Orionnnn/hub-stats huggingface dataset:
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for
  Embodied Interactive Tasks
Authors: name: Wenqi Zhang, name: Mengna Wang, name: Gangao Liu, name: Xu Huixin, name: Yiwei Jiang, name: Yongliang Shen, name: Guiyang Hou, name: Zhe Zheng, name: Hang Zhang, name: Xin Li, name: Weiming Lu, name: Peng Li, name: Yueting Zhuang
Published: 2025-03-27 17:00:51

Recent advances in deep thinking models have demonstrated remarkable
reasoning capabilities on mathematical and coding tasks. However, their
effectiveness in embodied domains which require continuous interaction with
environments through image action interleaved trajectories remains largely
-unexplored. We present Embodied Reasoner, a model that extends o1 style
reasoning to interactive embodied search tasks. Unlike mathematical reasoning
that relies primarily on logical deduction, embodied scenarios demand spatial
understanding, temporal reasoning, and ongoing self-reflection based on
interaction history. To address these challenges, we synthesize 9.3k coherent
Observation-Thought-Action trajectories containing 64k interactive images and
90k diverse thinking processes (analysis, spatial reasoning, reflection,
planning, and verification). We develop a three-stage training pipeline that
progressively enhances the model's capabilities through imitation learning,
self-exploration via rejection sampling, and self-correction through reflection
tuning. The evaluation shows that our model significantly outperforms those
advanced visual reasoning models, e.g., it exceeds OpenAI o1, o3-mini, and
Claude-3.7 by +9\%, 24\%, and +13\%. Analysis reveals our model exhibits fewer
repeated searches and logical inconsistencies, with particular advantages in
complex long-horizon tasks. Real-world environments also show our superiority
while exhibiting fewer repeated searches and logical inconsistency cases.
ResearchBench: Benchmarking LLMs in Scientific Discovery via
  Inspiration-Based Task Decomposition
Authors: name: Yujie Liu, name: Zonglin Yang, name: Tong Xie, name: Jinjie Ni, name: Ben Gao, name: Yuqiang Li, name: Shixiang Tang, name: Wanli Ouyang, name: Erik Cambria, name: Dongzhan Zhou
Published: 2025-03-27 08:09:15

Large language models (LLMs) have demonstrated potential in assisting
scientific research, yet their ability to discover high-quality research
hypotheses remains unexamined due to the lack of a dedicated benchmark. To
address this gap, we introduce the first large-scale benchmark for evaluating
LLMs with a near-sufficient set of sub-tasks of scientific discovery:
inspiration retrieval, hypothesis composition, and hypothesis ranking. We
develop an automated framework that extracts critical components - research
questions, background surveys, inspirations, and hypotheses - from scientific
papers across 12 disciplines, with expert validation confirming its accuracy.
To prevent data contamination, we focus exclusively on papers published in
2024, ensuring minimal overlap with LLM pretraining data. Our evaluation
reveals that LLMs perform well in retrieving inspirations, an
out-of-distribution task, suggesting their ability to surface novel knowledge
associations. This positions LLMs as "research hypothesis mines", capable of
facilitating automated scientific discovery by generating innovative hypotheses
at scale with minimal human intervention.
FinAudio: A Benchmark for Audio Large Language Models in Financial
  Applications
Authors: name: Yupeng Cao, name: Haohang Li, name: Yangyang Yu, name: Shashidhar Reddy Javaji, name: Yueru He, name: Jimin Huang, name: Zining Zhu, name: Qianqian Xie, name: Xiao-yang Liu, name: Koduvayur Subbalakshmi, name: Meikang Qiu, name: Sophia Ananiadou, name: Jian-Yun Nie
Published: 2025-03-26 21:07:51

