Orionnnn/hub-stats huggingface dataset:
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Authors: name: Yizhou Wang, name: Chen Tang, name: Han Deng, name: Jiabei Xiao, name: Jiaqi Liu, name: Jianyu Wu, name: Jun Yao, name: Pengze Li, name: Encheng Su, name: Lintao Wang, name: Guohang Zhuang, name: Yuchen Ren, name: Ben Fei, name: Ming Hu, name: Xin Chen, name: Dongzhan Zhou, name: Junjun He, name: Xiangyu Yue, name: Zhenfei Yin, name: Jiamin Wu, name: Qihao Zheng, name: Yuhao Zhou, name: Huihui Xu, name: Chenglong Ma, name: Yan Lu, name: Wenlong Zhang, name: Chunfeng Song, name: Philip Torr, name: Shixiang Tang, name: Xinzhu Ma, name: Wanli Ouyang, name: Lei Bai
Published: 2025-09-25 17:52:06

We present a scientific reasoning foundation model that aligns natural
language with heterogeneous scientific representations. The model is pretrained
on a 206B-token corpus spanning scientific text, pure sequences, and
sequence-text pairs, then aligned via SFT on 40M instructions, annealed
cold-start bootstrapping to elicit long-form chain-of-thought, and
reinforcement learning with task-specific reward shaping, which instills
deliberate scientific reasoning. It supports four capability families, covering
up to 103 tasks across workflows: (i) faithful translation between text and
scientific formats, (ii) text/knowledge extraction, (iii) property prediction,
(iv) property classification, (v) unconditional and conditional sequence
generation and design. Compared with specialist systems, our approach broadens
instruction coverage, improves cross-domain generalization, and enhances
fidelity. We detail data curation and training and show that cross-discipline
learning strengthens transfer and downstream reliability. The model, instruct
tuning datasets and the evaluation code are open-sourced at
https://huggingface.co/SciReason and
https://github.com/open-sciencelab/SciReason.
SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
Authors: name: Hmrishav Bandyopadhyay, name: Rahim Entezari, name: Jim Scott, name: Reshinth Adithyan, name: Yi-Zhe Song, name: Varun Jampani
Published: 2025-09-25 16:07:38

We present SD3.5-Flash, an efficient few-step distillation framework that
brings high-quality image generation to accessible consumer devices. Our
approach distills computationally prohibitive rectified flow models through a
reformulated distribution matching objective tailored specifically for few-step
generation. We introduce two key innovations: "timestep sharing" to reduce
gradient noise and "split-timestep fine-tuning" to improve prompt alignment.
Combined with comprehensive pipeline optimizations like text encoder
restructuring and specialized quantization, our system enables both rapid
generation and memory-efficient deployment across different hardware
configurations. This democratizes access across the full spectrum of devices,
from mobile phones to desktop computers. Through extensive evaluation including
large-scale user studies, we demonstrate that SD3.5-Flash consistently
outperforms existing few-step methods, making advanced generative AI truly
accessible for practical deployment.
StyleBench: Evaluating thinking styles in Large Language Models
Authors: name: Junyu Guo, name: Shangding Gu, name: Ming Jin, name: Costas Spanos, name: Javad Lavaei
Published: 2025-09-25 08:00:39

The effectiveness of Large Language Models (LLMs) is heavily influenced by
the reasoning strategies, or styles of thought, employed in their prompts.
However, the interplay between these reasoning styles, model architecture, and
task type remains poorly understood. To address this, we introduce StyleBench,
a comprehensive benchmark for systematically evaluating reasoning styles across
diverse tasks and models. We assess five representative reasoning styles,
including Chain of Thought (CoT), Tree of Thought (ToT), Algorithm of Thought
(AoT), Sketch of Thought (SoT), and Chain-of-Draft (CoD) on five reasoning
tasks, using 15 open-source models from major families (LLaMA, Qwen, Mistral,
Gemma, GPT-OSS, Phi, and DeepSeek) ranging from 270M to 120B parameters. Our
large-scale analysis reveals that no single style is universally optimal. We
demonstrate that strategy efficacy is highly contingent on both model scale and
task type: search-based methods (AoT, ToT) excel in open-ended problems but
require large-scale models, while concise styles (SoT, CoD) achieve radical
efficiency gains on well-defined tasks. Furthermore, we identify key behavioral
patterns: smaller models frequently fail to follow output instructions and
default to guessing, while reasoning robustness emerges as a function of scale.
Our findings offer a crucial roadmap for selecting optimal reasoning strategies
based on specific constraints, we open source the benchmark in
https://github.com/JamesJunyuGuo/Style_Bench.
MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and
  Open Resources
Authors: name: Sicong Leng, name: Jing Wang, name: Jiaxi Li, name: Hao Zhang, name: Zhiqiang Hu, name: Boqiang Zhang, name: Yuming Jiang, name: Hang Zhang, name: Xin Li, name: Lidong Bing, name: Deli Zhao, name: Wei Lu, name: Yu Rong, name: Aixin Sun, name: Shijian Lu
Published: 2025-09-25 14:58:29

