Orionnnn/hub-stats huggingface dataset:
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in
  Real-World Applications
Authors: name: Jinyang Li, name: Xiaolong Li, name: Ge Qu, name: Per Jacobsson, name: Bowen Qin, name: Binyuan Hui, name: Shuzheng Si, name: Nan Huo, name: Xiaohan Xu, name: Yue Zhang, name: Ziwei Tang, name: Yuanshuai Li, name: Florensia Widjaja, name: Xintong Zhu, name: Feige Zhou, name: Yongfeng Huang, name: Yannis Papakonstantinou, name: Fatma Ozcan, name: Chenhao Ma, name: Reynold Cheng
Published: 2025-06-23 09:41:37

Resolution of complex SQL issues persists as a significant bottleneck in
real-world database applications. Current Large Language Models (LLMs), while
adept at text-to-SQL translation, have not been rigorously evaluated on the
more challenging task of debugging SQL issues. To address this gap, we
introduce BIRD-CRITIC, a new SQL issue debugging benchmark comprising 530
PostgreSQL tasks (BIRD-CRITIC-PG) and 570 multi-dialect tasks
(BIRD-CRITIC-Multi), distilled from authentic user issues and replayed within
new environments to facilitate rigorous evaluation. Baseline evaluations
underscore the task's complexity, with the leading reasoning model O3-Mini
achieving only 38.87% success rate on BIRD-CRITIC-PG and 33.33% on
BIRD-CRITIC-Multi. Meanwhile, advancing open-source models for database tasks
is crucial for empowering local development while safeguarding data privacy.
Therefore, we present Six-Gym (Sql-fIX-Gym), a training environment for
elevating open-source model capabilities for SQL issue debugging. This
environment leverages SQL-Rewind strategy, which automatically generates
executable issue-solution datasets by reverse-engineering issues from verified
SQLs. However, popular trajectory-based fine-tuning methods do not explore
substantial supervisory signals. We further propose f-Plan Boosting, which
extracts high-level debugging plans from SQL solutions, enabling teacher LLMs
to produce 73.7% more successful trajectories for training. We integrate these
components into an open-source agent, Bird-Fixer. Based on Qwen-2.5-Coder-14B,
Bird-Fixer achieves 38.11% success rate on BIRD-CRITIC-PG and 29.65% on
BIRD-CRITIC-Multi, surpassing leading proprietary models such as
Claude-3.7-Sonnet and GPT-4.1, marking a significant step toward democratizing
sophisticated SQL-debugging capabilities. The leaderboard and source code are
available: https://bird-critic.github.io/
Unified Vision-Language-Action Model
Authors: name: Yuqi Wang, name: Xinghang Li, name: Wenxuan Wang, name: Junbo Zhang, name: Yingyan Li, name: Yuntao Chen, name: Xinlong Wang, name: Zhaoxiang Zhang
Published: 2025-06-24 17:59:57

Vision-language-action models (VLAs) have garnered significant attention for
their potential in advancing robotic manipulation. However, previous approaches
predominantly rely on the general comprehension capabilities of vision-language
models (VLMs) to generate action signals, often overlooking the rich temporal
and causal structure embedded in visual observations. In this paper, we present
UniVLA, a unified and native multimodal VLA model that autoregressively models
vision, language, and action signals as discrete token sequences. This
formulation enables flexible multimodal tasks learning, particularly from
large-scale video data. By incorporating world modeling during post-training,
UniVLA captures causal dynamics from videos, facilitating effective transfer to
downstream policy learning--especially for long-horizon tasks. Our approach
sets new state-of-the-art results across several widely used simulation
benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge, significantly
surpassing previous methods. For example, UniVLA achieves 95.5% average success
rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate
its broad applicability on real-world ALOHA manipulation and autonomous
driving.
Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low
  CFG Scales
Authors: name: Seyedmorteza Sadat, name: Tobias Vontobel, name: Farnood Salehi, name: Romann M. Weber
Published: 2025-06-24 15:19:42