Audio Large Language Models (AudioLLMs) have received widespread attention
and have significantly improved performance on audio tasks such as
conversation, audio understanding, and automatic speech recognition (ASR).
Despite these advancements, there is an absence of a benchmark for assessing
AudioLLMs in financial scenarios, where audio data, such as earnings conference
calls and CEO speeches, are crucial resources for financial analysis and
investment decisions. In this paper, we introduce FinAudio, the first
benchmark designed to evaluate the capacity of AudioLLMs in the financial
domain. We first define three tasks based on the unique characteristics of the
financial domain: 1) ASR for short financial audio, 2) ASR for long financial
audio, and 3) summarization of long financial audio. Then, we curate two short
and two long audio datasets, respectively, and develop a novel dataset for
financial audio summarization, comprising the FinAudio benchmark.
Then, we evaluate seven prevalent AudioLLMs on FinAudio. Our
evaluation reveals the limitations of existing AudioLLMs in the financial
domain and offers insights for improving AudioLLMs. All datasets and codes will
be released.
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large
  Reasoning Models with Iterative Retrieval Augmented Generation
Authors: name: Zhicheng Lee, name: Shulin Cao, name: Jinxin Liu, name: Jiajie Zhang, name: Weichuan Liu, name: Xiaoyin Che, name: Lei Hou, name: Juanzi Li
Published: 2025-03-27 17:44:18

Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely
primarily on parametric knowledge, limiting factual accuracy. While recent
works equip reinforcement learning (RL)-based LRMs with retrieval capabilities,
they suffer from overthinking and lack robustness in reasoning, reducing their
effectiveness in question answering (QA) tasks. To address this, we propose
ReaRAG, a factuality-enhanced reasoning model that explores diverse queries
without excessive iterations. Our solution includes a novel data construction
framework with an upper bound on the reasoning chain length. Specifically, we
first leverage an LRM to generate deliberate thinking, then select an action
from a predefined action space (Search and Finish). For Search action, a query
is executed against the RAG engine, where the result is returned as observation
to guide reasoning steps later. This process iterates until a Finish action is
chosen. Benefiting from ReaRAG's strong reasoning capabilities, our approach
outperforms existing baselines on multi-hop QA. Further analysis highlights its
strong reflective ability to recognize errors and refine its reasoning
trajectory. Our study enhances LRMs' factuality while effectively integrating
robust reasoning for Retrieval-Augmented Generation (RAG).
Synthetic Video Enhances Physical Fidelity in Video Synthesis
Authors: name: Qi Zhao, name: Xingyu Ni, name: Ziyu Wang, name: Feng Cheng, name: Ziyan Yang, name: Lu Jiang, name: Bohan Wang
Published: 2025-03-26 00:45:07

We investigate how to enhance the physical fidelity of video generation
models by leveraging synthetic videos derived from computer graphics pipelines.
These rendered videos respect real-world physics, such as maintaining 3D
consistency, and serve as a valuable resource that can potentially improve
video generation models. To harness this potential, we propose a solution that
curates and integrates synthetic data while introducing a method to transfer
its physical realism to the model, significantly reducing unwanted artifacts.
Through experiments on three representative tasks emphasizing physical
consistency, we demonstrate its efficacy in enhancing physical fidelity. While
our model still lacks a deep understanding of physics, our work offers one of
the first empirical demonstrations that synthetic video enhances physical
fidelity in video synthesis. Website: https://kevinz8866.github.io/simulation/
ChatAnyone: Stylized Real-time Portrait Video Generation with
  Hierarchical Motion Diffusion Model
Authors: name: Jinwei Qi, name: Chaonan Ji, name: Sheng Xu, name: Peng Zhang, name: Bang Zhang, name: Liefeng Bo
Published: 2025-03-27 04:18:53

Real-time interactive video-chat portraits have been increasingly recognized
as the future trend, particularly due to the remarkable progress made in text
and voice chat technologies. However, existing methods primarily focus on
real-time generation of head movements, but struggle to produce synchronized
body motions that match these head actions. Additionally, achieving
fine-grained control over the speaking style and nuances of facial expressions
remains a challenge. To address these limitations, we introduce a novel
framework for stylized real-time portrait video generation, enabling expressive
and flexible video chat that extends from talking head to upper-body
interaction. Our approach consists of the following two stages. The first stage
involves efficient hierarchical motion diffusion models, that take both
explicit and implicit motion representations into account based on audio
inputs, which can generate a diverse range of facial expressions with stylistic
control and synchronization between head and body movements. The second stage
aims to generate portrait video featuring upper-body movements, including hand
gestures. We inject explicit hand control signals into the generator to produce
more detailed hand movements, and further perform face refinement to enhance
the overall realism and expressiveness of the portrait video. Additionally, our
approach supports efficient and continuous generation of upper-body portrait
video in maximum 512 * 768 resolution at up to 30fps on 4090 GPU, supporting
interactive video-chat in real-time. Experimental results demonstrate the
capability of our approach to produce portrait videos with rich expressiveness
and natural upper-body movements.
Exploring the Evolution of Physics Cognition in Video Generation: A
  Survey
Authors: name: Minghui Lin, name: Xiang Wang, name: Yishan Wang, name: Shu Wang, name: Fengqi Dai, name: Pengxiang Ding, name: Cunxiang Wang, name: Zhengrong Zuo, name: Nong Sang, name: Siteng Huang, name: Donglin Wang
Published: 2025-03-27 17:58:33

