Orionnnn/hub-stats huggingface dataset:
RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth
  Segmentation in CBCT
Authors: name: Chuyu Zhao, name: Hao Huang, name: Jiashuo Guo, name: Ziyu Shen, name: Zhongwei Zhou, name: Jie Liu, name: Zekuan Yu
Published: 2025-05-06 13:50:57

Semi-supervised learning has become a compelling approach for 3D tooth
segmentation from CBCT scans, where labeled data is minimal. However, existing
methods still face two persistent challenges: limited corrective supervision in
structurally ambiguous or mislabeled regions during supervised training and
performance degradation caused by unreliable pseudo-labels on unlabeled data.
To address these problems, we propose Region-Aware Instructive Learning (RAIL),
a dual-group dual-student, semi-supervised framework. Each group contains two
student models guided by a shared teacher network. By alternating training
between the two groups, RAIL promotes intergroup knowledge transfer and
collaborative region-aware instruction while reducing overfitting to the
characteristics of any single model. Specifically, RAIL introduces two
instructive mechanisms. Disagreement-Focused Supervision (DFS) Controller
improves supervised learning by instructing predictions only within areas where
student outputs diverge from both ground truth and the best student, thereby
concentrating supervision on structurally ambiguous or mislabeled areas. In the
unsupervised phase, Confidence-Aware Learning (CAL) Modulator reinforces
agreement in regions with high model certainty while reducing the effect of
low-confidence predictions during training. This helps prevent our model from
learning unstable patterns and improves the overall reliability of
pseudo-labels. Extensive experiments on four CBCT tooth segmentation datasets
show that RAIL surpasses state-of-the-art methods under limited annotation. Our
code will be available at https://github.com/Tournesol-Saturday/RAIL.
Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI
  Knowledge Co-Creation
Authors: name: Xule Lin
Published: 2025-05-06 01:49:44

Scientific knowledge creation is fundamentally transforming as humans and AI
systems evolve beyond tool-user relationships into co-evolutionary epistemic
partnerships. When AlphaFold revolutionized protein structure prediction,
researchers described engaging with an epistemic partner that reshaped how they
conceptualized fundamental relationships. This article introduces Cognitio
Emergens (CE), a framework addressing critical limitations in existing models
that focus on static roles or narrow metrics while failing to capture how
scientific understanding emerges through recursive human-AI interaction over
time. CE integrates three components addressing these limitations: Agency
Configurations describing how authority distributes between humans and AI
(Directed, Contributory, Partnership), with partnerships dynamically
oscillating between configurations rather than following linear progression;
Epistemic Dimensions capturing six specific capabilities emerging through
collaboration across Discovery, Integration, and Projection axes, creating
distinctive "capability signatures" that guide development; and Partnership
Dynamics identifying forces shaping how these relationships evolve,
particularly the risk of epistemic alienation where researchers lose
interpretive control over knowledge they formally endorse. Drawing from
autopoiesis theory, social systems theory, and organizational modularity, CE
reveals how knowledge co-creation emerges through continuous negotiation of
roles, values, and organizational structures. By reconceptualizing human-AI
scientific collaboration as fundamentally co-evolutionary, CE offers a balanced
perspective that neither uncritically celebrates nor unnecessarily fears AI's
evolving role, instead providing conceptual tools for cultivating partnerships
that maintain meaningful human participation while enabling transformative
scientific breakthroughs.
OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub Issue
  Resolution
Authors: name: Lianghong Guo, name: Wei Tao, name: Runhan Jiang, name: Yanlin Wang, name: Jiachi Chen, name: Xilin Liu, name: Yuchi Ma, name: Mingzhi Mao, name: Hongyu Zhang, name: Zibin Zheng
Published: 2025-05-07 17:51:10

