Orionnnn/hub-stats huggingface dataset:
Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
Authors: name: Jiatong Li, name: Weida Wang, name: Qinggang Zhang, name: Junxian Li, name: Di Zhang, name: Changmeng Zheng, name: Shufei Zhang, name: Xiaoyong Wei, name: Qing Li
Published: 2025-08-11 18:50:05

Large language models (LLMs), especially Explicit Long Chain-of-Thought (CoT)
reasoning models like DeepSeek-R1 and QWQ, have demonstrated powerful reasoning
capabilities, achieving impressive performance in commonsense reasoning and
mathematical inference. Despite their effectiveness, Long-CoT reasoning models
are often criticized for their limited ability and low efficiency in
knowledge-intensive domains such as molecule discovery. Success in this field
requires a precise understanding of domain knowledge, including molecular
structures and chemical principles, which is challenging due to the inherent
complexity of molecular data and the scarcity of high-quality expert
annotations. To bridge this gap, we introduce Mol-R1, a novel framework
designed to improve explainability and reasoning performance of R1-like
Explicit Long-CoT reasoning LLMs in text-based molecule generation. Our
approach begins with a high-quality reasoning dataset curated through Prior
Regulation via In-context Distillation (PRID), a dedicated distillation
strategy to effectively generate paired reasoning traces guided by prior
regulations. Building upon this, we introduce MoIA, Molecular Iterative
Adaptation, a sophisticated training strategy that iteratively combines
Supervised Fine-tuning (SFT) with Reinforced Policy Optimization (RPO),
tailored to boost the reasoning performance of R1-like reasoning models for
molecule discovery. Finally, we examine the performance of Mol-R1 in the
text-based molecule reasoning generation task, showing superior performance
against existing baselines.
MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math
  Reasoning in Multimodal Large Language Models
Authors: name: Jun Feng, name: Zixin Wang, name: Zhentao Zhang, name: Yue Guo, name: Zhihan Zhou, name: Xiuyi Chen, name: Zhenyang Li, name: Dawei Yin
Published: 2025-08-08 04:39:16

Multimodal Large Language Models (MLLMs) have demonstrated remarkable
capabilities in visual mathematical reasoning across various existing
benchmarks. However, these benchmarks are predominantly based on clean or
processed multimodal inputs, without incorporating the images provided by
real-world Kindergarten through 12th grade (K-12) educational users. To address
this gap, we introduce MathReal, a meticulously curated dataset comprising
2,000 mathematical questions with images captured by handheld mobile devices in
authentic scenarios. Each question is an image, containing the question text
and visual element. We systematically classify the real images into three
primary categories: image quality degradation, perspective variation, and
irrelevant content interference, which are further delineated into 14
subcategories. Additionally, MathReal spans five core knowledge and ability
categories, which encompass three question types and are divided into three
difficulty levels. To comprehensively evaluate the multimodal mathematical
reasoning abilities of state-of-the-art MLLMs in real-world scenarios, we
design six experimental settings that enable a systematic analysis of their
performance. Through extensive experimentation, we find that the
problem-solving abilities of existing MLLMs are significantly challenged in
realistic educational contexts. Based on this, we conduct a thorough analysis
of their performance and error patterns, providing insights into their
recognition, comprehension, and reasoning capabilities, and outlining
directions for future improvements. Data and code:
https://github.com/junfeng0288/MathReal.
Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning
  for Large Language Models
Authors: name: Haitao Hong, name: Yuchen Yan, name: Xingyu Wu, name: Guiyang Hou, name: Wenqi Zhang, name: Weiming Lu, name: Yongliang Shen, name: Jun Xiao
Published: 2025-08-07 17:53:56

