Orionnnn/hub-stats huggingface dataset:
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking
  Services
Authors: name: Fei Zhao, name: Chonggang Lu, name: Yue Wang, name: Zheyong Xie, name: Ziyan Liu, name: Haofu Qian, name: JianZhao Huang, name: Fangcheng Shi, name: Zijie Meng, name: Hongcheng Guo, name: Mingqian He, name: Xinze Lyu, name: Yiming Lu, name: Ziyang Xiang, name: Zheyu Ye, name: Chengqiang Lu, name: Zhe Xu, name: Yi Wu, name: Yao Hu, name: Yan Gao, name: Jun Fan, name: Xiaolong Jiang, name: Weiting Liu, name: Boyang Wang, name: Shaosheng Cao
Published: 2025-07-13 02:22:59

As a primary medium for modern information dissemination, social networking
services (SNS) have experienced rapid growth, which has proposed significant
challenges for platform content management and interaction quality improvement.
Recently, the development of large language models (LLMs) has offered potential
solutions but existing studies focus on isolated tasks, which not only
encounter diminishing benefit from the data scaling within individual scenarios
but also fail to flexibly adapt to diverse real-world context. To address these
challenges, we introduce RedOne, a domain-specific LLM designed to break the
performance bottleneck of single-task baselines and establish a comprehensive
foundation for the SNS. RedOne was developed through a three-stage training
strategy consisting of continue pretraining, supervised fine-tuning, and
preference optimization, using a large-scale real-world dataset. Through
extensive experiments, RedOne maintains strong general capabilities, and
achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56%
in SNS bilingual evaluation benchmark, compared with base models. Furthermore,
through online testing, RedOne reduced the exposure rate in harmful content
detection by 11.23% and improved the click page rate in post-view search by
14.95% compared with single-tasks finetuned baseline models. These results
establish RedOne as a robust domain-specific LLM for SNS, demonstrating
excellent generalization across various tasks and promising applicability in
real-world scenarios.
RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA
  Optimization
Authors: name: Vladimir Bogachev, name: Vladimir Aletov, name: Alexander Molozhavenko, name: Denis Bobkov, name: Vera Soboleva, name: Aibek Alanov, name: Maxim Rakhuba
Published: 2025-07-16 11:17:12

Low-Rank Adaptation (LoRA) has become a widely adopted standard for
parameter-efficient fine-tuning of large language models (LLMs), significantly
reducing memory and computational demands. However, challenges remain,
including finding optimal initialization strategies or mitigating
overparametrization in low-rank matrix factorization. In this work, we propose
a novel approach that addresses both of the challenges simultaneously within a
unified framework. Our method treats a set of fixed-rank LoRA matrices as a
smooth manifold. Considering adapters as elements on this manifold removes
overparametrization, while determining the direction of the fastest loss
decrease along the manifold provides initialization. Special care is taken to
obtain numerically stable and computationally efficient implementation of our
method, using best practices from numerical linear algebra and Riemannian
optimization. Experimental results on LLM and diffusion model architectures
demonstrate that RiemannLoRA consistently improves both convergence speed and
final performance over standard LoRA and its state-of-the-art modifications.
Voxtral
Authors: name: Alexander H. Liu, name: Andy Ehrenberg, name: Andy Lo, name: Clément Denoix, name: Corentin Barreau, name: Guillaume Lample, name: Jean-Malo Delignon, name: Khyathi Raghavi Chandu, name: Patrick von Platen, name: Pavankumar Reddy Muddireddy, name: Sanchit Gandhi, name: Soham Ghosh, name: Srijan Mishra, name: Thomas Foubert, name: Abhinav Rastogi, name: Adam Yang, name: Albert Q. Jiang, name: Alexandre Sablayrolles, name: Amélie Héliou, name: Amélie Martin, name: Anmol Agarwal, name: Antoine Roux, name: Arthur Darcet, name: Arthur Mensch, name: Baptiste Bout, name: Baptiste Rozière, name: Baudouin De Monicault, name: Chris Bamford, name: Christian Wallenwein, name: Christophe Renaudin, name: Clémence Lanfranchi, name: Darius Dabert, name: Devendra Singh Chaplot, name: Devon Mizelle, name: Diego de las Casas, name: Elliot Chane-Sane, name: Emilien Fugier, name: Emma Bou Hanna, name: Gabrielle Berrada, name: Gauthier Delerce, name: Gauthier Guinet, name: Georgii Novikov, name: Guillaume Martin, name: Himanshu Jaju, name: Jan Ludziejewski, name: Jason Rute, name: Jean-Hadrien Chabran, name: Jessica Chudnovsky, name: Joachim Studnia, name: Joep Barmentlo, name: Jonas Amar, name: Josselin Somerville Roberts, name: Julien Denize, name: Karan Saxena, name: Karmesh Yadav, name: Kartik Khandelwal, name: Kush Jain, name: Lélio Renard Lavaud, name: Léonard Blier, name: Lingxiao Zhao, name: Louis Martin, name: Lucile Saulnier, name: Luyu Gao, name: Marie Pellat, name: Mathilde Guillaumin, name: Mathis Felardos, name: Matthieu Dinot, name: Maxime Darrin, name: Maximilian Augustin, name: Mickaël Seznec, name: Neha Gupta, name: Nikhil Raghuraman, name: Olivier Duchenne, name: Patricia Wang, name: Patryk Saffer, name: Paul Jacob, name: Paul Wambergue, name: Paula Kurylowicz, name: Philomène Chagniot, name: Pierre Stock, name: Pravesh Agrawal, name: Rémi Delacourt, name: Romain Sauvestre, name: Roman Soletskyi, name: Sagar Vaze, name: Sandeep Subramanian, name: Saurabh Garg, name: Shashwat Dalal, name: Siddharth Gandhi, name: Sumukh Aithal, name: Szymon Antoniak, name: Teven Le Scao, name: Thibault Schueller, name: Thibaut Lavril, name: Thomas Robert, name: Thomas Wang, name: Timothée Lacroix, name: Tom Bewley, name: Valeriia Nemychnikova, name: Victor Paltz, name: Virgile Richard, name: Wen-Ding Li, name: William Marshall, name: Xuanyu Zhang, name: Yihan Wan, name: Yunhao Tang
Published: 2025-07-17 16:17:37

