Orionnnn/hub-stats huggingface dataset:
VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D
  Space
Authors: name: Lin Li, name: Zehuan Huang, name: Haoran Feng, name: Gengxiong Zhuang, name: Rui Chen, name: Chunchao Guo, name: Lu Sheng
Published: 2025-08-26 17:59:47

3D local editing of specified regions is crucial for game industry and robot
interaction. Recent methods typically edit rendered multi-view images and then
reconstruct 3D models, but they face challenges in precisely preserving
unedited regions and overall coherence. Inspired by structured 3D generative
models, we propose VoxHammer, a novel training-free approach that performs
precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer
first predicts its inversion trajectory and obtains its inverted latents and
key-value tokens at each timestep. Subsequently, in the denoising and editing
phase, we replace the denoising features of preserved regions with the
corresponding inverted latents and cached key-value tokens. By retaining these
contextual features, this approach ensures consistent reconstruction of
preserved areas and coherent integration of edited parts. To evaluate the
consistency of preserved regions, we constructed Edit3D-Bench, a
human-annotated dataset comprising hundreds of samples, each with carefully
labeled 3D editing regions. Experiments demonstrate that VoxHammer
significantly outperforms existing methods in terms of both 3D consistency of
preserved regions and overall quality. Our method holds promise for
synthesizing high-quality edited paired data, thereby laying the data
foundation for in-context 3D generation. See our project page at
https://huanngzh.github.io/VoxHammer-Page/.
Understanding Tool-Integrated Reasoning
Authors: name: Heng Lin, name: Zhongwen Xu
Published: 2025-08-26 17:03:46

We study why Tool-Integrated Reasoning (TIR) makes Large Language Models
(LLMs) more capable. While LLMs integrated with tools like Python code
interpreters show great promise, a principled theory explaining why this
paradigm is effective has been missing. This work provides the first formal
proof that TIR fundamentally expands an LLM's capabilities. We demonstrate that
tools enable a strict expansion of the model's empirical and feasible support,
breaking the capability ceiling of pure-text models by unlocking
problem-solving strategies that are otherwise impossible or intractably
verbose. To guide model behavior without compromising training stability and
performance, we also introduce Advantage Shaping Policy Optimization (ASPO), a
novel algorithm that directly modifies the advantage function to guide the
policy behavior. We conduct comprehensive experiments on challenging
mathematical benchmarks, leveraging a Python interpreter as the external tool.
Our results show that the TIR model decisively outperforms its pure-text
counterpart on the pass@k metric. Crucially, this advantage is not confined to
computationally-intensive problems but extends to those requiring significant
abstract insight. We further identify the emergent cognitive patterns that
illustrate how models learn to think with tools. Finally, we report improved
tool usage behavior with early code invocation and much more interactive turns
with ASPO. Overall, our work provides the first principled explanation for
TIR's success, shifting the focus from the mere fact that tools work to why and
how they enable more powerful reasoning.
Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Authors: name: Tristan S. W. Stevens, name: Oisín Nolan, name: Ruud J. G. van Sloun
Published: 2025-08-24 12:20:18

Echocardiography plays a central role in cardiac imaging, offering dynamic
views of the heart that are essential for diagnosis and monitoring. However,
image quality can be significantly degraded by haze arising from multipath
reverberations, particularly in difficult-to-image patients. In this work, we
propose a semantic-guided, diffusion-based dehazing algorithm developed for the
MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method
integrates a pixel-wise noise model, derived from semantic segmentation of hazy
inputs into a diffusion posterior sampling framework guided by a generative
prior trained on clean ultrasound data. Quantitative evaluation on the
challenge dataset demonstrates strong performance across contrast and fidelity
metrics. Code for the submitted algorithm is available at
https://github.com/tristan-deep/semantic-diffusion-echo-dehazing.
Hermes 4 Technical Report
Authors: name: Ryan Teknium, name: Roger Jin, name: Jai Suphavadeeprasit, name: Dakota Mahan, name: Jeffrey Quesnelle, name: Joe Li, name: Chen Guang, name: Shannon Sands, name: Karan Malhotra
Published: 2025-08-25 17:45:06

