Orionnnn/hub-stats huggingface dataset:
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy
  Optimization
Authors: name: Xingyu Wu, name: Yuchen Yan, name: Shangke Lyu, name: Linjuan Wu, name: Yiwen Qiu, name: Yongliang Shen, name: Weiming Lu, name: Jian Shao, name: Jun Xiao, name: Yueting Zhuang
Published: 2025-07-21 16:14:41

Large reasoning models have achieved remarkable performance through extended
chain-of-thought sequences, yet this computational freedom leads to excessive
token generation even for simple problems. We present Length-Adaptive Policy
Optimization (LAPO), a novel framework that transforms reasoning length control
from an external constraint into an intrinsic model capability. Unlike existing
approaches that impose rigid limits or rely on post-hoc interventions, LAPO
enables models to internalize an understanding of appropriate reasoning depth
through a two-stage reinforcement learning process. In the first stage, models
learn natural reasoning patterns by discovering the statistical distribution of
successful solution lengths. The second stage leverages these patterns as
meta-cognitive guidance, embedding them directly within the model's reasoning
context to ensure inference-time flexibility. Experiments on mathematical
reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9\%
while improving accuracy by 2.3\%. Our analysis reveals that models trained
with LAPO develop emergent abilities to allocate computational resources based
on problem complexity, achieving efficient reasoning without sacrificing
quality.
TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance
Authors: name: Minghao Fu, name: Guo-Hua Wang, name: Xiaohao Chen, name: Qing-Guo Chen, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-07-24 08:45:40

Recent advances in text-to-image synthesis largely benefit from sophisticated
sampling strategies and classifier-free guidance (CFG) to ensure high-quality
generation. However, CFG's reliance on two forward passes, especially when
combined with intricate sampling algorithms, results in prohibitively high
inference costs. To address this, we introduce TeEFusion (Text
Embeddings Fusion), a novel and efficient distillation method
that directly incorporates the guidance magnitude into the text embeddings and
distills the teacher model's complex sampling strategy. By simply fusing
conditional and unconditional text embeddings using linear operations,
TeEFusion reconstructs the desired guidance without adding extra parameters,
simultaneously enabling the student model to learn from the teacher's output
produced via its sophisticated sampling approach. Extensive experiments on
state-of-the-art models such as SD3 demonstrate that our method allows the
student to closely mimic the teacher's performance with a far simpler and more
efficient sampling strategy. Consequently, the student model achieves inference
speeds up to 6times faster than the teacher model, while maintaining image
quality at levels comparable to those obtained through the teacher's complex
sampling approach. The code is publicly available at
https://github.com/AIDC-AI/TeEFusion{github.com/AIDC-AI/TeEFusion}.
Captain Cinema: Towards Short Movie Generation
Authors: name: Junfei Xiao, name: Ceyuan Yang, name: Lvmin Zhang, name: Shengqu Cai, name: Yang Zhao, name: Yuwei Guo, name: Gordon Wetzstein, name: Maneesh Agrawala, name: Alan Yuille, name: Lu Jiang
Published: 2025-07-24 17:59:56

We present Captain Cinema, a generation framework for short movie generation.
Given a detailed textual description of a movie storyline, our approach firstly
generates a sequence of keyframes that outline the entire narrative, which
ensures long-range coherence in both the storyline and visual appearance (e.g.,
scenes and characters). We refer to this step as top-down keyframe planning.
These keyframes then serve as conditioning signals for a video synthesis model,
which supports long context learning, to produce the spatio-temporal dynamics
between them. This step is referred to as bottom-up video synthesis. To support
stable and efficient generation of multi-scene long narrative cinematic works,
we introduce an interleaved training strategy for Multimodal Diffusion
Transformers (MM-DiT), specifically adapted for long-context video data. Our
model is trained on a specially curated cinematic dataset consisting of
interleaved data pairs. Our experiments demonstrate that Captain Cinema
performs favorably in the automated creation of visually coherent and narrative
consistent short movies in high quality and efficiency. Project page:
https://thecinema.ai
TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive
  Generation
Authors: name: Zhekai Chen, name: Ruihang Chu, name: Yukang Chen, name: Shiwei Zhang, name: Yujie Wei, name: Yingya Zhang, name: Xihui Liu
Published: 2025-07-24 16:04:55