Large language models (LLMs) have demonstrated remarkable performance in
reasoning tasks, where reinforcement learning (RL) serves as a key algorithm
for enhancing their reasoning capabilities. Currently, there are two mainstream
reward paradigms: model-based rewards and rule-based rewards. However, both
approaches suffer from limitations: rule-based rewards lack robustness, while
model-based rewards are vulnerable to reward hacking. To address these issues,
we propose Cooper(Co-optimizing Policy Model and Reward Model), a RL framework
that jointly optimizes both the policy model and the reward model. Cooper
leverages the high precision of rule-based rewards when identifying correct
responses, and dynamically constructs and selects positive-negative sample
pairs for continued training the reward model. This design enhances robustness
and mitigates the risk of reward hacking. To further support Cooper, we
introduce a hybrid annotation strategy that efficiently and accurately
generates training data for the reward model. We also propose a reference-based
reward modeling paradigm, where the reward model takes a reference answer as
input. Based on this design, we train a reward model named VerifyRM, which
achieves higher accuracy on VerifyBench compared to other models of the same
size. We conduct reinforcement learning using both VerifyRM and Cooper. Our
experiments show that Cooper not only alleviates reward hacking but also
improves end-to-end RL performance, for instance, achieving a 0.54% gain in
average accuracy on Qwen2.5-1.5B-Instruct. Our findings demonstrate that
dynamically updating reward model is an effective way to combat reward hacking,
providing a reference for better integrating reward models into RL.
AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust
  GAIA Problem Solving
Authors: name: Zhitian Xie, name: Qintong Wu, name: Chengyue Yu, name: Chenyi Zhuang, name: Jinjie Gu
Published: 2025-08-13 15:46:25

The rapid advancement of large language models (LLMs) has empowered
intelligent agents to leverage diverse external tools for solving complex
real-world problems. However, as agents increasingly depend on multiple tools,
they encounter new challenges: extended contexts from disparate sources and
noisy or irrelevant tool outputs can undermine system reliability and accuracy.
These challenges underscore the necessity for enhanced stability in agent-based
systems. To address this, we introduce dynamic supervision and maneuvering
mechanisms, constructing a robust and dynamic Multi-Agent System (MAS)
architecture within the AWorld framework. In our approach, the Execution Agent
invokes the Guard Agent at critical steps to verify and correct the reasoning
process, effectively reducing errors arising from noise and bolstering
problem-solving robustness. Extensive experiments on the GAIA test dataset
reveal that our dynamic maneuvering mechanism significantly improves both the
effectiveness and stability of solutions, outperforming single-agent system
(SAS) and standard tool-augmented systems. As a result, our dynamic MAS system
achieved first place among open-source projects on the prestigious GAIA
leaderboard. These findings highlight the practical value of collaborative
agent roles in developing more reliable and trustworthy intelligent systems.
StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation
Authors: name: Shuyuan Tu, name: Yueming Pan, name: Yinming Huang, name: Xintong Han, name: Zhen Xing, name: Qi Dai, name: Chong Luo, name: Zuxuan Wu, name: Yu-Gang Jiang
Published: 2025-08-11 17:58:24

Current diffusion models for audio-driven avatar video generation struggle to
synthesize long videos with natural audio synchronization and identity
consistency. This paper presents StableAvatar, the first end-to-end video
diffusion transformer that synthesizes infinite-length high-quality videos
without post-processing. Conditioned on a reference image and audio,
StableAvatar integrates tailored training and inference modules to enable
infinite-length video generation. We observe that the main reason preventing
existing models from generating long videos lies in their audio modeling. They
typically rely on third-party off-the-shelf extractors to obtain audio
embeddings, which are then directly injected into the diffusion model via
cross-attention. Since current diffusion backbones lack any audio-related
priors, this approach causes severe latent distribution error accumulation
across video clips, leading the latent distribution of subsequent segments to
drift away from the optimal distribution gradually. To address this,
StableAvatar introduces a novel Time-step-aware Audio Adapter that prevents
error accumulation via time-step-aware modulation. During inference, we propose
a novel Audio Native Guidance Mechanism to further enhance the audio
synchronization by leveraging the diffusion's own evolving joint audio-latent
prediction as a dynamic guidance signal. To enhance the smoothness of the
infinite-length videos, we introduce a Dynamic Weighted Sliding-window Strategy
that fuses latent over time. Experiments on benchmarks show the effectiveness
of StableAvatar both qualitatively and quantitatively.
AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of
  Visuomotor Policies
Authors: name: Yinpei Dai, name: Jayjun Lee, name: Yichi Zhang, name: Ziqiao Ma, name: Jed Yang, name: Amir Zadeh, name: Chuan Li, name: Nima Fazeli, name: Joyce Chai
Published: 2025-08-11 15:53:23