Classifier-free guidance (CFG) has become an essential component of modern
conditional diffusion models. Although highly effective in practice, the
underlying mechanisms by which CFG enhances quality, detail, and prompt
alignment are not fully understood. We present a novel perspective on CFG by
analyzing its effects in the frequency domain, showing that low and high
frequencies have distinct impacts on generation quality. Specifically,
low-frequency guidance governs global structure and condition alignment, while
high-frequency guidance mainly enhances visual fidelity. However, applying a
uniform scale across all frequencies -- as is done in standard CFG -- leads to
oversaturation and reduced diversity at high scales and degraded visual quality
at low scales. Based on these insights, we propose frequency-decoupled guidance
(FDG), an effective approach that decomposes CFG into low- and high-frequency
components and applies separate guidance strengths to each component. FDG
improves image quality at low guidance scales and avoids the drawbacks of high
CFG scales by design. Through extensive experiments across multiple datasets
and models, we demonstrate that FDG consistently enhances sample fidelity while
preserving diversity, leading to improved FID and recall compared to CFG,
establishing our method as a plug-and-play alternative to standard
classifier-free guidance.
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text
Authors: name: Amr Mohamed, name: Yang Zhang, name: Michalis Vazirgiannis, name: Guokan Shang
Published: 2025-06-16 21:19:27

Code-switching (CSW) is the act of alternating between two or more languages
within a single discourse. This phenomenon is widespread in multilingual
communities, and increasingly prevalent in online content, where users
naturally mix languages in everyday communication. As a result, Large Language
Models (LLMs), now central to content processing and generation, are frequently
exposed to code-switched inputs. Given their widespread use, it is crucial to
understand how LLMs process and reason about such mixed-language text. This
paper presents a systematic evaluation of LLM comprehension under
code-switching by generating CSW variants of established reasoning and
comprehension benchmarks. While degradation is evident when foreign tokens
disrupt English textx2013even under linguistic
constraintsx2013embedding English into other languages often
improves comprehension. Though prompting yields mixed results, fine-tuning
offers a more stable path to degradation mitigation.
ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality
  Debiasing
Authors: name: Long Xing, name: Qidong Huang, name: Xiaoyi Dong, name: Pan Zhang, name: Yuhang Zang, name: Yuhang Cao, name: Jinsong Li, name: Shuangrui Ding, name: Weiming Zhang, name: Nenghai Yu, name: Jiaqi Wang, name: Feng Wu, name: Dahua Lin
Published: 2025-06-24 17:59:55

This paper presents ScaleCap, an inference-time scalable image captioning
strategy that generates comprehensive and detailed image captions. The key
challenges of high-quality image captioning lie in the inherent biases of
LVLMs: multimodal bias resulting in imbalanced descriptive granularity,
offering detailed accounts of some elements while merely skimming over others;
linguistic bias leading to hallucinated descriptions of non-existent objects.
To address these issues, we propose a scalable debiased captioning strategy,
which continuously enriches and calibrates the caption with increased inference
budget. Specifically, we propose two novel components: heuristic question
answering and contrastive sentence rating. The former generates
content-specific questions based on the image and answers them to progressively
inject relevant information into the caption. The latter employs sentence-level
offline contrastive decoding to effectively identify and eliminate
hallucinations caused by linguistic biases. With increased inference cost, more
heuristic questions are raised by ScaleCap to progressively capture additional
visual details, generating captions that are more accurate, balanced, and
informative. Extensive modality alignment experiments demonstrate the
effectiveness of ScaleCap. Annotating 450K images with ScaleCap and using them
for LVLM pretraining leads to consistent performance gains across 11 widely
used benchmarks. Furthermore, ScaleCap showcases superb richness and fidelity
of generated captions with two additional tasks: replacing images with captions
in VQA task, and reconstructing images from captions to assess semantic
coverage. Code is available at https://github.com/Cooperx521/ScaleCap.
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo
  Retouching Agent
Authors: name: Yunlong Lin, name: Zixu Lin, name: Kunjie Lin, name: Jinbin Bai, name: Panwang Pan, name: Chenxin Li, name: Haoyu Chen, name: Zhongdao Wang, name: Xinghao Ding, name: Wenbo Li, name: Shuicheng Yan
Published: 2025-06-21 06:36:00

