Orionnnn/hub-stats huggingface dataset:
Rethinking Driving World Model as Synthetic Data Generator for
  Perception Tasks
Authors: name: Kai Zeng, name: Zhanqian Wu, name: Kaixin Xiong, name: Xiaobao Wei, name: Xiangyu Guo, name: Zhenxin Zhu, name: Kalok Ho, name: Lijun Zhou, name: Bohan Zeng, name: Ming Lu, name: Haiyang Sun, name: Bing Wang, name: Guang Chen, name: Hangjun Ye, name: Wentao Zhang
Published: 2025-10-22 03:02:38

Recent advancements in driving world models enable controllable generation of
high-quality RGB videos or multimodal videos. Existing methods primarily focus
on metrics related to generation quality and controllability. However, they
often overlook the evaluation of downstream perception tasks, which are
really crucial for the performance of autonomous driving. Existing
methods usually leverage a training strategy that first pretrains on synthetic
data and finetunes on real data, resulting in twice the epochs compared to the
baseline (real data only). When we double the epochs in the baseline, the
benefit of synthetic data becomes negligible. To thoroughly demonstrate the
benefit of synthetic data, we introduce Dream4Drive, a novel synthetic data
generation framework designed for enhancing the downstream perception tasks.
Dream4Drive first decomposes the input video into several 3D-aware guidance
maps and subsequently renders the 3D assets onto these guidance maps. Finally,
the driving world model is fine-tuned to produce the edited, multi-view
photorealistic videos, which can be used to train the downstream perception
models. Dream4Drive enables unprecedented flexibility in generating multi-view
corner cases at scale, significantly boosting corner case perception in
autonomous driving. To facilitate future research, we also contribute a
large-scale 3D asset dataset named DriveObj3D, covering the typical categories
in driving scenarios and enabling diverse 3D-aware video editing. We conduct
comprehensive experiments to show that Dream4Drive can effectively boost the
performance of downstream perception models under various training epochs.
Page: https://wm-research.github.io/Dream4Drive/ GitHub Link:
https://github.com/wm-research/Dream4Drive
Parallel Loop Transformer for Efficient Test-Time Computation Scaling
Authors: name: Bohong Wu, name: Mengzhao Chen, name: Xiang Luo, name: Shen Yan, name: Qifan Yu, name: Fan Xia, name: Tianqi Zhang, name: Hongrui Zhan, name: Zheng Zhong, name: Xun Zhou, name: Siyuan Qiao, name: Xingyan Bin
Published: 2025-10-28 15:35:50

Large Language Models (LLMs) are powerful but often too slow and costly for
real-world use during inference. Looped transformers save on parameters by
reusing the same weights for multiple computational steps, or "loops." However,
this approach has a major flaw: the loops run one after another, causing
inference latency and memory requirements to increase with each added loop.
This makes them impractical for fast applications. To solve this problem, we
introduce the Parallel Loop Transformer (PLT). PLT is a new architecture that
delivers the performance benefits of a deep, looped model but with the low
latency of a standard, non-looped model. PLT works using two key techniques.
First, Cross-Loop Parallelism (CLP) breaks the sequential dependency by
computing different loops for different tokens at the same time, all within a
single pass. Second, to prevent memory costs from growing, we use an Efficient
Representation Enhancement strategy. This method shares the memory (KV cache)
from the first loop with all other loops. It then uses a Gated Sliding-Window
Attention (G-SWA) to combine this shared global information with local
information, maintaining high accuracy. Our experiments show that PLT achieves
the high accuracy of a traditional looped model but with almost no extra
latency or memory cost compared to a standard transformer.
The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic,
  and Long-Horizon Task Execution
Authors: name: Junlong Li, name: Wenshuo Zhao, name: Jian Zhao, name: Weihao Zeng, name: Haoze Wu, name: Xiaochen Wang, name: Rui Ge, name: Yuxuan Cao, name: Yuzhen Huang, name: Wei Liu, name: Junteng Liu, name: Zhaochen Su, name: Yiyang Guo, name: Fan Zhou, name: Lueyang Zhang, name: Juan Michelini, name: Xingyao Wang, name: Xiang Yue, name: Shuyan Zhou, name: Graham Neubig, name: Junxian He
Published: 2025-10-29 17:32:49