In this paper, we propose AimBot, a lightweight visual augmentation technique
that provides explicit spatial cues to improve visuomotor policy learning in
robotic manipulation. AimBot overlays shooting lines and scope reticles onto
multi-view RGB images, offering auxiliary visual guidance that encodes the
end-effector's state. The overlays are computed from depth images, camera
extrinsics, and the current end-effector pose, explicitly conveying spatial
relationships between the gripper and objects in the scene. AimBot incurs
minimal computational overhead (less than 1 ms) and requires no changes to
model architectures, as it simply replaces original RGB images with augmented
counterparts. Despite its simplicity, our results show that AimBot consistently
improves the performance of various visuomotor policies in both simulation and
real-world settings, highlighting the benefits of spatially grounded visual
feedback.
Putnam-AXIOM: A Functional and Static Benchmark
Authors: name: Aryan Gulati, name: Brando Miranda, name: Eric Chen, name: Emily Xia, name: Kai Fronsdal, name: Bruno Dumont, name: Elyas Obbad, name: Sanmi Koyejo
Published: 2025-08-05 17:57:50

Current mathematical reasoning benchmarks for large language models (LLMs)
are approaching saturation, with some achieving > 90% accuracy, and are
increasingly compromised by training-set contamination. We introduce
Putnam-AXIOM, a benchmark of 522 university-level competition problems drawn
from the prestigious William Lowell Putnam Mathematical Competition, and
Putnam-AXIOM Variation, an unseen companion set of 100 functional variants
generated by programmatically perturbing variables and constants. The variation
protocol produces an unlimited stream of equally difficult, unseen instances --
yielding a contamination-resilient test bed. On the Original set, OpenAI's
o1-preview -- the strongest evaluated model -- scores 41.9%, but its accuracy
drops by 19.6% (46.8% relative decrease) on the paired Variations. The
remaining eighteen models show the same downward trend, ten of them with
non-overlapping 95% confidence intervals. These gaps suggest memorization and
highlight the necessity of dynamic benchmarks. We complement "boxed" accuracy
with Teacher-Forced Accuracy (TFA), a lightweight metric that directly scores
reasoning traces and automates natural language proof evaluations. Putnam-AXIOM
therefore provides a rigorous, contamination-resilient evaluation framework for
assessing advanced mathematical reasoning of LLMs. Data and evaluation code are
publicly available at https://github.com/brando90/putnam-axiom.
ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software
  Assistants
Authors: name: Xiangzhe Xu, name: Guangyu Shen, name: Zian Su, name: Siyuan Cheng, name: Hanxi Guo, name: Lu Yan, name: Xuan Chen, name: Jiasheng Jiang, name: Xiaolong Jin, name: Chengpeng Wang, name: Zhuo Zhang, name: Xiangyu Zhang
Published: 2025-08-05 21:57:52

AI coding assistants like GitHub Copilot are rapidly transforming software
development, but their safety remains deeply uncertain-especially in
high-stakes domains like cybersecurity. Current red-teaming tools often rely on
fixed benchmarks or unrealistic prompts, missing many real-world
vulnerabilities. We present ASTRA, an automated agent system designed to
systematically uncover safety flaws in AI-driven code generation and security
guidance systems. ASTRA works in three stages: (1) it builds structured
domain-specific knowledge graphs that model complex software tasks and known
weaknesses; (2) it performs online vulnerability exploration of each target
model by adaptively probing both its input space, i.e., the spatial
exploration, and its reasoning processes, i.e., the temporal exploration,
guided by the knowledge graphs; and (3) it generates high-quality
violation-inducing cases to improve model alignment. Unlike prior methods,
ASTRA focuses on realistic inputs-requests that developers might actually
ask-and uses both offline abstraction guided domain modeling and online domain
knowledge graph adaptation to surface corner-case vulnerabilities. Across two
major evaluation domains, ASTRA finds 11-66% more issues than existing
techniques and produces test cases that lead to 17% more effective alignment
training, showing its practical value for building safer AI systems.
Text-conditioned State Space Model For Domain-generalized Change
  Detection Visual Question Answering
Authors: name: Elman Ghazaei, name: Erchan Aptoula
Published: 2025-08-12 14:37:53

