Orionnnn/hub-stats huggingface dataset:
Exploring Conditions for Diffusion models in Robotic Control
Authors: name: Heeseong Shin, name: Byeongho Heo, name: Dongyoon Han, name: Seungryong Kim, name: Taekyung Kim
Published: 2025-10-17 10:24:14

While pre-trained visual representations have significantly advanced
imitation learning, they are often task-agnostic as they remain frozen during
policy learning. In this work, we explore leveraging pre-trained text-to-image
diffusion models to obtain task-adaptive visual representations for robotic
control, without fine-tuning the model itself. However, we find that naively
applying textual conditions - a successful strategy in other vision domains -
yields minimal or even negative gains in control tasks. We attribute this to
the domain gap between the diffusion model's training data and robotic control
environments, leading us to argue for conditions that consider the specific,
dynamic visual information required for control. To this end, we propose ORCA,
which introduces learnable task prompts that adapt to the control environment
and visual prompts that capture fine-grained, frame-specific details. Through
facilitating task-adaptive representations with our newly devised conditions,
our approach achieves state-of-the-art performance on various robotic control
benchmarks, significantly surpassing prior methods.
OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal
  Document Layout Generation
Authors: name: Hengrui Kang, name: Zhuangcheng Gu, name: Zhiyuan Zhao, name: Zichen Wen, name: Bin Wang, name: Weijia Li, name: Conghui He
Published: 2025-10-30 07:39:54

Document AI has advanced rapidly and is attracting increasing attention. Yet,
while most efforts have focused on document layout analysis (DLA), its
generative counterpart, document layout generation, remains underexplored. A
major obstacle lies in the scarcity of diverse layouts: academic papers with
Manhattan-style structures dominate existing studies, while open-world genres
such as newspapers and magazines remain severely underrepresented. To address
this gap, we curate OmniLayout-1M, the first million-scale dataset of diverse
document layouts, covering six common document types and comprising
contemporary layouts collected from multiple sources. Moreover, since existing
methods struggle in complex domains and often fail to arrange long sequences
coherently, we introduce OmniLayout-LLM, a 0.5B model with designed two-stage
Coarse-to-Fine learning paradigm: 1) learning universal layout principles from
OmniLayout-1M with coarse category definitions, and 2) transferring the
knowledge to a specific domain with fine-grained annotations. Extensive
experiments demonstrate that our approach achieves strong performance on
multiple domains in M^{6}Doc dataset, substantially surpassing both existing
layout generation experts and several latest general-purpose LLMs. Our code,
models, and dataset will be publicly released.
The Era of Agentic Organization: Learning to Organize with Language
  Models
Authors: name: Zewen Chi, name: Li Dong, name: Qingxiu Dong, name: Yaru Hao, name: Xun Wu, name: Shaohan Huang, name: Furu Wei
Published: 2025-10-30 16:25:10

We envision a new era of AI, termed agentic organization, where agents solve
complex problems by working collaboratively and concurrently, enabling outcomes
beyond individual intelligence. To realize this vision, we introduce
asynchronous thinking (AsyncThink) as a new paradigm of reasoning with large
language models, which organizes the internal thinking process into
concurrently executable structures. Specifically, we propose a thinking
protocol where an organizer dynamically assigns sub-queries to workers, merges
intermediate knowledge, and produces coherent solutions. More importantly, the
thinking structure in this protocol can be further optimized through
reinforcement learning. Experiments demonstrate that AsyncThink achieves 28%
lower inference latency compared to parallel thinking while improving accuracy
on mathematical reasoning. Moreover, AsyncThink generalizes its learned
asynchronous thinking capabilities, effectively tackling unseen tasks without
additional training.
AMO-Bench: Large Language Models Still Struggle in High School Math
  Competitions
Authors: name: Shengnan An, name: Xunliang Cai, name: Xuezhi Cao, name: Xiaoyu Li, name: Yehao Lin, name: Junlin Liu, name: Xinxuan Lv, name: Dan Ma, name: Xuanlin Wang, name: Ziwen Wang, name: Shuang Zhou
Published: 2025-10-30 17:52:02

