Orionnnn/hub-stats huggingface dataset:
X^{2}-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time
  Tomographic Reconstruction
Authors: name: Weihao Yu, name: Yuanhao Cai, name: Ruyi Zha, name: Zhiwen Fan, name: Chenxin Li, name: Yixuan Yuan
Published: 2025-03-27 17:59:57

Four-dimensional computed tomography (4D CT) reconstruction is crucial for
capturing dynamic anatomical changes but faces inherent limitations from
conventional phase-binning workflows. Current methods discretize temporal
resolution into fixed phases with respiratory gating devices, introducing
motion misalignment and restricting clinical practicality. In this paper, We
propose X^2-Gaussian, a novel framework that enables continuous-time 4D-CT
reconstruction by integrating dynamic radiative Gaussian splatting with
self-supervised respiratory motion learning. Our approach models anatomical
dynamics through a spatiotemporal encoder-decoder architecture that predicts
time-varying Gaussian deformations, eliminating phase discretization. To remove
dependency on external gating devices, we introduce a physiology-driven
periodic consistency loss that learns patient-specific breathing cycles
directly from projections via differentiable optimization. Extensive
experiments demonstrate state-of-the-art performance, achieving a 9.93 dB PSNR
gain over traditional methods and 2.25 dB improvement against prior Gaussian
splatting techniques. By unifying continuous motion modeling with hardware-free
period learning, X^2-Gaussian advances high-fidelity 4D CT reconstruction for
dynamic clinical imaging. Project website at: https://x2-gaussian.github.io/.
A Survey of Efficient Reasoning for Large Reasoning Models: Language,
  Multimodality, and Beyond
Authors: name: Xiaoye Qu, name: Yafu Li, name: Zhaochen Su, name: Weigao Sun, name: Jianhao Yan, name: Dongrui Liu, name: Ganqu Cui, name: Daizong Liu, name: Shuxian Liang, name: Junxian He, name: Peng Li, name: Wei Wei, name: Jing Shao, name: Chaochao Lu, name: Yue Zhang, name: Xian-Sheng Hua, name: Bowen Zhou, name: Yu Cheng
Published: 2025-03-27 15:36:30

Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have
demonstrated strong performance gains by scaling up the length of
Chain-of-Thought (CoT) reasoning during inference. However, a growing concern
lies in their tendency to produce excessively long reasoning traces, which are
often filled with redundant content (e.g., repeated definitions), over-analysis
of simple problems, and superficial exploration of multiple reasoning paths for
harder tasks. This inefficiency introduces significant challenges for training,
inference, and real-world deployment (e.g., in agent-based systems), where
token economy is critical. In this survey, we provide a comprehensive overview
of recent efforts aimed at improving reasoning efficiency in LRMs, with a
particular focus on the unique challenges that arise in this new paradigm. We
identify common patterns of inefficiency, examine methods proposed across the
LRM lifecycle, i.e., from pretraining to inference, and discuss promising
future directions for research. To support ongoing development, we also
maintain a real-time GitHub repository tracking recent progress in the field.
We hope this survey serves as a foundation for further exploration and inspires
innovation in this rapidly evolving area.
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal
  Consistency
Authors: name: Tianqi Liu, name: Zihao Huang, name: Zhaoxi Chen, name: Guangcong Wang, name: Shoukang Hu, name: Liao Shen, name: Huiqiang Sun, name: Zhiguo Cao, name: Wei Li, name: Ziwei Liu
Published: 2025-03-26 17:59:44