Scaling visual generation models is essential for real-world content
creation, yet requires substantial training and computational expenses.
Alternatively, test-time scaling has garnered growing attention due to resource
efficiency and promising performance. In this work, we present TTS-VAR, the
first general test-time scaling framework for visual auto-regressive (VAR)
models, modeling the generation process as a path searching problem. To
dynamically balance computational efficiency with exploration capacity, we
first introduce an adaptive descending batch size schedule throughout the
causal generation process. Besides, inspired by VAR's hierarchical
coarse-to-fine multi-scale generation, our framework integrates two key
components: (i) At coarse scales, we observe that generated tokens are hard for
evaluation, possibly leading to erroneous acceptance of inferior samples or
rejection of superior samples. Noticing that the coarse scales contain
sufficient structural information, we propose clustering-based diversity
search. It preserves structural variety through semantic feature clustering,
enabling later selection on samples with higher potential. (ii) In fine scales,
resampling-based potential selection prioritizes promising candidates using
potential scores, which are defined as reward functions incorporating
multi-scale generation history. Experiments on the powerful VAR model Infinity
show a notable 8.7% GenEval score improvement (from 0.69 to 0.75). Key insights
reveal that early-stage structural features effectively influence final
quality, and resampling efficacy varies across generation scales. Code is
available at https://github.com/ali-vilab/TTS-VAR.
DMOSpeech 2: Reinforcement Learning for Duration Prediction in
  Metric-Optimized Speech Synthesis
Authors: name: Yinghao Aaron Li, name: Xilin Jiang, name: Fei Tao, name: Cheng Niu, name: Kaifeng Xu, name: Juntong Song, name: Nima Mesgarani
Published: 2025-07-20 14:48:48

Diffusion-based text-to-speech (TTS) systems have made remarkable progress in
zero-shot speech synthesis, yet optimizing all components for perceptual
metrics remains challenging. Prior work with DMOSpeech demonstrated direct
metric optimization for speech generation components, but duration prediction
remained unoptimized. This paper presents DMOSpeech 2, which extends metric
optimization to the duration predictor through a reinforcement learning
approach. The proposed system implements a novel duration policy framework
using group relative preference optimization (GRPO) with speaker similarity and
word error rate as reward signals. By optimizing this previously unoptimized
component, DMOSpeech 2 creates a more complete metric-optimized synthesis
pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid
approach leveraging a teacher model for initial denoising steps before
transitioning to the student model, significantly improving output diversity
while maintaining efficiency. Comprehensive evaluations demonstrate superior
performance across all metrics compared to previous systems, while reducing
sampling steps by half without quality degradation. These advances represent a
significant step toward speech synthesis systems with metric optimization
across multiple components. The audio samples, code and pre-trained models are
available at https://dmospeech2.github.io/.
EarthCrafter: Scalable 3D Earth Generation via Dual-Sparse Latent
  Diffusion
Authors: name: Shang Liu, name: Chenjie Cao, name: Chaohui Yu, name: Wen Qian, name: Jing Wang, name: Fan Wang
Published: 2025-07-22 12:46:48

Despite the remarkable developments achieved by recent 3D generation works,
scaling these methods to geographic extents, such as modeling thousands of
square kilometers of Earth's surface, remains an open challenge. We address
this through a dual innovation in data infrastructure and model architecture.
First, we introduce Aerial-Earth3D, the largest 3D aerial dataset to date,
consisting of 50k curated scenes (each measuring 600m x 600m) captured across
the U.S. mainland, comprising 45M multi-view Google Earth frames. Each scene
provides pose-annotated multi-view images, depth maps, normals, semantic
segmentation, and camera poses, with explicit quality control to ensure terrain
diversity. Building on this foundation, we propose EarthCrafter, a tailored
framework for large-scale 3D Earth generation via sparse-decoupled latent
diffusion. Our architecture separates structural and textural generation: 1)
Dual sparse 3D-VAEs compress high-resolution geometric voxels and textural 2D
Gaussian Splats (2DGS) into compact latent spaces, largely alleviating the
costly computation suffering from vast geographic scales while preserving
critical information. 2) We propose condition-aware flow matching models
trained on mixed inputs (semantics, images, or neither) to flexibly model
latent geometry and texture features independently. Extensive experiments
demonstrate that EarthCrafter performs substantially better in extremely
large-scale generation. The framework further supports versatile applications,
from semantic-guided urban layout generation to unconditional terrain
synthesis, while maintaining geographic plausibility through our rich data
priors from Aerial-Earth3D. Our project page is available at
https://whiteinblue.github.io/earthcrafter/
Promptomatix: An Automatic Prompt Optimization Framework for Large
  Language Models
Authors: name: Rithesh Murthy, name: Ming Zhu, name: Liangwei Yang, name: Jielin Qiu, name: Juntao Tan, name: Shelby Heinecke, name: Caiming Xiong, name: Silvio Savarese, name: Huan Wang
Published: 2025-07-17 18:18:20

