Orionnnn/hub-stats huggingface dataset:
Magistral
Authors: name: Mistral-AI, name: Abhinav Rastogi, name: Albert Q. Jiang, name: Andy Lo, name: Gabrielle Berrada, name: Guillaume Lample, name: Jason Rute, name: Joep Barmentlo, name: Karmesh Yadav, name: Kartik Khandelwal, name: Khyathi Raghavi Chandu, name: Léonard Blier, name: Lucile Saulnier, name: Matthieu Dinot, name: Maxime Darrin, name: Neha Gupta, name: Roman Soletskyi, name: Sagar Vaze, name: Teven Le Scao, name: Yihan Wang, name: Adam Yang, name: Alexander H. Liu, name: Alexandre Sablayrolles, name: Amélie Héliou, name: Amélie Martin, name: Andy Ehrenberg, 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: Devon Mizelle, name: Diego de las Casas, name: Elliot Chane-Sane, name: Emilien Fugier, name: Emma Bou Hanna, name: Gauthier Delerce, name: Gauthier Guinet, name: Georgii Novikov, name: Guillaume Martin, name: Himanshu Jaju, name: Jan Ludziejewski, name: Jean-Hadrien Chabran, name: Jean-Malo Delignon, name: Joachim Studnia, name: Jonas Amar, name: Josselin Somerville Roberts, name: Julien Denize, name: Karan Saxena, name: Kush Jain, name: Lingxiao Zhao, name: Louis Martin, name: Luyu Gao, name: Lélio Renard Lavaud, name: Marie Pellat, name: Mathilde Guillaumin, name: Mathis Felardos, name: Maximilian Augustin, name: Mickaël Seznec, name: Nikhil Raghuraman, name: Olivier Duchenne, name: Patricia Wang, name: Patrick von Platen, name: Patryk Saffer, name: Paul Jacob, name: Paul Wambergue, name: Paula Kurylowicz, name: Pavankumar Reddy Muddireddy, name: Philomène Chagniot, name: Pierre Stock, name: Pravesh Agrawal, name: Romain Sauvestre, name: Rémi Delacourt, name: Sanchit Gandhi, name: Sandeep Subramanian, name: Shashwat Dalal, name: Siddharth Gandhi, name: Soham Ghosh, name: Srijan Mishra, name: Sumukh Aithal, name: Szymon Antoniak, name: Thibault Schueller, name: Thibaut Lavril, name: Thomas Robert, name: Thomas Wang, name: Timothée Lacroix, name: Valeriia Nemychnikova, name: Victor Paltz, name: Virgile Richard, name: Wen-Ding Li, name: William Marshall, name: Xuanyu Zhang, name: Yunhao Tang
Published: 2025-06-12 17:22:37

We introduce Magistral, Mistral's first reasoning model and our own scalable
reinforcement learning (RL) pipeline. Instead of relying on existing
implementations and RL traces distilled from prior models, we follow a ground
up approach, relying solely on our own models and infrastructure. Notably, we
demonstrate a stack that enabled us to explore the limits of pure RL training
of LLMs, present a simple method to force the reasoning language of the model,
and show that RL on text data alone maintains most of the initial checkpoint's
capabilities. We find that RL on text maintains or improves multimodal
understanding, instruction following and function calling. We present Magistral
Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we
open-source Magistral Small (Apache 2.0) which further includes cold-start data
from Magistral Medium.
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Authors: name: Yixin Ou, name: Yujie Luo, name: Jingsheng Zheng, name: Lanning Wei, name: Shuofei Qiao, name: Jintian Zhang, name: Da Zheng, name: Huajun Chen, name: Ningyu Zhang
Published: 2025-06-12 17:59:32

