Orionnnn/hub-stats huggingface dataset:
From Editor to Dense Geometry Estimator
Authors: name: JiYuan Wang, name: Chunyu Lin, name: Lei Sun, name: Rongying Liu, name: Lang Nie, name: Mingxing Li, name: Kang Liao, name: Xiangxiang Chu, name: Yao Zhao
Published: 2025-09-04 15:58:50

Leveraging visual priors from pre-trained text-to-image (T2I) generative
models has shown success in dense prediction. However, dense prediction is
inherently an image-to-image task, suggesting that image editing models, rather
than T2I generative models, may be a more suitable foundation for fine-tuning.
  Motivated by this, we conduct a systematic analysis of the fine-tuning
behaviors of both editors and generators for dense geometry estimation. Our
findings show that editing models possess inherent structural priors, which
enable them to converge more stably by ``refining" their innate features, and
ultimately achieve higher performance than their generative counterparts.
  Based on these findings, we introduce FE2E, a framework that
pioneeringly adapts an advanced editing model based on Diffusion Transformer
(DiT) architecture for dense geometry prediction. Specifically, to tailor the
editor for this deterministic task, we reformulate the editor's original flow
matching loss into the ``consistent velocity" training objective. And we use
logarithmic quantization to resolve the precision conflict between the editor's
native BFloat16 format and the high precision demand of our tasks.
Additionally, we leverage the DiT's global attention for a cost-free joint
estimation of depth and normals in a single forward pass, enabling their
supervisory signals to mutually enhance each other.
  Without scaling up the training data, FE2E achieves impressive performance
improvements in zero-shot monocular depth and normal estimation across multiple
datasets. Notably, it achieves over 35\% performance gains on the ETH3D dataset
and outperforms the DepthAnything series, which is trained on 100times data.
The project page can be accessed https://amap-ml.github.io/FE2E/{here}.
Durian: Dual Reference-guided Portrait Animation with Attribute Transfer
Authors: name: Hyunsoo Cha, name: Byungjun Kim, name: Hanbyul Joo
Published: 2025-09-04 17:53:03

We present Durian, the first method for generating portrait animation videos
with facial attribute transfer from a given reference image to a target
portrait in a zero-shot manner. To enable high-fidelity and spatially
consistent attribute transfer across frames, we introduce dual reference
networks that inject spatial features from both the portrait and attribute
images into the denoising process of a diffusion model. We train the model
using a self-reconstruction formulation, where two frames are sampled from the
same portrait video: one is treated as the attribute reference and the other as
the target portrait, and the remaining frames are reconstructed conditioned on
these inputs and their corresponding masks. To support the transfer of
attributes with varying spatial extent, we propose a mask expansion strategy
using keypoint-conditioned image generation for training. In addition, we
further augment the attribute and portrait images with spatial and
appearance-level transformations to improve robustness to positional
misalignment between them. These strategies allow the model to effectively
generalize across diverse attributes and in-the-wild reference combinations,
despite being trained without explicit triplet supervision. Durian achieves
state-of-the-art performance on portrait animation with attribute transfer, and
notably, its dual reference design enables multi-attribute composition in a
single generation pass without additional training.
Transition Models: Rethinking the Generative Learning Objective
Authors: name: Zidong Wang, name: Yiyuan Zhang, name: Xiaoyu Yue, name: Xiangyu Yue, name: Yangguang Li, name: Wanli Ouyang, name: Lei Bai
Published: 2025-09-04 17:05:59

