Orionnnn/hub-stats huggingface dataset:
PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image
  Generative Models
Authors: name: Junwen Chen, name: Heyang Jiang, name: Yanbin Wang, name: Keming Wu, name: Ji Li, name: Chao Zhang, name: Keiji Yanai, name: Dong Chen, name: Yuhui Yuan
Published: 2025-05-28 16:09:33

Generating high-quality, multi-layer transparent images from text prompts can
unlock a new level of creative control, allowing users to edit each layer as
effortlessly as editing text outputs from LLMs. However, the development of
multi-layer generative models lags behind that of conventional text-to-image
models due to the absence of a large, high-quality corpus of multi-layer
transparent data. In this paper, we address this fundamental challenge by: (i)
releasing the first open, ultra-high-fidelity PrismLayers (PrismLayersPro)
dataset of 200K (20K) multilayer transparent images with accurate alpha mattes,
(ii) introducing a trainingfree synthesis pipeline that generates such data on
demand using off-the-shelf diffusion models, and (iii) delivering a strong,
open-source multi-layer generation model, ART+, which matches the aesthetics of
modern text-to-image generation models. The key technical contributions
include: LayerFLUX, which excels at generating high-quality single transparent
layers with accurate alpha mattes, and MultiLayerFLUX, which composes multiple
LayerFLUX outputs into complete images, guided by human-annotated semantic
layout. To ensure higher quality, we apply a rigorous filtering stage to remove
artifacts and semantic mismatches, followed by human selection. Fine-tuning the
state-of-the-art ART model on our synthetic PrismLayersPro yields ART+, which
outperforms the original ART in 60% of head-to-head user study comparisons and
even matches the visual quality of images generated by the FLUX.1-[dev] model.
We anticipate that our work will establish a solid dataset foundation for the
multi-layer transparent image generation task, enabling research and
applications that require precise, editable, and visually compelling layered
imagery.
WebDancer: Towards Autonomous Information Seeking Agency
Authors: name: Jialong Wu, name: Baixuan Li, name: Runnan Fang, name: Wenbiao Yin, name: Liwen Zhang, name: Zhengwei Tao, name: Dingchu Zhang, name: Zekun Xi, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-05-28 17:57:07

Addressing intricate real-world problems necessitates in-depth information
seeking and multi-step reasoning. Recent progress in agentic systems,
exemplified by Deep Research, underscores the potential for autonomous
multi-step research. In this work, we present a cohesive paradigm for building
end-to-end agentic information seeking agents from a data-centric and
training-stage perspective. Our approach consists of four key stages: (1)
browsing data construction, (2) trajectories sampling, (3) supervised
fine-tuning for effective cold start, and (4) reinforcement learning for
enhanced generalisation. We instantiate this framework in a web agent based on
the ReAct, WebDancer. Empirical evaluations on the challenging information
seeking benchmarks, GAIA and WebWalkerQA, demonstrate the strong performance of
WebDancer, achieving considerable results and highlighting the efficacy of our
training paradigm. Further analysis of agent training provides valuable
insights and actionable, systematic pathways for developing more capable
agentic models. The codes and demo will be released in
https://github.com/Alibaba-NLP/WebAgent.
DeepResearchGym: A Free, Transparent, and Reproducible Evaluation
  Sandbox for Deep Research
Authors: name: João Coelho, name: Jingjie Ning, name: Jingyuan He, name: Kangrui Mao, name: Abhijay Paladugu, name: Pranav Setlur, name: Jiahe Jin, name: Jamie Callan, name: João Magalhães, name: Bruno Martins, name: Chenyan Xiong
Published: 2025-05-25 18:16:13