Photo retouching has become integral to contemporary visual storytelling,
enabling users to capture aesthetics and express creativity. While professional
tools such as Adobe Lightroom offer powerful capabilities, they demand
substantial expertise and manual effort. In contrast, existing AI-based
solutions provide automation but often suffer from limited adjustability and
poor generalization, failing to meet diverse and personalized editing needs. To
bridge this gap, we introduce JarvisArt, a multi-modal large language model
(MLLM)-driven agent that understands user intent, mimics the reasoning process
of professional artists, and intelligently coordinates over 200 retouching
tools within Lightroom. JarvisArt undergoes a two-stage training process: an
initial Chain-of-Thought supervised fine-tuning to establish basic reasoning
and tool-use skills, followed by Group Relative Policy Optimization for
Retouching (GRPO-R) to further enhance its decision-making and tool
proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate
seamless integration with Lightroom. To evaluate performance, we develop
MMArt-Bench, a novel benchmark constructed from real-world user edits.
JarvisArt demonstrates user-friendly interaction, superior generalization, and
fine-grained control over both global and local adjustments, paving a new
avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a
60% improvement in average pixel-level metrics on MMArt-Bench for content
fidelity, while maintaining comparable instruction-following capabilities.
Project Page: https://jarvisart.vercel.app/.
KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality
Authors: name: Baochang Ren, name: Shuofei Qiao, name: Wenhao Yu, name: Huajun Chen, name: Ningyu Zhang
Published: 2025-06-24 17:17:17

Large Language Models (LLMs), particularly slow-thinking models, often
exhibit severe hallucination, outputting incorrect content due to an inability
to accurately recognize knowledge boundaries during reasoning. While
Reinforcement Learning (RL) can enhance complex reasoning abilities, its
outcome-oriented reward mechanism often lacks factual supervision over the
thinking process, further exacerbating the hallucination problem. To address
the high hallucination in slow-thinking models, we propose Knowledge-enhanced
RL, KnowRL. KnowRL guides models to perform fact-based slow thinking by
integrating a factuality reward, based on knowledge verification, into the RL
training process, helping them recognize their knowledge boundaries. KnowRL
guides models to perform fact-based slow thinking by integrating a factuality
reward, based on knowledge verification, into the RL training process, helping
them recognize their knowledge boundaries. This targeted factual input during
RL training enables the model to learn and internalize fact-based reasoning
strategies. By directly rewarding adherence to facts within the reasoning
steps, KnowRL fosters a more reliable thinking process. Experimental results on
three hallucination evaluation datasets and two reasoning evaluation datasets
demonstrate that KnowRL effectively mitigates hallucinations in slow-thinking
models while maintaining their original strong reasoning capabilities. Our code
is available at https://github.com/zjunlp/KnowRL.
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic
  Empirical Study
Authors: name: Yuqi Zhu, name: Yi Zhong, name: Jintian Zhang, name: Ziheng Zhang, name: Shuofei Qiao, name: Yujie Luo, name: Lun Du, name: Da Zheng, name: Huajun Chen, name: Ningyu Zhang
Published: 2025-06-24 17:04:23

Large Language Models (LLMs) hold promise in automating data analysis tasks,
yet open-source models face significant limitations in these kinds of
reasoning-intensive scenarios. In this work, we investigate strategies to
enhance the data analysis capabilities of open-source LLMs. By curating a seed
dataset of diverse, realistic scenarios, we evaluate models across three
dimensions: data understanding, code generation, and strategic planning. Our
analysis reveals three key findings: (1) Strategic planning quality serves as
the primary determinant of model performance; (2) Interaction design and task
complexity significantly influence reasoning capabilities; (3) Data quality
demonstrates a greater impact than diversity in achieving optimal performance.
We leverage these insights to develop a data synthesis methodology,
demonstrating significant improvements in open-source LLMs' analytical
reasoning capabilities.
USAD: Universal Speech and Audio Representation via Distillation
Authors: name: Heng-Jui Chang, name: Saurabhchand Bhati, name: James Glass, name: Alexander H. Liu
Published: 2025-06-23 17:02:00

Self-supervised learning (SSL) has revolutionized audio representations, yet
models often remain domain-specific, focusing on either speech or non-speech
tasks. In this work, we present Universal Speech and Audio Distillation (USAD),
a unified approach to audio representation learning that integrates diverse
audio types - speech, sound, and music - into a single model. USAD employs
efficient layer-to-layer distillation from domain-specific SSL models to train
a student on a comprehensive audio dataset. USAD offers competitive performance
across various benchmarks and datasets, including frame and instance-level
speech processing tasks, audio tagging, and sound classification, achieving
near state-of-the-art results with a single encoder on SUPERB and HEAR
benchmarks.
GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal
  Reasoning
Authors: name: Yi Chen, name: Yuying Ge, name: Rui Wang, name: Yixiao Ge, name: Junhao Cheng, name: Ying Shan, name: Xihui Liu
Published: 2025-06-19 08:49:13