Real-world language agents must handle complex, multi-step workflows across
diverse Apps. For instance, an agent may manage emails by coordinating with
calendars and file systems, or monitor a production database to detect
anomalies and generate reports following an operating manual. However, existing
language agent benchmarks often focus on narrow domains or simplified tasks
that lack the diversity, realism, and long-horizon complexity required to
evaluate agents' real-world performance. To address this gap, we introduce the
Tool Decathlon (dubbed as Toolathlon), a benchmark for language agents offering
diverse Apps and tools, realistic environment setup, and reliable
execution-based evaluation. Toolathlon spans 32 software applications and 604
tools, ranging from everyday platforms such as Google Calendar and Notion to
professional ones like WooCommerce, Kubernetes, and BigQuery. Most of the tools
are based on a high-quality set of Model Context Protocol (MCP) servers that we
may have revised or implemented ourselves. Unlike prior works, which primarily
ensure functional realism but offer limited environment state diversity, we
provide realistic initial environment states from real software, such as Canvas
courses with dozens of students or real financial spreadsheets. This benchmark
includes 108 manually sourced or crafted tasks in total, requiring interacting
with multiple Apps over around 20 turns on average to complete. Each task is
strictly verifiable through dedicated evaluation scripts. Comprehensive
evaluation of SOTA models highlights their significant shortcomings: the
best-performing model, Claude-4.5-Sonnet, achieves only a 38.6% success rate
with 20.2 tool calling turns on average, while the top open-weights model
DeepSeek-V3.2-Exp reaches 20.1%. We expect Toolathlon to drive the development
of more capable language agents for real-world, long-horizon task execution.
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context
  Learning
Authors: name: Baolu Li, name: Yiming Zhang, name: Qinghe Wang, name: Liqian Ma, name: Xiaoyu Shi, name: Xintao Wang, name: Pengfei Wan, name: Zhenfei Yin, name: Yunzhi Zhuge, name: Huchuan Lu, name: Xu Jia
Published: 2025-10-29 17:59:53

Visual effects (VFX) are crucial to the expressive power of digital media,
yet their creation remains a major challenge for generative AI. Prevailing
methods often rely on the one-LoRA-per-effect paradigm, which is
resource-intensive and fundamentally incapable of generalizing to unseen
effects, thus limiting scalability and creation. To address this challenge, we
introduce VFXMaster, the first unified, reference-based framework for VFX video
generation. It recasts effect generation as an in-context learning task,
enabling it to reproduce diverse dynamic effects from a reference video onto
target content. In addition, it demonstrates remarkable generalization to
unseen effect categories. Specifically, we design an in-context conditioning
strategy that prompts the model with a reference example. An in-context
attention mask is designed to precisely decouple and inject the essential
effect attributes, allowing a single unified model to master the effect
imitation without information leakage. In addition, we propose an efficient
one-shot effect adaptation mechanism to boost generalization capability on
tough unseen effects from a single user-provided video rapidly. Extensive
experiments demonstrate that our method effectively imitates various categories
of effect information and exhibits outstanding generalization to out-of-domain
effects. To foster future research, we will release our code, models, and a
comprehensive dataset to the community.
RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
Authors: name: Pengtao Chen, name: Xianfang Zeng, name: Maosen Zhao, name: Mingzhu Shen, name: Peng Ye, name: Bangyin Xiang, name: Zhibo Wang, name: Wei Cheng, name: Gang Yu, name: Tao Chen
Published: 2025-10-29 14:58:37

Recently, instruction-based image editing (IIE) has received widespread
attention. In practice, IIE often modifies only specific regions of an image,
while the remaining areas largely remain unchanged. Although these two types of
regions differ significantly in generation difficulty and computational
redundancy, existing IIE models do not account for this distinction, instead
applying a uniform generation process across the entire image. This motivates
us to propose RegionE, an adaptive, region-aware generation framework that
accelerates IIE tasks without additional training. Specifically, the RegionE
framework consists of three main components: 1) Adaptive Region Partition. We
observed that the trajectory of unedited regions is straight, allowing for
multi-step denoised predictions to be inferred in a single step. Therefore, in
the early denoising stages, we partition the image into edited and unedited
regions based on the difference between the final estimated result and the
reference image. 2) Region-Aware Generation. After distinguishing the regions,
we replace multi-step denoising with one-step prediction for unedited areas.
For edited regions, the trajectory is curved, requiring local iterative
denoising. To improve the efficiency and quality of local iterative generation,
we propose the Region-Instruction KV Cache, which reduces computational cost
while incorporating global information. 3) Adaptive Velocity Decay Cache.
Observing that adjacent timesteps in edited regions exhibit strong velocity
similarity, we further propose an adaptive velocity decay cache to accelerate
the local denoising process. We applied RegionE to state-of-the-art IIE base
models, including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit. RegionE
achieved acceleration factors of 2.57, 2.41, and 2.06. Evaluations by GPT-4o
confirmed that semantic and perceptual fidelity were well preserved.
Scaling Latent Reasoning via Looped Language Models
Authors: name: Rui-Jie Zhu, name: Zixuan Wang, name: Kai Hua, name: Tianyu Zhang, name: Ziniu Li, name: Haoran Que, name: Boyi Wei, name: Zixin Wen, name: Fan Yin, name: He Xing, name: Lu Li, name: Jiajun Shi, name: Kaijing Ma, name: Shanda Li, name: Taylor Kergan, name: Andrew Smith, name: Xingwei Qu, name: Mude Hui, name: Bohong Wu, name: Qiyang Min, name: Hongzhi Huang, name: Xun Zhou, name: Wei Ye, name: Jiaheng Liu, name: Jian Yang, name: Yunfeng Shi, name: Chenghua Lin, name: Enduo Zhao, name: Tianle Cai, name: Ge Zhang, name: Wenhao Huang, name: Yoshua Bengio, name: Jason Eshraghian
Published: 2025-10-29 17:45:42