We present Hermes 4, a family of hybrid reasoning models that combine
structured, multi-turn reasoning with broad instruction-following ability. We
describe the challenges encountered during data curation, synthesis, training,
and evaluation, and outline the solutions employed to address these challenges
at scale. We comprehensively evaluate across mathematical reasoning, coding,
knowledge, comprehension, and alignment benchmarks, and we report both
quantitative performance and qualitative behavioral analysis. To support open
research, all model weights are published publicly at
https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728
MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
Authors: name: Sixun Dong, name: Juhua Hu, name: Mian Zhang, name: Ming Yin, name: Yanjie Fu, name: Qi Qian
Published: 2025-08-25 17:57:49

Vision-Language Models (VLMs) demonstrate impressive performance in
understanding visual content with language instruction by converting visual
input to vision tokens. However, redundancy in vision tokens results in the
degenerated inference efficiency of VLMs. While many algorithms have been
proposed to reduce the number of vision tokens, most of them apply only
unimodal information (i.e., vision/text) for pruning and ignore the inherent
multimodal property of vision-language tasks. Moreover, it lacks a generic
criterion that can be applied to different modalities. To mitigate this
limitation, in this work, we propose to leverage both vision and text tokens to
select informative vision tokens by the criterion of coverage. We first
formulate the subset selection problem as a maximum coverage problem.
Afterward, a subset of vision tokens is optimized to cover the text tokens and
the original set of vision tokens, simultaneously. Finally, a VLM agent can be
adopted to further improve the quality of text tokens for guiding vision
pruning. The proposed method MMTok is extensively evaluated on benchmark
datasets with different VLMs. The comparison illustrates that vision and text
information are complementary, and combining multimodal information can surpass
the unimodal baseline with a clear margin. Moreover, under the maximum coverage
criterion on the POPE dataset, our method achieves a 1.87x speedup while
maintaining 98.7% of the original performance on LLaVA-NeXT-13B. Furthermore,
with only four vision tokens, it still preserves 87.7% of the original
performance on LLaVA-1.5-7B. These results highlight the effectiveness of
coverage in token selection.
If We May De-Presuppose: Robustly Verifying Claims through
  Presupposition-Free Question Decomposition
Authors: name: Shubhashis Roy Dipta, name: Francis Ferraro
Published: 2025-08-22 23:34:24

Prior work has shown that presupposition in generated questions can introduce
unverified assumptions, leading to inconsistencies in claim verification.
Additionally, prompt sensitivity remains a significant challenge for large
language models (LLMs), resulting in performance variance as high as 3-6%.
While recent advancements have reduced this gap, our study demonstrates that
prompt sensitivity remains a persistent issue. To address this, we propose a
structured and robust claim verification framework that reasons through
presupposition-free, decomposed questions. Extensive experiments across
multiple prompts, datasets, and LLMs reveal that even state-of-the-art models
remain susceptible to prompt variance and presupposition. Our method
consistently mitigates these issues, achieving up to a 2-5% improvement.
REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage
  Generative Network Framework
Authors: name: Stefanos Pasios, name: Nikos Nikolaidis
Published: 2025-08-23 15:28:05