Large multimodal reasoning models have achieved rapid progress, but their
advancement is constrained by two major limitations: the absence of open,
large-scale, high-quality long chain-of-thought (CoT) data, and the instability
of reinforcement learning (RL) algorithms in post-training. Group Relative
Policy Optimization (GRPO), the standard framework for RL fine-tuning, is prone
to gradient vanishing when reward variance is low, which weakens optimization
signals and impairs convergence. This work makes three contributions: (1) We
propose Variance-Aware Sampling (VAS), a data selection strategy guided by
Variance Promotion Score (VPS) that combines outcome variance and trajectory
diversity to promote reward variance and stabilize policy optimization. (2) We
release large-scale, carefully curated resources containing ~1.6M long CoT
cold-start data and ~15k RL QA pairs, designed to ensure quality, difficulty,
and diversity, along with a fully reproducible end-to-end training codebase.
(3) We open-source a family of multimodal reasoning models in multiple scales,
establishing standardized baselines for the community. Experiments across
mathematical reasoning benchmarks demonstrate the effectiveness of both the
curated data and the proposed VAS. Comprehensive ablation studies and analyses
provide further insight into the contributions of each component. In addition,
we theoretically establish that reward variance lower-bounds the expected
policy gradient magnitude, with VAS serving as a practical mechanism to realize
this guarantee. Our code, data, and checkpoints are available at
https://github.com/LengSicong/MMR1.
Interactive Recommendation Agent with Active User Commands
Authors: name: Jiakai Tang, name: Yujie Luo, name: Xunke Xi, name: Fei Sun, name: Xueyang Feng, name: Sunhao Dai, name: Chao Yi, name: Dian Chen, name: Zhujin Gao, name: Yang Li, name: Xu Chen, name: Wen Chen, name: Jian Wu, name: Yuning Jiang, name: Bo Zheng
Published: 2025-09-25 15:38:27

Traditional recommender systems rely on passive feedback mechanisms that
limit users to simple choices such as like and dislike. However, these
coarse-grained signals fail to capture users' nuanced behavior motivations and
intentions. In turn, current systems cannot also distinguish which specific
item attributes drive user satisfaction or dissatisfaction, resulting in
inaccurate preference modeling. These fundamental limitations create a
persistent gap between user intentions and system interpretations, ultimately
undermining user satisfaction and harming system effectiveness.
  To address these limitations, we introduce the Interactive Recommendation
Feed (IRF), a pioneering paradigm that enables natural language commands within
mainstream recommendation feeds. Unlike traditional systems that confine users
to passive implicit behavioral influence, IRF empowers active explicit control
over recommendation policies through real-time linguistic commands. To support
this paradigm, we develop RecBot, a dual-agent architecture where a Parser
Agent transforms linguistic expressions into structured preferences and a
Planner Agent dynamically orchestrates adaptive tool chains for on-the-fly
policy adjustment. To enable practical deployment, we employ
simulation-augmented knowledge distillation to achieve efficient performance
while maintaining strong reasoning capabilities. Through extensive offline and
long-term online experiments, RecBot shows significant improvements in both
user satisfaction and business outcomes.
SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and
  Self-Reflective Agent
Authors: name: Yandan Yang, name: Baoxiong Jia, name: Shujie Zhang, name: Siyuan Huang
Published: 2025-09-24 09:06:41