Recent advancements in video generation have witnessed significant progress,
especially with the rapid advancement of diffusion models. Despite this, their
deficiencies in physical cognition have gradually received widespread attention
- generated content often violates the fundamental laws of physics, falling
into the dilemma of ''visual realism but physical absurdity". Researchers began
to increasingly recognize the importance of physical fidelity in video
generation and attempted to integrate heuristic physical cognition such as
motion representations and physical knowledge into generative systems to
simulate real-world dynamic scenarios. Considering the lack of a systematic
overview in this field, this survey aims to provide a comprehensive summary of
architecture designs and their applications to fill this gap. Specifically, we
discuss and organize the evolutionary process of physical cognition in video
generation from a cognitive science perspective, while proposing a three-tier
taxonomy: 1) basic schema perception for generation, 2) passive cognition of
physical knowledge for generation, and 3) active cognition for world
simulation, encompassing state-of-the-art methods, classical paradigms, and
benchmarks. Subsequently, we emphasize the inherent key challenges in this
domain and delineate potential pathways for future research, contributing to
advancing the frontiers of discussion in both academia and industry. Through
structured review and interdisciplinary analysis, this survey aims to provide
directional guidance for developing interpretable, controllable, and physically
consistent video generation paradigms, thereby propelling generative models
from the stage of ''visual mimicry'' towards a new phase of ''human-like
physical comprehension''.
Challenging the Boundaries of Reasoning: An Olympiad-Level Math
  Benchmark for Large Language Models
Authors: name: Haoxiang Sun, name: Yingqian Min, name: Zhipeng Chen, name: Wayne Xin Zhao, name: Zheng Liu, name: Zhongyuan Wang, name: Lei Fang, name: Ji-Rong Wen
Published: 2025-03-27 11:20:17

In recent years, the rapid development of large reasoning models has resulted
in the saturation of existing benchmarks for evaluating mathematical reasoning,
highlighting the urgent need for more challenging and rigorous evaluation
frameworks. To address this gap, we introduce OlymMATH, a novel Olympiad-level
mathematical benchmark, designed to rigorously test the complex reasoning
capabilities of LLMs. OlymMATH features 200 meticulously curated problems, each
manually verified and available in parallel English and Chinese versions. The
problems are systematically organized into two distinct difficulty tiers: (1)
AIME-level problems (easy) that establish a baseline for mathematical reasoning
assessment, and (2) significantly more challenging problems (hard) designed to
push the boundaries of current state-of-the-art models. In our benchmark, these
problems span four core mathematical fields, each including a verifiable
numerical solution to enable objective, rule-based evaluation. Empirical
results underscore the significant challenge presented by OlymMATH, with
state-of-the-art models including DeepSeek-R1 and OpenAI's o3-mini
demonstrating notably limited accuracy on the hard subset. Furthermore, the
benchmark facilitates comprehensive bilingual assessment of mathematical
reasoning abilities-a critical dimension that remains largely unaddressed in
mainstream mathematical reasoning benchmarks. We release the OlymMATH benchmark
at the STILL project: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs.
Large Language Model Agent: A Survey on Methodology, Applications and
  Challenges
Authors: name: Junyu Luo, name: Weizhi Zhang, name: Ye Yuan, name: Yusheng Zhao, name: Junwei Yang, name: Yiyang Gu, name: Bohan Wu, name: Binqi Chen, name: Ziyue Qiao, name: Qingqing Long, name: Rongcheng Tu, name: Xiao Luo, name: Wei Ju, name: Zhiping Xiao, name: Yifan Wang, name: Meng Xiao, name: Chenwu Liu, name: Jingyang Yuan, name: Shichang Zhang, name: Yiqiao Jin, name: Fan Zhang, name: Xian Wu, name: Hanqing Zhao, name: Dacheng Tao, name: Philip S. Yu, name: Ming Zhang
Published: 2025-03-27 12:50:17