Photorealism is an important aspect of modern video games since it can shape
the player experience and simultaneously impact the immersion, narrative
engagement, and visual fidelity. Although recent hardware technological
breakthroughs, along with state-of-the-art rendering technologies, have
significantly improved the visual realism of video games, achieving true
photorealism in dynamic environments at real-time frame rates still remains a
major challenge due to the tradeoff between visual quality and performance. In
this short paper, we present a novel approach for enhancing the photorealism of
rendered game frames using generative adversarial networks. To this end, we
propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative
Network framework (REGEN), which employs a robust unpaired image-to-image
translation model to produce semantically consistent photorealistic frames that
transform the problem into a simpler paired image-to-image translation task.
This enables training with a lightweight method that can achieve real-time
inference time without compromising visual quality. We demonstrate the
effectiveness of our framework on Grand Theft Auto V, showing that the approach
achieves visual results comparable to the ones produced by the robust unpaired
Im2Im method while improving inference speed by 32.14 times. Our findings also
indicate that the results outperform the photorealism-enhanced frames produced
by directly training a lightweight unpaired Im2Im translation method to
translate the video game frames towards the visual characteristics of
real-world images. Code, pre-trained models, and demos for this work are
available at: https://github.com/stefanos50/REGEN.
MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian
  Splatting
Authors: name: Hanzhi Chang, name: Ruijie Zhu, name: Wenjie Chang, name: Mulin Yu, name: Yanzhe Liang, name: Jiahao Lu, name: Zhuoyuan Li, name: Tianzhu Zhang
Published: 2025-08-25 09:04:20

Surface reconstruction has been widely studied in computer vision and
graphics. However, existing surface reconstruction works struggle to recover
accurate scene geometry when the input views are extremely sparse. To address
this issue, we propose MeshSplat, a generalizable sparse-view surface
reconstruction framework via Gaussian Splatting. Our key idea is to leverage
2DGS as a bridge, which connects novel view synthesis to learned geometric
priors and then transfers these priors to achieve surface reconstruction.
Specifically, we incorporate a feed-forward network to predict per-view
pixel-aligned 2DGS, which enables the network to synthesize novel view images
and thus eliminates the need for direct 3D ground-truth supervision. To improve
the accuracy of 2DGS position and orientation prediction, we propose a Weighted
Chamfer Distance Loss to regularize the depth maps, especially in overlapping
areas of input views, and also a normal prediction network to align the
orientation of 2DGS with normal vectors predicted by a monocular normal
estimator. Extensive experiments validate the effectiveness of our proposed
improvement, demonstrating that our method achieves state-of-the-art
performance in generalizable sparse-view mesh reconstruction tasks. Project
Page: https://hanzhichang.github.io/meshsplat_web
Limitations of Normalization in Attention Mechanism
Authors: name: Timur Mudarisov, name: Mikhail Burtsev, name: Tatiana Petrova, name: Radu State
Published: 2025-08-25 09:25:05

This paper investigates the limitations of the normalization in attention
mechanisms. We begin with a theoretical framework that enables the
identification of the model's selective ability and the geometric separation
involved in token selection. Our analysis includes explicit bounds on distances
and separation criteria for token vectors under softmax scaling. Through
experiments with pre-trained GPT-2 model, we empirically validate our
theoretical results and analyze key behaviors of the attention mechanism.
Notably, we demonstrate that as the number of selected tokens increases, the
model's ability to distinguish informative tokens declines, often converging
toward a uniform selection pattern. We also show that gradient sensitivity
under softmax normalization presents challenges during training, especially at
low temperature settings. These findings advance current understanding of
softmax-based attention mechanism and motivate the need for more robust
normalization and selection strategies in future attention architectures.
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory
  and Test-Time Compute Scaling
Authors: name: Ivan Rodkin, name: Daniil Orel, name: Konstantin Smirnov, name: Arman Bolatov, name: Bilal Elbouardi, name: Besher Hassan, name: Yuri Kuratov, name: Aydar Bulatov, name: Preslav Nakov, name: Timothy Baldwin, name: Artem Shelmanov, name: Mikhail Burtsev
Published: 2025-08-22 18:57:08