We present Voxtral Mini and Voxtral Small, two multimodal audio chat models.
Voxtral is trained to comprehend both spoken audio and text documents,
achieving state-of-the-art performance across a diverse range of audio
benchmarks, while preserving strong text capabilities. Voxtral Small
outperforms a number of closed-source models, while being small enough to run
locally. A 32K context window enables the model to handle audio files up to 40
minutes in duration and long multi-turn conversations. We also contribute three
benchmarks for evaluating speech understanding models on knowledge and trivia.
Both Voxtral models are released under Apache 2.0 license.
Automating Steering for Safe Multimodal Large Language Models
Authors: name: Lyucheng Wu, name: Mengru Wang, name: Ziwen Xu, name: Tri Cao, name: Nay Oo, name: Bryan Hooi, name: Shumin Deng
Published: 2025-07-17 16:04:55

Recent progress in Multimodal Large Language Models (MLLMs) has unlocked
powerful cross-modal reasoning abilities, but also raised new safety concerns,
particularly when faced with adversarial multimodal inputs. To improve the
safety of MLLMs during inference, we introduce a modular and adaptive
inference-time intervention technology, AutoSteer, without requiring any
fine-tuning of the underlying model. AutoSteer incorporates three core
components: (1) a novel Safety Awareness Score (SAS) that automatically
identifies the most safety-relevant distinctions among the model's internal
layers; (2) an adaptive safety prober trained to estimate the likelihood of
toxic outputs from intermediate representations; and (3) a lightweight Refusal
Head that selectively intervenes to modulate generation when safety risks are
detected. Experiments on LLaVA-OV and Chameleon across diverse safety-critical
benchmarks demonstrate that AutoSteer significantly reduces the Attack Success
Rate (ASR) for textual, visual, and cross-modal threats, while maintaining
general abilities. These findings position AutoSteer as a practical,
interpretable, and effective framework for safer deployment of multimodal AI
systems.
Einstein Fields: A Neural Perspective To Computational General
  Relativity
Authors: name: Sandeep Suresh Cranganore, name: Andrei Bodnar, name: Arturs Berzins, name: Johannes Brandstetter
Published: 2025-07-15 14:55:39