The era of intelligent agents is upon us, driven by revolutionary
advancements in large language models. Large Language Model (LLM) agents, with
goal-driven behaviors and dynamic adaptation capabilities, potentially
represent a critical pathway toward artificial general intelligence. This
survey systematically deconstructs LLM agent systems through a
methodology-centered taxonomy, linking architectural foundations, collaboration
mechanisms, and evolutionary pathways. We unify fragmented research threads by
revealing fundamental connections between agent design principles and their
emergent behaviors in complex environments. Our work provides a unified
architectural perspective, examining how agents are constructed, how they
collaborate, and how they evolve over time, while also addressing evaluation
methodologies, tool applications, practical challenges, and diverse application
domains. By surveying the latest developments in this rapidly evolving field,
we offer researchers a structured taxonomy for understanding LLM agents and
identify promising directions for future research. The collection is available
at https://github.com/luo-junyu/Awesome-Agent-Papers.
Video-R1: Reinforcing Video Reasoning in MLLMs
Authors: name: Kaituo Feng, name: Kaixiong Gong, name: Bohao Li, name: Zonghao Guo, name: Yibing Wang, name: Tianshuo Peng, name: Benyou Wang, name: Xiangyu Yue
Published: 2025-03-27 17:59:51

Inspired by DeepSeek-R1's success in eliciting reasoning abilities through
rule-based reinforcement learning (RL), we introduce Video-R1 as the first
attempt to systematically explore the R1 paradigm for eliciting video reasoning
within multimodal large language models (MLLMs). However, directly applying RL
training with the GRPO algorithm to video reasoning presents two primary
challenges: (i) a lack of temporal modeling for video reasoning, and (ii) the
scarcity of high-quality video-reasoning data. To address these issues, we
first propose the T-GRPO algorithm, which encourages models to utilize temporal
information in videos for reasoning. Additionally, instead of relying solely on
video data, we incorporate high-quality image-reasoning data into the training
process. We have constructed two datasets: Video-R1-COT-165k for SFT cold start
and Video-R1-260k for RL training, both comprising image and video data.
Experimental results demonstrate that Video-R1 achieves significant
improvements on video reasoning benchmarks such as VideoMMMU and VSI-Bench, as
well as on general video benchmarks including MVBench and TempCompass, etc.
Notably, Video-R1-7B attains a 35.8% accuracy on video spatial reasoning
benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o. All
codes, models, data are released.
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
Authors: name: Qi Qin, name: Le Zhuo, name: Yi Xin, name: Ruoyi Du, name: Zhen Li, name: Bin Fu, name: Yiting Lu, name: Jiakang Yuan, name: Xinyue Li, name: Dongyang Liu, name: Xiangyang Zhu, name: Manyuan Zhang, name: Will Beddow, name: Erwann Millon, name: Victor Perez, name: Wenhai Wang, name: Conghui He, name: Bo Zhang, name: Xiaohong Liu, name: Hongsheng Li, name: Yu Qiao, name: Chang Xu, name: Peng Gao
Published: 2025-03-27 17:57:07

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework
that achieves significant progress compared to previous work, Lumina-Next.
Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts
a unified architecture (Unified Next-DiT) that treats text and image tokens as
a joint sequence, enabling natural cross-modal interactions and allowing
seamless task expansion. Besides, since high-quality captioners can provide
semantically well-aligned text-image training pairs, we introduce a unified
captioning system, Unified Captioner (UniCap), specifically designed for T2I
generation tasks. UniCap excels at generating comprehensive and accurate
captions, accelerating convergence and enhancing prompt adherence. (2)
Efficiency - to improve the efficiency of our proposed model, we develop
multi-stage progressive training strategies and introduce inference
acceleration techniques without compromising image quality. Extensive
evaluations on academic benchmarks and public text-to-image arenas show that
Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters,
highlighting its scalability and design efficiency. We have released our
training details, code, and models at
https://github.com/Alpha-VLLM/Lumina-Image-2.0.
Optimal Stepsize for Diffusion Sampling
Authors: name: Jianning Pei, name: Han Hu, name: Shuyang Gu
Published: 2025-03-27 17:59:46