We present Free4D, a novel tuning-free framework for 4D scene generation from
a single image. Existing methods either focus on object-level generation,
making scene-level generation infeasible, or rely on large-scale multi-view
video datasets for expensive training, with limited generalization ability due
to the scarcity of 4D scene data. In contrast, our key insight is to distill
pre-trained foundation models for consistent 4D scene representation, which
offers promising advantages such as efficiency and generalizability. 1) To
achieve this, we first animate the input image using image-to-video diffusion
models followed by 4D geometric structure initialization. 2) To turn this
coarse structure into spatial-temporal consistent multiview videos, we design
an adaptive guidance mechanism with a point-guided denoising strategy for
spatial consistency and a novel latent replacement strategy for temporal
coherence. 3) To lift these generated observations into consistent 4D
representation, we propose a modulation-based refinement to mitigate
inconsistencies while fully leveraging the generated information. The resulting
4D representation enables real-time, controllable rendering, marking a
significant advancement in single-image-based 4D scene generation.
MedAgent-Pro: Towards Multi-modal Evidence-based Medical Diagnosis via
  Reasoning Agentic Workflow
Authors: name: Ziyue Wang, name: Junde Wu, name: Chang Han Low, name: Yueming Jin
Published: 2025-03-21 14:04:18

Developing reliable AI systems to assist human clinicians in multi-modal
medical diagnosis has long been a key objective for researchers. Recently,
Multi-modal Large Language Models (MLLMs) have gained significant attention and
achieved success across various domains. With strong reasoning capabilities and
the ability to perform diverse tasks based on user instructions, they hold
great potential for enhancing medical diagnosis. However, directly applying
MLLMs to the medical domain still presents challenges. They lack detailed
perception of visual inputs, limiting their ability to perform quantitative
image analysis, which is crucial for medical diagnostics. Additionally, MLLMs
often exhibit hallucinations and inconsistencies in reasoning, whereas clinical
diagnoses must adhere strictly to established criteria. To address these
challenges, we propose MedAgent-Pro, an evidence-based reasoning agentic system
designed to achieve reliable, explainable, and precise medical diagnoses. This
is accomplished through a hierarchical workflow: at the task level,
knowledge-based reasoning generate reliable diagnostic plans for specific
diseases following retrieved clinical criteria. While at the case level,
multiple tool agents process multi-modal inputs, analyze different indicators
according to the plan, and provide a final diagnosis based on both quantitative
and qualitative evidence. Comprehensive experiments on both 2D and 3D medical
diagnosis tasks demonstrate the superiority and effectiveness of MedAgent-Pro,
while case studies further highlight its reliability and interpretability. The
code is available at https://github.com/jinlab-imvr/MedAgent-Pro.
SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
Authors: name: Xianglong He, name: Zi-Xin Zou, name: Chia-Hao Chen, name: Yuan-Chen Guo, name: Ding Liang, name: Chun Yuan, name: Wanli Ouyang, name: Yan-Pei Cao, name: Yangguang Li
Published: 2025-03-27 17:46:42

Creating high-fidelity 3D meshes with arbitrary topology, including open
surfaces and complex interiors, remains a significant challenge. Existing
implicit field methods often require costly and detail-degrading watertight
conversion, while other approaches struggle with high resolutions. This paper
introduces SparseFlex, a novel sparse-structured isosurface representation that
enables differentiable mesh reconstruction at resolutions up to 1024^3
directly from rendering losses. SparseFlex combines the accuracy of Flexicubes
with a sparse voxel structure, focusing computation on surface-adjacent regions
and efficiently handling open surfaces. Crucially, we introduce a frustum-aware
sectional voxel training strategy that activates only relevant voxels during
rendering, dramatically reducing memory consumption and enabling
high-resolution training. This also allows, for the first time, the
reconstruction of mesh interiors using only rendering supervision. Building
upon this, we demonstrate a complete shape modeling pipeline by training a
variational autoencoder (VAE) and a rectified flow transformer for high-quality
3D shape generation. Our experiments show state-of-the-art reconstruction
accuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in
F-score compared to previous methods, and demonstrate the generation of
high-resolution, detailed 3D shapes with arbitrary topology. By enabling
high-resolution, differentiable mesh reconstruction and generation with
rendering losses, SparseFlex significantly advances the state-of-the-art in 3D
shape representation and modeling.
A Refined Analysis of Massive Activations in LLMs
Authors: name: Louis Owen, name: Nilabhra Roy Chowdhury, name: Abhay Kumar, name: Fabian Güra
Published: 2025-03-28 11:08:34