A fundamental dilemma in generative modeling persists: iterative diffusion
models achieve outstanding fidelity, but at a significant computational cost,
while efficient few-step alternatives are constrained by a hard quality
ceiling. This conflict between generation steps and output quality arises from
restrictive training objectives that focus exclusively on either infinitesimal
dynamics (PF-ODEs) or direct endpoint prediction. We address this challenge by
introducing an exact, continuous-time dynamics equation that analytically
defines state transitions across any finite time interval. This leads to a
novel generative paradigm, Transition Models (TiM), which adapt to
arbitrary-step transitions, seamlessly traversing the generative trajectory
from single leaps to fine-grained refinement with more steps. Despite having
only 865M parameters, TiM achieves state-of-the-art performance, surpassing
leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across
all evaluated step counts. Importantly, unlike previous few-step generators,
TiM demonstrates monotonic quality improvement as the sampling budget
increases. Additionally, when employing our native-resolution strategy, TiM
delivers exceptional fidelity at resolutions up to 4096x4096.
Towards a Unified View of Large Language Model Post-Training
Authors: name: Xingtai Lv, name: Yuxin Zuo, name: Youbang Sun, name: Hongyi Liu, name: Yuntian Wei, name: Zhekai Chen, name: Lixuan He, name: Xuekai Zhu, name: Kaiyan Zhang, name: Bingning Wang, name: Ning Ding, name: Bowen Zhou
Published: 2025-09-04 17:40:33

Two major sources of training data exist for post-training modern language
models: online (model-generated rollouts) data, and offline (human or
other-model demonstrations) data. These two types of data are typically used by
approaches like Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT),
respectively. In this paper, we show that these approaches are not in
contradiction, but are instances of a single optimization process. We derive a
Unified Policy Gradient Estimator, and present the calculations of a wide
spectrum of post-training approaches as the gradient of a common objective
under different data distribution assumptions and various bias-variance
tradeoffs. The gradient estimator is constructed with four interchangeable
parts: stabilization mask, reference policy denominator, advantage estimate,
and likelihood gradient. Motivated by our theoretical findings, we propose
Hybrid Post-Training (HPT), an algorithm that dynamically selects different
training signals. HPT is designed to yield both effective exploitation of
demonstration and stable exploration without sacrificing learned reasoning
patterns. We provide extensive experiments and ablation studies to verify the
effectiveness of our unified theoretical framework and HPT. Across six
mathematical reasoning benchmarks and two out-of-distribution suites, HPT
consistently surpasses strong baselines across models of varying scales and
families.
False Sense of Security: Why Probing-based Malicious Input Detection
  Fails to Generalize
Authors: name: Cheng Wang, name: Zeming Wei, name: Qin Liu, name: Muhao Chen
Published: 2025-09-04 05:15:55

Large Language Models (LLMs) can comply with harmful instructions, raising
serious safety concerns despite their impressive capabilities. Recent work has
leveraged probing-based approaches to study the separability of malicious and
benign inputs in LLMs' internal representations, and researchers have proposed
using such probing methods for safety detection. We systematically re-examine
this paradigm. Motivated by poor out-of-distribution performance, we
hypothesize that probes learn superficial patterns rather than semantic
harmfulness. Through controlled experiments, we confirm this hypothesis and
identify the specific patterns learned: instructional patterns and trigger
words. Our investigation follows a systematic approach, progressing from
demonstrating comparable performance of simple n-gram methods, to controlled
experiments with semantically cleaned datasets, to detailed analysis of pattern
dependencies. These results reveal a false sense of security around current
probing-based approaches and highlight the need to redesign both models and
evaluation protocols, for which we provide further discussions in the hope of
suggesting responsible further research in this direction. We have open-sourced
the project at https://github.com/WangCheng0116/Why-Probe-Fails.
DeepResearch Arena: The First Exam of LLMs' Research Abilities via
  Seminar-Grounded Tasks
Authors: name: Haiyuan Wan, name: Chen Yang, name: Junchi Yu, name: Meiqi Tu, name: Jiaxuan Lu, name: Di Yu, name: Jianbao Cao, name: Ben Gao, name: Jiaqing Xie, name: Aoran Wang, name: Wenlong Zhang, name: Philip Torr, name: Dongzhan Zhou
Published: 2025-09-01 11:42:47