Indoor scene synthesis has become increasingly important with the rise of
Embodied AI, which requires 3D environments that are not only visually
realistic but also physically plausible and functionally diverse. While recent
approaches have advanced visual fidelity, they often remain constrained to
fixed scene categories, lack sufficient object-level detail and physical
consistency, and struggle to align with complex user instructions. In this
work, we present SceneWeaver, a reflective agentic framework that unifies
diverse scene synthesis paradigms through tool-based iterative refinement. At
its core, SceneWeaver employs a language model-based planner to select from a
suite of extensible scene generation tools, ranging from data-driven generative
models to visual- and LLM-based methods, guided by self-evaluation of physical
plausibility, visual realism, and semantic alignment with user input. This
closed-loop reason-act-reflect design enables the agent to identify semantic
inconsistencies, invoke targeted tools, and update the environment over
successive iterations. Extensive experiments on both common and open-vocabulary
room types demonstrate that SceneWeaver not only outperforms prior methods on
physical, visual, and semantic metrics, but also generalizes effectively to
complex scenes with diverse instructions, marking a step toward general-purpose
3D environment generation. Project website: https://scene-weaver.github.io/.
Understanding the Thinking Process of Reasoning Models: A Perspective
  from Schoenfeld's Episode Theory
Authors: name: Ming Li, name: Nan Zhang, name: Chenrui Fan, name: Hong Jiao, name: Yanbin Fu, name: Sydney Peters, name: Qingshu Xu, name: Robert Lissitz, name: Tianyi Zhou
Published: 2025-09-18 06:42:41

While Large Reasoning Models (LRMs) generate extensive chain-of-thought
reasoning, we lack a principled framework for understanding how these thoughts
are structured. In this paper, we introduce a novel approach by applying
Schoenfeld's Episode Theory, a classic cognitive framework for human
mathematical problem-solving, to analyze the reasoning traces of LRMs. We
annotated thousands of sentences and paragraphs from model-generated solutions
to math problems using seven cognitive labels (e.g., Plan, Implement, Verify).
The result is the first publicly available benchmark for the fine-grained
analysis of machine reasoning, including a large annotated corpus and detailed
annotation guidebooks. Our preliminary analysis reveals distinct patterns in
LRM reasoning, such as the transition dynamics between cognitive states. This
framework provides a theoretically grounded methodology for interpreting LRM
cognition and enables future work on more controllable and transparent
reasoning systems.
Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D
  Assets
Authors: name: Team Hunyuan3D, name: Bowen Zhang, name: Chunchao Guo, name: Haolin Liu, name: Hongyu Yan, name: Huiwen Shi, name: Jingwei Huang, name: Junlin Yu, name: Kunhong Li, name: Linus, name: Penghao Wang, name: Qingxiang Lin, name: Sicong Liu, name: Xianghui Yang, name: Yixuan Tang, name: Yunfei Zhao, name: Zeqiang Lai, name: Zhihao Liang, name: Zibo Zhao
Published: 2025-09-25 14:39:17

Recent advances in 3D-native generative models have accelerated asset
creation for games, film, and design. However, most methods still rely
primarily on image or text conditioning and lack fine-grained, cross-modal
controls, which limits controllability and practical adoption. To address this
gap, we present Hunyuan3D-Omni, a unified framework for fine-grained,
controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images,
Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose
priors as conditioning signals, enabling precise control over geometry,
topology, and pose. Instead of separate heads for each modality, our model
unifies all signals in a single cross-modal architecture. We train with a
progressive, difficulty-aware sampling strategy that selects one control
modality per example and biases sampling toward harder signals (e.g., skeletal
pose) while downweighting easier ones (e.g., point clouds), encouraging robust
multi-modal fusion and graceful handling of missing inputs. Experiments show
that these additional controls improve generation accuracy, enable
geometry-aware transformations, and increase robustness for production
workflows.
Seedream 4.0: Toward Next-generation Multimodal Image Generation
Authors: name: Team Seedream, name: Yunpeng Chen, name: Yu Gao, name: Lixue Gong, name: Meng Guo, name: Qiushan Guo, name: Zhiyao Guo, name: Xiaoxia Hou, name: Weilin Huang, name: Yixuan Huang, name: Xiaowen Jian, name: Huafeng Kuang, name: Zhichao Lai, name: Fanshi Li, name: Liang Li, name: Xiaochen Lian, name: Chao Liao, name: Liyang Liu, name: Wei Liu, name: Yanzuo Lu, name: Zhengxiong Luo, name: Tongtong Ou, name: Guang Shi, name: Yichun Shi, name: Shiqi Sun, name: Yu Tian, name: Zhi Tian, name: Peng Wang, name: Rui Wang, name: Xun Wang, name: Ye Wang, name: Guofeng Wu, name: Jie Wu, name: Wenxu Wu, name: Yonghui Wu, name: Xin Xia, name: Xuefeng Xiao, name: Shuang Xu, name: Xin Yan, name: Ceyuan Yang, name: Jianchao Yang, name: Zhonghua Zhai, name: Chenlin Zhang, name: Heng Zhang, name: Qi Zhang, name: Xinyu Zhang, name: Yuwei Zhang, name: Shijia Zhao, name: Wenliang Zhao, name: Wenjia Zhu
Published: 2025-09-24 17:59:04