Reasoning is a core capability of large language models, yet understanding
how they learn and perform multi-step reasoning remains an open problem. In
this study, we explore how different architectures and training methods affect
model multi-step reasoning capabilities within a cellular automata framework.
By training on state sequences generated with random Boolean functions for
random initial conditions to exclude memorization, we demonstrate that most
neural architectures learn to abstract the underlying rules. While models
achieve high accuracy in next-state prediction, their performance declines
sharply if multi-step reasoning is required. We confirm that increasing model
depth plays a crucial role for sequential computations. We demonstrate that an
extension of the effective model depth with recurrence, memory, and test-time
compute scaling substantially enhances reasoning capabilities.
German4All - A Dataset and Model for Readability-Controlled Paraphrasing
  in German
Authors: name: Miriam Anschütz, name: Thanh Mai Pham, name: Eslam Nasrallah, name: Maximilian Müller, name: Cristian-George Craciun, name: Georg Groh
Published: 2025-08-25 12:40:32

The ability to paraphrase texts across different complexity levels is
essential for creating accessible texts that can be tailored toward diverse
reader groups. Thus, we introduce German4All, the first large-scale German
dataset of aligned readability-controlled, paragraph-level paraphrases. It
spans five readability levels and comprises over 25,000 samples. The dataset is
automatically synthesized using GPT-4 and rigorously evaluated through both
human and LLM-based judgments. Using German4All, we train an open-source,
readability-controlled paraphrasing model that achieves state-of-the-art
performance in German text simplification, enabling more nuanced and
reader-specific adaptations. We opensource both the dataset and the model to
encourage further research on multi-level paraphrasing
MV-RAG: Retrieval Augmented Multiview Diffusion
Authors: name: Yosef Dayani, name: Omer Benishu, name: Sagie Benaim
Published: 2025-08-22 17:59:40

Text-to-3D generation approaches have advanced significantly by leveraging
pretrained 2D diffusion priors, producing high-quality and 3D-consistent
outputs. However, they often fail to produce out-of-domain (OOD) or rare
concepts, yielding inconsistent or inaccurate results. To this end, we propose
MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images
from a large in-the-wild 2D database and then conditions a multiview diffusion
model on these images to synthesize consistent and accurate multiview outputs.
Training such a retrieval-conditioned model is achieved via a novel hybrid
strategy bridging structured multiview data and diverse 2D image collections.
This involves training on multiview data using augmented conditioning views
that simulate retrieval variance for view-specific reconstruction, alongside
training on sets of retrieved real-world 2D images using a distinctive held-out
view prediction objective: the model predicts the held-out view from the other
views to infer 3D consistency from 2D data. To facilitate a rigorous OOD
evaluation, we introduce a new collection of challenging OOD prompts.
Experiments against state-of-the-art text-to-3D, image-to-3D, and
personalization baselines show that our approach significantly improves 3D
consistency, photorealism, and text adherence for OOD/rare concepts, while
maintaining competitive performance on standard benchmarks.
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for
  N-level Assessment
Authors: name: Omid Ghahroodi, name: Arshia Hemmat, name: Marzia Nouri, name: Seyed Mohammad Hadi Hosseini, name: Doratossadat Dastgheib, name: Mohammad Vali Sanian, name: Alireza Sahebi, name: Reihaneh Zohrabi, name: Mohammad Hossein Rohban, name: Ehsaneddin Asgari, name: Mahdieh Soleymani Baghshah
Published: 2025-08-24 10:32:37

Recent advancements in large vision-language models (VLMs) have primarily
focused on English, with limited attention given to other languages. To address
this gap, we introduce MEENA (also known as PersianMMMU), the first dataset
designed to evaluate Persian VLMs across scientific, reasoning, and human-level
understanding tasks. Our dataset comprises approximately 7,500 Persian and
3,000 English questions, covering a wide range of topics such as reasoning,
mathematics, physics, diagrams, charts, and Persian art and literature. Key
features of MEENA include: (1) diverse subject coverage spanning various
educational levels, from primary to upper secondary school, (2) rich metadata,
including difficulty levels and descriptive answers, (3) original Persian data
that preserves cultural nuances, (4) a bilingual structure to assess
cross-linguistic performance, and (5) a series of diverse experiments assessing
various capabilities, including overall performance, the model's ability to
attend to images, and its tendency to generate hallucinations. We hope this
benchmark contributes to enhancing VLM capabilities beyond English.
Explain Before You Answer: A Survey on Compositional Visual Reasoning
Authors: name: Fucai Ke, name: Joy Hsu, name: Zhixi Cai, name: Zixian Ma, name: Xin Zheng, name: Xindi Wu, name: Sukai Huang, name: Weiqing Wang, name: Pari Delir Haghighi, name: Gholamreza Haffari, name: Ranjay Krishna, name: Jiajun Wu, name: Hamid Rezatofighi
Published: 2025-08-24 11:01:51