Motivated in part by their relevance for low-precision training and
quantization, massive activations in large language models (LLMs) have recently
emerged as a topic of interest. However, existing analyses are limited in
scope, and generalizability across architectures is unclear. This paper helps
address some of these gaps by conducting an analysis of massive activations
across a broad range of LLMs, including both GLU-based and non-GLU-based
architectures. Our findings challenge several prior assumptions, most
importantly: (1) not all massive activations are detrimental, i.e. suppressing
them does not lead to an explosion of perplexity or a collapse in downstream
task performance; (2) proposed mitigation strategies such as Attention KV bias
are model-specific and ineffective in certain cases. We consequently
investigate novel hybrid mitigation strategies; in particular pairing Target
Variance Rescaling (TVR) with Attention KV bias or Dynamic Tanh (DyT)
successfully balances the mitigation of massive activations with preserved
downstream model performance in the scenarios we investigated. Our code is
available at: https://github.com/bluorion-com/refine_massive_activations.
PHYSICS: Benchmarking Foundation Models on University-Level Physics
  Problem Solving
Authors: name: Kaiyue Feng, name: Yilun Zhao, name: Yixin Liu, name: Tianyu Yang, name: Chen Zhao, name: John Sous, name: Arman Cohan
Published: 2025-03-26 06:21:56

We introduce PHYSICS, a comprehensive benchmark for university-level physics
problem solving. It contains 1297 expert-annotated problems covering six core
areas: classical mechanics, quantum mechanics, thermodynamics and statistical
mechanics, electromagnetism, atomic physics, and optics. Each problem requires
advanced physics knowledge and mathematical reasoning. We develop a robust
automated evaluation system for precise and reliable validation. Our evaluation
of leading foundation models reveals substantial limitations. Even the most
advanced model, o3-mini, achieves only 59.9% accuracy, highlighting significant
challenges in solving high-level scientific problems. Through comprehensive
error analysis, exploration of diverse prompting strategies, and
Retrieval-Augmented Generation (RAG)-based knowledge augmentation, we identify
key areas for improvement, laying the foundation for future advancements.
Segment Any Motion in Videos
Authors: name: Nan Huang, name: Wenzhao Zheng, name: Chenfeng Xu, name: Kurt Keutzer, name: Shanghang Zhang, name: Angjoo Kanazawa, name: Qianqian Wang
Published: 2025-03-28 09:34:11

Moving object segmentation is a crucial task for achieving a high-level
understanding of visual scenes and has numerous downstream applications. Humans
can effortlessly segment moving objects in videos. Previous work has largely
relied on optical flow to provide motion cues; however, this approach often
results in imperfect predictions due to challenges such as partial motion,
complex deformations, motion blur and background distractions. We propose a
novel approach for moving object segmentation that combines long-range
trajectory motion cues with DINO-based semantic features and leverages SAM2 for
pixel-level mask densification through an iterative prompting strategy. Our
model employs Spatio-Temporal Trajectory Attention and Motion-Semantic
Decoupled Embedding to prioritize motion while integrating semantic support.
Extensive testing on diverse datasets demonstrates state-of-the-art
performance, excelling in challenging scenarios and fine-grained segmentation
of multiple objects. Our code is available at https://motion-seg.github.io/.
Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal
  Bridging
Authors: name: Chongjie Ye, name: Yushuang Wu, name: Ziteng Lu, name: Jiahao Chang, name: Xiaoyang Guo, name: Jiaqing Zhou, name: Hao Zhao, name: Xiaoguang Han
Published: 2025-03-28 08:39:20