We present AMO-Bench, an Advanced Mathematical reasoning benchmark with
Olympiad level or even higher difficulty, comprising 50 human-crafted problems.
Existing benchmarks have widely leveraged high school math competitions for
evaluating mathematical reasoning capabilities of large language models (LLMs).
However, many existing math competitions are becoming less effective for
assessing top-tier LLMs due to performance saturation (e.g., AIME24/25). To
address this, AMO-Bench introduces more rigorous challenges by ensuring all 50
problems are (1) cross-validated by experts to meet at least the International
Mathematical Olympiad (IMO) difficulty standards, and (2) entirely original
problems to prevent potential performance leakages from data memorization.
Moreover, each problem in AMO-Bench requires only a final answer rather than a
proof, enabling automatic and robust grading for evaluation. Experimental
results across 26 LLMs on AMO-Bench show that even the best-performing model
achieves only 52.4% accuracy on AMO-Bench, with most LLMs scoring below 40%.
Beyond these poor performances, our further analysis reveals a promising
scaling trend with increasing test-time compute on AMO-Bench. These results
highlight the significant room for improving the mathematical reasoning in
current LLMs. We release AMO-Bench to facilitate further research into
advancing the reasoning abilities of language models.
https://amo-bench.github.io/
Magentic Marketplace: An Open-Source Environment for Studying Agentic
  Markets
Authors: name: Gagan Bansal, name: Wenyue Hua, name: Zezhou Huang, name: Adam Fourney, name: Amanda Swearngin, name: Will Epperson, name: Tyler Payne, name: Jake M. Hofman, name: Brendan Lucier, name: Chinmay Singh, name: Markus Mobius, name: Akshay Nambi, name: Archana Yadav, name: Kevin Gao, name: David M. Rothschild, name: Aleksandrs Slivkins, name: Daniel G. Goldstein, name: Hussein Mozannar, name: Nicole Immorlica, name: Maya Murad, name: Matthew Vogel, name: Subbarao Kambhampati, name: Eric Horvitz, name: Saleema Amershi
Published: 2025-10-27 18:35:59

As LLM agents advance, they are increasingly mediating economic decisions,
ranging from product discovery to transactions, on behalf of users. Such
applications promise benefits but also raise many questions about agent
accountability and value for users. Addressing these questions requires
understanding how agents behave in realistic market conditions. However,
previous research has largely evaluated agents in constrained settings, such as
single-task marketplaces (e.g., negotiation) or structured two-agent
interactions. Real-world markets are fundamentally different: they require
agents to handle diverse economic activities and coordinate within large,
dynamic ecosystems where multiple agents with opaque behaviors may engage in
open-ended dialogues. To bridge this gap, we investigate two-sided agentic
marketplaces where Assistant agents represent consumers and Service agents
represent competing businesses. To study these interactions safely, we develop
Magentic-Marketplace-- a simulated environment where Assistants and Services
can operate. This environment enables us to study key market dynamics: the
utility agents achieve, behavioral biases, vulnerability to manipulation, and
how search mechanisms shape market outcomes. Our experiments show that frontier
models can approach optimal welfare-- but only under ideal search conditions.
Performance degrades sharply with scale, and all models exhibit severe
first-proposal bias, creating 10-30x advantages for response speed over
quality. These findings reveal how behaviors emerge across market conditions,
informing the design of fair and efficient agentic marketplaces.
EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic
  Health Record Analysis
Authors: name: Yusheng Liao, name: Chaoyi Wu, name: Junwei Liu, name: Shuyang Jiang, name: Pengcheng Qiu, name: Haowen Wang, name: Yun Yue, name: Shuai Zhen, name: Jian Wang, name: Qianrui Fan, name: Jinjie Gu, name: Ya Zhang, name: Yanfeng Wang, name: Yu Wang, name: Weidi Xie
Published: 2025-10-29 15:32:47