We introduce Einstein Fields, a neural representation that is designed to
compress computationally intensive four-dimensional numerical relativity
simulations into compact implicit neural network weights. By modeling the
metric, which is the core tensor field of general relativity, Einstein
Fields enable the derivation of physical quantities via automatic
differentiation. However, unlike conventional neural fields (e.g., signed
distance, occupancy, or radiance fields), Einstein Fields are Neural
Tensor Fields with the key difference that when encoding the spacetime
geometry of general relativity into neural field representations, dynamics
emerge naturally as a byproduct. Einstein Fields show remarkable potential,
including continuum modeling of 4D spacetime, mesh-agnosticity, storage
efficiency, derivative accuracy, and ease of use. We address these challenges
across several canonical test beds of general relativity and release an open
source JAX-based library, paving the way for more scalable and expressive
approaches to numerical relativity. Code is made available at
https://github.com/AndreiB137/EinFields
Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos
  with Spatio-Temporal Diffusion Models
Authors: name: Yudong Jin, name: Sida Peng, name: Xuan Wang, name: Tao Xie, name: Zhen Xu, name: Yifan Yang, name: Yujun Shen, name: Hujun Bao, name: Xiaowei Zhou
Published: 2025-07-17 17:59:17

This paper addresses the challenge of high-fidelity view synthesis of humans
with sparse-view videos as input. Previous methods solve the issue of
insufficient observation by leveraging 4D diffusion models to generate videos
at novel viewpoints. However, the generated videos from these models often lack
spatio-temporal consistency, thus degrading view synthesis quality. In this
paper, we propose a novel sliding iterative denoising process to enhance the
spatio-temporal consistency of the 4D diffusion model. Specifically, we define
a latent grid in which each latent encodes the image, camera pose, and human
pose for a certain viewpoint and timestamp, then alternately denoising the
latent grid along spatial and temporal dimensions with a sliding window, and
finally decode the videos at target viewpoints from the corresponding denoised
latents. Through the iterative sliding, information flows sufficiently across
the latent grid, allowing the diffusion model to obtain a large receptive field
and thus enhance the 4D consistency of the output, while making the GPU memory
consumption affordable. The experiments on the DNA-Rendering and ActorsHQ
datasets demonstrate that our method is able to synthesize high-quality and
consistent novel-view videos and significantly outperforms the existing
approaches. See our project page for interactive demos and video results:
https://diffuman4d.github.io/ .
Teach Old SAEs New Domain Tricks with Boosting
Authors: name: Nikita Koriagin, name: Yaroslav Aksenov, name: Daniil Laptev, name: Gleb Gerasimov, name: Nikita Balagansky, name: Daniil Gavrilov
Published: 2025-07-17 10:57:49

Sparse Autoencoders have emerged as powerful tools for interpreting the
internal representations of Large Language Models, yet they often fail to
capture domain-specific features not prevalent in their training corpora. This
paper introduces a residual learning approach that addresses this feature
blindness without requiring complete retraining. We propose training a
secondary SAE specifically to model the reconstruction error of a pretrained
SAE on domain-specific texts, effectively capturing features missed by the
primary model. By summing the outputs of both models during inference, we
demonstrate significant improvements in both LLM cross-entropy and explained
variance metrics across multiple specialized domains. Our experiments show that
this method efficiently incorporates new domain knowledge into existing SAEs
while maintaining their performance on general tasks. This approach enables
researchers to selectively enhance SAE interpretability for specific domains of
interest, opening new possibilities for targeted mechanistic interpretability
of LLMs.
FantasyPortrait: Enhancing Multi-Character Portrait Animation with
  Expression-Augmented Diffusion Transformers
Authors: name: Qiang Wang, name: Mengchao Wang, name: Fan Jiang, name: Yaqi Fan, name: Yonggang Qi, name: Mu Xu
Published: 2025-07-17 09:50:43

Producing expressive facial animations from static images is a challenging
task. Prior methods relying on explicit geometric priors (e.g., facial
landmarks or 3DMM) often suffer from artifacts in cross reenactment and
struggle to capture subtle emotions. Furthermore, existing approaches lack
support for multi-character animation, as driving features from different
individuals frequently interfere with one another, complicating the task. To
address these challenges, we propose FantasyPortrait, a diffusion transformer
based framework capable of generating high-fidelity and emotion-rich animations
for both single- and multi-character scenarios. Our method introduces an
expression-augmented learning strategy that utilizes implicit representations
to capture identity-agnostic facial dynamics, enhancing the model's ability to
render fine-grained emotions. For multi-character control, we design a masked
cross-attention mechanism that ensures independent yet coordinated expression
generation, effectively preventing feature interference. To advance research in
this area, we propose the Multi-Expr dataset and ExprBench, which are
specifically designed datasets and benchmarks for training and evaluating
multi-character portrait animations. Extensive experiments demonstrate that
FantasyPortrait significantly outperforms state-of-the-art methods in both
quantitative metrics and qualitative evaluations, excelling particularly in
challenging cross reenactment and multi-character contexts. Our project page is
https://fantasy-amap.github.io/fantasy-portrait/.
AbGen: Evaluating Large Language Models in Ablation Study Design and
  Evaluation for Scientific Research
Authors: name: Yilun Zhao, name: Weiyuan Chen, name: Zhijian Xu, name: Manasi Patwardhan, name: Yixin Liu, name: Chengye Wang, name: Lovekesh Vig, name: Arman Cohan
Published: 2025-07-17 17:09:22

