Orionnnn/hub-stats huggingface dataset:
UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
Authors: name: Yixun Liang, name: Kunming Luo, name: Xiao Chen, name: Rui Chen, name: Hongyu Yan, name: Weiyu Li, name: Jiarui Liu, name: Ping Tan
Published: 2025-05-29 08:58:41

We present UniTEX, a novel two-stage 3D texture generation framework to
create high-quality, consistent textures for 3D assets. Existing approaches
predominantly rely on UV-based inpainting to refine textures after reprojecting
the generated multi-view images onto the 3D shapes, which introduces challenges
related to topological ambiguity. To address this, we propose to bypass the
limitations of UV mapping by operating directly in a unified 3D functional
space. Specifically, we first propose that lifts texture generation into 3D
space via Texture Functions (TFs)--a continuous, volumetric representation that
maps any 3D point to a texture value based solely on surface proximity,
independent of mesh topology. Then, we propose to predict these TFs directly
from images and geometry inputs using a transformer-based Large Texturing Model
(LTM). To further enhance texture quality and leverage powerful 2D priors, we
develop an advanced LoRA-based strategy for efficiently adapting large-scale
Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as
our first stage. Extensive experiments demonstrate that UniTEX achieves
superior visual quality and texture integrity compared to existing approaches,
offering a generalizable and scalable solution for automated 3D texture
generation. Code will available in: https://github.com/YixunLiang/UniTEX.
Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV
  Cache and Parallel Decoding
Authors: name: Chengyue Wu, name: Hao Zhang, name: Shuchen Xue, name: Zhijian Liu, name: Shizhe Diao, name: Ligeng Zhu, name: Ping Luo, name: Song Han, name: Enze Xie
Published: 2025-05-28 17:39:15

Diffusion-based large language models (Diffusion LLMs) have shown promise for
non-autoregressive text generation with parallel decoding capabilities.
However, the practical inference speed of open-sourced Diffusion LLMs often
lags behind autoregressive models due to the lack of Key-Value (KV) Cache and
quality degradation when decoding multiple tokens simultaneously. To bridge
this gap, we introduce a novel block-wise approximate KV Cache mechanism
tailored for bidirectional diffusion models, enabling cache reuse with
negligible performance drop. Additionally, we identify the root cause of
generation quality degradation in parallel decoding as the disruption of token
dependencies under the conditional independence assumption. To address this, we
propose a confidence-aware parallel decoding strategy that selectively decodes
tokens exceeding a confidence threshold, mitigating dependency violations and
maintaining generation quality. Experimental results on LLaDA and Dream models
across multiple LLM benchmarks demonstrate up to 27.6times
throughput improvement with minimal accuracy loss, closing the performance gap
with autoregressive models and paving the way for practical deployment of
Diffusion LLMs.
Evaluating Text Creativity across Diverse Domains: A Dataset and Large
  Language Model Evaluator
Authors: name: Qian Cao, name: Xiting Wang, name: Yuzhuo Yuan, name: Yahui Liu, name: Fang Luo, name: Ruihua Song
Published: 2025-05-25 17:25:23

Creativity evaluation remains a challenging frontier for large language
models (LLMs). Current evaluations heavily rely on inefficient and costly human
judgments, hindering progress in enhancing machine creativity. While automated
methods exist, ranging from psychological testing to heuristic- or
prompting-based approaches, they often lack generalizability or alignment with
human judgment. To address these issues, in this paper, we propose a novel
pairwise-comparison framework for assessing textual creativity, leveraging
shared contextual instructions to improve evaluation consistency. We introduce
CreataSet, a large-scale dataset with 100K+ human-level and 1M+ synthetic
creative instruction-response pairs spanning diverse open-domain tasks. Through
training on CreataSet, we develop an LLM-based evaluator named CrEval. CrEval
demonstrates remarkable superiority over existing methods in alignment with
human judgments. Experimental results underscore the indispensable significance
of integrating both human-generated and synthetic data in training highly
robust evaluators, and showcase the practical utility of CrEval in boosting the
creativity of LLMs. We will release all data, code, and models publicly soon to
support further research.
UniRL: Self-Improving Unified Multimodal Models via Supervised and
  Reinforcement Learning
Authors: name: Weijia Mao, name: Zhenheng Yang, name: Mike Zheng Shou
Published: 2025-05-29 12:00:15

