Orionnnn/hub-stats huggingface dataset:
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
Authors: name: Yuetong Liu, name: Yunqiu Xu, name: Yang Wei, name: Xiuli Bi, name: Bin Xiao
Published: 2025-05-22 10:06:35

Restoring nighttime images affected by multiple adverse weather conditions is
a practical yet under-explored research problem, as multiple weather conditions
often coexist in the real world alongside various lighting effects at night.
This paper first explores the challenging multi-weather nighttime image
restoration task, where various types of weather degradations are intertwined
with flare effects. To support the research, we contribute the AllWeatherNight
dataset, featuring large-scale high-quality nighttime images with diverse
compositional degradations, synthesized using our introduced illumination-aware
degradation generation. Moreover, we present ClearNight, a unified nighttime
image restoration framework, which effectively removes complex degradations in
one go. Specifically, ClearNight extracts Retinex-based dual priors and
explicitly guides the network to focus on uneven illumination regions and
intrinsic texture contents respectively, thereby enhancing restoration
effectiveness in nighttime scenarios. In order to better represent the common
and unique characters of multiple weather degradations, we introduce a
weather-aware dynamic specific-commonality collaboration method, which
identifies weather degradations and adaptively selects optimal candidate units
associated with specific weather types. Our ClearNight achieves
state-of-the-art performance on both synthetic and real-world images.
Comprehensive ablation experiments validate the necessity of AllWeatherNight
dataset as well as the effectiveness of ClearNight. Project page:
https://henlyta.github.io/ClearNight/mainpage.html
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation
  via Reinforcement Learning
Authors: name: Mingrui Wu, name: Lu Wang, name: Pu Zhao, name: Fangkai Yang, name: Jianjin Zhang, name: Jianfeng Liu, name: Yuefeng Zhan, name: Weihao Han, name: Hao Sun, name: Jiayi Ji, name: Xiaoshuai Sun, name: Qingwei Lin, name: Weiwei Deng, name: Dongmei Zhang, name: Feng Sun, name: Qi Zhang, name: Rongrong Ji
Published: 2025-05-23 06:44:26

Despite recent progress in text-to-image (T2I) generation, existing models
often struggle to faithfully capture user intentions from short and
under-specified prompts. While prior work has attempted to enhance prompts
using large language models (LLMs), these methods frequently generate stylistic
or unrealistic content due to insufficient grounding in visual semantics and
real-world composition. Inspired by recent advances in reasoning for language
model, we propose RePrompt, a novel reprompting framework that introduces
explicit reasoning into the prompt enhancement process via reinforcement
learning. Instead of relying on handcrafted rules or stylistic rewrites, our
method trains a language model to generate structured, self-reflective prompts
by optimizing for image-level outcomes. The tailored reward models assesse the
generated images in terms of human preference, semantic alignment, and visual
composition, providing indirect supervision to refine prompt generation. Our
approach enables end-to-end training without human-annotated data. Experiments
on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial
layout fidelity and compositional generalization across diverse T2I backbones,
establishing new state-of-the-art results.
QwenLong-CPRS: Towards infty-LLMs with Dynamic Context Optimization
Authors: name: Weizhou Shen, name: Chenliang Li, name: Fanqi Wan, name: Shengyi Liao, name: Shaopeng Lai, name: Bo Zhang, name: Yingcheng Shi, name: Yuning Wu, name: Gang Fu, name: Zhansheng Li, name: Bin Yang, name: Ji Zhang, name: Fei Huang, name: Jingren Zhou, name: Ming Yan
Published: 2025-05-23 16:47:00