Deep research systems represent an emerging class of agentic information
retrieval methods that generate comprehensive and well-supported reports to
complex queries. However, most existing frameworks rely on dynamic commercial
search APIs, which pose reproducibility and transparency challenges in addition
to their cost. To address these limitations, we introduce DeepResearchGym, an
open-source sandbox that combines a reproducible search API with a rigorous
evaluation protocol for benchmarking deep research systems. The API indexes
large-scale public web corpora, namely ClueWeb22 and FineWeb, using a
state-of-the-art dense retriever and approximate nearest neighbor search via
DiskANN. It achieves lower latency than popular commercial APIs while ensuring
stable document rankings across runs, and is freely available for research use.
To evaluate deep research systems' outputs, we extend the Researchy Questions
benchmark with automatic metrics through LLM-as-a-judge assessments to measure
alignment with users' information needs, retrieval faithfulness, and report
quality. Experimental results show that systems integrated with DeepResearchGym
achieve performance comparable to those using commercial APIs, with performance
rankings remaining consistent across evaluation metrics. A human evaluation
study further confirms that our automatic protocol aligns with human
preferences, validating the framework's ability to help support controlled
assessment of deep research systems. Our code and API documentation are
available at https://www.deepresearchgym.ai.
R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large
  Model Token Routing
Authors: name: Tianyu Fu, name: Yi Ge, name: Yichen You, name: Enshu Liu, name: Zhihang Yuan, name: Guohao Dai, name: Shengen Yan, name: Huazhong Yang, name: Yu Wang
Published: 2025-05-27 16:57:20

Large Language Models (LLMs) achieve impressive reasoning capabilities at the
cost of substantial inference overhead, posing substantial deployment
challenges. Although distilled Small Language Models (SLMs) significantly
enhance efficiency, their performance suffers as they fail to follow LLMs'
reasoning paths. Luckily, we reveal that only a small fraction of tokens
genuinely diverge reasoning paths between LLMs and SLMs. Most generated tokens
are either identical or exhibit neutral differences, such as minor variations
in abbreviations or expressions. Leveraging this insight, we introduce **Roads
to Rome (R2R)**, a neural token routing method that selectively utilizes LLMs
only for these critical, path-divergent tokens, while leaving the majority of
token generation to the SLM. We also develop an automatic data generation
pipeline that identifies divergent tokens and generates token-level routing
labels to train the lightweight router. We apply R2R to combine R1-1.5B and
R1-32B models from the DeepSeek family, and evaluate on challenging math,
coding, and QA benchmarks. With an average activated parameter size of 5.6B,
R2R surpasses the average accuracy of R1-7B by 1.6x, outperforming even the
R1-14B model. Compared to R1-32B, it delivers a 2.8x wall-clock speedup with
comparable performance, advancing the Pareto frontier of test-time scaling
efficiency. Our code is available at https://github.com/thu-nics/R2R.
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
Authors: name: Jinheon Baek, name: Horst Samulowitz, name: Oktie Hassanzadeh, name: Dharmashankar Subramanian, name: Sola Shirai, name: Alfio Gliozzo, name: Debarun Bhattacharjya
Published: 2025-05-28 08:17:58

Text-to-SQL aims to translate natural language queries into SQL statements,
which is practical as it enables anyone to easily retrieve the desired
information from databases. Recently, many existing approaches tackle this
problem with Large Language Models (LLMs), leveraging their strong capability
in understanding user queries and generating corresponding SQL code. Yet, the
parametric knowledge in LLMs might be limited to covering all the diverse and
domain-specific queries that require grounding in various database schemas,
which makes generated SQLs less accurate oftentimes. To tackle this, we propose
constructing the knowledge base for text-to-SQL, a foundational source of
knowledge, from which we retrieve and generate the necessary knowledge for
given queries. In particular, unlike existing approaches that either manually
annotate knowledge or generate only a few pieces of knowledge for each query,
our knowledge base is comprehensive, which is constructed based on a
combination of all the available questions and their associated database
schemas along with their relevant knowledge, and can be reused for unseen
databases from different datasets and domains. We validate our approach on
multiple text-to-SQL datasets, considering both the overlapping and
non-overlapping database scenarios, where it outperforms relevant baselines
substantially.
Reverse Preference Optimization for Complex Instruction Following
Authors: name: Xiang Huang, name: Ting-En Lin, name: Feiteng Fang, name: Yuchuan Wu, name: Hangyu Li, name: Yuzhong Qu, name: Fei Huang, name: Yongbin Li
Published: 2025-05-28 09:44:27