Unified multimodal large language models such as Show-o and Janus have
achieved strong performance across both generation and understanding tasks.
However, these models typically rely on large-scale datasets and require
substantial computation during the pretraining stage. In addition, several
post-training methods have been proposed, but they often depend on external
data or are limited to task-specific customization. In this work, we introduce
UniRL, a self-improving post-training approach. Our approach enables the model
to generate images from prompts and use them as training data in each
iteration, without relying on any external image data. Moreover, it enables the
two tasks to enhance each other: the generated images are used for
understanding, and the understanding results are used to supervise generation.
We explore supervised fine-tuning (SFT) and Group Relative Policy Optimization
(GRPO) to optimize the models. UniRL offers three key advantages: (1) it
requires no external image data, as all training samples are generated by the
model itself during training; (2) it not only improves individual task
performance, but also reduces the imbalance between generation and
understanding; and (3) it requires only several additional training steps
during the post-training stage. We evaluate UniRL on top of Show-o and Janus,
achieving a GenEval score of 0.77 for Show-o and 0.65 for Janus. Code and
models will be released in https://github.com/showlab/UniRL.
Uni-Instruct: One-step Diffusion Model through Unified Diffusion
  Divergence Instruction
Authors: name: Yifei Wang, name: Weimin Bai, name: Colin Zhang, name: Debing Zhang, name: Weijian Luo, name: He Sun
Published: 2025-05-27 05:55:45

In this paper, we unify more than 10 existing one-step diffusion distillation
approaches, such as Diff-Instruct, DMD, SIM, SiD, f-distill, etc, inside a
theory-driven framework which we name the \emph{Uni-Instruct}.
Uni-Instruct is motivated by our proposed diffusion expansion theory of the
f-divergence family. Then we introduce key theories that overcome the
intractability issue of the original expanded f-divergence, resulting in an
equivalent yet tractable loss that effectively trains one-step diffusion models
by minimizing the expanded f-divergence family. The novel unification
introduced by Uni-Instruct not only offers new theoretical contributions that
help understand existing approaches from a high-level perspective but also
leads to state-of-the-art one-step diffusion generation performances. On the
CIFAR10 generation benchmark, Uni-Instruct achieves record-breaking Frechet
Inception Distance (FID) values of \emph{1.46} for unconditional
generation and \emph{1.38} for conditional generation. On the
ImageNet-64times 64 generation benchmark, Uni-Instruct achieves a new SoTA
one-step generation FID of \emph{1.02}, which outperforms its 79-step
teacher diffusion with a significant improvement margin of 1.33 (1.02 vs 2.35).
We also apply Uni-Instruct on broader tasks like text-to-3D generation. For
text-to-3D generation, Uni-Instruct gives decent results, which slightly
outperforms previous methods, such as SDS and VSD, in terms of both generation
quality and diversity. Both the solid theoretical and empirical contributions
of Uni-Instruct will potentially help future studies on one-step diffusion
distillation and knowledge transferring of diffusion models.
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
  Rectified Flow Transformers
Authors: name: Yusuf Dalva, name: Hidir Yesiltepe, name: Pinar Yanardag
Published: 2025-05-29 17:59:46

