Orionnnn/hub-stats huggingface dataset:
RecoWorld: Building Simulated Environments for Agentic Recommender
  Systems
Authors: name: Fei Liu, name: Xinyu Lin, name: Hanchao Yu, name: Mingyuan Wu, name: Jianyu Wang, name: Qiang Zhang, name: Zhuokai Zhao, name: Yinglong Xia, name: Yao Zhang, name: Weiwei Li, name: Mingze Gao, name: Qifan Wang, name: Lizhu Zhang, name: Benyu Zhang, name: Xiangjun Fan
Published: 2025-09-12 16:44:34

We present RecoWorld, a blueprint for building simulated environments
tailored to agentic recommender systems. Such environments give agents a proper
training space where they can learn from errors without impacting real users.
RecoWorld distinguishes itself with a dual-view architecture: a simulated user
and an agentic recommender engage in multi-turn interactions aimed at
maximizing user retention. The user simulator reviews recommended items,
updates its mindset, and when sensing potential user disengagement, generates
reflective instructions. The agentic recommender adapts its recommendations by
incorporating these user instructions and reasoning traces, creating a dynamic
feedback loop that actively engages users. This process leverages the
exceptional reasoning capabilities of modern LLMs. We explore diverse content
representations within the simulator, including text-based, multimodal, and
semantic ID modeling, and discuss how multi-turn RL enables the recommender to
refine its strategies through iterative interactions. RecoWorld also supports
multi-agent simulations, allowing creators to simulate the responses of
targeted user populations. It marks an important first step toward recommender
systems where users and agents collaboratively shape personalized information
streams. We envision new interaction paradigms where "user instructs,
recommender responds," jointly optimizing user retention and engagement.
Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question
  Answering with LLMs
Authors: name: Mario Sanz-Guerrero, name: Minh Duc Bui, name: Katharina von der Wense
Published: 2025-09-18 14:47:58

When evaluating large language models (LLMs) with multiple-choice question
answering (MCQA), it is common to end the prompt with the string "Answer:" to
facilitate automated answer extraction via next-token probabilities. However,
there is no consensus on how to tokenize the space following the colon, often
overlooked as a trivial choice. In this paper, we uncover accuracy differences
of up to 11% due to this (seemingly irrelevant) tokenization variation as well
as reshuffled model rankings, raising concerns about the reliability of LLM
comparisons in prior work. Surprisingly, we are able to recommend one specific
strategy -- tokenizing the space together with the answer letter -- as we
observe consistent and statistically significant performance improvements.
Additionally, it improves model calibration, enhancing the reliability of the
model's confidence estimates. Our findings underscore the importance of careful
evaluation design and highlight the need for standardized, transparent
evaluation protocols to ensure reliable and comparable results.
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal
  Ultrasound Intelligence
Authors: name: Chaoyin She, name: Ruifang Lu, name: Lida Chen, name: Wei Wang, name: Qinghua Huang
Published: 2025-09-18 14:07:53

Ultrasound imaging has become the preferred imaging modality for early cancer
screening due to its advantages of non-ionizing radiation, low cost, and
real-time imaging capabilities. However, conventional ultrasound diagnosis
heavily relies on physician expertise, presenting challenges of high
subjectivity and low diagnostic efficiency. Vision-language models (VLMs) offer
promising solutions for this issue, but existing general-purpose models
demonstrate limited knowledge in ultrasound medical tasks, with poor
generalization in multi-organ lesion recognition and low efficiency across
multi-task diagnostics. To address these limitations, we propose EchoVLM, a
vision-language model specifically designed for ultrasound medical imaging. The
model employs a Mixture of Experts (MoE) architecture trained on data spanning
seven anatomical regions. This design enables the model to perform multiple
tasks, including ultrasound report generation, diagnosis and visual
question-answering (VQA). The experimental results demonstrated that EchoVLM
achieved significant improvements of 10.15 and 4.77 points in BLEU-1 scores and
ROUGE-1 scores respectively compared to Qwen2-VL on the ultrasound report
generation task. These findings suggest that EchoVLM has substantial potential
to enhance diagnostic accuracy in ultrasound imaging, thereby providing a
viable technical solution for future clinical applications. Source code and
model weights are available at https://github.com/Asunatan/EchoVLM.
Reasoning over Boundaries: Enhancing Specification Alignment via
  Test-time Delibration
Authors: name: Haoran Zhang, name: Yafu Li, name: Xuyang Hu, name: Dongrui Liu, name: Zhilin Wang, name: Bo Li, name: Yu Cheng
Published: 2025-09-18 09:08:53