We introduce Seedream 4.0, an efficient and high-performance multimodal image
generation system that unifies text-to-image (T2I) synthesis, image editing,
and multi-image composition within a single framework. We develop a highly
efficient diffusion transformer with a powerful VAE which also can reduce the
number of image tokens considerably. This allows for efficient training of our
model, and enables it to fast generate native high-resolution images (e.g.,
1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning
diverse taxonomies and knowledge-centric concepts. Comprehensive data
collection across hundreds of vertical scenarios, coupled with optimized
strategies, ensures stable and large-scale training, with strong
generalization. By incorporating a carefully fine-tuned VLM model, we perform
multi-modal post-training for training both T2I and image editing tasks
jointly. For inference acceleration, we integrate adversarial distillation,
distribution matching, and quantization, as well as speculative decoding. It
achieves an inference time of up to 1.8 seconds for generating a 2K image
(without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream
4.0 can achieve state-of-the-art results on both T2I and multimodal image
editing. In particular, it demonstrates exceptional multimodal capabilities in
complex tasks, including precise image editing and in-context reasoning, and
also allows for multi-image reference, and can generate multiple output images.
This extends traditional T2I systems into an more interactive and
multidimensional creative tool, pushing the boundary of generative AI for both
creativity and professional applications. Seedream 4.0 is now accessible on
https://www.volcengine.com/experience/ark?launch=seedream.
CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy
  Optimization in Reinforcement Learning
Authors: name: Zhenpeng Su, name: Leiyu Pan, name: Minxuan Lv, name: Yuntao Li, name: Wenping Hu, name: Fuzheng Zhang, name: Kun Gai, name: Guorui Zhou
Published: 2025-09-25 03:22:04

Reinforcement learning (RL) has become a powerful paradigm for optimizing
large language models (LLMs) to handle complex reasoning tasks. A core
challenge in this process lies in managing policy entropy, which reflects the
balance between exploration and exploitation during training. Existing methods,
such as proximal policy optimization (PPO) and its variants, discard valuable
gradient signals from low-probability tokens due to the clipping mechanism. We
systematically analyze the entropy dynamics and reveal that these clipped
tokens play a critical yet overlooked role in regulating entropy evolution. We
propose Controlling Entropy via
Gradient-Preserving Policy Optimization
(CE-GPPO), a novel algorithm that reintroduces gradients from clipped tokens in
native PPO in a gentle and bounded manner. By controlling the magnitude of
gradients from tokens outside the clipping interval, CE-GPPO is able to achieve
an exploration-exploitation trade-off. We provide theoretical justification and
empirical evidence showing that CE-GPPO effectively mitigates entropy
instability. Extensive experiments on mathematical reasoning benchmarks show
that CE-GPPO consistently outperforms strong baselines across different model
scales.
Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just
  What They Say
Authors: name: Jacob Fein-Ashley, name: Dhruv Parikh, name: Rajgopal Kannan, name: Viktor Prasanna
Published: 2025-09-25 13:50:09