Large Language Model (LLM) agents have shown great potential in addressing
real-world data science problems. LLM-driven data science agents promise to
automate the entire machine learning pipeline, yet their real-world
effectiveness remains limited. Existing frameworks depend on rigid, pre-defined
workflows and inflexible coding strategies; consequently, they excel only on
relatively simple, classical problems and fail to capture the empirical
expertise that human practitioners bring to complex, innovative tasks. In this
work, we introduce AutoMind, an adaptive, knowledgeable LLM-agent framework
that overcomes these deficiencies through three key advances: (1) a curated
expert knowledge base that grounds the agent in domain expert knowledge, (2) an
agentic knowledgeable tree search algorithm that strategically explores
possible solutions, and (3) a self-adaptive coding strategy that dynamically
tailors code generation to task complexity. Evaluations on two automated data
science benchmarks demonstrate that AutoMind delivers superior performance
versus state-of-the-art baselines. Additional analyses confirm favorable
effectiveness, efficiency, and qualitative solution quality, highlighting
AutoMind as an efficient and robust step toward fully automated data science.
ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
Authors: name: Kangwei Liu, name: Siyuan Cheng, name: Bozhong Tian, name: Xiaozhuan Liang, name: Yuyang Yin, name: Meng Han, name: Ningyu Zhang, name: Bryan Hooi, name: Xi Chen, name: Shumin Deng
Published: 2025-06-12 17:57:05

Large language models (LLMs) have been increasingly applied to automated
harmful content detection tasks, assisting moderators in identifying policy
violations and improving the overall efficiency and accuracy of content review.
However, existing resources for harmful content detection are predominantly
focused on English, with Chinese datasets remaining scarce and often limited in
scope. We present a comprehensive, professionally annotated benchmark for
Chinese content harm detection, which covers six representative categories and
is constructed entirely from real-world data. Our annotation process further
yields a knowledge rule base that provides explicit expert knowledge to assist
LLMs in Chinese harmful content detection. In addition, we propose a
knowledge-augmented baseline that integrates both human-annotated knowledge
rules and implicit knowledge from large language models, enabling smaller
models to achieve performance comparable to state-of-the-art LLMs. Code and
data are available at https://github.com/zjunlp/ChineseHarm-bench.
Discovering Hierarchical Latent Capabilities of Language Models via
  Causal Representation Learning
Authors: name: Jikai Jin, name: Vasilis Syrgkanis, name: Sham Kakade, name: Hanlin Zhang
Published: 2025-06-12 06:07:42

Faithful evaluation of language model capabilities is crucial for deriving
actionable insights that can inform model development. However, rigorous causal
evaluations in this domain face significant methodological challenges,
including complex confounding effects and prohibitive computational costs
associated with extensive retraining. To tackle these challenges, we propose a
causal representation learning framework wherein observed benchmark performance
is modeled as a linear transformation of a few latent capability factors.
Crucially, these latent factors are identified as causally interrelated after
appropriately controlling for the base model as a common confounder. Applying
this approach to a comprehensive dataset encompassing over 1500 models
evaluated across six benchmarks from the Open LLM Leaderboard, we identify a
concise three-node linear causal structure that reliably explains the observed
performance variations. Further interpretation of this causal structure
provides substantial scientific insights beyond simple numerical rankings:
specifically, we reveal a clear causal direction starting from general
problem-solving capabilities, advancing through instruction-following
proficiency, and culminating in mathematical reasoning ability. Our results
underscore the essential role of carefully controlling base model variations
during evaluation, a step critical to accurately uncovering the underlying
causal relationships among latent model capabilities.
AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven
  Clip Generation
Authors: name: Haoyuan Shi, name: Yunxin Li, name: Xinyu Chen, name: Longyue Wang, name: Baotian Hu, name: Min Zhang
Published: 2025-06-12 10:06:21

Despite rapid advancements in video generation models, generating coherent
storytelling videos that span multiple scenes and characters remains
challenging. Current methods often rigidly convert pre-generated keyframes into
fixed-length clips, resulting in disjointed narratives and pacing issues.
Furthermore, the inherent instability of video generation models means that
even a single low-quality clip can significantly degrade the entire output
animation's logical coherence and visual continuity. To overcome these
obstacles, we introduce AniMaker, a multi-agent framework enabling efficient
multi-candidate clip generation and storytelling-aware clip selection, thus
creating globally consistent and story-coherent animation solely from text
input. The framework is structured around specialized agents, including the
Director Agent for storyboard generation, the Photography Agent for video clip
generation, the Reviewer Agent for evaluation, and the Post-Production Agent
for editing and voiceover. Central to AniMaker's approach are two key technical
components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search
(MCTS)-inspired strategy that intelligently navigates the candidate space to
generate high-potential clips while optimizing resource usage; and AniEval in
Reviewer Agent, the first framework specifically designed for multi-shot
animation evaluation, which assesses critical aspects such as story-level
consistency, action completion, and animation-specific features by considering
each clip in the context of its preceding and succeeding clips. Experiments
demonstrate that AniMaker achieves superior quality as measured by popular
metrics including VBench and our proposed AniEval framework, while
significantly improving the efficiency of multi-candidate generation, pushing
AI-generated storytelling animation closer to production standards.
What Makes a Good Natural Language Prompt?
Authors: name: Do Xuan Long, name: Duy Dinh, name: Ngoc-Hai Nguyen, name: Kenji Kawaguchi, name: Nancy F. Chen, name: Shafiq Joty, name: Min-Yen Kan
Published: 2025-06-07 23:19:27