Deep research agents have attracted growing attention for their potential to
orchestrate multi-stage research workflows, spanning literature synthesis,
methodological design, and empirical verification. Despite these strides,
evaluating their research capability faithfully is rather challenging due to
the difficulty of collecting frontier research questions that genuinely capture
researchers' attention and intellectual curiosity. To address this gap, we
introduce DeepResearch Arena, a benchmark grounded in academic seminars that
capture rich expert discourse and interaction, better reflecting real-world
research environments and reducing the risk of data leakage. To automatically
construct DeepResearch Arena, we propose a Multi-Agent Hierarchical Task
Generation (MAHTG) system that extracts research-worthy inspirations from
seminar transcripts. The MAHTG system further translates research-worthy
inspirations into high-quality research tasks, ensuring the traceability of
research task formulation while filtering noise. With the MAHTG system, we
curate DeepResearch Arena with over 10,000 high-quality research tasks from
over 200 academic seminars, spanning 12 disciplines, such as literature,
history, and science. Our extensive evaluation shows that DeepResearch Arena
presents substantial challenges for current state-of-the-art agents, with clear
performance gaps observed across different models.
Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from
  Vector Drawings
Authors: name: Feiwei Qin, name: Shichao Lu, name: Junhao Hou, name: Changmiao Wang, name: Meie Fang, name: Ligang Liu
Published: 2025-08-26 07:01:58

Computer-Aided Design (CAD) generative modeling is driving significant
innovations across industrial applications. Recent works have shown remarkable
progress in creating solid models from various inputs such as point clouds,
meshes, and text descriptions. However, these methods fundamentally diverge
from traditional industrial workflows that begin with 2D engineering drawings.
The automatic generation of parametric CAD models from these 2D vector drawings
remains underexplored despite being a critical step in engineering design. To
address this gap, our key insight is to reframe CAD generation as a
sequence-to-sequence learning problem where vector drawing primitives directly
inform the generation of parametric CAD operations, preserving geometric
precision and design intent throughout the transformation process. We propose
Drawing2CAD, a framework with three key technical components: a
network-friendly vector primitive representation that preserves precise
geometric information, a dual-decoder transformer architecture that decouples
command type and parameter generation while maintaining precise correspondence,
and a soft target distribution loss function accommodating inherent flexibility
in CAD parameters. To train and evaluate Drawing2CAD, we create CAD-VGDrawing,
a dataset of paired engineering drawings and parametric CAD models, and conduct
thorough experiments to demonstrate the effectiveness of our method. Code and
dataset are available at https://github.com/lllssc/Drawing2CAD.
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL
  Training
Authors: name: Chenlu Ye, name: Zhou Yu, name: Ziji Zhang, name: Hao Chen, name: Narayanan Sadagopan, name: Jing Huang, name: Tong Zhang, name: Anurag Beniwal
Published: 2025-09-03 15:28:51

Reinforcement learning with verifiable rewards (RLVR) has emerged to be a
predominant paradigm for mathematical reasoning tasks, offering stable
improvements in reasoning ability. However, Outcome Reward Models (ORMs) in
RLVR are too coarse-grained to distinguish flawed reasoning within correct
answers or valid reasoning within incorrect answers. This lack of granularity
introduces noisy and misleading gradients significantly and hinders further
progress in reasoning process quality. While Process Reward Models (PRMs) offer
fine-grained guidance for intermediate steps, they frequently suffer from
inaccuracies and are susceptible to reward hacking.
  To resolve this dilemma, we introduce PRocess cOnsistency Filter (PROF), an
effective data process curation method that harmonizes noisy, fine-grained
process rewards with accurate, coarse-grained outcome rewards. Rather than
naively blending PRM and ORM in the objective function
(arXiv:archive/2506.18896), PROF leverages their complementary strengths
through consistency-driven sample selection. Our approach retains correct
responses with higher averaged process values and incorrect responses with
lower averaged process values, while maintaining positive/negative training
sample balance. Extensive experiments demonstrate that our method not only
consistently improves the final accuracy over 4% compared to the blending
approaches, but also strengthens the quality of intermediate reasoning steps.
Codes and training recipes are available at https://github.com/Chenluye99/PROF.
Manipulation as in Simulation: Enabling Accurate Geometry Perception in
  Robots
Authors: name: Minghuan Liu, name: Zhengbang Zhu, name: Xiaoshen Han, name: Peng Hu, name: Haotong Lin, name: Xinyao Li, name: Jingxiao Chen, name: Jiafeng Xu, name: Yichu Yang, name: Yunfeng Lin, name: Xinghang Li, name: Yong Yu, name: Weinan Zhang, name: Tao Kong, name: Bingyi Kang
Published: 2025-09-02 17:29:38