This technical report presents QwenLong-CPRS, a context compression framework
designed for explicit long-context optimization, addressing prohibitive
computation overhead during the prefill stage and the "lost in the middle"
performance degradation of large language models (LLMs) during long sequence
processing. Implemented through a novel dynamic context optimization mechanism,
QwenLong-CPRS enables multi-granularity context compression guided by natural
language instructions, achieving both efficiency gains and improved
performance.
  Evolved from the Qwen architecture series, QwenLong-CPRS introduces four key
innovations: (1) Natural language-guided dynamic optimization, (2)
Bidirectional reasoning layers for enhanced boundary awareness, (3) Token
critic mechanisms with language modeling heads, and (4) Window-parallel
inference.
  Comprehensive evaluations across five benchmarks (4K-2M word contexts)
demonstrate QwenLong-CPRS's threefold effectiveness: (1) Consistent superiority
over other context management methods like RAG and sparse attention in both
accuracy and efficiency. (2) Architecture-agnostic integration with all
flagship LLMs, including GPT-4o, Gemini2.0-pro, Claude3.7-sonnet, DeepSeek-v3,
and Qwen2.5-max, achieves 21.59times context compression alongside
19.15-point average performance gains; (3) Deployed with Qwen2.5-32B-Instruct,
QwenLong-CPRS surpasses leading proprietary LLMs by 4.85 and 10.88 points on
Ruler-128K and InfiniteBench, establishing new SOTA performance.
Scaling Image and Video Generation via Test-Time Evolutionary Search
Authors: name: Haoran He, name: Jiajun Liang, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Kun Gai, name: Ling Pan
Published: 2025-05-23 08:25:46

As the marginal cost of scaling computation (data and parameters) during
model pre-training continues to increase substantially, test-time scaling (TTS)
has emerged as a promising direction for improving generative model performance
by allocating additional computation at inference time. While TTS has
demonstrated significant success across multiple language tasks, there remains
a notable gap in understanding the test-time scaling behaviors of image and
video generative models (diffusion-based or flow-based models). Although recent
works have initiated exploration into inference-time strategies for vision
tasks, these approaches face critical limitations: being constrained to
task-specific domains, exhibiting poor scalability, or falling into reward
over-optimization that sacrifices sample diversity. In this paper, we propose
Evolutionary Search (EvoSearch), a novel, generalist, and
efficient TTS method that effectively enhances the scalability of both image
and video generation across diffusion and flow models, without requiring
additional training or model expansion. EvoSearch reformulates test-time
scaling for diffusion and flow models as an evolutionary search problem,
leveraging principles from biological evolution to efficiently explore and
refine the denoising trajectory. By incorporating carefully designed selection
and mutation mechanisms tailored to the stochastic differential equation
denoising process, EvoSearch iteratively generates higher-quality offspring
while preserving population diversity. Through extensive evaluation across both
diffusion and flow architectures for image and video generation tasks, we
demonstrate that our method consistently outperforms existing approaches,
achieves higher diversity, and shows strong generalizability to unseen
evaluation metrics. Our project is available at the website
https://tinnerhrhe.github.io/evosearch.
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA
Authors: name: Rishabh Maheshwary, name: Masoud Hashemi, name: Khyati Mahajan, name: Shiva Krishna Reddy Malay, name: Sai Rajeswar, name: Sathwik Tejaswi Madhusudhan, name: Spandana Gella, name: Vikas Yadav
Published: 2025-05-22 06:45:05

Iterative RAG for multi-hop question answering faces challenges with lengthy
contexts and the buildup of irrelevant information. This hinders a model's
capacity to process and reason over retrieved content and limits performance.
While recent methods focus on compressing retrieved information, they are
either restricted to single-round RAG, require finetuning or lack scalability
in iterative RAG. To address these challenges, we propose Notes Writing, a
method that generates concise and relevant notes from retrieved documents at
each step, thereby reducing noise and retaining only essential information.
This indirectly increases the effective context length of Large Language Models
(LLMs), enabling them to reason and plan more effectively while processing
larger volumes of input text. Notes Writing is framework agnostic and can be
integrated with different iterative RAG methods. We demonstrate its
effectiveness with three iterative RAG methods, across two models and four
evaluation datasets. Notes writing yields an average improvement of 15.6
percentage points overall, with minimal increase in output tokens.
TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in
  Real-World Scenarios
Authors: name: Shaohang Wei, name: Wei Li, name: Feifan Song, name: Wen Luo, name: Tianyi Zhuang, name: Haochen Tan, name: Zhijiang Guo, name: Houfeng Wang
Published: 2025-05-19 09:22:02

Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend
the real world. However, existing works neglect the real-world challenges for
temporal reasoning: (1) intensive temporal information, (2) fast-changing event
dynamics, and (3) complex temporal dependencies in social interactions. To
bridge this gap, we propose a multi-level benchmark TIME, designed for temporal
reasoning in real-world scenarios. TIME consists of 38,522 QA pairs, covering 3
levels with 11 fine-grained sub-tasks. This benchmark encompasses 3
sub-datasets reflecting different real-world challenges: TIME-Wiki, TIME-News,
and TIME-Dial. We conduct extensive experiments on reasoning models and
non-reasoning models. And we conducted an in-depth analysis of temporal
reasoning performance across diverse real-world scenarios and tasks, and
summarized the impact of test-time scaling on temporal reasoning capabilities.
Additionally, we release TIME-Lite, a human-annotated subset to foster future
research and standardized evaluation in temporal reasoning. The code is
available at https://github.com/sylvain-wei/TIME , and the dataset is available
at https://huggingface.co/datasets/SylvainWei/TIME .
TabSTAR: A Foundation Tabular Model With Semantically Target-Aware
  Representations
Authors: name: Alan Arazi, name: Eilam Shapira, name: Roi Reichart
Published: 2025-05-23 17:34:28

While deep learning has achieved remarkable success across many domains, it
has historically underperformed on tabular learning tasks, which remain
dominated by gradient boosting decision trees (GBDTs). However, recent
advancements are paving the way for Tabular Foundation Models, which can
leverage real-world knowledge and generalize across diverse datasets,
particularly when the data contains free-text. Although incorporating language
model capabilities into tabular tasks has been explored, most existing methods
utilize static, target-agnostic textual representations, limiting their
effectiveness. We introduce TabSTAR: a Foundation Tabular Model with
Semantically Target-Aware Representations. TabSTAR is designed to enable
transfer learning on tabular data with textual features, with an architecture
free of dataset-specific parameters. It unfreezes a pretrained text encoder and
takes as input target tokens, which provide the model with the context needed
to learn task-specific embeddings. TabSTAR achieves state-of-the-art
performance for both medium- and large-sized datasets across known benchmarks
of classification tasks with text features, and its pretraining phase exhibits
scaling laws in the number of datasets, offering a pathway for further
performance improvements.
Interactive Post-Training for Vision-Language-Action Models
Authors: name: Shuhan Tan, name: Kairan Dou, name: Yue Zhao, name: Philipp Krähenbühl
Published: 2025-05-22 17:59:45

We introduce RIPT-VLA, a simple and scalable reinforcement-learning-based
interactive post-training paradigm that fine-tunes pretrained
Vision-Language-Action (VLA) models using only sparse binary success rewards.
Existing VLA training pipelines rely heavily on offline expert demonstration
data and supervised imitation, limiting their ability to adapt to new tasks and
environments under low-data regimes. RIPT-VLA addresses this by enabling
interactive post-training with a stable policy optimization algorithm based on
dynamic rollout sampling and leave-one-out advantage estimation.
  RIPT-VLA has the following characteristics. First, it applies to various VLA
models, resulting in an improvement on the lightweight QueST model by 21.2%,
and the 7B OpenVLA-OFT model to an unprecedented 97.5% success rate. Second, it
is computationally efficient and data-efficient: with only one demonstration,
RIPT-VLA enables an unworkable SFT model (4%) to succeed with a 97% success
rate within 15 iterations. Furthermore, we demonstrate that the policy learned
by RIPT-VLA generalizes across different tasks and scenarios and is robust to
the initial state context. These results highlight RIPT-VLA as a practical and
effective paradigm for post-training VLA models through minimal supervision.
QwenLong-L1: Towards Long-Context Large Reasoning Models with
  Reinforcement Learning
Authors: name: Fanqi Wan, name: Weizhou Shen, name: Shengyi Liao, name: Yingcheng Shi, name: Chenliang Li, name: Ziyi Yang, name: Ji Zhang, name: Fei Huang, name: Jingren Zhou, name: Ming Yan
Published: 2025-05-23 09:31:55