Compositional visual reasoning has emerged as a key research frontier in
multimodal AI, aiming to endow machines with the human-like ability to
decompose visual scenes, ground intermediate concepts, and perform multi-step
logical inference. While early surveys focus on monolithic vision-language
models or general multimodal reasoning, a dedicated synthesis of the rapidly
expanding compositional visual reasoning literature is still missing. We fill
this gap with a comprehensive survey spanning 2023 to 2025 that systematically
reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We
first formalize core definitions and describe why compositional approaches
offer advantages in cognitive alignment, semantic fidelity, robustness,
interpretability, and data efficiency. Next, we trace a five-stage paradigm
shift: from prompt-enhanced language-centric pipelines, through tool-enhanced
LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and
unified agentic VLMs, highlighting their architectural designs, strengths, and
limitations. We then catalog 60+ benchmarks and corresponding metrics that
probe compositional visual reasoning along dimensions such as grounding
accuracy, chain-of-thought faithfulness, and high-resolution perception.
Drawing on these analyses, we distill key insights, identify open challenges
(e.g., limitations of LLM-based reasoning, hallucination, a bias toward
deductive reasoning, scalable supervision, tool integration, and benchmark
limitations), and outline future directions, including world-model integration,
human-AI collaborative reasoning, and richer evaluation protocols. By offering
a unified taxonomy, historical roadmap, and critical outlook, this survey aims
to serve as a foundational reference and inspire the next generation of
compositional visual reasoning research.
T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image
  Generation
Authors: name: Kaiyue Sun, name: Rongyao Fang, name: Chengqi Duan, name: Xian Liu, name: Xihui Liu
Published: 2025-08-24 17:59:38

We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of
text-to-image (T2I) models. It consists of four dimensions: Idiom
Interpretation, Textual Image Design, Entity-Reasoning and
Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the
reasoning accuracy and image quality. We benchmark various T2I generation
models, and provide comprehensive analysis on their performances.
Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement
  Learning for General LLM Reasoning
Authors: name: Yang Zhou, name: Sunzhu Li, name: Shunyu Liu, name: Wenkai Fang, name: Jiale Zhao, name: Jingwen Yang, name: Jianwei Lv, name: Kongcheng Zhang, name: Yihe Zhou, name: Hengtong Lu, name: Wei Chen, name: Yan Xie, name: Mingli Song
Published: 2025-08-23 08:47:31

Recent advances in Large Language Models (LLMs) have underscored the
potential of Reinforcement Learning (RL) to facilitate the emergence of
reasoning capabilities. Despite the encouraging results, a fundamental dilemma
persists as RL improvement relies on learning from high-quality samples, yet
the exploration for such samples remains bounded by the inherent limitations of
LLMs. This, in effect, creates an undesirable cycle in which what cannot be
explored cannot be learned. In this work, we propose Rubric-Scaffolded
Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework
designed to break the exploration bottleneck for general LLM reasoning.
Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit
scaffolding for exploration during rollout generation, where different rubrics
are provided as external guidance within task instructions to steer diverse
high-quality responses. This guidance is gradually decayed over time,
encouraging the model to internalize the underlying reasoning patterns; (2)
verifiable rewards for exploitation during model training, where we can obtain
robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL
on general reasoning tasks. Extensive experiments demonstrate the superiority
of the proposed RuscaRL across various benchmarks, effectively expanding
reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL
significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500,
surpassing GPT-4.1. Furthermore, our fine-tuned variant on
Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading
LLMs including OpenAI-o3.