Modern LLMs are trained to "think" primarily via explicit text generation,
such as chain-of-thought (CoT), which defers reasoning to post-training and
under-leverages pre-training data. We present and open-source Ouro, named after
the recursive Ouroboros, a family of pre-trained Looped Language Models
(LoopLM) that instead build reasoning into the pre-training phase through (i)
iterative computation in latent space, (ii) an entropy-regularized objective
for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and
2.6B models enjoy superior performance that match the results of up to 12B SOTA
LLMs across a wide range of benchmarks. Through controlled experiments, we show
this advantage stems not from increased knowledge capacity, but from superior
knowledge manipulation capabilities. We also show that LoopLM yields reasoning
traces more aligned with final outputs than explicit CoT. We hope our results
show the potential of LoopLM as a novel scaling direction in the reasoning era.
Our model could be found in: http://ouro-llm.github.io.
Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal
  Perception and Generation
Authors: name: Inclusion AI, name: Bowen Ma, name: Cheng Zou, name: Canxiang Yan, name: Chunxiang Jin, name: Chunjie Shen, name: Dandan Zheng, name: Fudong Wang, name: Furong Xu, name: GuangMing Yao, name: Jun Zhou, name: Jingdong Chen, name: Jianing Li, name: Jianxin Sun, name: Jiajia Liu, name: Jianjiang Zhu, name: Jianping Jiang, name: Jun Peng, name: Kaixiang Ji, name: Kaimeng Ren, name: Libin Wang, name: Lixiang Ru, name: Longhua Tan, name: Lan Wang, name: Mochen Bai, name: Ning Gao, name: Qingpei Guo, name: Qinglong Zhang, name: Qiang Xu, name: Rui Liu, name: Ruijie Xiong, name: Ruobing Zheng, name: Sirui Gao, name: Tianqi Li, name: Tinghao Liu, name: Weilong Chai, name: Xinyu Xiao, name: Xiaomei Wang, name: Xiaolong Wang, name: Xiao Lu, name: Xiaoyu Li, name: Xingning Dong, name: Xuzheng Yu, name: Yi Yuan, name: Yuting Gao, name: Yuting Xiao, name: Yunxiao Sun, name: Yipeng Chen, name: Yifan Mao, name: Yifei Wu, name: Yongjie Lyu, name: Ziping Ma, name: Zhiqiang Fang, name: Zhihao Qiu, name: Ziyuan Huang, name: Zizheng Yang, name: Zhengyu He
Published: 2025-10-28 15:24:13