Instruction following (IF) is a critical capability for large language models
(LLMs). However, handling complex instructions with multiple constraints
remains challenging. Previous methods typically select preference pairs based
on the number of constraints they satisfy, introducing noise where chosen
examples may fail to follow some constraints and rejected examples may excel in
certain respects over the chosen ones. To address the challenge of aligning
with multiple preferences, we propose a simple yet effective method called
Reverse Preference Optimization (RPO). It mitigates noise in preference pairs
by dynamically reversing the constraints within the instruction to ensure the
chosen response is perfect, alleviating the burden of extensive sampling and
filtering to collect perfect responses. Besides, reversal also enlarges the gap
between chosen and rejected responses, thereby clarifying the optimization
direction and making it more robust to noise. We evaluate RPO on two multi-turn
IF benchmarks, Sysbench and Multi-IF, demonstrating average improvements over
the DPO baseline of 4.6 and 2.5 points (on Llama-3.1 8B), respectively.
Moreover, RPO scales effectively across model sizes (8B to 70B parameters),
with the 70B RPO model surpassing GPT-4o.
Spatial Knowledge Graph-Guided Multimodal Synthesis
Authors: name: Yida Xue, name: Zhen Bi, name: Jinnan Yang, name: Jungang Lou, name: Huajun Chen, name: Ningyu Zhang
Published: 2025-05-28 17:50:21

Recent advances in multimodal large language models (MLLMs) have
significantly enhanced their capabilities; however, their spatial perception
abilities remain a notable limitation. To address this challenge, multimodal
data synthesis offers a promising solution. Yet, ensuring that synthesized data
adhere to spatial common sense is a non-trivial task. In this work, we
introduce SKG2Data, a novel multimodal synthesis approach guided by spatial
knowledge graphs, grounded in the concept of knowledge-to-data generation.
SKG2Data automatically constructs a Spatial Knowledge Graph (SKG) to emulate
human-like perception of spatial directions and distances, which is
subsequently utilized to guide multimodal data synthesis. Extensive experiments
demonstrate that data synthesized from diverse types of spatial knowledge,
including direction and distance, not only enhance the spatial perception and
reasoning abilities of MLLMs but also exhibit strong generalization
capabilities. We hope that the idea of knowledge-based data synthesis can
advance the development of spatial intelligence.
Inverse Virtual Try-On: Generating Multi-Category Product-Style Images
  from Clothed Individuals
Authors: name: Davide Lobba, name: Fulvio Sanguigni, name: Bin Ren, name: Marcella Cornia, name: Rita Cucchiara, name: Nicu Sebe
Published: 2025-05-27 11:47:51

While virtual try-on (VTON) systems aim to render a garment onto a target
person image, this paper tackles the novel task of virtual try-off (VTOFF),
which addresses the inverse problem: generating standardized product images of
garments from real-world photos of clothed individuals. Unlike VTON, which must
resolve diverse pose and style variations, VTOFF benefits from a consistent and
well-defined output format -- typically a flat, lay-down-style representation
of the garment -- making it a promising tool for data generation and dataset
enhancement. However, existing VTOFF approaches face two major limitations: (i)
difficulty in disentangling garment features from occlusions and complex poses,
often leading to visual artifacts, and (ii) restricted applicability to
single-category garments (e.g., upper-body clothes only), limiting
generalization. To address these challenges, we present Text-Enhanced
MUlti-category Virtual Try-Off (TEMU-VTOFF), a novel architecture featuring a
dual DiT-based backbone with a modified multimodal attention mechanism for
robust garment feature extraction. Our architecture is designed to receive
garment information from multiple modalities like images, text, and masks to
work in a multi-category setting. Finally, we propose an additional alignment
module to further refine the generated visual details. Experiments on VITON-HD
and Dress Code datasets show that TEMU-VTOFF sets a new state-of-the-art on the
VTOFF task, significantly improving both visual quality and fidelity to the
target garments.
FinTagging: An LLM-ready Benchmark for Extracting and Structuring
  Financial Information
Authors: name: Yan Wang, name: Yang Ren, name: Lingfei Qian, name: Xueqing Peng, name: Keyi Wang, name: Yi Han, name: Dongji Feng, name: Xiao-Yang Liu, name: Jimin Huang, name: Qianqian Xie
Published: 2025-05-27 02:55:53