Recent reinforcement learning approaches, such as outcome-supervised GRPO,
have advanced Chain-of-Thought reasoning in large language models (LLMs), yet
their adaptation to multimodal LLMs (MLLMs) is unexplored. To address the lack
of rigorous evaluation for MLLM post-training methods, we introduce
SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced
perception and reasoning. It offers a large training set and evaluates
generalization across three escalating challenges: in-distribution,
cross-environment, and cross-environment-task scenarios. Using SEED-Bench-R1,
we find that standard GRPO, while improving answer accuracy, often reduces
logical coherence between reasoning steps and answers, with only a 57.9%
consistency rate. This stems from reward signals focusing solely on final
answers, encouraging shortcuts, and strict KL penalties limiting exploration.To
address this, we propose GRPO-CARE, a consistency-aware RL framework optimizing
both answer correctness and reasoning coherence without explicit supervision.
GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer
correctness, and (2) an adaptive consistency bonus, computed by comparing the
model's reasoning-to-answer likelihood (via a slowly-evolving reference model)
against group peers.This dual mechanism amplifies rewards for reasoning paths
that are both correct and logically consistent. Replacing KL penalties with
this adaptive bonus, GRPO-CARE outperforms standard GRPO on SEED-Bench-R1,
achieving a 6.7% performance gain on the hardest evaluation level and a 24.5%
improvement in consistency. It also shows strong transferability, improving
model performance across diverse video understanding benchmarks. Our work
contributes a systematically designed benchmark and a generalizable
post-training framework, advancing the development of more interpretable and
robust MLLMs.
Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in
  LLMs
Authors: name: Liang Zeng, name: Yongcong Li, name: Yuzhen Xiao, name: Changshi Li, name: Chris Yuhao Liu, name: Rui Yan, name: Tianwen Wei, name: Jujie He, name: Xuchen Song, name: Yang Liu, name: Yahui Zhou
Published: 2025-06-24 03:53:36

Software engineering (SWE) has recently emerged as a crucial testbed for
next-generation LLM agents, demanding inherent capabilities in two critical
dimensions: sustained iterative problem-solving (e.g., >50 interaction rounds)
and long-context dependency resolution (e.g., >32k tokens). However, the data
curation process in SWE remains notoriously time-consuming, as it heavily
relies on manual annotation for code file filtering and the setup of dedicated
runtime environments to execute and validate unit tests. Consequently, most
existing datasets are limited to only a few thousand GitHub-sourced instances.
To this end, we propose an incremental, automated data-curation pipeline that
systematically scales both the volume and diversity of SWE datasets. Our
dataset comprises 10,169 real-world Python task instances from 2,531 distinct
GitHub repositories, each accompanied by a task specified in natural language
and a dedicated runtime-environment image for automated unit-test validation.
We have carefully curated over 8,000 successfully runtime-validated training
trajectories from our proposed SWE dataset. When fine-tuning the Skywork-SWE
model on these trajectories, we uncover a striking data scaling phenomenon: the
trained model's performance for software engineering capabilities in LLMs
continues to improve as the data size increases, showing no signs of
saturation. Notably, our Skywork-SWE model achieves 38.0% pass@1 accuracy on
the SWE-bench Verified benchmark without using verifiers or multiple rollouts,
establishing a new state-of-the-art (SOTA) among the Qwen2.5-Coder-32B-based
LLMs built on the OpenHands agent framework. Furthermore, with the
incorporation of test-time scaling techniques, the performance further improves
to 47.0% accuracy, surpassing the previous SOTA results for sub-32B parameter
models. We release the Skywork-SWE-32B model checkpoint to accelerate future
research.
SRFT: A Single-Stage Method with Supervised and Reinforcement
  Fine-Tuning for Reasoning
Authors: name: Yuqian Fu, name: Tinghong Chen, name: Jiajun Chai, name: Xihuai Wang, name: Songjun Tu, name: Guojun Yin, name: Wei Lin, name: Qichao Zhang, name: Yuanheng Zhu, name: Dongbin Zhao
Published: 2025-06-24 16:31:37