The GitHub issue resolution task aims to resolve issues reported in
repositories automatically. With advances in large language models (LLMs), this
task has gained increasing attention, and several benchmarks are proposed to
evaluate the issue resolution ability of LLMs. However, existing benchmarks
have three main limitations. First, current benchmarks focus on a single
programming language, limiting the evaluation of issues from repositories
across different languages. Second, they usually cover a narrow range of
domains, which may fail to represent the diversity of real-world issues. Third,
existing benchmarks rely solely on textual information in issue descriptions,
overlooking multimodal information such as images in issues. In this paper, we
propose OmniGIRL, a GitHub Issue ResoLution benchmark that is multilingual,
multimodal, and multi-domain. OmniGIRL includes 959 task instances, which are
collected from repositories across four programming languages (i.e., Python,
JavaScript, TypeScript, and Java) and eight different domains. Our evaluation
shows that current LLMs show limited performances on OmniGIRL. Notably, the
best-performing model, GPT-4o, resolves only 8.6% of the issues. Besides, we
find that current LLMs struggle to resolve issues requiring understanding
images. The best performance is achieved by Claude-3.5-Sonnet, which resolves
only 10.5% of the issues with image information. Finally, we analyze the
reasons behind current LLMs' failure on OmniGIRL, providing insights for future
improvements.
Beyond Recognition: Evaluating Visual Perspective Taking in Vision
  Language Models
Authors: name: Gracjan Góral, name: Alicja Ziarko, name: Piotr Miłoś, name: Michał Nauman, name: Maciej Wołczyk, name: Michał Kosiński
Published: 2025-05-03 00:10:41

We investigate the ability of Vision Language Models (VLMs) to perform visual
perspective taking using a novel set of visual tasks inspired by established
human tests. Our approach leverages carefully controlled scenes, in which a
single humanoid minifigure is paired with a single object. By systematically
varying spatial configurations - such as object position relative to the
humanoid minifigure and the humanoid minifigure's orientation - and using both
bird's-eye and surface-level views, we created 144 unique visual tasks. Each
visual task is paired with a series of 7 diagnostic questions designed to
assess three levels of visual cognition: scene understanding, spatial
reasoning, and visual perspective taking. Our evaluation of several
state-of-the-art models, including GPT-4-Turbo, GPT-4o,
Llama-3.2-11B-Vision-Instruct, and variants of Claude Sonnet, reveals that
while they excel in scene understanding, the performance declines significantly
on spatial reasoning and further deteriorates on perspective-taking. Our
analysis suggests a gap between surface-level object recognition and the deeper
spatial and perspective reasoning required for complex visual tasks, pointing
to the need for integrating explicit geometric representations and tailored
training protocols in future VLM development.
Unified Multimodal Understanding and Generation Models: Advances,
  Challenges, and Opportunities
Authors: name: Xinjie Zhang, name: Jintao Guo, name: Shanshan Zhao, name: Minghao Fu, name: Lunhao Duan, name: Guo-Hua Wang, name: Qing-Guo Chen, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-05-05 11:18:03

Recent years have seen remarkable progress in both multimodal understanding
models and image generation models. Despite their respective successes, these
two domains have evolved independently, leading to distinct architectural
paradigms: While autoregressive-based architectures have dominated multimodal
understanding, diffusion-based models have become the cornerstone of image
generation. Recently, there has been growing interest in developing unified
frameworks that integrate these tasks. The emergence of GPT-4o's new
capabilities exemplifies this trend, highlighting the potential for
unification. However, the architectural differences between the two domains
pose significant challenges. To provide a clear overview of current efforts
toward unification, we present a comprehensive survey aimed at guiding future
research. First, we introduce the foundational concepts and recent advancements
in multimodal understanding and text-to-image generation models. Next, we
review existing unified models, categorizing them into three main architectural
paradigms: diffusion-based, autoregressive-based, and hybrid approaches that
fuse autoregressive and diffusion mechanisms. For each category, we analyze the
structural designs and innovations introduced by related works. Additionally,
we compile datasets and benchmarks tailored for unified models, offering
resources for future exploration. Finally, we discuss the key challenges facing
this nascent field, including tokenization strategy, cross-modal attention, and
data. As this area is still in its early stages, we anticipate rapid
advancements and will regularly update this survey. Our goal is to inspire
further research and provide a valuable reference for the community. The
references associated with this survey are available on GitHub
(https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models).
OSUniverse: Benchmark for Multimodal GUI-navigation AI Agents
Authors: name: Mariya Davydova, name: Daniel Jeffries, name: Patrick Barker, name: Arturo Márquez Flores, name: Sinéad Ryan
Published: 2025-05-06 14:29:47