As large language models (LLMs) have progressed towards more human-like and
human--AI communications have become prevalent, prompting has emerged as a
decisive component. However, there is limited conceptual consensus on what
exactly quantifies natural language prompts. We attempt to address this
question by conducting a meta-analysis surveying more than 150
prompting-related papers from leading NLP and AI conferences from 2022 to 2025
and blogs. We propose a property- and human-centric framework for evaluating
prompt quality, encompassing 21 properties categorized into six dimensions. We
then examine how existing studies assess their impact on LLMs, revealing their
imbalanced support across models and tasks, and substantial research gaps.
Further, we analyze correlations among properties in high-quality natural
language prompts, deriving prompting recommendations. We then empirically
explore multi-property prompt enhancements in reasoning tasks, observing that
single-property enhancements often have the greatest impact. Finally, we
discover that instruction-tuning on property-enhanced prompts can result in
better reasoning models. Our findings establish a foundation for
property-centric prompt evaluation and optimization, bridging the gaps between
human--AI communication and opening new prompting research directions.
Breaking Data Silos: Towards Open and Scalable Mobility Foundation
  Models via Generative Continual Learning
Authors: name: Yuan Yuan, name: Yukun Liu, name: Chonghua Han, name: Jie Feng, name: Yong Li
Published: 2025-06-07 07:19:11

Foundation models have revolutionized fields such as natural language
processing and computer vision by enabling general-purpose learning across
diverse tasks and datasets. However, building analogous models for human
mobility remains challenging due to the privacy-sensitive nature of mobility
data and the resulting data silos across institutions. To bridge this gap, we
propose MoveGCL, a scalable and privacy-preserving framework for training
mobility foundation models via generative continual learning. Without sharing
raw data, MoveGCL enables decentralized and progressive model evolution by
replaying synthetic trajectories generated from a frozen teacher model, and
reinforces knowledge retention through a tailored distillation strategy that
mitigates catastrophic forgetting. To address the heterogeneity of mobility
patterns, MoveGCL incorporates a Mixture-of-Experts Transformer with a
mobility-aware expert routing mechanism, and employs a layer-wise progressive
adaptation strategy to stabilize continual updates. Experiments on six
real-world urban datasets demonstrate that MoveGCL achieves performance
comparable to joint training and significantly outperforms federated learning
baselines, while offering strong privacy protection. MoveGCL marks a crucial
step toward unlocking foundation models for mobility, offering a practical
blueprint for open, scalable, and privacy-preserving model development in the
era of foundation models.
Resa: Transparent Reasoning Models via SAEs
Authors: name: Shangshang Wang, name: Julian Asilis, name: Ömer Faruk Akgül, name: Enes Burak Bilgin, name: Ollie Liu, name: Deqing Fu, name: Willie Neiswanger
Published: 2025-06-11 17:44:01

How cost-effectively can we elicit strong reasoning in language models by
leveraging their underlying representations? We answer this question with Resa,
a family of 1.5B reasoning models trained via a novel and efficient sparse
autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to
capture reasoning abilities from a source model, and then uses the trained SAE
to guide a standard supervised fine-tuning process to elicit such abilities in
a target model, all using verified question-answer data without any reasoning
traces. Notably, when applied to certain base models before further RL
post-training, SAE-Tuning retains >97% of its RL-trained counterpart's
reasoning performance while reducing training costs by >2000x to roughly \1
and training time by >450x to around 20 minutes. Furthermore, when applied to
lightly RL-trained models (e.g., within 1 hour on 2 GPUs), it enables reasoning
performance such as 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23 for only
around 1 additional cost. Surprisingly, the reasoning abilities extracted via
SAEs are potentially both generalizable and modular. Generality means abilities
extracted from one dataset still elevate performance on a larger and
overlapping corpus. Modularity means abilities extracted from Qwen or Qwen-Math
can be attached to the R1-Distill model at test time, without any retraining,
and yield comparable gains. Extensive ablations validate these findings and all
artifacts are fully open-sourced.
Build the web for agents, not agents for the web
Authors: name: Xing Han Lù, name: Gaurav Kamath, name: Marius Mosbach, name: Siva Reddy
Published: 2025-06-12 17:53:58