Electronic Health Records (EHRs) contain rich yet complex information, and
their automated analysis is critical for clinical decision-making. Despite
recent advances of large language models (LLMs) in clinical workflows, their
ability to analyze EHRs remains limited due to narrow task coverage and lack of
EHR-oriented reasoning capabilities. This paper aims to bridge the gap,
specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning
instruction dataset, comprising 300k high-quality reasoning cases and 4M
non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a
thinking-graph-driven framework that enables to generate high-quality reasoning
data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced
LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage
training paradigm, including domain adaptation, reasoning enhancement, and
reinforcement learning, EHR-R1 systematically acquires domain knowledge and
diverse reasoning capabilities, enabling accurate and robust EHR analysis.
Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning
42 tasks, to comprehensively assess reasoning and prediction across EHR
scenarios. In experiments, we show that the resulting EHR-R1 consistently
outperforms state-of-the-art commercial and open-source LLMs (including
DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and
achieving a 10\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins,
EHR-R1, and EHR-Bench have significantly advanced the development for more
reliable and clinically relevant EHR analysis.
The Quest for Generalizable Motion Generation: Data, Model, and
  Evaluation
Authors: name: Jing Lin, name: Ruisi Wang, name: Junzhe Lu, name: Ziqi Huang, name: Guorui Song, name: Ailing Zeng, name: Xian Liu, name: Chen Wei, name: Wanqi Yin, name: Qingping Sun, name: Zhongang Cai, name: Lei Yang, name: Ziwei Liu
Published: 2025-10-30 17:59:27

Despite recent advances in 3D human motion generation (MoGen) on standard
benchmarks, existing models still face a fundamental bottleneck in their
generalization capability. In contrast, adjacent generative fields, most
notably video generation (ViGen), have demonstrated remarkable generalization
in modeling human behaviors, highlighting transferable insights that MoGen can
leverage. Motivated by this observation, we present a comprehensive framework
that systematically transfers knowledge from ViGen to MoGen across three key
pillars: data, modeling, and evaluation. First, we introduce ViMoGen-228K, a
large-scale dataset comprising 228,000 high-quality motion samples that
integrates high-fidelity optical MoCap data with semantically annotated motions
from web videos and synthesized samples generated by state-of-the-art ViGen
models. The dataset includes both text-motion pairs and text-video-motion
triplets, substantially expanding semantic diversity. Second, we propose
ViMoGen, a flow-matching-based diffusion transformer that unifies priors from
MoCap data and ViGen models through gated multimodal conditioning. To enhance
efficiency, we further develop ViMoGen-light, a distilled variant that
eliminates video generation dependencies while preserving strong
generalization. Finally, we present MBench, a hierarchical benchmark designed
for fine-grained evaluation across motion quality, prompt fidelity, and
generalization ability. Extensive experiments show that our framework
significantly outperforms existing approaches in both automatic and human
evaluations. The code, data, and benchmark will be made publicly available.
EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme
  Backbone Generation
Authors: name: Chao Song, name: Zhiyuan Liu, name: Han Huang, name: Liang Wang, name: Qiong Wang, name: Jianyu Shi, name: Hui Yu, name: Yihang Zhou, name: Yang Zhang
Published: 2025-10-29 03:22:32

Designing enzyme backbones with substrate-specific functionality is a
critical challenge in computational protein engineering. Current generative
models excel in protein design but face limitations in binding data,
substrate-specific control, and flexibility for de novo enzyme backbone
generation. To address this, we introduce EnzyBind, a dataset with 11,100
experimentally validated enzyme-substrate pairs specifically curated from
PDBbind. Building on this, we propose EnzyControl, a method that enables
functional and substrate-specific control in enzyme backbone generation. Our
approach generates enzyme backbones conditioned on MSA-annotated catalytic
sites and their corresponding substrates, which are automatically extracted
from curated enzyme-substrate data. At the core of EnzyControl is EnzyAdapter,
a lightweight, modular component integrated into a pretrained motif-scaffolding
model, allowing it to become substrate-aware. A two-stage training paradigm
further refines the model's ability to generate accurate and functional enzyme
structures. Experiments show that our EnzyControl achieves the best performance
across structural and functional metrics on EnzyBind and EnzyBench benchmarks,
with particularly notable improvements of 13\% in designability and 13\% in
catalytic efficiency compared to the baseline models. The code is released at
https://github.com/Vecteur-libre/EnzyControl.
FullPart: Generating each 3D Part at Full Resolution
Authors: name: Lihe Ding, name: Shaocong Dong, name: Yaokun Li, name: Chenjian Gao, name: Xiao Chen, name: Rui Han, name: Yihao Kuang, name: Hong Zhang, name: Bo Huang, name: Zhanpeng Huang, name: Zibin Wang, name: Dan Xu, name: Tianfan Xue
Published: 2025-10-30 04:51:05

