Orionnnn/hub-stats huggingface dataset:
Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for
  Culturally Diverse Art Style Classification
Authors: name: Abdellah Zakaria Sellam, name: Salah Eddine Bekhouche, name: Cosimo Distante, name: Abdelmalik Taleb-Ahmed
Published: 2025-07-31 11:16:00

Art style classification remains a formidable challenge in computational
aesthetics due to the scarcity of expertly labeled datasets and the intricate,
often nonlinear interplay of stylistic elements. While recent dual-teacher
self-supervised frameworks reduce reliance on labeled data, their linear
projection layers and localized focus struggle to model global compositional
context and complex style-feature interactions. We enhance the dual-teacher
knowledge distillation framework to address these limitations by replacing
conventional MLP projection and prediction heads with Kolmogorov-Arnold
Networks (KANs). Our approach retains complementary guidance from two teacher
networks, one emphasizing localized texture and brushstroke patterns, the other
capturing broader stylistic hierarchies while leveraging KANs' spline-based
activations to model nonlinear feature correlations with mathematical
precision. Experiments on WikiArt and Pandora18k demonstrate that our approach
outperforms the base dual teacher architecture in Top-1 accuracy. Our findings
highlight the importance of KANs in disentangling complex style manifolds,
leading to better linear probe accuracy than MLP projections.
Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Authors: name: Salah Eddine Bekhouche, name: Azeddine Benlamoudi, name: Yazid Bounab, name: Fadi Dornaika, name: Abdenour Hadid
Published: 2025-07-31 10:18:28

Arabic poses a particular challenge for natural language processing (NLP) and
information retrieval (IR) due to its complex morphology, optional diacritics
and the coexistence of Modern Standard Arabic (MSA) and various dialects.
Despite the growing global significance of Arabic, it is still underrepresented
in NLP research and benchmark resources. In this paper, we present an enhanced
Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At
the core of our approach is a novel Attentive Relevance Scoring (ARS) that
replaces standard interaction mechanisms with an adaptive scoring function that
more effectively models the semantic relevance between questions and passages.
Our method integrates pre-trained Arabic language models and architectural
refinements to improve retrieval performance and significantly increase ranking
accuracy when answering Arabic questions. The code is made publicly available
at https://github.com/Bekhouche/APR{GitHub}.
Phi-Ground Tech Report: Advancing Perception in GUI Grounding
Authors: name: Miaosen Zhang, name: Ziqiang Xu, name: Jialiang Zhu, name: Qi Dai, name: Kai Qiu, name: Yifan Yang, name: Chong Luo, name: Tianyi Chen, name: Justin Wagle, name: Tim Franklin, name: Baining Guo
Published: 2025-07-31 17:59:09

With the development of multimodal reasoning models, Computer Use Agents
(CUAs), akin to Jarvis from "Iron Man", are becoming a reality. GUI
grounding is a core component for CUAs to execute actual actions, similar to
mechanical control in robotics, and it directly leads to the success or failure
of the system. It determines actions such as clicking and typing, as well as
related parameters like the coordinates for clicks. Current end-to-end
grounding models still achieve less than 65\% accuracy on challenging
benchmarks like ScreenSpot-pro and UI-Vision, indicating they are far from
being ready for deployment. % , as a single misclick can result in unacceptable
consequences. In this work, we conduct an empirical study on the training of
grounding models, examining details from data collection to model training.
Ultimately, we developed the Phi-Ground model family, which achieves
state-of-the-art performance across all five grounding benchmarks for models
under 10B parameters in agent settings. In the end-to-end model setting, our
model still achieves SOTA results with scores of \textbf{43.2} on
ScreenSpot-pro and \textbf{27.2} on UI-Vision. We believe that the
various details discussed in this paper, along with our successes and failures,
not only clarify the construction of grounding models but also benefit other
perception tasks. Project homepage:
https://zhangmiaosen2000.github.io/Phi-Ground/{https://zhangmiaosen2000.github.io/Phi-Ground/}
Persona Vectors: Monitoring and Controlling Character Traits in Language
  Models
Authors: name: Runjin Chen, name: Andy Arditi, name: Henry Sleight, name: Owain Evans, name: Jack Lindsey
Published: 2025-07-29 05:20:14