We introduce LoRAShop, the first framework for multi-concept image editing
with LoRA models. LoRAShop builds on a key observation about the feature
interaction patterns inside Flux-style diffusion transformers: concept-specific
transformer features activate spatially coherent regions early in the denoising
process. We harness this observation to derive a disentangled latent mask for
each concept in a prior forward pass and blend the corresponding LoRA weights
only within regions bounding the concepts to be personalized. The resulting
edits seamlessly integrate multiple subjects or styles into the original scene
while preserving global context, lighting, and fine details. Our experiments
demonstrate that LoRAShop delivers better identity preservation compared to
baselines. By eliminating retraining and external constraints, LoRAShop turns
personalized diffusion models into a practical `photoshop-with-LoRAs' tool and
opens new avenues for compositional visual storytelling and rapid creative
iteration.
ZeroSep: Separate Anything in Audio with Zero Training
Authors: name: Chao Huang, name: Yuesheng Ma, name: Junxuan Huang, name: Susan Liang, name: Yunlong Tang, name: Jing Bi, name: Wenqiang Liu, name: Nima Mesgarani, name: Chenliang Xu
Published: 2025-05-29 16:31:45

Audio source separation is fundamental for machines to understand complex
acoustic environments and underpins numerous audio applications. Current
supervised deep learning approaches, while powerful, are limited by the need
for extensive, task-specific labeled data and struggle to generalize to the
immense variability and open-set nature of real-world acoustic scenes. Inspired
by the success of generative foundation models, we investigate whether
pre-trained text-guided audio diffusion models can overcome these limitations.
We make a surprising discovery: zero-shot source separation can be achieved
purely through a pre-trained text-guided audio diffusion model under the right
configuration. Our method, named ZeroSep, works by inverting the mixed audio
into the diffusion model's latent space and then using text conditioning to
guide the denoising process to recover individual sources. Without any
task-specific training or fine-tuning, ZeroSep repurposes the generative
diffusion model for a discriminative separation task and inherently supports
open-set scenarios through its rich textual priors. ZeroSep is compatible with
a variety of pre-trained text-guided audio diffusion backbones and delivers
strong separation performance on multiple separation benchmarks, surpassing
even supervised methods.
One-shot Entropy Minimization
Authors: name: Zitian Gao, name: Lynx Chen, name: Joey Zhou, name: Bryan Dai
Published: 2025-05-26 17:58:30

We trained 13,440 large language models and found that entropy minimization
requires only a single unlabeled data and 10 steps optimization to achieve
performance improvements comparable to or even greater than those obtained
using thousands of data and carefully designed rewards in rule-based
reinforcement learning. This striking result may prompt a rethinking of
post-training paradigms for large language models. Our code is avaliable at
https://github.com/zitian-gao/one-shot-em.
ChartLens: Fine-grained Visual Attribution in Charts
Authors: name: Manan Suri, name: Puneet Mathur, name: Nedim Lipka, name: Franck Dernoncourt, name: Ryan A. Rossi, name: Dinesh Manocha
Published: 2025-05-25 23:17:32

The growing capabilities of multimodal large language models (MLLMs) have
advanced tasks like chart understanding. However, these models often suffer
from hallucinations, where generated text sequences conflict with the provided
visual data. To address this, we introduce Post-Hoc Visual Attribution for
Charts, which identifies fine-grained chart elements that validate a given
chart-associated response. We propose ChartLens, a novel chart attribution
algorithm that uses segmentation-based techniques to identify chart objects and
employs set-of-marks prompting with MLLMs for fine-grained visual attribution.
Additionally, we present ChartVA-Eval, a benchmark with synthetic and
real-world charts from diverse domains like finance, policy, and economics,
featuring fine-grained attribution annotations. Our evaluations show that
ChartLens improves fine-grained attributions by 26-66%.
A Graph Perspective to Probe Structural Patterns of Knowledge in Large
  Language Models
Authors: name: Utkarsh Sahu, name: Zhisheng Qi, name: Yongjia Lei, name: Ryan A. Rossi, name: Franck Dernoncourt, name: Nesreen K. Ahmed, name: Mahantesh M Halappanavar, name: Yao Ma, name: Yu Wang
Published: 2025-05-25 19:34:15