In this paper, we introduce OSUniverse: a benchmark of complex, multimodal
desktop-oriented tasks for advanced GUI-navigation AI agents that focuses on
ease of use, extensibility, comprehensive coverage of test cases, and automated
validation. We divide the tasks in increasing levels of complexity, from basic
precision clicking to multistep, multiapplication tests requiring dexterity,
precision, and clear thinking from the agent. In version one of the benchmark,
presented here, we have calibrated the complexity of the benchmark test cases
to ensure that the SOTA (State of the Art) agents (at the time of publication)
do not achieve results higher than 50%, while the average white collar worker
can perform all these tasks with perfect accuracy. The benchmark can be scored
manually, but we also introduce an automated validation mechanism that has an
average error rate less than 2%. Therefore, this benchmark presents solid
ground for fully automated measuring of progress, capabilities and the
effectiveness of GUI-navigation AI agents over the short and medium-term
horizon. The source code of the benchmark is available at
https://github.com/agentsea/osuniverse.
PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
  Auto-Regressive Transformer
Authors: name: Jingwen Ye, name: Yuze He, name: Yanning Zhou, name: Yiqin Zhu, name: Kaiwen Xiao, name: Yong-Jin Liu, name: Wei Yang, name: Xiao Han
Published: 2025-05-07 17:59:46

Shape primitive abstraction, which decomposes complex 3D shapes into simple
geometric elements, plays a crucial role in human visual cognition and has
broad applications in computer vision and graphics. While recent advances in 3D
content generation have shown remarkable progress, existing primitive
abstraction methods either rely on geometric optimization with limited semantic
understanding or learn from small-scale, category-specific datasets, struggling
to generalize across diverse shape categories. We present PrimitiveAnything, a
novel framework that reformulates shape primitive abstraction as a primitive
assembly generation task. PrimitiveAnything includes a shape-conditioned
primitive transformer for auto-regressive generation and an ambiguity-free
parameterization scheme to represent multiple types of primitives in a unified
manner. The proposed framework directly learns the process of primitive
assembly from large-scale human-crafted abstractions, enabling it to capture
how humans decompose complex shapes into primitive elements. Through extensive
experiments, we demonstrate that PrimitiveAnything can generate high-quality
primitive assemblies that better align with human perception while maintaining
geometric fidelity across diverse shape categories. It benefits various 3D
applications and shows potential for enabling primitive-based user-generated
content (UGC) in games. Project page: https://primitiveanything.github.io
R&B: Domain Regrouping and Data Mixture Balancing for Efficient
  Foundation Model Training
Authors: name: Albert Ge, name: Tzu-Heng Huang, name: John Cooper, name: Avi Trost, name: Ziyi Chu, name: Satya Sai Srinath Namburi GNVV, name: Ziyang Cai, name: Kendall Park, name: Nicholas Roberts, name: Frederic Sala
Published: 2025-05-01 07:08:19

Data mixing strategies have successfully reduced the costs involved in
training language models. While promising, such methods suffer from two flaws.
First, they rely on predetermined data domains (e.g., data sources, task
types), which may fail to capture critical semantic nuances, leaving
performance on the table. Second, these methods scale with the number of
domains in a computationally prohibitive way. We address these challenges via
R&B, a framework that re-partitions training data based on semantic similarity
(Regroup) to create finer-grained domains, and efficiently optimizes the data
composition (Balance) by leveraging a Gram matrix induced by domain gradients
obtained throughout training. Unlike prior works, it removes the need for
additional compute to obtain evaluation information such as losses or
gradients. We analyze this technique under standard regularity conditions and
provide theoretical insights that justify R&B's effectiveness compared to
non-adaptive mixing approaches. Empirically, we demonstrate the effectiveness
of R&B on five diverse datasets ranging from natural language to reasoning and
multimodal tasks. With as little as 0.01% additional compute overhead, R&B
matches or exceeds the performance of state-of-the-art data mixing strategies.
OpenHelix: A Short Survey, Empirical Analysis, and Open-Source
  Dual-System VLA Model for Robotic Manipulation
Authors: name: Can Cui, name: Pengxiang Ding, name: Wenxuan Song, name: Shuanghao Bai, name: Xinyang Tong, name: Zirui Ge, name: Runze Suo, name: Wanqi Zhou, name: Yang Liu, name: Bofang Jia, name: Han Zhao, name: Siteng Huang, name: Donglin Wang
Published: 2025-05-06 18:35:07