With the growing demand for high-fidelity 3D models from 2D images, existing
methods still face significant challenges in accurately reproducing
fine-grained geometric details due to limitations in domain gaps and inherent
ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel
framework for generating high-fidelity 3D geometry from images via normal
bridging. Hi3DGen consists of three key components: (1) an image-to-normal
estimator that decouples the low-high frequency image pattern with noise
injection and dual-stream training to achieve generalizable, stable, and sharp
estimation; (2) a normal-to-geometry learning approach that uses
normal-regularized latent diffusion learning to enhance 3D geometry generation
fidelity; and (3) a 3D data synthesis pipeline that constructs a high-quality
dataset to support training. Extensive experiments demonstrate the
effectiveness and superiority of our framework in generating rich geometric
details, outperforming state-of-the-art methods in terms of fidelity. Our work
provides a new direction for high-fidelity 3D geometry generation from images
by leveraging normal maps as an intermediate representation.
Exploring Data Scaling Trends and Effects in Reinforcement Learning from
  Human Feedback
Authors: name: Wei Shen, name: Guanlin Liu, name: Zheng Wu, name: Ruofei Zhu, name: Qingping Yang, name: Chao Xin, name: Yu Yue, name: Lin Yan
Published: 2025-03-28 08:26:41

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning
large language models with human preferences. While recent research has focused
on algorithmic improvements, the importance of prompt-data construction has
been overlooked. This paper addresses this gap by exploring data-driven
bottlenecks in RLHF performance scaling, particularly reward hacking and
decreasing response diversity. We introduce a hybrid reward system combining
reasoning task verifiers (RTV) and a generative reward model (GenRM) to
mitigate reward hacking. We also propose a novel prompt-selection method,
Pre-PPO, to maintain response diversity and enhance learning effectiveness.
Additionally, we find that prioritizing mathematical and coding tasks early in
RLHF training significantly improves performance. Experiments across two model
sizes validate our methods' effectiveness and scalability. Results show that
RTV is most resistant to reward hacking, followed by GenRM with ground truth,
and then GenRM with SFT Best-of-N responses. Our strategies enable rapid
capture of subtle task-specific distinctions, leading to substantial
improvements in overall RLHF performance. This work highlights the importance
of careful data construction and provides practical methods to overcome
performance barriers in RLHF.
ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
Authors: name: Yunhong Min, name: Daehyeon Choi, name: Kyeongmin Yeo, name: Jihyun Lee, name: Minhyuk Sung
Published: 2025-03-28 07:23:12

We introduce ORIGEN, the first zero-shot method for 3D orientation grounding
in text-to-image generation across multiple objects and diverse categories.
While previous work on spatial grounding in image generation has mainly focused
on 2D positioning, it lacks control over 3D orientation. To address this, we
propose a reward-guided sampling approach using a pretrained discriminative
model for 3D orientation estimation and a one-step text-to-image generative
flow model. While gradient-ascent-based optimization is a natural choice for
reward-based guidance, it struggles to maintain image realism. Instead, we
adopt a sampling-based approach using Langevin dynamics, which extends gradient
ascent by simply injecting random noise--requiring just a single additional
line of code. Additionally, we introduce adaptive time rescaling based on the
reward function to accelerate convergence. Our experiments show that ORIGEN
outperforms both training-based and test-time guidance methods across
quantitative metrics and user studies.
Think Before Recommend: Unleashing the Latent Reasoning Power for
  Sequential Recommendation
Authors: name: Jiakai Tang, name: Sunhao Dai, name: Teng Shi, name: Jun Xu, name: Xu Chen, name: Wen Chen, name: Wu Jian, name: Yuning Jiang
Published: 2025-03-28 17:59:03

Sequential Recommendation (SeqRec) aims to predict the next item by capturing
sequential patterns from users' historical interactions, playing a crucial role
in many real-world recommender systems. However, existing approaches
predominantly adopt a direct forward computation paradigm, where the final
hidden state of the sequence encoder serves as the user representation. We
argue that this inference paradigm, due to its limited computational depth,
struggles to model the complex evolving nature of user preferences and lacks a
nuanced understanding of long-tail items, leading to suboptimal performance. To
address this issue, we propose ReaRec, the first inference-time
computing framework for recommender systems, which enhances user
representations through implicit multi-step reasoning. Specifically, ReaRec
autoregressively feeds the sequence's last hidden state into the sequential
recommender while incorporating special reasoning position embeddings to
decouple the original item encoding space from the multi-step reasoning space.
Moreover, we introduce two lightweight reasoning-based learning methods,
Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to
further effectively exploit ReaRec's reasoning potential. Extensive experiments
on five public real-world datasets and different SeqRec architectures
demonstrate the generality and effectiveness of our proposed ReaRec.
Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the
performance ceiling of multiple sequential recommendation backbones by
approximately 30\%-50\%. Thus, we believe this work can open a new and
promising avenue for future research in inference-time computing for sequential
recommendation.
Tracktention: Leveraging Point Tracking to Attend Videos Faster and
  Better
Authors: name: Zihang Lai, name: Andrea Vedaldi
Published: 2025-03-25 17:58:48