Large language models (LLMs) are increasingly applied in diverse real-world
scenarios, each governed by bespoke behavioral and safety specifications (spec)
custom-tailored by users or organizations. These spec, categorized into
safety-spec and behavioral-spec, vary across scenarios and evolve with changing
preferences and requirements. We formalize this challenge as specification
alignment, focusing on LLMs' ability to follow dynamic, scenario-specific spec
from both behavioral and safety perspectives. To address this challenge, we
propose Align3, a lightweight method that employs Test-Time Deliberation (TTD)
with hierarchical reflection and revision to reason over the specification
boundaries. We further present SpecBench, a unified benchmark for measuring
specification alignment, covering 5 scenarios, 103 spec, and 1,500 prompts.
Experiments on 15 reasoning and 18 instruct models with several TTD methods,
including Self-Refine, TPO, and MoreThink, yield three key findings: (i)
test-time deliberation enhances specification alignment; (ii) Align3 advances
the safety-helpfulness trade-off frontier with minimal overhead; (iii)
SpecBench effectively reveals alignment gaps. These results highlight the
potential of test-time deliberation as an effective strategy for reasoning over
the real-world specification boundaries.
Understand Before You Generate: Self-Guided Training for Autoregressive
  Image Generation
Authors: name: Xiaoyu Yue, name: Zidong Wang, name: Yuqing Wang, name: Wenlong Zhang, name: Xihui Liu, name: Wanli Ouyang, name: Lei Bai, name: Luping Zhou
Published: 2025-09-18 17:47:40

Recent studies have demonstrated the importance of high-quality visual
representations in image generation and have highlighted the limitations of
generative models in image understanding. As a generative paradigm originally
designed for natural language, autoregressive models face similar challenges.
In this work, we present the first systematic investigation into the mechanisms
of applying the next-token prediction paradigm to the visual domain. We
identify three key properties that hinder the learning of high-level visual
semantics: local and conditional dependence, inter-step semantic inconsistency,
and spatial invariance deficiency. We show that these issues can be effectively
addressed by introducing self-supervised objectives during training, leading to
a novel training framework, Self-guided Training for AutoRegressive models
(ST-AR). Without relying on pre-trained representation models, ST-AR
significantly enhances the image understanding ability of autoregressive models
and leads to improved generation quality. Specifically, ST-AR brings
approximately 42% FID improvement for LlamaGen-L and 49% FID improvement for
LlamaGen-XL, while maintaining the same sampling strategy.
FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution
  Remote Sensing Change Detection
Authors: name: Zhongxiang Xie, name: Shuangxi Miao, name: Yuhan Jiang, name: Zhewei Zhang, name: Jing Yao, name: Xuecao Li, name: Jianxi Huang, name: Pedram Ghamisi
Published: 2025-09-08 09:46:33

Change detection from high-resolution remote sensing images lies as a
cornerstone of Earth observation applications, yet its efficacy is often
compromised by two critical challenges. First, false alarms are prevalent as
models misinterpret radiometric variations from temporal shifts (e.g.,
illumination, season) as genuine changes. Second, a non-negligible semantic gap
between deep abstract features and shallow detail-rich features tends to
obstruct their effective fusion, culminating in poorly delineated boundaries.
To step further in addressing these issues, we propose the Frequency-Spatial
Synergistic Gated Network (FSG-Net), a novel paradigm that aims to
systematically disentangle semantic changes from nuisance variations.
Specifically, FSG-Net first operates in the frequency domain, where a
Discrepancy-Aware Wavelet Interaction Module (DAWIM) adaptively mitigates
pseudo-changes by discerningly processing different frequency components.
Subsequently, the refined features are enhanced in the spatial domain by a
Synergistic Temporal-Spatial Attention Module (STSAM), which amplifies the
saliency of genuine change regions. To finally bridge the semantic gap, a
Lightweight Gated Fusion Unit (LGFU) leverages high-level semantics to
selectively gate and integrate crucial details from shallow layers.
Comprehensive experiments on the CDD, GZ-CD, and LEVIR-CD benchmarks validate
the superiority of FSG-Net, establishing a new state-of-the-art with F1-scores
of 94.16%, 89.51%, and 91.27%, respectively. The code will be made available at
https://github.com/zxXie-Air/FSG-Net after a possible publication.
Unleashing the Potential of Multimodal LLMs for Zero-Shot
  Spatio-Temporal Video Grounding
Authors: name: Zaiquan Yang, name: Yuhao Liu, name: Gerhard Hancke, name: Rynson W. H. Lau
Published: 2025-09-18 17:35:50