The Earth's surface is constantly changing, and detecting these changes
provides valuable insights that benefit various aspects of human society. While
traditional change detection methods have been employed to detect changes from
bi-temporal images, these approaches typically require expert knowledge for
accurate interpretation. To enable broader and more flexible access to change
information by non-expert users, the task of Change Detection Visual Question
Answering (CDVQA) has been introduced. However, existing CDVQA methods have
been developed under the assumption that training and testing datasets share
similar distributions. This assumption does not hold in real-world
applications, where domain shifts often occur. In this paper, the CDVQA task is
revisited with a focus on addressing domain shift. To this end, a new
multi-modal and multi-domain dataset, BrightVQA, is introduced to facilitate
domain generalization research in CDVQA. Furthermore, a novel state space
model, termed Text-Conditioned State Space Model (TCSSM), is proposed. The
TCSSM framework is designed to leverage both bi-temporal imagery and
geo-disaster-related textual information in an unified manner to extract
domain-invariant features across domains. Input-dependent parameters existing
in TCSSM are dynamically predicted by using both bi-temporal images and
geo-disaster-related description, thereby facilitating the alignment between
bi-temporal visual data and the associated textual descriptions. Extensive
experiments are conducted to evaluate the proposed method against
state-of-the-art models, and superior performance is consistently demonstrated.
The code and dataset will be made publicly available upon acceptance at
https://github.com/Elman295/TCSSM.
RedDino: A foundation model for red blood cell analysis
Authors: name: Luca Zedda, name: Andrea Loddo, name: Cecilia Di Ruberto, name: Carsten Marr
Published: 2025-08-11 16:59:31

Red blood cells (RBCs) are essential to human health, and their precise
morphological analysis is important for diagnosing hematological disorders.
Despite the promise of foundation models in medical diagnostics, comprehensive
AI solutions for RBC analysis remain scarce. We present RedDino, a
self-supervised foundation model designed for RBC image analysis. RedDino uses
an RBC-specific adaptation of the DINOv2 self-supervised learning framework and
is trained on a curated dataset of 1.25 million RBC images from diverse
acquisition modalities and sources. Extensive evaluations show that RedDino
outperforms existing state-of-the-art models on RBC shape classification.
Through assessments including linear probing and nearest neighbor
classification, we confirm its strong feature representations and
generalization ability. Our main contributions are: (1) a foundation model
tailored for RBC analysis, (2) ablation studies exploring DINOv2 configurations
for RBC modeling, and (3) a detailed evaluation of generalization performance.
RedDino addresses key challenges in computational hematology by capturing
nuanced morphological features, advancing the development of reliable
diagnostic tools. The source code and pretrained models for RedDino are
available at https://github.com/Snarci/RedDino, and the pretrained models can
be downloaded from our Hugging Face collection at
https://huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
Complex Logical Instruction Generation
Authors: name: Mian Zhang, name: Shujian Liu, name: Sixun Dong, name: Ming Yin, name: Yebowen Hu, name: Xun Wang, name: Steven Ma, name: Song Wang, name: Sathish Reddy Indurthi, name: Haoyun Deng, name: Zhiyu Zoey Chen, name: Kaiqiang Song
Published: 2025-08-12 17:54:27