Recent large reasoning models (LRMs) have demonstrated strong reasoning
capabilities through reinforcement learning (RL). These improvements have
primarily been observed within the short-context reasoning tasks. In contrast,
extending LRMs to effectively process and reason on long-context inputs via RL
remains a critical unsolved challenge. To bridge this gap, we first formalize
the paradigm of long-context reasoning RL, and identify key challenges in
suboptimal training efficiency and unstable optimization process. To address
these issues, we propose QwenLong-L1, a framework that adapts short-context
LRMs to long-context scenarios via progressive context scaling. Specifically,
we utilize a warm-up supervised fine-tuning (SFT) stage to establish a robust
initial policy, followed by a curriculum-guided phased RL technique to
stabilize the policy evolution, and enhanced with a difficulty-aware
retrospective sampling strategy to incentivize the policy exploration.
Experiments on seven long-context document question-answering benchmarks
demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini
and Qwen3-235B-A22B, achieving performance on par with
Claude-3.7-Sonnet-Thinking, demonstrating leading performance among
state-of-the-art LRMs. This work advances the development of practical
long-context LRMs capable of robust reasoning across information-intensive
environments.
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse
  Attention
Authors: name: Shuang Wu, name: Youtian Lin, name: Feihu Zhang, name: Yifei Zeng, name: Yikang Yang, name: Yajie Bao, name: Jiachen Qian, name: Siyu Zhu, name: Philip Torr, name: Xun Cao, name: Yao Yao
Published: 2025-05-23 02:58:01

Generating high resolution 3D shapes using volumetric representations such as
Signed Distance Functions presents substantial computational and memory
challenges. We introduce Direct3D S2, a scalable 3D generation framework based
on sparse volumes that achieves superior output quality with dramatically
reduced training costs. Our key innovation is the Spatial Sparse Attention
mechanism, which greatly enhances the efficiency of Diffusion Transformer
computations on sparse volumetric data. SSA allows the model to effectively
process large token sets within sparse volumes, significantly reducing
computational overhead and achieving a 3.9x speedup in the forward pass and a
9.6x speedup in the backward pass. Our framework also includes a variational
autoencoder that maintains a consistent sparse volumetric format across input,
latent, and output stages. Compared to previous methods with heterogeneous
representations in 3D VAE, this unified design significantly improves training
efficiency and stability. Our model is trained on public available datasets,
and experiments demonstrate that Direct3D S2 not only surpasses
state-of-the-art methods in generation quality and efficiency, but also enables
training at 1024 resolution using only 8 GPUs, a task typically requiring at
least 32 GPUs for volumetric representations at 256 resolution, thus making
gigascale 3D generation both practical and accessible. Project page:
https://nju3dv.github.io/projects/Direct3D-S2/.
Revisiting Residual Connections: Orthogonal Updates for Stable and
  Efficient Deep Networks
Authors: name: Giyeong Oh, name: Woohyun Cho, name: Siyeol Kim, name: Suhwan Choi, name: Younjae Yu
Published: 2025-05-17 07:16:11

Residual connections are pivotal for deep neural networks, enabling greater
depth by mitigating vanishing gradients. However, in standard residual updates,
the module's output is directly added to the input stream. This can lead to
updates that predominantly reinforce or modulate the existing stream direction,
potentially underutilizing the module's capacity for learning entirely novel
features. In this work, we introduce Orthogonal Residual Update: we decompose
the module's output relative to the input stream and add only the component
orthogonal to this stream. This design aims to guide modules to contribute
primarily new representational directions, fostering richer feature learning
while promoting more efficient training. We demonstrate that our orthogonal
update strategy improves generalization accuracy and training stability across
diverse architectures (ResNetV2, Vision Transformers) and datasets (CIFARs,
TinyImageNet, ImageNet-1k), achieving, for instance, a +4.3\%p top-1 accuracy
gain for ViT-B on ImageNet-1k.
Synthetic Data RL: Task Definition Is All You Need
Authors: name: Yiduo Guo, name: Zhen Guo, name: Chuanwei Huang, name: Zi-Ang Wang, name: Zekai Zhang, name: Haofei Yu, name: Huishuai Zhang, name: Yikang Shen
Published: 2025-05-18 05:35:13