Recent advancements in Large Language Models (LLMs) and multimodal
counterparts have spurred significant interest in developing web agents -- AI
systems capable of autonomously navigating and completing tasks within web
environments. While holding tremendous promise for automating complex web
interactions, current approaches face substantial challenges due to the
fundamental mismatch between human-designed interfaces and LLM capabilities.
Current methods struggle with the inherent complexity of web inputs, whether
processing massive DOM trees, relying on screenshots augmented with additional
information, or bypassing the user interface entirely through API interactions.
This position paper advocates for a paradigm shift in web agent research:
rather than forcing web agents to adapt to interfaces designed for humans, we
should develop a new interaction paradigm specifically optimized for agentic
capabilities. To this end, we introduce the concept of an Agentic Web Interface
(AWI), an interface specifically designed for agents to navigate a website. We
establish six guiding principles for AWI design, emphasizing safety,
efficiency, and standardization, to account for the interests of all primary
stakeholders. This reframing aims to overcome fundamental limitations of
existing interfaces, paving the way for more efficient, reliable, and
transparent web agent design, which will be a collaborative effort involving
the broader ML community.
Ming-Omni: A Unified Multimodal Model for Perception and Generation
Authors: name: Inclusion AI, name: Biao Gong, name: Cheng Zou, name: Chuanyang Zheng, name: Chunluan Zhou, name: Canxiang Yan, name: Chunxiang Jin, name: Chunjie Shen, name: Dandan Zheng, name: Fudong Wang, name: Furong Xu, name: GuangMing Yao, name: Jun Zhou, name: Jingdong Chen, name: Jianxin Sun, name: Jiajia Liu, name: Jianjiang Zhu, name: Jun Peng, name: Kaixiang Ji, name: Kaiyou Song, name: Kaimeng Ren, name: Libin Wang, name: Lixiang Ru, name: Lele Xie, name: Longhua Tan, name: Lyuxin Xue, name: Lan Wang, name: Mochen Bai, name: Ning Gao, name: Pei Chen, name: Qingpei Guo, name: Qinglong Zhang, name: Qiang Xu, name: Rui Liu, name: Ruijie Xiong, name: Sirui Gao, name: Tinghao Liu, name: Taisong Li, name: Weilong Chai, name: Xinyu Xiao, name: Xiaomei Wang, name: Xiaoxue Chen, name: Xiao Lu, name: Xiaoyu Li, name: Xingning Dong, name: Xuzheng Yu, name: Yi Yuan, name: Yuting Gao, name: Yunxiao Sun, name: Yipeng Chen, name: Yifei Wu, name: Yongjie Lyu, name: Ziping Ma, name: Zipeng Feng, name: Zhijiang Fang, name: Zhihao Qiu, name: Ziyuan Huang, name: Zhengyu He
Published: 2025-06-11 02:50:49

We propose Ming-Omni, a unified multimodal model capable of processing
images, text, audio, and video, while demonstrating strong proficiency in both
speech and image generation. Ming-Omni employs dedicated encoders to extract
tokens from different modalities, which are then processed by Ling, an MoE
architecture equipped with newly proposed modality-specific routers. This
design enables a single model to efficiently process and fuse multimodal inputs
within a unified framework, thereby facilitating diverse tasks without
requiring separate models, task-specific fine-tuning, or structural redesign.
Importantly, Ming-Omni extends beyond conventional multimodal models by
supporting audio and image generation. This is achieved through the integration
of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for
high-quality image generation, which also allow the model to engage in
context-aware chatting, perform text-to-speech conversion, and conduct
versatile image editing. Our experimental results showcase Ming-Omni offers a
powerful solution for unified perception and generation across all modalities.
Notably, our proposed Ming-Omni is the first open-source model we are aware of
to match GPT-4o in modality support, and we release all code and model weights
to encourage further research and development in the community.
Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture
  without Training
Authors: name: Mozhi Zhang, name: Howe Tissue, name: Lu Wang, name: Xipeng Qiu
Published: 2025-06-12 17:53:51