Spatio-temporal video grounding (STVG) aims at localizing the spatio-temporal
tube of a video, as specified by the input text query. In this paper, we
utilize multimodal large language models (MLLMs) to explore a zero-shot
solution in STVG. We reveal two key insights about MLLMs: (1) MLLMs tend to
dynamically assign special tokens, referred to as grounding tokens,
for grounding the text query; and (2) MLLMs often suffer from suboptimal
grounding due to the inability to fully integrate the cues in the text query
(e.g., attributes, actions) for inference. Based on these insights, we
propose a MLLM-based zero-shot framework for STVG, which includes novel
decomposed spatio-temporal highlighting (DSTH) and temporal-augmented
assembling (TAS) strategies to unleash the reasoning ability of MLLMs. The DSTH
strategy first decouples the original query into attribute and action
sub-queries for inquiring the existence of the target both spatially and
temporally. It then uses a novel logit-guided re-attention (LRA) module to
learn latent variables as spatial and temporal prompts, by regularizing token
predictions for each sub-query. These prompts highlight attribute and action
cues, respectively, directing the model's attention to reliable spatial and
temporal related visual regions. In addition, as the spatial grounding by the
attribute sub-query should be temporally consistent, we introduce the TAS
strategy to assemble the predictions using the original video frames and the
temporal-augmented frames as inputs to help improve temporal consistency. We
evaluate our method on various MLLMs, and show that it outperforms SOTA methods
on three common STVG benchmarks.
  The code will be available at https://github.com/zaiquanyang/LLaVA_Next_STVG.
FlowRL: Matching Reward Distributions for LLM Reasoning
Authors: name: Xuekai Zhu, name: Daixuan Cheng, name: Dinghuai Zhang, name: Hengli Li, name: Kaiyan Zhang, name: Che Jiang, name: Youbang Sun, name: Ermo Hua, name: Yuxin Zuo, name: Xingtai Lv, name: Qizheng Zhang, name: Lin Chen, name: Fanghao Shao, name: Bo Xue, name: Yunchong Song, name: Zhenjie Yang, name: Ganqu Cui, name: Ning Ding, name: Jianfeng Gao, name: Xiaodong Liu, name: Bowen Zhou, name: Hongyuan Mei, name: Zhouhan Lin
Published: 2025-09-18 17:56:36

We propose FlowRL: matching the full reward distribution via flow balancing
instead of maximizing rewards in large language model (LLM) reinforcement
learning (RL). Recent advanced reasoning models adopt reward-maximizing methods
(\eg, PPO and GRPO), which tend to over-optimize dominant reward signals while
neglecting less frequent but valid reasoning paths, thus reducing diversity. In
contrast, we transform scalar rewards into a normalized target distribution
using a learnable partition function, and then minimize the reverse KL
divergence between the policy and the target distribution. We implement this
idea as a flow-balanced optimization method that promotes diverse exploration
and generalizable reasoning trajectories. We conduct experiments on math and
code reasoning tasks: FlowRL achieves a significant average improvement of
10.0% over GRPO and 5.1% over PPO on math benchmarks, and performs
consistently better on code reasoning tasks. These results highlight reward
distribution-matching as a key step toward efficient exploration and diverse
reasoning in LLM reinforcement learning.
FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial
  Search and Reasoning
Authors: name: Liang Hu, name: Jianpeng Jiao, name: Jiashuo Liu, name: Yanle Ren, name: Zhoufutu Wen, name: Kaiyuan Zhang, name: Xuanliang Zhang, name: Xiang Gao, name: Tianci He, name: Fei Hu, name: Yali Liao, name: Zaiyuan Wang, name: Chenghao Yang, name: Qianyu Yang, name: Mingren Yin, name: Zhiyuan Zeng, name: Ge Zhang, name: Xinyi Zhang, name: Xiying Zhao, name: Zhenwei Zhu, name: Hongseok Namkoong, name: Wenhao Huang, name: Yuwen Tang
Published: 2025-09-16 15:13:13