Large Language Models (LLMs) perform best with well-crafted prompts, yet
prompt engineering remains manual, inconsistent, and inaccessible to
non-experts. We introduce Promptomatix, an automatic prompt optimization
framework that transforms natural language task descriptions into high-quality
prompts without requiring manual tuning or domain expertise. Promptomatix
supports both a lightweight meta-prompt-based optimizer and a DSPy-powered
compiler, with modular design enabling future extension to more advanced
frameworks. The system analyzes user intent, generates synthetic training data,
selects prompting strategies, and refines prompts using cost-aware objectives.
Evaluated across 5 task categories, Promptomatix achieves competitive or
superior performance compared to existing libraries, while reducing prompt
length and computational overhead making prompt optimization scalable and
efficient.
PUSA V1.0: Surpassing Wan-I2V with $500 Training Cost by Vectorized
  Timestep Adaptation
Authors: name: Yaofang Liu, name: Yumeng Ren, name: Aitor Artola, name: Yuxuan Hu, name: Xiaodong Cun, name: Xiaotong Zhao, name: Alan Zhao, name: Raymond H. Chan, name: Suiyun Zhang, name: Rui Liu, name: Dandan Tu, name: Jean-Michel Morel
Published: 2025-07-22 00:09:37

The rapid advancement of video diffusion models has been hindered by
fundamental limitations in temporal modeling, particularly the rigid
synchronization of frame evolution imposed by conventional scalar timestep
variables. While task-specific adaptations and autoregressive models have
sought to address these challenges, they remain constrained by computational
inefficiency, catastrophic forgetting, or narrow applicability. In this work,
we present Pusa, a groundbreaking paradigm that leverages vectorized timestep
adaptation (VTA) to enable fine-grained temporal control within a unified video
diffusion framework. Besides, VTA is a non-destructive adaptation, which means
it fully preserves the capabilities of the base model. By finetuning the SOTA
Wan2.1-T2V-14B model with VTA, we achieve unprecedented efficiency --
surpassing the performance of Wan-I2V-14B with leq 1/200 of the training
cost (\500 vs. \geq 100,000) and leq 1/2500 of the dataset size (4K vs.
geq 10M samples). Pusa not only sets a new standard for image-to-video (I2V)
generation, achieving a VBench-I2V total score of 87.32\% (vs. 86.86\% of
Wan-I2V-14B), but also unlocks many zero-shot multi-task capabilities such as
start-end frames and video extension -- all without task-specific training.
Meanwhile, Pusa can still perform text-to-video generation. Mechanistic
analyses reveal that our approach preserves the foundation model's generative
priors while surgically injecting temporal dynamics, avoiding the combinatorial
explosion inherent to vectorized timesteps. This work establishes a scalable,
efficient, and versatile paradigm for next-generation video synthesis,
democratizing high-fidelity video generation for research and industry alike.
Code is open-sourced at https://github.com/Yaofang-Liu/Pusa-VidGen
RAVine: Reality-Aligned Evaluation for Agentic Search
Authors: name: Yilong Xu, name: Xiang Long, name: Zhi Zheng, name: Jinhua Gao
Published: 2025-07-22 16:08:12