Part-based 3D generation holds great potential for various applications.
Previous part generators that represent parts using implicit vector-set tokens
often suffer from insufficient geometric details. Another line of work adopts
an explicit voxel representation but shares a global voxel grid among all
parts; this often causes small parts to occupy too few voxels, leading to
degraded quality. In this paper, we propose FullPart, a novel framework that
combines both implicit and explicit paradigms. It first derives the bounding
box layout through an implicit box vector-set diffusion process, a task that
implicit diffusion handles effectively since box tokens contain little
geometric detail. Then, it generates detailed parts, each within its own fixed
full-resolution voxel grid. Instead of sharing a global low-resolution space,
each part in our method - even small ones - is generated at full resolution,
enabling the synthesis of intricate details. We further introduce a
center-point encoding strategy to address the misalignment issue when
exchanging information between parts of different actual sizes, thereby
maintaining global coherence. Moreover, to tackle the scarcity of reliable part
data, we present PartVerse-XL, the largest human-annotated 3D part dataset to
date with 40K objects and 320K parts. Extensive experiments demonstrate that
FullPart achieves state-of-the-art results in 3D part generation. We will
release all code, data, and model to benefit future research in 3D part
generation.
OmniX: From Unified Panoramic Generation and Perception to
  Graphics-Ready 3D Scenes
Authors: name: Yukun Huang, name: Jiwen Yu, name: Yanning Zhou, name: Jianan Wang, name: Xintao Wang, name: Pengfei Wan, name: Xihui Liu
Published: 2025-10-30 17:59:51

There are two prevalent ways to constructing 3D scenes: procedural generation
and 2D lifting. Among them, panorama-based 2D lifting has emerged as a
promising technique, leveraging powerful 2D generative priors to produce
immersive, realistic, and diverse 3D environments. In this work, we advance
this technique to generate graphics-ready 3D scenes suitable for physically
based rendering (PBR), relighting, and simulation. Our key insight is to
repurpose 2D generative models for panoramic perception of geometry, textures,
and PBR materials. Unlike existing 2D lifting approaches that emphasize
appearance generation and ignore the perception of intrinsic properties, we
present OmniX, a versatile and unified framework. Based on a lightweight and
efficient cross-modal adapter structure, OmniX reuses 2D generative priors for
a broad range of panoramic vision tasks, including panoramic perception,
generation, and completion. Furthermore, we construct a large-scale synthetic
panorama dataset containing high-quality multimodal panoramas from diverse
indoor and outdoor scenes. Extensive experiments demonstrate the effectiveness
of our model in panoramic visual perception and graphics-ready 3D scene
generation, opening new possibilities for immersive and physically realistic
virtual world generation.
CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark
Authors: name: Jiaqi Wang, name: Xiao Yang, name: Kai Sun, name: Parth Suresh, name: Sanat Sharma, name: Adam Czyzewski, name: Derek Andersen, name: Surya Appini, name: Arkav Banerjee, name: Sajal Choudhary, name: Shervin Ghasemlou, name: Ziqiang Guan, name: Akil Iyer, name: Haidar Khan, name: Lingkun Kong, name: Roy Luo, name: Tiffany Ma, name: Zhen Qiao, name: David Tran, name: Wenfang Xu, name: Skyler Yeatman, name: Chen Zhou, name: Gunveer Gujral, name: Yinglong Xia, name: Shane Moon, name: Nicolas Scheffer, name: Nirav Shah, name: Eun Chang, name: Yue Liu, name: Florian Metze, name: Tammy Stark, name: Zhaleh Feizollahi, name: Andrea Jessee, name: Mangesh Pujari, name: Ahmed Aly, name: Babak Damavandi, name: Rakesh Wanga, name: Anuj Kumar, name: Rohit Patel, name: Wen-tau Yih, name: Xin Luna Dong
Published: 2025-10-30 05:50:48