Dual-system VLA (Vision-Language-Action) architectures have become a hot
topic in embodied intelligence research, but there is a lack of sufficient
open-source work for further performance analysis and optimization. To address
this problem, this paper will summarize and compare the structural designs of
existing dual-system architectures, and conduct systematic empirical
evaluations on the core design elements of existing dual-system architectures.
Ultimately, it will provide a low-cost open-source model for further
exploration. Of course, this project will continue to update with more
experimental conclusions and open-source models with improved performance for
everyone to choose from. Project page: https://openhelix-robot.github.io/.
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
  Generation
Authors: name: Teng Hu, name: Zhentao Yu, name: Zhengguang Zhou, name: Sen Liang, name: Yuan Zhou, name: Qin Lin, name: Qinglin Lu
Published: 2025-05-07 15:33:18

Customized video generation aims to produce videos featuring specific
subjects under flexible user-defined conditions, yet existing methods often
struggle with identity consistency and limited input modalities. In this paper,
we propose HunyuanCustom, a multi-modal customized video generation framework
that emphasizes subject consistency while supporting image, audio, video, and
text conditions. Built upon HunyuanVideo, our model first addresses the
image-text conditioned generation task by introducing a text-image fusion
module based on LLaVA for enhanced multi-modal understanding, along with an
image ID enhancement module that leverages temporal concatenation to reinforce
identity features across frames. To enable audio- and video-conditioned
generation, we further propose modality-specific condition injection
mechanisms: an AudioNet module that achieves hierarchical alignment via spatial
cross-attention, and a video-driven injection module that integrates
latent-compressed conditional video through a patchify-based feature-alignment
network. Extensive experiments on single- and multi-subject scenarios
demonstrate that HunyuanCustom significantly outperforms state-of-the-art open-
and closed-source methods in terms of ID consistency, realism, and text-video
alignment. Moreover, we validate its robustness across downstream tasks,
including audio and video-driven customized video generation. Our results
highlight the effectiveness of multi-modal conditioning and identity-preserving
strategies in advancing controllable video generation. All the code and models
are available at https://hunyuancustom.github.io.
Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal
  Problem-Solving
Authors: name: Qi Liu, name: Xinhao Zheng, name: Renqiu Xia, name: Xingzhi Qi, name: Qinxiang Cao, name: Junchi Yan
Published: 2025-05-07 16:02:14

As a seemingly self-explanatory task, problem-solving has been a significant
component of science and engineering. However, a general yet concrete
formulation of problem-solving itself is missing. With the recent development
of AI-based problem-solving agents, the demand for process-level verifiability
is rapidly increasing yet underexplored. To fill these gaps, we present a
principled formulation of problem-solving as a deterministic Markov decision
process; a novel framework, FPS (Formal Problem-Solving), which utilizes
existing FTP (formal theorem proving) environments to perform process-verified
problem-solving; and D-FPS (Deductive FPS), decoupling solving and answer
verification for better human-alignment. The expressiveness, soundness and
completeness of the frameworks are proven. We construct three benchmarks on
problem-solving: FormalMath500, a formalization of a subset of the MATH500
benchmark; MiniF2F-Solving and PutnamBench-Solving, adaptations of FTP
benchmarks MiniF2F and PutnamBench. For faithful, interpretable, and
human-aligned evaluation, we propose RPE (Restricted Propositional
Equivalence), a symbolic approach to determine the correctness of answers by
formal verification. We evaluate four prevalent FTP models and two prompting
methods as baselines, solving at most 23.77% of FormalMath500, 27.47% of
MiniF2F-Solving, and 0.31% of PutnamBench-Solving.
Knowledge Augmented Complex Problem Solving with Large Language Models:
  A Survey
Authors: name: Da Zheng, name: Lun Du, name: Junwei Su, name: Yuchen Tian, name: Yuqi Zhu, name: Jintian Zhang, name: Lanning Wei, name: Ningyu Zhang, name: Huajun Chen
Published: 2025-05-06 10:53:58