Instruction following has catalyzed the recent era of Large Language Models
(LLMs) and is the foundational skill underpinning more advanced capabilities
such as reasoning and agentic behaviors. As tasks grow more challenging, the
logic structures embedded in natural language instructions becomes increasingly
intricate. However, how well LLMs perform on such logic-rich instructions
remains under-explored. We propose LogicIFGen and LogicIFEval. LogicIFGen is a
scalable, automated framework for generating verifiable instructions from code
functions, which can naturally express rich logic such as conditionals,
nesting, recursion, and function calls. We further curate a collection of
complex code functions and use LogicIFGen to construct LogicIFEval, a benchmark
comprising 426 verifiable logic-rich instructions. Our experiments demonstrate
that current state-of-the-art LLMs still struggle to correctly follow the
instructions in LogicIFEval. Most LLMs can only follow fewer than 60% of the
instructions, revealing significant deficiencies in the instruction-following
ability. Code and Benchmark: https://github.com/mianzhang/LogicIF
Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
Authors: name: Brendan R. Hogan, name: Will Brown, name: Adel Boyarsky, name: Anderson Schneider, name: Yuriy Nevmyvaka
Published: 2025-08-09 04:22:07

Even though large language models are becoming increasingly capable, it is
still unreasonable to expect them to excel at tasks that are under-represented
on the Internet. Leveraging LLMs for specialized applications, particularly in
niche programming languages and private domains, remains challenging and
largely unsolved. In this work, we address this gap by presenting a
comprehensive, open-source approach for adapting LLMs to the Q programming
language, a popular tool in quantitative finance that is much less present on
the Internet compared to Python, C, Java, and other ``mainstream" languages and
is therefore not a strong suit of general-purpose AI models. We introduce a new
Leetcode style evaluation dataset for Q, benchmark major frontier models on the
dataset, then do pretraining, supervised fine tuning, and reinforcement
learning to train a suite of reasoning and non-reasoning models based on the
Qwen-2.5 series, spanning five parameter sizes (1.5B, 3B, 7B, 14B, 32B). Our
best model achieves a pass@1 accuracy of 59 percent on our Q benchmark,
surpassing the best-performing frontier model, Claude Opus-4 by 29.5 percent.
Additionally, all models, even our 1.5B model, outperform GPT-4.1 on this task.
In addition to releasing models, code, and data, we provide a detailed
blueprint for dataset construction, model pretraining, supervised fine-tuning,
and reinforcement learning. Our methodology is broadly applicable, and we
discuss how these techniques can be extended to other tasks, including those
where evaluation may rely on soft or subjective signals.
Optimization-Free Style Transfer for 3D Gaussian Splats
Authors: name: Raphael Du Sablon, name: David Hart
Published: 2025-08-07 19:35:01

The task of style transfer for 3D Gaussian splats has been explored in many
previous works, but these require reconstructing or fine-tuning the splat while
incorporating style information or optimizing a feature extraction network on
the splat representation. We propose a reconstruction- and optimization-free
approach to stylizing 3D Gaussian splats. This is done by generating a graph
structure across the implicit surface of the splat representation. A
feed-forward, surface-based stylization method is then used and interpolated
back to the individual splats in the scene. This allows for any style image and
3D Gaussian splat to be used without any additional training or optimization.
This also allows for fast stylization of splats, achieving speeds under 2
minutes even on consumer-grade hardware. We demonstrate the quality results
this approach achieves and compare to other 3D Gaussian splat style transfer
methods. Code is publicly available at
https://github.com/davidmhart/FastSplatStyler.
Improving Masked Style Transfer using Blended Partial Convolution
Authors: name: Seyed Hadi Seyed, name: Ayberk Cansever, name: David Hart
Published: 2025-08-07 18:35:44

Artistic style transfer has long been possible with the advancements of
convolution- and transformer-based neural networks. Most algorithms apply the
artistic style transfer to the whole image, but individual users may only need
to apply a style transfer to a specific region in the image. The standard
practice is to simply mask the image after the stylization. This work shows
that this approach tends to improperly capture the style features in the region
of interest. We propose a partial-convolution-based style transfer network that
accurately applies the style features exclusively to the region of interest.
Additionally, we present network-internal blending techniques that account for
imperfections in the region selection. We show that this visually and
quantitatively improves stylization using examples from the SA-1B dataset. Code
is publicly available at https://github.com/davidmhart/StyleTransferMasked.