We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a
sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion
total parameters, of which only 6.1 billion are active per token. This
architecture enables highly efficient scaling (dramatically improving
computational efficiency while significantly expanding model capacity) and
empowers stronger unified multimodal intelligence across vision, speech, and
language, representing a key step toward Artificial General Intelligence (AGI).
Compared to its predecessor, the upgraded version exhibits substantial
improvements across multimodal understanding and generation. We significantly
advance speech recognition capabilities, achieving state-of-the-art performance
in contextual ASR and highly competitive results in dialect-aware ASR. In image
generation, Ming-Flash-Omni introduces high-fidelity text rendering and
demonstrates marked gains in scene consistency and identity preservation during
image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation,
a capability that not only achieves strong standalone segmentation performance
but also enhances spatial control in image generation and improves editing
consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in
text-to-image generation and generative segmentation, and sets new records on
all 12 contextual ASR benchmarks, all within a single unified architecture.
BhashaBench V1: A Comprehensive Benchmark for the Quadrant of Indic
  Domains
Authors: name: Vijay Devane, name: Mohd Nauman, name: Bhargav Patel, name: Aniket Mahendra Wakchoure, name: Yogeshkumar Sant, name: Shyam Pawar, name: Viraj Thakur, name: Ananya Godse, name: Sunil Patra, name: Neha Maurya, name: Suraj Racha, name: Nitish Kamal Singh, name: Ajay Nagpal, name: Piyush Sawarkar, name: Kundeshwar Vijayrao Pundalik, name: Rohit Saluja, name: Ganesh Ramakrishnan
Published: 2025-10-29 11:27:08

The rapid advancement of large language models(LLMs) has intensified the need
for domain and culture specific evaluation. Existing benchmarks are largely
Anglocentric and domain-agnostic, limiting their applicability to India-centric
contexts. To address this gap, we introduce BhashaBench V1, the first
domain-specific, multi-task, bilingual benchmark focusing on critical Indic
knowledge systems. BhashaBench V1 contains 74,166 meticulously curated
question-answer pairs, with 52,494 in English and 21,672 in Hindi, sourced from
authentic government and domain-specific exams. It spans four major domains:
Agriculture, Legal, Finance, and Ayurveda, comprising 90+ subdomains and
covering 500+ topics, enabling fine-grained evaluation. Evaluation of 29+ LLMs
reveals significant domain and language specific performance gaps, with
especially large disparities in low-resource domains. For instance, GPT-4o
achieves 76.49% overall accuracy in Legal but only 59.74% in Ayurveda. Models
consistently perform better on English content compared to Hindi across all
domains. Subdomain-level analysis shows that areas such as Cyber Law,
International Finance perform relatively well, while Panchakarma, Seed Science,
and Human Rights remain notably weak. BhashaBench V1 provides a comprehensive
dataset for evaluating large language models across India's diverse knowledge
domains. It enables assessment of models' ability to integrate domain-specific
knowledge with bilingual understanding. All code, benchmarks, and resources are
publicly available to support open research.
S-Chain: Structured Visual Chain-of-Thought For Medicine
Authors: name: Khai Le-Duc, name: Duy M. H. Nguyen, name: Phuong T. H. Trinh, name: Tien-Phat Nguyen, name: Nghiem T. Diep, name: An Ngo, name: Tung Vu, name: Trinh Vuong, name: Anh-Tien Nguyen, name: Mau Nguyen, name: Van Trung Hoang, name: Khai-Nguyen Nguyen, name: Hy Nguyen, name: Chris Ngo, name: Anji Liu, name: Nhat Ho, name: Anne-Christin Hauschild, name: Khanh Xuan Nguyen, name: Thanh Nguyen-Tang, name: Pengtao Xie, name: Daniel Sonntag, name: James Zou, name: Mathias Niepert, name: Anh Totti Nguyen
Published: 2025-10-26 15:57:14

Faithful reasoning in medical vision-language models (VLMs) requires not only
accurate predictions but also transparent alignment between textual rationales
and visual evidence. While Chain-of-Thought (CoT) prompting has shown promise
in medical visual question answering (VQA), no large-scale expert-level dataset
has captured stepwise reasoning with precise visual grounding. We introduce
S-Chain, the first large-scale dataset of 12,000 expert-annotated medical
images with bounding boxes and structured visual CoT (SV-CoT), explicitly
linking visual regions to reasoning steps. The dataset further supports 16
languages, totaling over 700k VQA pairs for broad multilingual applicability.
Using S-Chain, we benchmark state-of-the-art medical VLMs (ExGra-Med,
LLaVA-Med) and general-purpose VLMs (Qwen2.5-VL, InternVL2.5), showing that
SV-CoT supervision significantly improves interpretability, grounding fidelity,
and robustness. Beyond benchmarking, we study its synergy with
retrieval-augmented generation, revealing how domain knowledge and visual
grounding interact during autoregressive reasoning. Finally, we propose a new
mechanism that strengthens the alignment between visual evidence and reasoning,
improving both reliability and efficiency. S-Chain establishes a new benchmark
for grounded medical reasoning and paves the way toward more trustworthy and
explainable medical VLMs.
ATOM: AdapTive and OptiMized dynamic temporal knowledge graph
  construction using LLMs
Authors: name: Yassir Lairgi, name: Ludovic Moncla, name: Khalid Benabdeslem, name: Rémy Cazabet, name: Pierre Cléau
Published: 2025-10-26 09:10:26