Modern robotic manipulation primarily relies on visual observations in a 2D
color space for skill learning but suffers from poor generalization. In
contrast, humans, living in a 3D world, depend more on physical properties-such
as distance, size, and shape-than on texture when interacting with objects.
Since such 3D geometric information can be acquired from widely available depth
cameras, it appears feasible to endow robots with similar perceptual
capabilities. Our pilot study found that using depth cameras for manipulation
is challenging, primarily due to their limited accuracy and susceptibility to
various types of noise. In this work, we propose Camera Depth Models (CDMs) as
a simple plugin on daily-use depth cameras, which take RGB images and raw depth
signals as input and output denoised, accurate metric depth. To achieve this,
we develop a neural data engine that generates high-quality paired data from
simulation by modeling a depth camera's noise pattern. Our results show that
CDMs achieve nearly simulation-level accuracy in depth prediction, effectively
bridging the sim-to-real gap for manipulation tasks. Notably, our experiments
demonstrate, for the first time, that a policy trained on raw simulated depth,
without the need for adding noise or real-world fine-tuning, generalizes
seamlessly to real-world robots on two challenging long-horizon tasks involving
articulated, reflective, and slender objects, with little to no performance
degradation. We hope our findings will inspire future research in utilizing
simulation data and 3D information in general robot policies.
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement
  Fine-Tuning of LLMs
Authors: name: Yanxiao Zhao, name: Yaqian Li, name: Zihao Bo, name: Rinyoichi Takezoe, name: Haojia Hui, name: Mo Guang, name: Lei Ren, name: Xiaolin Qin, name: Kaiwen Long
Published: 2025-08-31 16:56:06

Recent advances in Large Language Models (LLMs) have demonstrated remarkable
general reasoning capabilities. However, systematically evaluating and
enhancing these reasoning capabilities is challenging due to the lack of
controllable and scalable tools for fine-grained analysis. Existing benchmarks
and datasets often lack the necessary variable control for multi-dimensional,
systematic analysis and training, or have narrow problem types and formats. To
address these limitations, we introduce SATQuest, a systematic verifier
designed to evaluate and enhance logical reasoning in LLMs by generating
diverse, Satisfiability-based logical reasoning problems directly from
Conjunctive Normal Form (CNF) instances. SATQuest structures these problems
along three orthogonal dimensions: instance scale, problem type, and question
format, employing randomized, SAT-based problem generation and objective answer
verification via PySAT. This design mitigates memorization issues, allows for
nuanced insights into reasoning performance, and enables effective
reinforcement fine-tuning. Our extensive evaluation of various LLMs using
SATQuest identified significant limitations in their logical reasoning,
particularly in generalizing beyond familiar mathematical formats. Furthermore,
we show that reinforcement fine-tuning with SATQuest rewards substantially
improves targeted task performance and generalizes to more complex instances,
while highlighting remaining challenges in cross-format adaptation. Through
these demonstrations, we showcase SATQuest's potential as a foundational tool
and a valuable starting point for advancing LLM logical reasoning.
Planning with Reasoning using Vision Language World Model
Authors: name: Delong Chen, name: Theo Moutakanni, name: Willy Chung, name: Yejin Bang, name: Ziwei Ji, name: Allen Bolourchi, name: Pascale Fung
Published: 2025-09-02 18:18:57