Diffusion models achieve remarkable generation quality but suffer from
computational intensive sampling due to suboptimal step discretization. While
existing works focus on optimizing denoising directions, we address the
principled design of stepsize schedules. This paper proposes Optimal Stepsize
Distillation, a dynamic programming framework that extracts theoretically
optimal schedules by distilling knowledge from reference trajectories. By
reformulating stepsize optimization as recursive error minimization, our method
guarantees global discretization bounds through optimal substructure
exploitation. Crucially, the distilled schedules demonstrate strong robustness
across architectures, ODE solvers, and noise schedules. Experiments show 10x
accelerated text-to-image generation while preserving 99.4% performance on
GenEval. Our code is available at https://github.com/bebebe666/OptimalSteps.
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
  Synthesis
Authors: name: Shitian Zhao, name: Qilong Wu, name: Xinyue Li, name: Bo Zhang, name: Ming Li, name: Qi Qin, name: Dongyang Liu, name: Kaipeng Zhang, name: Hongsheng Li, name: Yu Qiao, name: Peng Gao, name: Bin Fu, name: Zhen Li
Published: 2025-03-27 17:56:15

We introduce LeX-Art, a comprehensive suite for high-quality text-image
synthesis that systematically bridges the gap between prompt expressiveness and
text rendering fidelity. Our approach follows a data-centric paradigm,
constructing a high-quality data synthesis pipeline based on Deepseek-R1 to
curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined
1024times1024 images. Beyond dataset construction, we develop LeX-Enhancer,
a robust prompt enrichment model, and train two text-to-image models, LeX-FLUX
and LeX-Lumina, achieving state-of-the-art text rendering performance. To
systematically evaluate visual text generation, we introduce LeX-Bench, a
benchmark that assesses fidelity, aesthetics, and alignment, complemented by
Pairwise Normalized Edit Distance (PNED), a novel metric for robust text
accuracy evaluation. Experiments demonstrate significant improvements, with
LeX-Lumina achieving a 79.81% PNED gain on CreateBench, and LeX-FLUX
outperforming baselines in color (+3.18%), positional (+4.45%), and font
accuracy (+3.81%). Our codes, models, datasets, and demo are publicly
available.
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
  Faithfulness
Authors: name: Dian Zheng, name: Ziqi Huang, name: Hongbo Liu, name: Kai Zou, name: Yinan He, name: Fan Zhang, name: Yuanhan Zhang, name: Jingwen He, name: Wei-Shi Zheng, name: Yu Qiao, name: Ziwei Liu
Published: 2025-03-27 17:57:01

Video generation has advanced significantly, evolving from producing
unrealistic outputs to generating videos that appear visually convincing and
temporally coherent. To evaluate these video generative models, benchmarks such
as VBench have been developed to assess their faithfulness, measuring factors
like per-frame aesthetics, temporal consistency, and basic prompt adherence.
However, these aspects mainly represent superficial faithfulness, which focus
on whether the video appears visually convincing rather than whether it adheres
to real-world principles. While recent models perform increasingly well on
these metrics, they still struggle to generate videos that are not just
visually plausible but fundamentally realistic. To achieve real "world models"
through video generation, the next frontier lies in intrinsic faithfulness to
ensure that generated videos adhere to physical laws, commonsense reasoning,
anatomical correctness, and compositional integrity. Achieving this level of
realism is essential for applications such as AI-assisted filmmaking and
simulated world modeling. To bridge this gap, we introduce VBench-2.0, a
next-generation benchmark designed to automatically evaluate video generative
models for their intrinsic faithfulness. VBench-2.0 assesses five key
dimensions: Human Fidelity, Controllability, Creativity, Physics, and
Commonsense, each further broken down into fine-grained capabilities. Tailored
for individual dimensions, our evaluation framework integrates generalists such
as state-of-the-art VLMs and LLMs, and specialists, including anomaly detection
methods proposed for video generation. We conduct extensive annotations to
ensure alignment with human judgment. By pushing beyond superficial
faithfulness toward intrinsic faithfulness, VBench-2.0 aims to set a new
standard for the next generation of video generative models in pursuit of
intrinsic faithfulness.