Problem-solving has been a fundamental driver of human progress in numerous
domains. With advancements in artificial intelligence, Large Language Models
(LLMs) have emerged as powerful tools capable of tackling complex problems
across diverse domains. Unlike traditional computational systems, LLMs combine
raw computational power with an approximation of human reasoning, allowing them
to generate solutions, make inferences, and even leverage external
computational tools. However, applying LLMs to real-world problem-solving
presents significant challenges, including multi-step reasoning, domain
knowledge integration, and result verification. This survey explores the
capabilities and limitations of LLMs in complex problem-solving, examining
techniques including Chain-of-Thought (CoT) reasoning, knowledge augmentation,
and various LLM-based and tool-based verification techniques. Additionally, we
highlight domain-specific challenges in various domains, such as software
engineering, mathematical reasoning and proving, data analysis and modeling,
and scientific research. The paper further discusses the fundamental
limitations of the current LLM solutions and the future directions of LLM-based
complex problems solving from the perspective of multi-step reasoning, domain
knowledge integration and result verification.
Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly
  Detection
Authors: name: Sungheon Jeong, name: Jihong Park, name: Mohsen Imani
Published: 2025-05-05 06:33:20

Most existing video anomaly detectors rely solely on RGB frames, which lack
the temporal resolution needed to capture abrupt or transient motion cues, key
indicators of anomalous events. To address this limitation, we propose
Image-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that
synthesizes event representations directly from RGB videos and fuses them with
image features through a principled, uncertainty-aware process. The system (i)
models heavy-tailed sensor noise with a Student`s-t likelihood, deriving
value-level inverse-variance weights via a Laplace approximation; (ii) applies
Kalman-style frame-wise updates to balance modalities over time; and (iii)
iteratively refines the fused latent state to erase residual cross-modal noise.
Without any dedicated event sensor or frame-level labels, IEF-VAD sets a new
state of the art across multiple real-world anomaly detection benchmarks. These
findings highlight the utility of synthetic event representations in
emphasizing motion cues that are often underrepresented in RGB frames, enabling
accurate and robust video understanding across diverse applications without
requiring dedicated event sensors. Code and models are available at
https://github.com/EavnJeong/IEF-VAD.
Benchmarking LLMs' Swarm intelligence
Authors: name: Kai Ruan, name: Mowen Huang, name: Ji-Rong Wen, name: Hao Sun
Published: 2025-05-07 12:32:01

Large Language Models (LLMs) show potential for complex reasoning, yet their
capacity for emergent coordination in Multi-Agent Systems (MAS) when operating
under strict constraints-such as limited local perception and communication,
characteristic of natural swarms-remains largely unexplored, particularly
concerning the nuances of swarm intelligence. Existing benchmarks often do not
fully capture the unique challenges of decentralized coordination that arise
when agents operate with incomplete spatio-temporal information. To bridge this
gap, we introduce SwarmBench, a novel benchmark designed to systematically
evaluate the swarm intelligence capabilities of LLMs acting as decentralized
agents. SwarmBench features five foundational MAS coordination tasks within a
configurable 2D grid environment, forcing agents to rely primarily on local
sensory input (k x k view) and local communication. We propose metrics for
coordination effectiveness and analyze emergent group dynamics. Evaluating
several leading LLMs in a zero-shot setting, we find significant performance
variations across tasks, highlighting the difficulties posed by local
information constraints. While some coordination emerges, results indicate
limitations in robust planning and strategy formation under uncertainty in
these decentralized scenarios. Assessing LLMs under swarm-like conditions is
crucial for realizing their potential in future decentralized systems. We
release SwarmBench as an open, extensible toolkit-built upon a customizable and
scalable physical system with defined mechanical properties. It provides
environments, prompts, evaluation scripts, and the comprehensive experimental
datasets generated, aiming to foster reproducible research into LLM-based MAS
coordination and the theoretical underpinnings of Embodied MAS. Our code
repository is available at https://github.com/x66ccff/swarmbench.