Large language models interact with users through a simulated 'Assistant'
persona. While the Assistant is typically trained to be helpful, harmless, and
honest, it sometimes deviates from these ideals. In this paper, we identify
directions in the model's activation space-persona vectors-underlying several
traits, such as evil, sycophancy, and propensity to hallucinate. We confirm
that these vectors can be used to monitor fluctuations in the Assistant's
personality at deployment time. We then apply persona vectors to predict and
control personality shifts that occur during training. We find that both
intended and unintended personality changes after finetuning are strongly
correlated with shifts along the relevant persona vectors. These shifts can be
mitigated through post-hoc intervention, or avoided in the first place with a
new preventative steering method. Moreover, persona vectors can be used to flag
training data that will produce undesirable personality changes, both at the
dataset level and the individual sample level. Our method for extracting
persona vectors is automated and can be applied to any personality trait of
interest, given only a natural-language description.
NeRF Is a Valuable Assistant for 3D Gaussian Splatting
Authors: name: Shuangkang Fang, name: I-Chao Shen, name: Takeo Igarashi, name: Yufeng Wang, name: ZeSheng Wang, name: Yi Yang, name: Wenrui Ding, name: Shuchang Zhou
Published: 2025-07-31 09:43:31

We introduce NeRF-GS, a novel framework that jointly optimizes Neural
Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This framework
leverages the inherent continuous spatial representation of NeRF to mitigate
several limitations of 3DGS, including sensitivity to Gaussian initialization,
limited spatial awareness, and weak inter-Gaussian correlations, thereby
enhancing its performance. In NeRF-GS, we revisit the design of 3DGS and
progressively align its spatial features with NeRF, enabling both
representations to be optimized within the same scene through shared 3D spatial
information. We further address the formal distinctions between the two
approaches by optimizing residual vectors for both implicit features and
Gaussian positions to enhance the personalized capabilities of 3DGS.
Experimental results on benchmark datasets show that NeRF-GS surpasses existing
methods and achieves state-of-the-art performance. This outcome confirms that
NeRF and 3DGS are complementary rather than competing, offering new insights
into hybrid approaches that combine 3DGS and NeRF for efficient 3D scene
representation.
Flow Equivariant Recurrent Neural Networks
Authors: name: T. Anderson Keller
Published: 2025-07-20 02:52:21

Data arrives at our senses as a continuous stream, smoothly transforming from
one instant to the next. These smooth transformations can be viewed as
continuous symmetries of the environment that we inhabit, defining equivalence
relations between stimuli over time. In machine learning, neural network
architectures that respect symmetries of their data are called equivariant and
have provable benefits in terms of generalization ability and sample
efficiency. To date, however, equivariance has been considered only for static
transformations and feed-forward networks, limiting its applicability to
sequence models, such as recurrent neural networks (RNNs), and corresponding
time-parameterized sequence transformations. In this work, we extend
equivariant network theory to this regime of `flows' -- one-parameter Lie
subgroups capturing natural transformations over time, such as visual motion.
We begin by showing that standard RNNs are generally not flow equivariant:
their hidden states fail to transform in a geometrically structured manner for
moving stimuli. We then show how flow equivariance can be introduced, and
demonstrate that these models significantly outperform their non-equivariant
counterparts in terms of training speed, length generalization, and velocity
generalization, on both next step prediction and sequence classification. We
present this work as a first step towards building sequence models that respect
the time-parameterized symmetries which govern the world around us.
AgroBench: Vision-Language Model Benchmark in Agriculture
Authors: name: Risa Shinoda, name: Nakamasa Inoue, name: Hirokatsu Kataoka, name: Masaki Onishi, name: Yoshitaka Ushiku
Published: 2025-07-28 04:58:29