Search has emerged as core infrastructure for LLM-based agents and is widely
viewed as critical on the path toward more general intelligence. Finance is a
particularly demanding proving ground: analysts routinely conduct complex,
multi-step searches over time-sensitive, domain-specific data, making it ideal
for assessing both search proficiency and knowledge-grounded reasoning. Yet no
existing open financial datasets evaluate data searching capability of
end-to-end agents, largely because constructing realistic, complicated tasks
requires deep financial expertise and time-sensitive data is hard to evaluate.
We present FinSearchComp, the first fully open-source agent benchmark for
realistic, open-domain financial search and reasoning. FinSearchComp comprises
three tasks -- Time-Sensitive Data Fetching, Simple Historical Lookup, and
Complex Historical Investigation -- closely reproduce real-world financial
analyst workflows. To ensure difficulty and reliability, we engage 70
professional financial experts for annotation and implement a rigorous
multi-stage quality-assurance pipeline. The benchmark includes 635 questions
spanning global and Greater China markets, and we evaluate 21 models (products)
on it. Grok 4 (web) tops the global subset, approaching expert-level accuracy.
DouBao (web) leads on the Greater China subset. Experimental analyses show that
equipping agents with web search and financial plugins substantially improves
results on FinSearchComp, and the country origin of models and tools impact
performance significantly.By aligning with realistic analyst tasks and
providing end-to-end evaluation, FinSearchComp offers a professional,
high-difficulty testbed for complex financial search and reasoning.
AToken: A Unified Tokenizer for Vision
Authors: name: Jiasen Lu, name: Liangchen Song, name: Mingze Xu, name: Byeongjoo Ahn, name: Yanjun Wang, name: Chen Chen, name: Afshin Dehghan, name: Yinfei Yang
Published: 2025-09-17 23:11:18

We present AToken, the first unified visual tokenizer that achieves both
high-fidelity reconstruction and semantic understanding across images, videos,
and 3D assets. Unlike existing tokenizers that specialize in either
reconstruction or understanding for single modalities, AToken encodes these
diverse visual inputs into a shared 4D latent space, unifying both tasks and
modalities in a single framework. Specifically, we introduce a pure transformer
architecture with 4D rotary position embeddings to process visual inputs of
arbitrary resolutions and temporal durations. To ensure stable training, we
introduce an adversarial-free training objective that combines perceptual and
Gram matrix losses, achieving state-of-the-art reconstruction quality. By
employing a progressive training curriculum, AToken gradually expands from
single images, videos, and 3D, and supports both continuous and discrete latent
tokens. AToken achieves 0.21 rFID with 82.2% ImageNet accuracy for images, 3.01
rFVD with 32.6% MSRVTT retrieval for videos, and 28.19 PSNR with 90.9%
classification accuracy for 3D. In downstream applications, AToken enables both
visual generation tasks (e.g., image generation with continuous and discrete
tokens, text-to-video generation, image-to-3D synthesis) and understanding
tasks (e.g., multimodal LLMs), achieving competitive performance across all
benchmarks. These results shed light on the next-generation multimodal AI
systems built upon unified visual tokenization.
WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model
  via Training-Free Guidance
Authors: name: Chenxi Song, name: Yanming Yang, name: Tong Zhao, name: Ruibo Li, name: Chi Zhang
Published: 2025-09-18 16:40:47