We introduce FinTagging, the first full-scope, table-aware XBRL benchmark
designed to evaluate the structured information extraction and semantic
alignment capabilities of large language models (LLMs) in the context of
XBRL-based financial reporting. Unlike prior benchmarks that oversimplify XBRL
tagging as flat multi-class classification and focus solely on narrative text,
FinTagging decomposes the XBRL tagging problem into two subtasks: FinNI for
financial entity extraction and FinCL for taxonomy-driven concept alignment. It
requires models to jointly extract facts and align them with the full 10k+
US-GAAP taxonomy across both unstructured text and structured tables, enabling
realistic, fine-grained evaluation. We assess a diverse set of LLMs under
zero-shot settings, systematically analyzing their performance on both subtasks
and overall tagging accuracy. Our results reveal that, while LLMs demonstrate
strong generalization in information extraction, they struggle with
fine-grained concept alignment, particularly in disambiguating closely related
taxonomy entries. These findings highlight the limitations of existing LLMs in
fully automating XBRL tagging and underscore the need for improved semantic
reasoning and schema-aware modeling to meet the demands of accurate financial
disclosure. Code is available at our GitHub repository and data is at our
Hugging Face repository.
VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D
  Reconstruction
Authors: name: Zhiwen Fan, name: Jian Zhang, name: Renjie Li, name: Junge Zhang, name: Runjin Chen, name: Hezhen Hu, name: Kevin Wang, name: Huaizhi Qu, name: Dilin Wang, name: Zhicheng Yan, name: Hongyu Xu, name: Justin Theiss, name: Tianlong Chen, name: Jiachen Li, name: Zhengzhong Tu, name: Zhangyang Wang, name: Rakesh Ranjan
Published: 2025-05-26 17:56:30

The rapid advancement of Large Multimodal Models (LMMs) for 2D images and
videos has motivated extending these models to understand 3D scenes, aiming for
human-like visual-spatial intelligence. Nevertheless, achieving deep spatial
understanding comparable to human capabilities poses significant challenges in
model encoding and data acquisition. Existing methods frequently depend on
external depth sensors for geometry capture or utilize off-the-shelf algorithms
for pre-constructing 3D maps, thereby limiting their scalability, especially
with prevalent monocular video inputs and for time-sensitive applications. In
this work, we introduce VLM-3R, a unified framework for Vision-Language Models
(VLMs) that incorporates 3D Reconstructive instruction tuning. VLM-3R processes
monocular video frames by employing a geometry encoder to derive implicit 3D
tokens that represent spatial understanding. Leveraging our Spatial-Visual-View
Fusion and over 200K curated 3D reconstructive instruction tuning
question-answer (QA) pairs, VLM-3R effectively aligns real-world spatial
context with language instructions. This enables monocular 3D spatial
assistance and embodied reasoning. To facilitate the evaluation of temporal
reasoning, we introduce the Vision-Spatial-Temporal Intelligence benchmark,
featuring over 138.6K QA pairs across five distinct tasks focused on evolving
spatial relationships. Extensive experiments demonstrate that our model,
VLM-3R, not only facilitates robust visual-spatial reasoning but also enables
the understanding of temporal 3D context changes, excelling in both accuracy
and scalability.
Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic
  Confidence
Authors: name: Amirhosein Ghasemabadi, name: Keith G. Mills, name: Baochun Li, name: Di Niu
Published: 2025-05-23 18:19:09

Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM)
reasoning often incur substantial computational costs, primarily due to
extensive reliance on external Process Reward Models (PRMs) or sampling methods
like Best-of-N (BoN). This paper introduces Guided by Gut (GG), an efficient
self-guided TTS framework that achieves PRM-level performance without costly
external verifier models. Our method employs a lightweight tree search guided
solely by intrinsic LLM signals, token-level confidence and step novelty. One
critical innovation is improving the reliability of internal confidence
estimates via a targeted reinforcement learning fine-tuning phase. Empirical
evaluations on challenging mathematical reasoning benchmarks demonstrate that
GG enables smaller models (e.g., 1.5B parameters) to achieve accuracy matching
or surpassing significantly larger models (e.g., 32B-70B parameters), while
reducing GPU memory usage by up to 10x. Compared to PRM-based methods, GG
achieves comparable accuracy with 8x faster inference speeds and 4-5x lower
memory usage. Additionally, GG reduces KV cache memory usage by approximately
50% compared to the BoN strategy, facilitating more efficient and practical
deployment of TTS techniques.
SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for
  Enterprise Use
Authors: name: Hitesh Laxmichand Patel, name: Amit Agarwal, name: Arion Das, name: Bhargava Kumar, name: Srikant Panda, name: Priyaranjan Pattnayak, name: Taki Hasan Rafi, name: Tejaswini Kumar, name: Dong-Kyu Chae
Published: 2025-05-22 22:56:58