Precise automated understanding of agricultural tasks such as disease
identification is essential for sustainable crop production. Recent advances in
vision-language models (VLMs) are expected to further expand the range of
agricultural tasks by facilitating human-model interaction through easy,
text-based communication. Here, we introduce AgroBench (Agronomist AI
Benchmark), a benchmark for evaluating VLM models across seven agricultural
topics, covering key areas in agricultural engineering and relevant to
real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is
annotated by expert agronomists. Our AgroBench covers a state-of-the-art range
of categories, including 203 crop categories and 682 disease categories, to
thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal
that VLMs have room for improvement in fine-grained identification tasks.
Notably, in weed identification, most open-source VLMs perform close to random.
With our wide range of topics and expert-annotated categories, we analyze the
types of errors made by VLMs and suggest potential pathways for future VLM
development. Our dataset and code are available at
https://dahlian00.github.io/AgroBenchPage/ .
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial
  Intelligence in Visuomotor Agents
Authors: name: Shaofei Cai, name: Zhancun Mu, name: Haiwen Xia, name: Bowei Zhang, name: Anji Liu, name: Yitao Liang
Published: 2025-07-31 16:20:02

While Reinforcement Learning (RL) has achieved remarkable success in language
modeling, its triumph hasn't yet fully translated to visuomotor agents. A
primary challenge in RL models is their tendency to overfit specific tasks or
environments, thereby hindering the acquisition of generalizable behaviors
across diverse settings. This paper provides a preliminary answer to this
challenge by demonstrating that RL-finetuned visuomotor agents in Minecraft can
achieve zero-shot generalization to unseen worlds. Specifically, we explore
RL's potential to enhance generalizable spatial reasoning and interaction
capabilities in 3D worlds. To address challenges in multi-task RL
representation, we analyze and establish cross-view goal specification as a
unified multi-task goal space for visuomotor policies. Furthermore, to overcome
the significant bottleneck of manual task design, we propose automated task
synthesis within the highly customizable Minecraft environment for large-scale
multi-task RL training, and we construct an efficient distributed RL framework
to support this. Experimental results show RL significantly boosts interaction
success rates by 4times and enables zero-shot generalization of spatial
reasoning across diverse environments, including real-world settings. Our
findings underscore the immense potential of RL training in 3D simulated
environments, especially those amenable to large-scale task generation, for
significantly advancing visuomotor agents' spatial reasoning.
On the Expressiveness of Softmax Attention: A Recurrent Neural Network
  Perspective
Authors: name: Gabriel Mongaras, name: Eric C. Larson
Published: 2025-07-31 15:10:03

Since its introduction, softmax attention has become the backbone of modern
transformer architectures due to its expressiveness and scalability across a
wide range of tasks. However, the main drawback of softmax attention is the
quadratic memory requirement and computational complexity with respect to the
sequence length. By replacing the softmax nonlinearity, linear attention and
similar methods have been introduced to avoid the quadratic bottleneck of
softmax attention. Despite these linear forms of attention being derived from
the original softmax formulation, they typically lag in terms of downstream
accuracy. While strong intuition of the softmax nonlinearity on the query and
key inner product suggests that it has desirable properties compared to other
nonlinearities, the question of why this discrepancy exists still remains
unanswered. This work demonstrates that linear attention is an approximation of
softmax attention by deriving the recurrent form of softmax attention. Using
this form, each part of softmax attention can be described in the language of
recurrent neural networks (RNNs). Describing softmax attention as an RNN allows
for the ablation of the components of softmax attention to understand the
importance of each part and how they interact. In this way, our work helps
explain why softmax attention is more expressive than its counterparts.
villa-X: Enhancing Latent Action Modeling in Vision-Language-Action
  Models
Authors: name: Xiaoyu Chen, name: Hangxing Wei, name: Pushi Zhang, name: Chuheng Zhang, name: Kaixin Wang, name: Yanjiang Guo, name: Rushuai Yang, name: Yucen Wang, name: Xinquan Xiao, name: Li Zhao, name: Jianyu Chen, name: Jiang Bian
Published: 2025-07-31 15:57:46