Agentic search, as a more autonomous and adaptive paradigm of retrieval
augmentation, is driving the evolution of intelligent search systems. However,
existing evaluation frameworks fail to align well with the goals of agentic
search. First, the complex queries commonly used in current benchmarks often
deviate from realistic user search scenarios. Second, prior approaches tend to
introduce noise when extracting ground truth for end-to-end evaluations,
leading to distorted assessments at a fine-grained level. Third, most current
frameworks focus solely on the quality of final answers, neglecting the
evaluation of the iterative process inherent to agentic search. To address
these limitations, we propose RAVine -- a Reality-Aligned eValuation framework
for agentic LLMs with search. RAVine targets multi-point queries and long-form
answers that better reflect user intents, and introduces an attributable ground
truth construction strategy to enhance the accuracy of fine-grained evaluation.
Moreover, RAVine examines model's interaction with search tools throughout the
iterative process, and accounts for factors of efficiency. We benchmark a
series of models using RAVine and derive several insights, which we hope will
contribute to advancing the development of agentic search systems. The code and
datasets are available at https://github.com/SwordFaith/RAVine.
Finding Dori: Memorization in Text-to-Image Diffusion Models Is Less
  Local Than Assumed
Authors: name: Antoni Kowalczuk, name: Dominik Hintersdorf, name: Lukas Struppek, name: Kristian Kersting, name: Adam Dziedzic, name: Franziska Boenisch
Published: 2025-07-22 15:02:38

Text-to-image diffusion models (DMs) have achieved remarkable success in
image generation. However, concerns about data privacy and intellectual
property remain due to their potential to inadvertently memorize and replicate
training data. Recent mitigation efforts have focused on identifying and
pruning weights responsible for triggering replication, based on the assumption
that memorization can be localized. Our research assesses the robustness of
these pruning-based approaches. We demonstrate that even after pruning, minor
adjustments to text embeddings of input prompts are sufficient to re-trigger
data replication, highlighting the fragility of these defenses. Furthermore, we
challenge the fundamental assumption of memorization locality, by showing that
replication can be triggered from diverse locations within the text embedding
space, and follows different paths in the model. Our findings indicate that
existing mitigation strategies are insufficient and underscore the need for
methods that truly remove memorized content, rather than attempting to suppress
its retrieval. As a first step in this direction, we introduce a novel
adversarial fine-tuning method that iteratively searches for replication
triggers and updates the model to increase robustness. Through our research, we
provide fresh insights into the nature of memorization in text-to-image DMs and
a foundation for building more trustworthy and compliant generative AI.
Re:Form -- Reducing Human Priors in Scalable Formal Software
  Verification with RL in LLMs: A Preliminary Study on Dafny
Authors: name: Chuanhao Yan, name: Fengdi Che, name: Xuhan Huang, name: Xu Xu, name: Xin Li, name: Yizhi Li, name: Xingwei Qu, name: Jingzhe Shi, name: Zhuangzhuang He, name: Chenghua Lin, name: Yaodong Yang, name: Binhang Yuan, name: Hang Zhao, name: Yu Qiao, name: Bowen Zhou, name: Jie Fu
Published: 2025-07-22 08:13:01

Existing informal language-based (e.g., human language) Large Language Models
(LLMs) trained with Reinforcement Learning (RL) face a significant challenge:
their verification processes, which provide crucial training signals, are
neither reliable nor scalable. In fact, the prevalent large proprietary models
could hardly generate verifiable programs. A promising yet largely uncharted
alternative is formal language-based reasoning. Grounding LLMs in rigorous
formal systems where generative models operate in formal language spaces (e.g.,
Dafny) enables the automatic and mathematically provable verification of their
reasoning processes and outcomes. This capability is pivotal for achieving
large-scale, reliable formal software verification. It is a common practice to
employ human-annotated chain-of-thought and other human priors to induce the
reasoning and coding capabilities of LLMs. Unfortunately, it becomes
unacceptably all-consuming to provide such priors for supervising complex
programming tasks. In this work, we systematically explore ways to reduce human
priors with the formal language, Dafny, as the main environment for our pilot
study. Our pipeline mainly relies on introducing an automatic and scalable data
curation pipeline, and careful RL designs integrated with feedback from the
formal language verifier. We introduce DafnyComp, a benchmark of compositional
formal programs with auto-formalized specifications for specification
reasoning. Our supervised fine-tuning (SFT) stage enables even small models
(e.g., 0.5B) to generate syntactically valid and verifiable Dafny code,
surpassing proprietary models. RL with regularization further improves
performance, achieving stronger generalization to out-of-domain tasks and
outperforming all strong baselines on the challenging DafnyComp benchmark.
Yume: An Interactive World Generation Model
Authors: name: Xiaofeng Mao, name: Shaoheng Lin, name: Zhen Li, name: Chuanhao Li, name: Wenshuo Peng, name: Tong He, name: Jiangmiao Pang, name: Mingmin Chi, name: Yu Qiao, name: Kaipeng Zhang
Published: 2025-07-23 17:57:09

