDO GRPO, which leverages tailored reward functions to further enhance
temporal coherence and dialogue accuracy while regularizing caption length and
reducing collapse. Experimental results demonstrate that AVoCaDO significantly
outperforms existing open-source models across four audiovisual video
captioning benchmarks, and also achieves competitive performance on the VDC and
DREAM-1K benchmark under visual-only settings.
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing
  Assessment
Authors: name: Yinan Chen, name: Jiangning Zhang, name: Teng Hu, name: Yuxiang Zeng, name: Zhucun Xue, name: Qingdong He, name: Chengjie Wang, name: Yong Liu, name: Xiaobin Hu, name: Shuicheng Yan
Published: 2025-10-13 17:27:08

Instruction-guided video editing has emerged as a rapidly advancing research
direction, offering new opportunities for intuitive content transformation
while also posing significant challenges for systematic evaluation. Existing
video editing benchmarks fail to support the evaluation of instruction-guided
video editing adequately and further suffer from limited source diversity,
narrow task coverage and incomplete evaluation metrics. To address the above
limitations, we introduce IVEBench, a modern benchmark suite specifically
designed for instruction-guided video editing assessment. IVEBench comprises a
diverse database of 600 high-quality source videos, spanning seven semantic
dimensions, and covering video lengths ranging from 32 to 1,024 frames. It
further includes 8 categories of editing tasks with 35 subcategories, whose
prompts are generated and refined through large language models and expert
review. Crucially, IVEBench establishes a three-dimensional evaluation protocol
encompassing video quality, instruction compliance and video fidelity,
integrating both traditional metrics and multimodal large language model-based
assessments. Extensive experiments demonstrate the effectiveness of IVEBench in
benchmarking state-of-the-art instruction-guided video editing methods, showing
its ability to provide comprehensive and human-aligned evaluation outcomes.
InfiniHuman: Infinite 3D Human Creation with Precise Control
Authors: name: Yuxuan Xue, name: Xianghui Xie, name: Margaret Kostyrko, name: Gerard Pons-Moll
Published: 2025-10-13 17:29:55

Generating realistic and controllable 3D human avatars is a long-standing
challenge, particularly when covering broad attribute ranges such as ethnicity,
age, clothing styles, and detailed body shapes. Capturing and annotating
large-scale human datasets for training generative models is prohibitively
expensive and limited in scale and diversity. The central question we address
in this paper is: Can existing foundation models be distilled to generate
theoretically unbounded, richly annotated 3D human data? We introduce
InfiniHuman, a framework that synergistically distills these models to produce
richly annotated human data at minimal cost and with theoretically unlimited
scalability. We propose InfiniHumanData, a fully automatic pipeline that
leverages vision-language and image generation models to create a large-scale
multi-modal dataset. User study shows our automatically generated identities
are undistinguishable from scan renderings. InfiniHumanData contains 111K
identities spanning unprecedented diversity. Each identity is annotated with
multi-granularity text descriptions, multi-view RGB images, detailed clothing
images, and SMPL body-shape parameters. Building on this dataset, we propose
InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body
shape, and clothing assets. InfiniHumanGen enables fast, realistic, and
precisely controllable avatar generation. Extensive experiments demonstrate
significant improvements over state-of-the-art methods in visual quality,
generation speed, and controllability. Our approach enables high-quality avatar
generation with fine-grained control at effectively unbounded scale through a
practical and affordable solution. We will publicly release the automatic data
generation pipeline, the comprehensive InfiniHumanData dataset, and the
InfiniHumanGen models at https://yuxuan-xue.com/infini-human.
Diffusion Transformers with Representation Autoencoders
Authors: name: Boyang Zheng, name: Nanye Ma, name: Shengbang Tong, name: Saining Xie
Published: 2025-10-13 17:51:39