In today's rapidly expanding data landscape, knowledge extraction from
unstructured text is vital for real-time analytics, temporal inference, and
dynamic memory frameworks. However, traditional static knowledge graph (KG)
construction often overlooks the dynamic and time-sensitive nature of
real-world data, limiting adaptability to continuous changes. Moreover, recent
zero- or few-shot approaches that avoid domain-specific fine-tuning or reliance
on prebuilt ontologies often suffer from instability across multiple runs, as
well as incomplete coverage of key facts. To address these challenges, we
introduce ATOM (AdapTive and OptiMized), a few-shot and scalable approach that
builds and continuously updates Temporal Knowledge Graphs (TKGs) from
unstructured texts. ATOM splits input documents into minimal, self-contained
"atomic" facts, improving extraction exhaustivity and stability. Then, it
constructs atomic TKGs from these facts while employing a dual-time modeling
that distinguishes when information is observed from when it is valid. The
resulting atomic TKGs are subsequently merged in parallel. Empirical
evaluations demonstrate that ATOM achieves ~18% higher exhaustivity, ~17%
better stability, and over 90% latency reduction compared to baseline methods,
demonstrating a strong scalability potential for dynamic TKG construction.
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs' Cultural
  Processing of Figurative Language
Authors: name: Mena Attia, name: Aashiq Muhamed, name: Mai Alkhamissi, name: Thamar Solorio, name: Mona Diab
Published: 2025-10-27 20:13:32

We present a comprehensive evaluation of the ability of large language models
(LLMs) to process culturally grounded language, specifically to understand and
pragmatically use figurative expressions that encode local knowledge and
cultural nuance. Using figurative language as a proxy for cultural nuance and
local knowledge, we design evaluation tasks for contextual understanding,
pragmatic use, and connotation interpretation in Arabic and English. We
evaluate 22 open- and closed-source LLMs on Egyptian Arabic idioms,
multidialectal Arabic proverbs, and English proverbs. Our results show a
consistent hierarchy: the average accuracy for Arabic proverbs is 4.29% lower
than for English proverbs, and performance for Egyptian idioms is 10.28% lower
than for Arabic proverbs. For the pragmatic use task, accuracy drops by 14.07%
relative to understanding, though providing contextual idiomatic sentences
improves accuracy by 10.66%. Models also struggle with connotative meaning,
reaching at most 85.58% agreement with human annotators on idioms with 100%
inter-annotator agreement. These findings demonstrate that figurative language
serves as an effective diagnostic for cultural reasoning: while LLMs can often
interpret figurative meaning, they face challenges in using it appropriately.
To support future research, we release Kinayat, the first dataset of Egyptian
Arabic idioms designed for both figurative understanding and pragmatic use
evaluation.
Batch Speculative Decoding Done Right
Authors: name: Ranran Haoran Zhang, name: Soumik Dey, name: Ashirbad Mishra, name: Hansi Wu, name: Binbin Li, name: Rui Zhang
Published: 2025-10-26 23:59:23

Speculative decoding speeds up LLM inference by using a small draft model to
propose multiple tokens that a target model verifies in parallel. Extending
this idea to batches is essential for production serving, but it introduces the
ragged tensor problem: sequences in the same batch accept different numbers of
draft tokens, breaking right-alignment and corrupting position IDs, attention
masks, and KV-cache state. We show that several existing batch implementations
violate output equivalence-the fundamental requirement that speculative
decoding must produce identical token sequences to standard autoregressive
generation. These violations occur precisely due to improper handling of the
ragged tensor problem. In response, we (1) characterize the synchronization
requirements that guarantee correctness, (2) present a correctness-first batch
speculative decoding EQSPEC that exposes realignment as consuming 40% of
overhead, and (3) introduce EXSPEC, which maintains a sliding pool of sequences
and dynamically forms same-length groups, to reduce the realignment overhead
while preserving per-sequence speculative speedups. On the SpecBench dataset,
across Vicuna-7B/68M, Qwen3-8B/0.6B, and GLM-4-9B/0.6B target/draft pairs, our
approach achieves up to 3times throughput improvement at batch size 8
compared to batch size 1, with efficient scaling through batch size 8, while
maintaining 95% output equivalence. Our method requires no custom kernels and
integrates cleanly with existing inference stacks. Our code is available at
https://github.com/eBay/spec_dec.