Yume aims to use images, text, or videos to create an interactive, realistic,
and dynamic world, which allows exploration and control using peripheral
devices or neural signals. In this report, we present a preview version of
\method, which creates a dynamic world from an input image and allows
exploration of the world using keyboard actions. To achieve this high-fidelity
and interactive video world generation, we introduce a well-designed framework,
which consists of four main components, including camera motion quantization,
video generation architecture, advanced sampler, and model acceleration. First,
we quantize camera motions for stable training and user-friendly interaction
using keyboard inputs. Then, we introduce the Masked Video Diffusion
Transformer~(MVDT) with a memory module for infinite video generation in an
autoregressive manner. After that, training-free Anti-Artifact Mechanism (AAM)
and Time Travel Sampling based on Stochastic Differential Equations (TTS-SDE)
are introduced to the sampler for better visual quality and more precise
control. Moreover, we investigate model acceleration by synergistic
optimization of adversarial distillation and caching mechanisms. We use the
high-quality world exploration dataset \sekai to train \method, and it achieves
remarkable results in diverse scenes and applications. All data, codebase, and
model weights are available on https://github.com/stdstu12/YUME. Yume will
update monthly to achieve its original goal. Project page:
https://stdstu12.github.io/YUME-Project/.
DesignLab: Designing Slides Through Iterative Detection and Correction
Authors: name: Jooyeol Yun, name: Heng Wang, name: Yotaro Shimose, name: Jaegul Choo, name: Shingo Takamatsu
Published: 2025-07-23 04:49:48

Designing high-quality presentation slides can be challenging for non-experts
due to the complexity involved in navigating various design choices. Numerous
automated tools can suggest layouts and color schemes, yet often lack the
ability to refine their own output, which is a key aspect in real-world
workflows. We propose DesignLab, which separates the design process into two
roles, the design reviewer, who identifies design-related issues, and the
design contributor who corrects them. This decomposition enables an iterative
loop where the reviewer continuously detects issues and the contributor
corrects them, allowing a draft to be further polished with each iteration,
reaching qualities that were unattainable. We fine-tune large language models
for these roles and simulate intermediate drafts by introducing controlled
perturbations, enabling the design reviewer learn design errors and the
contributor learn how to fix them. Our experiments show that DesignLab
outperforms existing design-generation methods, including a commercial tool, by
embracing the iterative nature of designing which can result in polished,
professional slides.
Pixels, Patterns, but No Poetry: To See The World like Humans
Authors: name: Hongcheng Gao, name: Zihao Huang, name: Lin Xu, name: Jingyi Tang, name: Xinhao Li, name: Yue Liu, name: Haoyang Li, name: Taihang Hu, name: Minhua Lin, name: Xinlong Yang, name: Ge Wu, name: Balong Bi, name: Hongyu Chen, name: Wentao Zhang
Published: 2025-07-21 21:50:16

Achieving human-like perception and reasoning in Multimodal Large Language
Models (MLLMs) remains a central challenge in artificial intelligence. While
recent research has primarily focused on enhancing reasoning capabilities in
MLLMs, a fundamental question persists: Can Multimodal Large Language Models
truly perceive the world as humans do? This paper shifts focus from reasoning
to perception. Rather than constructing benchmarks specifically for reasoning,
we introduce the Turing Eye Test (TET), a challenging perception-oriented
benchmark comprising four diagnostic tasks that evaluate MLLMs' performance on
synthetic images that humans process intuitively. Our findings reveal that
state-of-the-art MLLMs exhibit catastrophic failures on our perceptual tasks
trivial for humans. Both in-context learning and training on language
backbone-effective for previous benchmarks-fail to improve performance on our
tasks, while fine-tuning the vision tower enables rapid adaptation, suggesting
that our benchmark poses challenges for vision tower generalization rather than
for the knowledge and reasoning capabilities of the language backbone-a key gap
between current MLLMs and human perception. We release a representative subset
of TET tasks in this version, and will introduce more diverse tasks and methods
to enhance visual generalization in future work.