Large language models have been extensively studied as neural knowledge bases
for their knowledge access, editability, reasoning, and explainability.
However, few works focus on the structural patterns of their knowledge.
Motivated by this gap, we investigate these structural patterns from a graph
perspective. We quantify the knowledge of LLMs at both the triplet and entity
levels, and analyze how it relates to graph structural properties such as node
degree. Furthermore, we uncover the knowledge homophily, where topologically
close entities exhibit similar levels of knowledgeability, which further
motivates us to develop graph machine learning models to estimate entity
knowledge based on its local neighbors. This model further enables valuable
knowledge checking by selecting triplets less known to LLMs. Empirical results
show that using selected triplets for fine-tuning leads to superior
performance.
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
Authors: name: Lihan Jiang, name: Yucheng Mao, name: Linning Xu, name: Tao Lu, name: Kerui Ren, name: Yichen Jin, name: Xudong Xu, name: Mulin Yu, name: Jiangmiao Pang, name: Feng Zhao, name: Dahua Lin, name: Bo Dai
Published: 2025-05-29 17:49:56

We introduce AnySplat, a feed forward network for novel view synthesis from
uncalibrated image collections. In contrast to traditional neural rendering
pipelines that demand known camera poses and per scene optimization, or recent
feed forward methods that buckle under the computational weight of dense views,
our model predicts everything in one shot. A single forward pass yields a set
of 3D Gaussian primitives encoding both scene geometry and appearance, and the
corresponding camera intrinsics and extrinsics for each input image. This
unified design scales effortlessly to casually captured, multi view datasets
without any pose annotations. In extensive zero shot evaluations, AnySplat
matches the quality of pose aware baselines in both sparse and dense view
scenarios while surpassing existing pose free approaches. Moreover, it greatly
reduce rendering latency compared to optimization based neural fields, bringing
real time novel view synthesis within reach for unconstrained capture
settings.Project page: https://city-super.github.io/anysplat/
D-AR: Diffusion via Autoregressive Models
Authors: name: Ziteng Gao, name: Mike Zheng Shou
Published: 2025-05-29 17:09:25

This paper presents Diffusion via Autoregressive models (D-AR), a new
paradigm recasting the image diffusion process as a vanilla autoregressive
procedure in the standard next-token-prediction fashion. We start by designing
the tokenizer that converts images into sequences of discrete tokens, where
tokens in different positions can be decoded into different diffusion denoising
steps in the pixel space. Thanks to the diffusion properties, these tokens
naturally follow a coarse-to-fine order, which directly lends itself to
autoregressive modeling. Therefore, we apply standard next-token prediction on
these tokens, without modifying any underlying designs (either causal masks or
training/inference strategies), and such sequential autoregressive token
generation directly mirrors the diffusion procedure in image space. That is,
once the autoregressive model generates an increment of tokens, we can directly
decode these tokens into the corresponding diffusion denoising step in the
streaming manner. Our pipeline naturally reveals several intriguing properties,
for example, it supports consistent previews when generating only a subset of
tokens and enables zero-shot layout-controlled synthesis. On the standard
ImageNet benchmark, our method achieves 2.09 FID using a 775M Llama backbone
with 256 discrete tokens. We hope our work can inspire future research on
unified autoregressive architectures of visual synthesis, especially with large
language models. Code and models will be available at
https://github.com/showlab/D-AR
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action
  Control
Authors: name: Anthony Chen, name: Wenzhao Zheng, name: Yida Wang, name: Xueyang Zhang, name: Kun Zhan, name: Peng Jia, name: Kurt Keutzer, name: Shanghang Zhang
Published: 2025-05-28 14:46:51