Visual-Language-Action (VLA) models have emerged as a popular paradigm for
learning robot manipulation policies that can follow language instructions and
generalize to novel scenarios. Recent work has begun to explore the
incorporation of latent actions, an abstract representation of visual change
between two frames, into VLA pre-training. In this paper, we introduce villa-X,
a novel Visual-Language-Latent-Action (ViLLA) framework that advances latent
action modeling for learning generalizable robot manipulation policies. Our
approach improves both how latent actions are learned and how they are
incorporated into VLA pre-training. Together, these contributions enable
villa-X to achieve superior performance across simulated environments including
SIMPLER and LIBERO, as well as on two real-world robot setups including gripper
and dexterous hand manipulation. We believe the ViLLA paradigm holds
significant promise, and that our villa-X provides a strong foundation for
future research.
C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring
  Challenges in Complex Conversations
Authors: name: Chengqian Ma, name: Wei Tao, name: Yiwen Guo
Published: 2025-07-30 17:56:23

Spoken Dialogue Models (SDMs) have recently attracted significant attention
for their ability to generate voice responses directly to users' spoken
queries. Despite their increasing popularity, there exists a gap in research
focused on comprehensively understanding their practical effectiveness in
comprehending and emulating human conversations. This is especially true
compared to text-based Large Language Models (LLMs), which benefit from
extensive benchmarking. Human voice interactions are inherently more complex
than text due to characteristics unique to spoken dialogue. Ambiguity poses one
challenge, stemming from semantic factors like polysemy, as well as
phonological aspects such as heterograph, heteronyms, and stress patterns.
Additionally, context-dependency, like omission, coreference, and multi-turn
interaction, adds further complexity to human conversational dynamics. To
illuminate the current state of SDM development and to address these
challenges, we present a benchmark dataset in this paper, which comprises 1,079
instances in English and Chinese. Accompanied by an LLM-based evaluation method
that closely aligns with human judgment, this dataset facilitates a
comprehensive exploration of the performance of SDMs in tackling these
practical challenges.
Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Authors: name: Luoxin Chen, name: Jinming Gu, name: Liankai Huang, name: Wenhao Huang, name: Zhicheng Jiang, name: Allan Jie, name: Xiaoran Jin, name: Xing Jin, name: Chenggang Li, name: Kaijing Ma, name: Cheng Ren, name: Jiawei Shen, name: Wenlei Shi, name: Tong Sun, name: He Sun, name: Jiahui Wang, name: Siran Wang, name: Zhihong Wang, name: Chenrui Wei, name: Shufa Wei, name: Yonghui Wu, name: Yuchen Wu, name: Yihang Xia, name: Huajian Xin, name: Fan Yang, name: Huaiyuan Ying, name: Hongyi Yuan, name: Zheng Yuan, name: Tianyang Zhan, name: Chi Zhang, name: Yue Zhang, name: Ge Zhang, name: Tianyun Zhao, name: Jianqiu Zhao, name: Yichi Zhou, name: Thomas Hanwen Zhu
Published: 2025-07-31 17:00:30