Large language models (LLMs) have achieved remarkable progress in reasoning
tasks, yet the optimal integration of Supervised Fine-Tuning (SFT) and
Reinforcement Learning (RL) remains a fundamental challenge. Through
comprehensive analysis of token distributions, learning dynamics, and
integration mechanisms from entropy-based perspectives, we reveal key
differences between these paradigms: SFT induces coarse-grained global changes
to LLM policy distributions, while RL performs fine-grained selective
optimizations, with entropy serving as a critical indicator of training
effectiveness. Building on these observations, we propose Supervised
Reinforcement Fine-Tuning (SRFT), a single-stage method that unifies both
fine-tuning paradigms through entropy-aware weighting mechanisms. Our approach
simultaneously applies SFT and RL to directly optimize the LLM using
demonstrations and self-exploration rollouts rather than through two-stage
sequential methods. Extensive experiments show that SRFT achieves 59.1% average
accuracy, outperforming zero-RL methods by 9.0% on five mathematical reasoning
benchmarks and 10.9% on three out-of-distribution benchmarks.
AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion
  Models
Authors: name: Zehuan Huang, name: Haoran Feng, name: Yangtian Sun, name: Yuanchen Guo, name: Yanpei Cao, name: Lu Sheng
Published: 2025-06-24 17:59:58

We present AnimaX, a feed-forward 3D animation framework that bridges the
motion priors of video diffusion models with the controllable structure of
skeleton-based animation. Traditional motion synthesis methods are either
restricted to fixed skeletal topologies or require costly optimization in
high-dimensional deformation spaces. In contrast, AnimaX effectively transfers
video-based motion knowledge to the 3D domain, supporting diverse articulated
meshes with arbitrary skeletons. Our method represents 3D motion as multi-view,
multi-frame 2D pose maps, and enables joint video-pose diffusion conditioned on
template renderings and a textual motion prompt. We introduce shared positional
encodings and modality-aware embeddings to ensure spatial-temporal alignment
between video and pose sequences, effectively transferring video priors to
motion generation task. The resulting multi-view pose sequences are
triangulated into 3D joint positions and converted into mesh animation via
inverse kinematics. Trained on a newly curated dataset of 160,000 rigged
sequences, AnimaX achieves state-of-the-art results on VBench in
generalization, motion fidelity, and efficiency, offering a scalable solution
for category-agnostic 3D animation. Project page:
https://anima-x.github.io/{https://anima-x.github.io/}.
SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution
Authors: name: Liangbin Xie, name: Yu Li, name: Shian Du, name: Menghan Xia, name: Xintao Wang, name: Fanghua Yu, name: Ziyan Chen, name: Pengfei Wan, name: Jiantao Zhou, name: Chao Dong
Published: 2025-06-24 17:57:26

Latent diffusion models have emerged as a leading paradigm for efficient
video generation. However, as user expectations shift toward higher-resolution
outputs, relying solely on latent computation becomes inadequate. A promising
approach involves decoupling the process into two stages: semantic content
generation and detail synthesis. The former employs a computationally intensive
base model at lower resolutions, while the latter leverages a lightweight
cascaded video super-resolution (VSR) model to achieve high-resolution output.
In this work, we focus on studying key design principles for latter cascaded
VSR models, which are underexplored currently. First, we propose two
degradation strategies to generate training pairs that better mimic the output
characteristics of the base model, ensuring alignment between the VSR model and
its upstream generator. Second, we provide critical insights into VSR model
behavior through systematic analysis of (1) timestep sampling strategies, (2)
noise augmentation effects on low-resolution (LR) inputs. These findings
directly inform our architectural and training innovations. Finally, we
introduce interleaving temporal unit and sparse local attention to achieve
efficient training and inference, drastically reducing computational overhead.
Extensive experiments demonstrate the superiority of our framework over
existing methods, with ablation studies confirming the efficacy of each design
choice. Our work establishes a simple yet effective baseline for cascaded video
super-resolution generation, offering practical insights to guide future
advancements in efficient cascaded synthesis systems.