Latent generative modeling, where a pretrained autoencoder maps pixels into a
latent space for the diffusion process, has become the standard strategy for
Diffusion Transformers (DiT); however, the autoencoder component has barely
evolved. Most DiTs continue to rely on the original VAE encoder, which
introduces several limitations: outdated backbones that compromise
architectural simplicity, low-dimensional latent spaces that restrict
information capacity, and weak representations that result from purely
reconstruction-based training and ultimately limit generative quality. In this
work, we explore replacing the VAE with pretrained representation encoders
(e.g., DINO, SigLIP, MAE) paired with trained decoders, forming what we term
Representation Autoencoders (RAEs). These models provide both high-quality
reconstructions and semantically rich latent spaces, while allowing for a
scalable transformer-based architecture. Since these latent spaces are
typically high-dimensional, a key challenge is enabling diffusion transformers
to operate effectively within them. We analyze the sources of this difficulty,
propose theoretically motivated solutions, and validate them empirically. Our
approach achieves faster convergence without auxiliary representation alignment
losses. Using a DiT variant equipped with a lightweight, wide DDT head, we
achieve strong image generation results on ImageNet: 1.51 FID at 256x256 (no
guidance) and 1.13 at both 256x256 and 512x512 (with guidance). RAE offers
clear advantages and should be the new default for diffusion transformer
training.
CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven
  Images
Authors: name: Chengqi Duan, name: Kaiyue Sun, name: Rongyao Fang, name: Manyuan Zhang, name: Yan Feng, name: Ying Luo, name: Yufang Liu, name: Ke Wang, name: Peng Pei, name: Xunliang Cai, name: Hongsheng Li, name: Yi Ma, name: Xihui Liu
Published: 2025-10-13 17:59:55

Recent advances in Large Language Models (LLMs) and Vision Language Models
(VLMs) have shown significant progress in mathematical reasoning, yet they
still face a critical bottleneck with problems requiring visual assistance,
such as drawing auxiliary lines or plotting functions to solve the problems.
Most LLMs and VLMs are constrained to text-only reasoning chains, while
multimodal unified models that can generate interleaved text and images lack
the necessary precision and controllability for such tasks. To address this, we
propose CodePlot-CoT, a code-driven Chain-of-Thought paradigm for "thinking
with images" in mathematics. Our approach leverages the VLM to generate text
reasoning as well as executable plotting code, which is then rendered into
images as "visual thought", to solve mathematical problems. To achieve this, we
first construct Math-VR, the first large-scale, bilingual dataset and benchmark
for Mathematics problems with Visual Reasoning, comprising 178K samples.
Second, to create high-quality training data, we develop a state-of-the-art
image-to-code converter specialized for parsing complex mathematical figures
into codes. Finally, using these training data, we train the CodePlot-CoT model
for solving mathematical problems. Experimental results show that our model
achieves up to 21% increase over base model on our new benchmark, fully
validating the efficacy of our proposed code-driven reasoning paradigm. Our
work opens a new direction for multimodal mathematical reasoning and provides
the community with the first large-scale dataset, comprehensive benchmark, and
strong approach for such problems. To facilitate future research, we make our
datasets, code, and pretrained models publicly available at
https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT.
FastHMR: Accelerating Human Mesh Recovery via Token and Layer Merging
  with Diffusion Decoding
Authors: name: Soroush Mehraban, name: Andrea Iaboni, name: Babak Taati
Published: 2025-10-13 00:23:17

Recent transformer-based models for 3D Human Mesh Recovery (HMR) have
achieved strong performance but often suffer from high computational cost and
complexity due to deep transformer architectures and redundant tokens. In this
paper, we introduce two HMR-specific merging strategies: Error-Constrained
Layer Merging (ECLM) and Mask-guided Token Merging (Mask-ToMe). ECLM
selectively merges transformer layers that have minimal impact on the Mean Per
Joint Position Error (MPJPE), while Mask-ToMe focuses on merging background
tokens that contribute little to the final prediction. To further address the
potential performance drop caused by merging, we propose a diffusion-based
decoder that incorporates temporal context and leverages pose priors learned
from large-scale motion capture datasets. Experiments across multiple
benchmarks demonstrate that our method achieves up to 2.3x speed-up while
slightly improving performance over the baseline.