Enterprise customers are increasingly adopting Large Language Models (LLMs)
for critical communication tasks, such as drafting emails, crafting sales
pitches, and composing casual messages. Deploying such models across different
regions requires them to understand diverse cultural and linguistic contexts
and generate safe and respectful responses. For enterprise applications, it is
crucial to mitigate reputational risks, maintain trust, and ensure compliance
by effectively identifying and handling unsafe or offensive language. To
address this, we introduce SweEval, a benchmark simulating real-world scenarios
with variations in tone (positive or negative) and context (formal or
informal). The prompts explicitly instruct the model to include specific swear
words while completing the task. This benchmark evaluates whether LLMs comply
with or resist such inappropriate instructions and assesses their alignment
with ethical frameworks, cultural nuances, and language comprehension
capabilities. In order to advance research in building ethically aligned AI
systems for enterprise use and beyond, we release the dataset and code:
https://github.com/amitbcp/multilingual_profanity.
MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask
  Multimodal Generation
Authors: name: Jihan Yao, name: Yushi Hu, name: Yujie Yi, name: Bin Han, name: Shangbin Feng, name: Guang Yang, name: Bingbing Wen, name: Ranjay Krishna, name: Lucy Lu Wang, name: Yulia Tsvetkov, name: Noah A. Smith, name: Banghua Zhu
Published: 2025-05-23 08:21:28

Automatically evaluating multimodal generation presents a significant
challenge, as automated metrics often struggle to align reliably with human
evaluation, especially for complex tasks that involve multiple modalities. To
address this, we present MMMG, a comprehensive and human-aligned benchmark for
multimodal generation across 4 modality combinations (image, audio, interleaved
text and image, interleaved text and audio), with a focus on tasks that present
significant challenges for generation models, while still enabling reliable
automatic evaluation through a combination of models and programs. MMMG
encompasses 49 tasks (including 29 newly developed ones), each with a carefully
designed evaluation pipeline, and 937 instructions to systematically assess
reasoning, controllability, and other key capabilities of multimodal generation
models. Extensive validation demonstrates that MMMG is highly aligned with
human evaluation, achieving an average agreement of 94.3%. Benchmarking results
on 24 multimodal generation models reveal that even though the state-of-the-art
model, GPT Image, achieves 78.3% accuracy for image generation, it falls short
on multimodal reasoning and interleaved generation. Furthermore, results
suggest considerable headroom for improvement in audio generation, highlighting
an important direction for future research.
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
  Verifiable Rewards
Authors: name: Chuming Shen, name: Wei Wei, name: Xiaoye Qu, name: Yu Cheng
Published: 2025-05-25 11:11:06

DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text
domain through stable reinforcement learning (RL). Recently, in the multimodal
domain, works have begun to directly apply RL to generate R1-like free-form
reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks
share an intrinsically different nature from textual tasks, which heavily rely
on the understanding of the input image to solve the problem. Therefore, such
free-form reasoning faces two critical limitations in the VQA task: (1)
Extended reasoning chains diffuse visual focus away from task-critical regions,
degrading answer accuracy. (2) Unverifiable intermediate steps amplify
policy-gradient variance and computational costs overhead. To address these
issues, in this paper, we introduce SATORI (Spatially
Anchored Task Optimization with
ReInforcement Learning), which decomposes VQA into three
verifiable stages, including global image captioning, region localization, and
answer prediction, each supplying explicit reward signals. Furthermore, we also
introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and
bounding-boxes to facilitate training. Experiments demonstrate consistent
performance improvements across seven VQA benchmarks, achieving up to 15.7%
improvement in accuracy in accuracy compared to the R1-like baseline. Our
analysis of the attention map confirms enhanced focus on critical regions,
which brings improvements in accuracy. Our code is available at
https://github.com/justairr/SATORI-R1.