Reinforcement learning (RL) is a powerful way to adapt foundation models to
specialized tasks, but its reliance on large-scale human-labeled data limits
broad adoption. We introduce Synthetic Data RL, a simple and general framework
that reinforcement fine-tunes models using only synthetic data generated from a
task definition. Our method first generates question and answer pairs from the
task definition and retrieved documents, then adapts the difficulty of the
question based on model solvability, and selects questions using the average
pass rate of the model across samples for RL training. On Qwen-2.5-7B, our
method achieves a 29.2% absolute improvement over the base model on GSM8K (+2.9
pp vs. instruction-tuned, +6.6 pp vs. Self-Instruct), 8.7% on MATH, 13.1% on
GPQA (+7.0 pp vs. SynthLLM), 8.9% on MedQA, 17.7% on CQA (law) and 13.7% on CFA
(finance). It surpasses supervised fine-tuning under the same data budget and
nearly matches RL with full human data across datasets (e.g., +17.2 pp on
GSM8K). Adding 100 human demonstrations improves the performance of GSM8K only
by 0.4 pp, showing a limited added value. By reducing human data annotation,
Synthetic Data RL enables scalable and efficient RL-based model adaptation.
Code and demos are available at https://github.com/gydpku/Data_Synthesis_RL/.
AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large
  Language Models
Authors: name: Kai Li, name: Can Shen, name: Yile Liu, name: Jirui Han, name: Kelong Zheng, name: Xuechao Zou, name: Zhe Wang, name: Xingjian Du, name: Shun Zhang, name: Hanjun Luo, name: Yingbin Jin, name: Xinxin Xing, name: Ziyang Ma, name: Yue Liu, name: Xiaojun Jia, name: Yifan Zhang, name: Junfeng Fang, name: Kun Wang, name: Yibo Yan, name: Haoyang Li, name: Yiming Li, name: Xiaobin Zhuang, name: Yang Liu, name: Haibo Hu, name: Zhuo Chen, name: Zhizheng Wu, name: Xiaolin Hu, name: Eng-Siong Chng, name: XiaoFeng Wang, name: Wenyuan Xu, name: Wei Dong, name: Xinfeng Li
Published: 2025-05-22 04:27:46

The rapid advancement and expanding applications of Audio Large Language
Models (ALLMs) demand a rigorous understanding of their trustworthiness.
However, systematic research on evaluating these models, particularly
concerning risks unique to the audio modality, remains largely unexplored.
Existing evaluation frameworks primarily focus on the text modality or address
only a restricted set of safety dimensions, failing to adequately account for
the unique characteristics and application scenarios inherent to the audio
modality. We introduce AudioTrust-the first multifaceted trustworthiness
evaluation framework and benchmark specifically designed for ALLMs. AudioTrust
facilitates assessments across six key dimensions: fairness, hallucination,
safety, privacy, robustness, and authentication. To comprehensively evaluate
these dimensions, AudioTrust is structured around 18 distinct experimental
setups. Its core is a meticulously constructed dataset of over 4,420 audio/text
samples, drawn from real-world scenarios (e.g., daily conversations, emergency
calls, voice assistant interactions), specifically designed to probe the
multifaceted trustworthiness of ALLMs. For assessment, the benchmark carefully
designs 9 audio-specific evaluation metrics, and we employ a large-scale
automated pipeline for objective and scalable scoring of model outputs.
Experimental results reveal the trustworthiness boundaries and limitations of
current state-of-the-art open-source and closed-source ALLMs when confronted
with various high-risk audio scenarios, offering valuable insights for the
secure and trustworthy deployment of future audio models. Our platform and
benchmark are available at https://github.com/JusperLee/AudioTrust.
Thought-Augmented Policy Optimization: Bridging External Guidance and
  Internal Capabilities
Authors: name: Jinyang Wu, name: Chonghua Liao, name: Mingkuan Feng, name: Shuai Zhang, name: Zhengqi Wen, name: Pengpeng Shao, name: Huazhe Xu, name: Jianhua Tao
Published: 2025-05-21 16:06:10

Reinforcement learning (RL) has emerged as an effective method for training
reasoning models. However, existing RL approaches typically bias the model's
output distribution toward reward-maximizing paths without introducing external
knowledge. This limits their exploration capacity and results in a narrower
reasoning capability boundary compared to base models. To address this
limitation, we propose TAPO (Thought-Augmented Policy Optimization), a novel
framework that augments RL by incorporating external high-level guidance
("thought patterns"). By adaptively integrating structured thoughts during
training, TAPO effectively balances model-internal exploration and external
guidance exploitation. Extensive experiments show that our approach
significantly outperforms GRPO by 99% on AIME, 41% on AMC, and 17% on Minerva
Math. Notably, these high-level thought patterns, abstracted from only 500
prior samples, generalize effectively across various tasks and models. This
highlights TAPO's potential for broader applications across multiple tasks and
domains. Our further analysis reveals that introducing external guidance
produces powerful reasoning models with superior explainability of inference
behavior and enhanced output readability.