Wearable devices such as smart glasses are transforming the way people
interact with their surroundings, enabling users to seek information regarding
entities in their view. Multi-Modal Retrieval-Augmented Generation (MM-RAG)
plays a key role in supporting such questions, yet there is still no
comprehensive benchmark for this task, especially regarding wearables
scenarios. To fill this gap, we present CRAG-MM -- a Comprehensive RAG
benchmark for Multi-modal Multi-turn conversations. CRAG-MM contains a diverse
set of 6.5K (image, question, answer) triplets and 2K visual-based multi-turn
conversations across 13 domains, including 6.2K egocentric images designed to
mimic captures from wearable devices. We carefully constructed the questions to
reflect real-world scenarios and challenges, including five types of
image-quality issues, six question types, varying entity popularity, differing
information dynamism, and different conversation turns. We design three tasks:
single-source augmentation, multi-source augmentation, and multi-turn
conversations -- each paired with an associated retrieval corpus and APIs for
both image-KG retrieval and webpage retrieval. Our evaluation shows that
straightforward RAG approaches achieve only 32% and 43% truthfulness on CRAG-MM
single- and multi-turn QA, respectively, whereas state-of-the-art industry
solutions have similar quality (32%/45%), underscoring ample room for
improvement. The benchmark has hosted KDD Cup 2025, attracting about 1K
participants and 5K submissions, with winning solutions improving baseline
performance by 28%, highlighting its early impact on advancing the field.
Counteracting Matthew Effect in Self-Improvement of LVLMs through
  Head-Tail Re-balancing
Authors: name: Xin Guo, name: Zhiheng Xi, name: Yiwen Ding, name: Yitao Zhai, name: Xiaowei Shi, name: Xunliang Cai, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang
Published: 2025-10-30 13:26:58

Self-improvement has emerged as a mainstream paradigm for advancing the
reasoning capabilities of large vision-language models (LVLMs), where models
explore and learn from successful trajectories iteratively. However, we
identify a critical issue during this process: the model excels at generating
high-quality trajectories for simple queries (i.e., head data) but struggles
with more complex ones (i.e., tail data). This leads to an imbalanced
optimization that drives the model to prioritize simple reasoning skills, while
hindering its ability to tackle more complex reasoning tasks. Over iterations,
this imbalance becomes increasingly pronounced--a dynamic we term the "Matthew
effect"--which ultimately hinders further model improvement and leads to
performance bottlenecks. To counteract this challenge, we introduce four
efficient strategies from two perspectives: distribution-reshaping and
trajectory-resampling, to achieve head-tail re-balancing during the
exploration-and-learning self-improvement process. Extensive experiments on
Qwen2-VL-7B-Instruct and InternVL2.5-4B models across visual reasoning tasks
demonstrate that our methods consistently improve visual reasoning
capabilities, outperforming vanilla self-improvement by 3.86 points on average.
Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with
  the MME-CoF Benchmark
Authors: name: Ziyu Guo, name: Xinyan Chen, name: Renrui Zhang, name: Ruichuan An, name: Yu Qi, name: Dongzhi Jiang, name: Xiangtai Li, name: Manyuan Zhang, name: Hongsheng Li, name: Pheng-Ann Heng
Published: 2025-10-30 17:59:55

Recent video generation models can produce high-fidelity, temporally coherent
videos, indicating that they may encode substantial world knowledge. Beyond
realistic synthesis, they also exhibit emerging behaviors indicative of visual
perception, modeling, and manipulation. Yet, an important question still
remains: Are video models ready to serve as zero-shot reasoners in challenging
visual reasoning scenarios? In this work, we conduct an empirical study to
comprehensively investigate this question, focusing on the leading and popular
Veo-3. We evaluate its reasoning behavior across 12 dimensions, including
spatial, geometric, physical, temporal, and embodied logic, systematically
characterizing both its strengths and failure modes. To standardize this study,
we curate the evaluation data into MME-CoF, a compact benchmark that enables
in-depth and thorough assessment of Chain-of-Frame (CoF) reasoning. Our
findings reveal that while current video models demonstrate promising reasoning
patterns on short-horizon spatial coherence, fine-grained grounding, and
locally consistent dynamics, they remain limited in long-horizon causal
reasoning, strict geometric constraints, and abstract logic. Overall, they are
not yet reliable as standalone zero-shot reasoners, but exhibit encouraging
signs as complementary visual engines alongside dedicated reasoning models.
Project page: https://video-cof.github.io