We introduce AbGen, the first benchmark designed to evaluate the capabilities
of LLMs in designing ablation studies for scientific research. AbGen consists
of 1,500 expert-annotated examples derived from 807 NLP papers. In this
benchmark, LLMs are tasked with generating detailed ablation study designs for
a specified module or process based on the given research context. Our
evaluation of leading LLMs, such as DeepSeek-R1-0528 and o4-mini, highlights a
significant performance gap between these models and human experts in terms of
the importance, faithfulness, and soundness of the ablation study designs.
Moreover, we demonstrate that current automated evaluation methods are not
reliable for our task, as they show a significant discrepancy when compared to
human assessment. To better investigate this, we develop AbGen-Eval, a
meta-evaluation benchmark designed to assess the reliability of commonly used
automated evaluation systems in measuring LLM performance on our task. We
investigate various LLM-as-Judge systems on AbGen-Eval, providing insights for
future research on developing more effective and reliable LLM-based evaluation
systems for complex scientific tasks.
AnyCap Project: A Unified Framework, Dataset, and Benchmark for
  Controllable Omni-modal Captioning
Authors: name: Yiming Ren, name: Zhiqiang Lin, name: Yu Li, name: Gao Meng, name: Weiyun Wang, name: Junjie Wang, name: Zicheng Lin, name: Jifeng Dai, name: Yujiu Yang, name: Wenhai Wang, name: Ruihang Chu
Published: 2025-07-17 07:04:05

Controllable captioning is essential for precise multimodal alignment and
instruction following, yet existing models often lack fine-grained control and
reliable evaluation protocols. To address this gap, we present the AnyCap
Project, an integrated solution spanning model, dataset, and evaluation. We
introduce AnyCapModel (ACM), a lightweight plug-and-play framework that
enhances the controllability of existing foundation models for omni-modal
captioning without retraining the base model. ACM reuses the original captions
from base models while incorporating user instructions and modality features to
generate improved captions. To remedy the data scarcity in controllable
multimodal captioning, we build AnyCapDataset (ACD), covering three modalities,
28 user-instruction types, and 300\,k high-quality data entries. We further
propose AnyCapEval, a new benchmark that provides more reliable evaluation
metrics for controllable captioning by decoupling content accuracy and
stylistic fidelity. ACM markedly improves caption quality across a diverse set
of base models on AnyCapEval. Notably, ACM-8B raises GPT-4o\'s content scores
by 45\% and style scores by 12\%, and it also achieves substantial gains on
widely used benchmarks such as MIA-Bench and VidCapBench.
VisionThink: Smart and Efficient Vision Language Model via Reinforcement
  Learning
Authors: name: Senqiao Yang, name: Junyi Li, name: Xin Lai, name: Bei Yu, name: Hengshuang Zhao, name: Jiaya Jia
Published: 2025-07-17 17:59:55

Recent advancements in vision-language models (VLMs) have improved
performance by increasing the number of visual tokens, which are often
significantly longer than text tokens. However, we observe that most real-world
scenarios do not require such an extensive number of visual tokens. While the
performance drops significantly in a small subset of OCR-related tasks, models
still perform accurately in most other general VQA tasks with only 1/4
resolution. Therefore, we propose to dynamically process distinct samples with
different resolutions, and present a new paradigm for visual token compression,
namely, VisionThink. It starts with a downsampled image and smartly decides
whether it is sufficient for problem solving. Otherwise, the model could output
a special token to request the higher-resolution image. Compared to existing
Efficient VLM methods that compress tokens using fixed pruning ratios or
thresholds, VisionThink autonomously decides whether to compress tokens case by
case. As a result, it demonstrates strong fine-grained visual understanding
capability on OCR-related tasks, and meanwhile saves substantial visual tokens
on simpler tasks. We adopt reinforcement learning and propose the LLM-as-Judge
strategy to successfully apply RL to general VQA tasks. Moreover, we carefully
design a reward function and penalty mechanism to achieve a stable and
reasonable image resize call ratio. Extensive experiments demonstrate the
superiority, efficiency, and effectiveness of our method. Our code is available
at https://github.com/dvlab-research/VisionThink.
TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame
  Interpolation
Authors: name: Zonglin Lyu, name: Chen Chen
Published: 2025-07-07 13:25:32