Recent video diffusion models demonstrate strong potential in spatial
intelligence tasks due to their rich latent world priors. However, this
potential is hindered by their limited controllability and geometric
inconsistency, creating a gap between their strong priors and their practical
use in 3D/4D tasks. As a result, current approaches often rely on retraining or
fine-tuning, which risks degrading pretrained knowledge and incurs high
computational costs. To address this, we propose WorldForge, a training-free,
inference-time framework composed of three tightly coupled modules. Intra-Step
Recursive Refinement introduces a recursive refinement mechanism during
inference, which repeatedly optimizes network predictions within each denoising
step to enable precise trajectory injection. Flow-Gated Latent Fusion leverages
optical flow similarity to decouple motion from appearance in the latent space
and selectively inject trajectory guidance into motion-related channels.
Dual-Path Self-Corrective Guidance compares guided and unguided denoising paths
to adaptively correct trajectory drift caused by noisy or misaligned structural
signals. Together, these components inject fine-grained, trajectory-aligned
guidance without training, achieving both accurate motion control and
photorealistic content generation. Extensive experiments across diverse
benchmarks validate our method's superiority in realism, trajectory
consistency, and visual fidelity. This work introduces a novel plug-and-play
paradigm for controllable video synthesis, offering a new perspective on
leveraging generative priors for spatial intelligence.
RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
Authors: name: Yuming Jiang, name: Siteng Huang, name: Shengke Xue, name: Yaxi Zhao, name: Jun Cen, name: Sicong Leng, name: Kehan Li, name: Jiayan Guo, name: Kexiang Wang, name: Mingxiu Chen, name: Fan Wang, name: Deli Zhao, name: Xin Li
Published: 2025-09-18 17:58:02

This paper presents RynnVLA-001, a vision-language-action(VLA) model built
upon large-scale video generative pretraining from human demonstrations. We
propose a novel two-stage pretraining methodology. The first stage, Ego-Centric
Video Generative Pretraining, trains an Image-to-Video model on 12M ego-centric
manipulation videos to predict future frames conditioned on an initial frame
and a language instruction. The second stage, Human-Centric Trajectory-Aware
Modeling, extends this by jointly predicting future keypoint trajectories,
thereby effectively bridging visual frame prediction with action prediction.
Furthermore, to enhance action representation, we propose ActionVAE, a
variational autoencoder that compresses sequences of actions into compact
latent embeddings, reducing the complexity of the VLA output space. When
finetuned on the same downstream robotics datasets, RynnVLA-001 achieves
superior performance over state-of-the-art baselines, demonstrating that the
proposed pretraining strategy provides a more effective initialization for VLA
models.
MultiEdit: Advancing Instruction-based Image Editing on Diverse and
  Challenging Tasks
Authors: name: Mingsong Li, name: Lin Liu, name: Hongjun Wang, name: Haoxing Chen, name: Xijun Gu, name: Shizhan Liu, name: Dong Gong, name: Junbo Zhao, name: Zhenzhong Lan, name: Jianguo Li
Published: 2025-09-18 05:33:38

Current instruction-based image editing (IBIE) methods struggle with
challenging editing tasks, as both editing types and sample counts of existing
datasets are limited. Moreover, traditional dataset construction often contains
noisy image-caption pairs, which may introduce biases and limit model
capabilities in complex editing scenarios. To address these limitations, we
introduce MultiEdit, a comprehensive dataset featuring over 107K high-quality
image editing samples. It encompasses 6 challenging editing tasks through a
diverse collection of 18 non-style-transfer editing types and 38 style transfer
operations, covering a spectrum from sophisticated style transfer to complex
semantic operations like person reference editing and in-image text editing. We
employ a novel dataset construction pipeline that utilizes two multi-modal
large language models (MLLMs) to generate visual-adaptive editing instructions
and produce high-fidelity edited images, respectively. Extensive experiments
demonstrate that fine-tuning foundational open-source models with our
MultiEdit-Train set substantially improves models' performance on sophisticated
editing tasks in our proposed MultiEdit-Test benchmark, while effectively
preserving their capabilities on the standard editing benchmark. We believe
MultiEdit provides a valuable resource for advancing research into more diverse
and challenging IBIE capabilities. Our dataset is available at
https://huggingface.co/datasets/inclusionAI/MultiEdit.