Effective planning requires strong world models, but high-level world models
that can understand and reason about actions with semantic and temporal
abstraction remain largely underdeveloped. We introduce the Vision Language
World Model (VLWM), a foundation model trained for language-based world
modeling on natural videos. Given visual observations, the VLWM first infers
the overall goal achievements then predicts a trajectory composed of
interleaved actions and world state changes. Those targets are extracted by
iterative LLM Self-Refine conditioned on compressed future observations
represented by Tree of Captions. The VLWM learns both an action policy and a
dynamics model, which respectively facilitates reactive system-1 plan decoding
and reflective system-2 planning via cost minimization. The cost evaluates the
semantic distance between the hypothetical future states given by VLWM
roll-outs and the expected goal state, and is measured by a critic model that
we trained in a self-supervised manner. The VLWM achieves state-of-the-art
Visual Planning for Assistance (VPA) performance on both benchmark evaluations
and our proposed PlannerArena human evaluations, where system-2 improves the
Elo score by +27% upon system-1. The VLWM models also outperforms strong VLM
baselines on RoboVQA and WorldPrediction benchmark.
LMEnt: A Suite for Analyzing Knowledge in Language Models from
  Pretraining Data to Representations
Authors: name: Daniela Gottesman, name: Alon Gilae-Dotan, name: Ido Cohen, name: Yoav Gur-Arieh, name: Marius Mosbach, name: Ori Yoran, name: Mor Geva
Published: 2025-09-03 15:31:18

Language models (LMs) increasingly drive real-world applications that require
world knowledge. However, the internal processes through which models turn data
into representations of knowledge and beliefs about the world, are poorly
understood. Insights into these processes could pave the way for developing LMs
with knowledge representations that are more consistent, robust, and complete.
To facilitate studying these questions, we present LMEnt, a suite for analyzing
knowledge acquisition in LMs during pretraining. LMEnt introduces: (1) a
knowledge-rich pretraining corpus, fully annotated with entity mentions, based
on Wikipedia, (2) an entity-based retrieval method over pretraining data that
outperforms previous approaches by as much as 80.4%, and (3) 12 pretrained
models with up to 1B parameters and 4K intermediate checkpoints, with
comparable performance to popular open-sourced models on knowledge benchmarks.
Together, these resources provide a controlled environment for analyzing
connections between entity mentions in pretraining and downstream performance,
and the effects of causal interventions in pretraining data. We show the
utility of LMEnt by studying knowledge acquisition across checkpoints, finding
that fact frequency is key, but does not fully explain learning trends. We
release LMEnt to support studies of knowledge in LMs, including knowledge
representations, plasticity, editing, attribution, and learning dynamics.
Mixture of Global and Local Experts with Diffusion Transformer for
  Controllable Face Generation
Authors: name: Xuechao Zou, name: Shun Zhang, name: Xing Fu, name: Yue Li, name: Kai Li, name: Yushe Cao, name: Congyan Lang, name: Pin Tao, name: Junliang Xing
Published: 2025-08-30 09:21:07

Controllable face generation poses critical challenges in generative modeling
due to the intricate balance required between semantic controllability and
photorealism. While existing approaches struggle with disentangling semantic
controls from generation pipelines, we revisit the architectural potential of
Diffusion Transformers (DiTs) through the lens of expert specialization. This
paper introduces Face-MoGLE, a novel framework featuring: (1)
Semantic-decoupled latent modeling through mask-conditioned space
factorization, enabling precise attribute manipulation; (2) A mixture of global
and local experts that captures holistic structure and region-level semantics
for fine-grained controllability; (3) A dynamic gating network producing
time-dependent coefficients that evolve with diffusion steps and spatial
locations. Face-MoGLE provides a powerful and flexible solution for
high-quality, controllable face generation, with strong potential in generative
modeling and security applications. Extensive experiments demonstrate its
effectiveness in multimodal and monomodal face generation settings and its
robust zero-shot generalization capability. Project page is available at
https://github.com/XavierJiezou/Face-MoGLE.