We introduce~Domain2Vec, a novel approach that decomposes any
dataset into a linear combination of several meta-domains, a new concept
designed to capture the key underlying features of datasets.
Domain2Vec maintains a vocabulary of meta-domains and uses a
classifier to decompose any given dataset into a domain vector that corresponds
to a distribution over this vocabulary. These domain vectors enable the
identification of the optimal data mixture for language model (LM) pretraining
in a training-free manner under the \textbf{Distribution
Alignment Assumption} (DA^{2}), which suggests that when
the data distributions of the training set and the validation set are better
aligned, a lower validation loss is achieved. Moreover, Domain2vec can
be seamlessly integrated into previous works to model the relationship between
domain vectors and LM performance, greatly enhancing the efficiency and
scalability of previous methods. Extensive experiments demonstrate that
Domain2Vec helps find the data mixture that enhances downstream task
performance with minimal computational overhead. Specifically,
Domain2Vec achieves the same validation loss on Pile-CC using only
51.5% of the computation required when training on the original mixture of
The Pile dataset. Under equivalent compute budget, Domain2Vec improves
downstream performance by an average of 2.83%.
VideoDeepResearch: Long Video Understanding With Agentic Tool Using
Authors: name: Huaying Yuan, name: Zheng Liu, name: Junjie Zhou, name: Ji-Rong Wen, name: Zhicheng Dou
Published: 2025-06-12 15:39:10

Long video understanding (LVU) presents a significant challenge for current
multi-modal large language models (MLLMs) due to the task's inherent complexity
and context window constraint. It is widely assumed that addressing LVU tasks
requires foundation MLLMs with extended context windows, strong visual
perception capabilities, and proficient domain expertise. In this work, we
challenge this common belief by introducing VideoDeepResearch, a novel agentic
framework for long video understanding. Our approach relies solely on a
text-only large reasoning model (LRM) combined with a modular multi-modal
toolkit, including multimodal retrievers and visual perceivers, all of which
are readily available in practice. For each LVU task, the system formulates a
problem-solving strategy through reasoning, while selectively accessing and
utilizing essential video content via tool using. We conduct extensive
experiments on popular LVU benchmarks, including MLVU, Video-MME, and LVBench.
Our results demonstrate that VideoDeepResearch achieves substantial
improvements over existing MLLM baselines, surpassing the previous
state-of-the-art by 9.6%, 6.6%, and 3.9% on MLVU (test), LVBench, and
LongVideoBench, respectively. These findings highlight the promise of agentic
systems in overcoming key challenges in LVU problems.
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical
  Reasoning
Authors: name: Yu Sun, name: Xingyu Qian, name: Weiwen Xu, name: Hao Zhang, name: Chenghao Xiao, name: Long Li, name: Yu Rong, name: Wenbing Huang, name: Qifeng Bai, name: Tingyang Xu
Published: 2025-06-11 08:36:55

Though reasoning-based large language models (LLMs) have excelled in
mathematics and programming, their capabilities in knowledge-intensive medical
question answering remain underexplored. To address this, we introduce
ReasonMed, the largest medical reasoning dataset, comprising 370k high-quality
examples distilled from 1.7 million initial reasoning paths generated by
various LLMs. ReasonMed is constructed through a multi-agent
verification and refinement process, where we design an Error Refiner
to enhance the reasoning paths by identifying and correcting error-prone steps
flagged by a verifier. Leveraging ReasonMed, we systematically investigate best
practices for training medical reasoning models and find that combining
detailed Chain-of-Thought (CoT) reasoning with concise answer summaries yields
the most effective fine-tuning strategy. Based on this strategy, we train
ReasonMed-7B, which sets a new benchmark for sub-10B models, outperforming the
prior best by 4.17\% and even exceeding LLaMA3.1-70B on PubMedQA by 4.60\%.