Open-source Large Language Models (LLMs) increasingly specialize by domain
(e.g., math, code, general reasoning), motivating systems that leverage
complementary strengths across models. Prior multi-LLM approaches either (i)
route a query to one or a few experts and generate independently, (ii)
aggregate outputs from each model via costly multi-turn exchanges, or (iii)
fuse weights into a single model-typically requiring architectural homogeneity.
We introduce Mixture of Thoughts (MoT), a simple method for latent-level
collaboration among heterogeneous experts under a global routing scheme. For
each query, a lightweight router selects top-K experts and designates a
primary expert; uniformly placed interaction layers project hidden states into
a shared latent space where the primary expert performs cross-attention over
its active (selected) peers. Pre-trained experts remain frozen; only the router
and the lightweight interaction layers are trained with a novel joint training
objective that improves both the expert selection and inter-expert
collaboration. Across five in-distribution (ID) and three out-of-distribution
(OOD) benchmarks, MoT surpasses the current routing and aggregation-based
state-of-the-art, Avengers, by +0.38% and +2.92%, respectively. Further,
MoT significantly outperforms the best-performing single model. It achieves
this with single-pass inference, runtime comparable to routing baselines, and
none of the overheads of iterative aggregation. MoT offers a simple
latent-space mechanism for combining heterogeneous LLMs, a practical step
toward broader multi-LLM collaboration. Our code is publicly available at
https://github.com/jacobfa/mot.
kh2d-solver: A Python Library for Idealized Two-Dimensional
  Incompressible Kelvin-Helmholtz Instability
Authors: name: Sandy H. S. Herho, name: Nurjanna J. Trilaksono, name: Faiz R. Fajary, name: Gandhi Napitupulu, name: Iwan P. Anwar, name: Faruq Khadami, name: Dasapta E. Irawan
Published: 2025-09-19 15:26:37

We present an open-source Python library for simulating two-dimensional
incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The
solver employs a fractional-step projection method with spectral Poisson
solution via Fast Sine Transform, achieving second-order spatial accuracy.
Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient
computation. Four canonical test cases explore Reynolds numbers 1000--5000 and
Richardson numbers 0.1--0.3: classical shear layer, double shear configuration,
rotating flow, and forced turbulence. Statistical analysis using Shannon
entropy and complexity indices reveals that double shear layers achieve
2.8times higher mixing rates than forced turbulence despite lower Reynolds
numbers. The solver runs efficiently on standard desktop hardware, with
384times192 grid simulations completing in approximately 31 minutes. Results
demonstrate that mixing efficiency depends on instability generation pathways
rather than intensity measures alone, challenging Richardson number-based
parameterizations and suggesting refinements for subgrid-scale representation
in climate models.
SimpleFold: Folding Proteins is Simpler than You Think
Authors: name: Yuyang Wang, name: Jiarui Lu, name: Navdeep Jaitly, name: Josh Susskind, name: Miguel Angel Bautista
Published: 2025-09-23 00:33:32

Protein folding models have achieved groundbreaking results typically via a
combination of integrating domain knowledge into the architectural blocks and
training pipelines. Nonetheless, given the success of generative models across
different but related problems, it is natural to question whether these
architectural designs are a necessary condition to build performant models. In
this paper, we introduce SimpleFold, the first flow-matching based protein
folding model that solely uses general purpose transformer blocks. Protein
folding models typically employ computationally expensive modules involving
triangular updates, explicit pair representations or multiple training
objectives curated for this specific domain. Instead, SimpleFold employs
standard transformer blocks with adaptive layers and is trained via a
generative flow-matching objective with an additional structural term. We scale
SimpleFold to 3B parameters and train it on approximately 9M distilled protein
structures together with experimental PDB data. On standard folding benchmarks,
SimpleFold-3B achieves competitive performance compared to state-of-the-art
baselines, in addition SimpleFold demonstrates strong performance in ensemble
prediction which is typically difficult for models trained via deterministic
reconstruction objectives. Due to its general-purpose architecture, SimpleFold
shows efficiency in deployment and inference on consumer-level hardware.
SimpleFold challenges the reliance on complex domain-specific architectures
designs in protein folding, opening up an alternative design space for future
progress.
ATLAS: Benchmarking and Adapting LLMs for Global Trade via Harmonized
  Tariff Code Classification
Authors: name: Pritish Yuvraj, name: Siva Devarakonda
Published: 2025-09-22 20:32:24

Accurate classification of products under the Harmonized Tariff Schedule
(HTS) is a critical bottleneck in global trade, yet it has received little
attention from the machine learning community. Misclassification can halt
shipments entirely, with major postal operators suspending deliveries to the
U.S. due to incomplete customs documentation. We introduce the first benchmark
for HTS code classification, derived from the U.S. Customs Rulings Online
Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned
Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit
classifications and 57.5 percent correct 6-digit classifications, improvements
of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking.
Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and
eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to
guarantee data privacy in high-stakes trade and compliance workflows. While
Atlas sets a strong baseline, the benchmark remains highly challenging, with
only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim
to position HTS classification as a new community benchmark task and invite
future work in retrieval, reasoning, and alignment.