Video Frame Interpolation (VFI) aims to predict the intermediate frame I_n
(we use n to denote time in videos to avoid notation overload with the timestep
t in diffusion models) based on two consecutive neighboring frames I_0 and
I_1. Recent approaches apply diffusion models (both image-based and
video-based) in this task and achieve strong performance. However, image-based
diffusion models are unable to extract temporal information and are relatively
inefficient compared to non-diffusion methods. Video-based diffusion models can
extract temporal information, but they are too large in terms of training
scale, model size, and inference time. To mitigate the above issues, we propose
Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation
(TLB-VFI), an efficient video-based diffusion model. By extracting rich
temporal information from video inputs through our proposed 3D-wavelet gating
and temporal-aware autoencoder, our method achieves 20% improvement in FID on
the most challenging datasets over recent SOTA of image-based diffusion models.
Meanwhile, due to the existence of rich temporal information, our method
achieves strong performance while having 3times fewer parameters. Such a
parameter reduction results in 2.3x speed up. By incorporating optical flow
guidance, our method requires 9000x less training data and achieves over 20x
fewer parameters than video-based diffusion models. Codes and results are
available at our project page: https://zonglinl.github.io/tlbvfi_page.
π^3: Scalable Permutation-Equivariant Visual Geometry Learning
Authors: name: Yifan Wang, name: Jianjun Zhou, name: Haoyi Zhu, name: Wenzheng Chang, name: Yang Zhou, name: Zizun Li, name: Junyi Chen, name: Jiangmiao Pang, name: Chunhua Shen, name: Tong He
Published: 2025-07-17 17:59:53

We introduce pi^3, a feed-forward neural network that offers a novel
approach to visual geometry reconstruction, breaking the reliance on a
conventional fixed reference view. Previous methods often anchor their
reconstructions to a designated viewpoint, an inductive bias that can lead to
instability and failures if the reference is suboptimal. In contrast, pi^3
employs a fully permutation-equivariant architecture to predict
affine-invariant camera poses and scale-invariant local point maps without any
reference frames. This design makes our model inherently robust to input
ordering and highly scalable. These advantages enable our simple and bias-free
approach to achieve state-of-the-art performance on a wide range of tasks,
including camera pose estimation, monocular/video depth estimation, and dense
point map reconstruction. Code and models are publicly available.
The Imitation Game: Turing Machine Imitator is Length Generalizable
  Reasoner
Authors: name: Zhouqi Hua, name: Wenwei Zhang, name: Chengqi Lyu, name: Yuzhe Gu, name: Songyang Gao, name: Kuikun Liu, name: Kai Chen
Published: 2025-07-17 17:50:07

Length generalization, the ability to solve problems of longer sequences than
those observed during training, poses a core challenge of Transformer-based
large language models (LLM). Although existing studies have predominantly
focused on data-driven approaches for arithmetic operations and symbolic
manipulation tasks, these approaches tend to be task-specific with limited
overall performance. To pursue a more general solution, this paper focuses on a
broader case of reasoning problems that are computable, i.e., problems that
algorithms can solve, thus can be solved by the Turing Machine. From this
perspective, this paper proposes Turing MAchine Imitation Learning (TAIL) to
improve the length generalization ability of LLMs. TAIL synthesizes
chain-of-thoughts (CoT) data that imitate the execution process of a Turing
Machine by computer programs, which linearly expands the reasoning steps into
atomic states to alleviate shortcut learning and explicit memory fetch
mechanism to reduce the difficulties of dynamic and long-range data access in
elementary operations. To validate the reliability and universality of TAIL, we
construct a challenging synthetic dataset covering 8 classes of algorithms and
18 tasks. Without bells and whistles, TAIL significantly improves the length
generalization ability as well as the performance of Qwen2.5-7B on various
tasks using only synthetic data, surpassing previous methods and DeepSeek-R1.
The experimental results reveal that the key concepts in the Turing Machine,
instead of the thinking styles, are indispensable for TAIL for length
generalization, through which the model exhibits read-and-write behaviors
consistent with the properties of the Turing Machine in their attention layers.
This work provides a promising direction for future research in the learning of
LLM reasoning from synthetic data.