Temporal consistency is critical in video prediction to ensure that outputs
are coherent and free of artifacts. Traditional methods, such as temporal
attention and 3D convolution, may struggle with significant object motion and
may not capture long-range temporal dependencies in dynamic scenes. To address
this gap, we propose the Tracktention Layer, a novel architectural component
that explicitly integrates motion information using point tracks, i.e.,
sequences of corresponding points across frames. By incorporating these motion
cues, the Tracktention Layer enhances temporal alignment and effectively
handles complex object motions, maintaining consistent feature representations
over time. Our approach is computationally efficient and can be seamlessly
integrated into existing models, such as Vision Transformers, with minimal
modification. It can be used to upgrade image-only models to state-of-the-art
video ones, sometimes outperforming models natively designed for video
prediction. We demonstrate this on video depth prediction and video
colorization, where models augmented with the Tracktention Layer exhibit
significantly improved temporal consistency compared to baselines.
Semantic Library Adaptation: LoRA Retrieval and Fusion for
  Open-Vocabulary Semantic Segmentation
Authors: name: Reza Qorbani, name: Gianluca Villani, name: Theodoros Panagiotakopoulos, name: Marc Botet Colomer, name: Linus Härenstam-Nielsen, name: Mattia Segu, name: Pier Luigi Dovesi, name: Jussi Karlgren, name: Daniel Cremers, name: Federico Tombari, name: Matteo Poggi
Published: 2025-03-27 17:59:58

Open-vocabulary semantic segmentation models associate vision and text to
label pixels from an undefined set of classes using textual queries, providing
versatile performance on novel datasets. However, large shifts between training
and test domains degrade their performance, requiring fine-tuning for effective
real-world applications. We introduce Semantic Library Adaptation (SemLA), a
novel framework for training-free, test-time domain adaptation. SemLA leverages
a library of LoRA-based adapters indexed with CLIP embeddings, dynamically
merging the most relevant adapters based on proximity to the target domain in
the embedding space. This approach constructs an ad-hoc model tailored to each
specific input without additional training. Our method scales efficiently,
enhances explainability by tracking adapter contributions, and inherently
protects data privacy, making it ideal for sensitive applications.
Comprehensive experiments on a 20-domain benchmark built over 10 standard
datasets demonstrate SemLA's superior adaptability and performance across
diverse settings, establishing a new standard in domain adaptation for
open-vocabulary semantic segmentation.
LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized
  Text-Guided Image Editing
Authors: name: Achint Soni, name: Meet Soni, name: Sirisha Rambhatla
Published: 2025-03-27 14:32:17

Text-guided image editing aims to modify specific regions of an image
according to natural language instructions while maintaining the general
structure and the background fidelity. Existing methods utilize masks derived
from cross-attention maps generated from diffusion models to identify the
target regions for modification. However, since cross-attention mechanisms
focus on semantic relevance, they struggle to maintain the image integrity. As
a result, these methods often lack spatial consistency, leading to editing
artifacts and distortions. In this work, we address these limitations and
introduce LOCATEdit, which enhances cross-attention maps through a graph-based
approach utilizing self-attention-derived patch relationships to maintain
smooth, coherent attention across image regions, ensuring that alterations are
limited to the designated items while retaining the surrounding structure.
\method consistently and substantially outperforms existing baselines on
PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on
various editing tasks. Code can be found on
https://github.com/LOCATEdit/LOCATEdit/