LLMs have demonstrated strong mathematical reasoning abilities by leveraging
reinforcement learning with long chain-of-thought, yet they continue to
struggle with theorem proving due to the lack of clear supervision signals when
solely using natural language. Dedicated domain-specific languages like Lean
provide clear supervision via formal verification of proofs, enabling effective
training through reinforcement learning. In this work, we propose
Seed-Prover, a lemma-style whole-proof reasoning model. Seed-Prover
can iteratively refine its proof based on Lean feedback, proved lemmas, and
self-summarization. To solve IMO-level contest problems, we design three
test-time inference strategies that enable both deep and broad reasoning.
Seed-Prover proves 78.1% of formalized past IMO problems, saturates MiniF2F,
and achieves over 50\% on PutnamBench, outperforming the previous
state-of-the-art by a large margin. To address the lack of geometry support in
Lean, we introduce a geometry reasoning engine Seed-Geometry, which
outperforms previous formal geometry engines. We use these two systems to
participate in IMO 2025 and fully prove 5 out of 6 problems. This work
represents a significant advancement in automated mathematical reasoning,
demonstrating the effectiveness of formal verification with long
chain-of-thought reasoning.
RecGPT Technical Report
Authors: name: Chao Yi, name: Dian Chen, name: Gaoyang Guo, name: Jiakai Tang, name: Jian Wu, name: Jing Yu, name: Sunhao Dai, name: Wen Chen, name: Wenjun Yang, name: Yuning Jiang, name: Zhujin Gao, name: Bo Zheng, name: Chi Li, name: Dimin Wang, name: Dixuan Wang, name: Fan Li, name: Fan Zhang, name: Haibin Chen, name: Haozhuang Liu, name: Jialin Zhu, name: Jiamang Wang, name: Jiawei Wu, name: Jin Cui, name: Ju Huang, name: Kai Zhang, name: Kan Liu, name: Lang Tian, name: Liang Rao, name: Longbin Li, name: Lulu Zhao, name: Mao Zhang, name: Na He, name: Peiyang Wang, name: Qiqi Huang, name: Tao Luo, name: Wenbo Su, name: Xiaoxiao He, name: Xin Tong, name: Xu Chen, name: Xunke Xi, name: Yang Li, name: Yaxuan Wu, name: Yeqiu Yang, name: Yi Hu, name: Yinnan Song, name: Yuchen Li, name: Yujie Luo, name: Yujin Yuan, name: Yuliang Yan, name: Zhengyang Wang, name: Zhibo Xiao, name: Zhixin Ma, name: Zile Zhou
Published: 2025-07-30 17:55:06

Recommender systems are among the most impactful applications of artificial
intelligence, serving as critical infrastructure connecting users, merchants,
and platforms. However, most current industrial systems remain heavily reliant
on historical co-occurrence patterns and log-fitting objectives, i.e.,
optimizing for past user interactions without explicitly modeling user intent.
This log-fitting approach often leads to overfitting to narrow historical
preferences, failing to capture users' evolving and latent interests. As a
result, it reinforces filter bubbles and long-tail phenomena, ultimately
harming user experience and threatening the sustainability of the whole
recommendation ecosystem.
  To address these challenges, we rethink the overall design paradigm of
recommender systems and propose RecGPT, a next-generation framework that places
user intent at the center of the recommendation pipeline. By integrating large
language models (LLMs) into key stages of user interest mining, item retrieval,
and explanation generation, RecGPT transforms log-fitting recommendation into
an intent-centric process. To effectively align general-purpose LLMs to the
above domain-specific recommendation tasks at scale, RecGPT incorporates a
multi-stage training paradigm, which integrates reasoning-enhanced
pre-alignment and self-training evolution, guided by a Human-LLM cooperative
judge system. Currently, RecGPT has been fully deployed on the Taobao App.
Online experiments demonstrate that RecGPT achieves consistent performance
gains across stakeholders: users benefit from increased content diversity and
satisfaction, merchants and the platform gain greater exposure and conversions.
These comprehensive improvement results across all stakeholders validates that
LLM-driven, intent-centric design can foster a more sustainable and mutually
beneficial recommendation ecosystem.
DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation
Authors: name: Haoran Li, name: Yuli Tian, name: Kun Lan, name: Yong Liao, name: Lin Wang, name: Pan Hui, name: Peng Yuan Zhou
Published: 2025-07-18 14:45:54

Generating 3D scenes from natural language holds great promise for
applications in gaming, film, and design. However, existing methods struggle
with automation, 3D consistency, and fine-grained control. We present
DreamScene, an end-to-end framework for high-quality and editable 3D scene
generation from text or dialogue. DreamScene begins with a scene planning
module, where a GPT-4 agent infers object semantics and spatial constraints to
construct a hybrid graph. A graph-based placement algorithm then produces a
structured, collision-free layout. Based on this layout, Formation Pattern
Sampling (FPS) generates object geometry using multi-timestep sampling and
reconstructive optimization, enabling fast and realistic synthesis. To ensure
global consistent, DreamScene employs a progressive camera sampling strategy
tailored to both indoor and outdoor settings. Finally, the system supports
fine-grained scene editing, including object movement, appearance changes, and
4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior
methods in quality, consistency, and flexibility, offering a practical solution
for open-domain 3D content creation. Code and demos are available at
https://jahnsonblack.github.io/DreamScene-Full/.