Recent advancements in world models have revolutionized dynamic environment
simulation, allowing systems to foresee future states and assess potential
actions. In autonomous driving, these capabilities help vehicles anticipate the
behavior of other road users, perform risk-aware planning, accelerate training
in simulation, and adapt to novel scenarios, thereby enhancing safety and
reliability. Current approaches exhibit deficiencies in maintaining robust 3D
geometric consistency or accumulating artifacts during occlusion handling, both
critical for reliable safety assessment in autonomous navigation tasks. To
address this, we introduce GeoDrive, which explicitly integrates robust 3D
geometry conditions into driving world models to enhance spatial understanding
and action controllability. Specifically, we first extract a 3D representation
from the input frame and then obtain its 2D rendering based on the
user-specified ego-car trajectory. To enable dynamic modeling, we propose a
dynamic editing module during training to enhance the renderings by editing the
positions of the vehicles. Extensive experiments demonstrate that our method
significantly outperforms existing models in both action accuracy and 3D
spatial awareness, leading to more realistic, adaptable, and reliable scene
modeling for safer autonomous driving. Additionally, our model can generalize
to novel trajectories and offers interactive scene editing capabilities, such
as object editing and object trajectory control.
PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient
  Interactions
Authors: name: Daeun Kyung, name: Hyunseung Chung, name: Seongsu Bae, name: Jiho Kim, name: Jae Ho Sohn, name: Taerim Kim, name: Soo Kyung Kim, name: Edward Choi
Published: 2025-05-23 12:34:48

Doctor-patient consultations require multi-turn, context-aware communication
tailored to diverse patient personas. Training or evaluating doctor LLMs in
such settings requires realistic patient interaction systems. However, existing
simulators often fail to reflect the full range of personas seen in clinical
practice. To address this, we introduce PatientSim, a patient simulator that
generates realistic and diverse patient personas for clinical scenarios,
grounded in medical expertise. PatientSim operates using: 1) clinical profiles,
including symptoms and medical history, derived from real-world data in the
MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by four axes:
personality, language proficiency, medical history recall level, and cognitive
confusion level, resulting in 37 unique combinations. We evaluated eight LLMs
for factual accuracy and persona consistency. The top-performing open-source
model, Llama 3.3, was validated by four clinicians to confirm the robustness of
our framework. As an open-source, customizable platform, PatientSim provides a
reproducible and scalable solution that can be customized for specific training
needs. Offering a privacy-compliant environment, it serves as a robust testbed
for evaluating medical dialogue systems across diverse patient presentations
and shows promise as an educational tool for healthcare.
DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural
  Language and Reinforcement Learning
Authors: name: Ziyin Zhang, name: Jiahao Xu, name: Zhiwei He, name: Tian Liang, name: Qiuzhi Liu, name: Yansi Li, name: Linfeng Song, name: Zhengwen Liang, name: Zhuosheng Zhang, name: Rui Wang, name: Zhaopeng Tu, name: Haitao Mi, name: Dong Yu
Published: 2025-05-29 17:59:39

Theorem proving serves as a major testbed for evaluating complex reasoning
abilities in large language models (LLMs). However, traditional automated
theorem proving (ATP) approaches rely heavily on formal proof systems that
poorly align with LLMs' strength derived from informal, natural language
knowledge acquired during pre-training. In this work, we propose DeepTheorem, a
comprehensive informal theorem-proving framework exploiting natural language to
enhance LLM mathematical reasoning. DeepTheorem includes a large-scale
benchmark dataset consisting of 121K high-quality IMO-level informal theorems
and proofs spanning diverse mathematical domains, rigorously annotated for
correctness, difficulty, and topic categories, accompanied by systematically
constructed verifiable theorem variants. We devise a novel reinforcement
learning strategy (RL-Zero) explicitly tailored to informal theorem proving,
leveraging the verified theorem variants to incentivize robust mathematical
inference. Additionally, we propose comprehensive outcome and process
evaluation metrics examining proof correctness and the quality of reasoning
steps. Extensive experimental analyses demonstrate DeepTheorem significantly
improves LLM theorem-proving performance compared to existing datasets and
supervised fine-tuning protocols, achieving state-of-the-art accuracy and
reasoning quality. Our findings highlight DeepTheorem's potential to
fundamentally advance automated informal theorem proving and mathematical
exploration.
Muddit: Liberating Generation Beyond Text-to-Image with a Unified
  Discrete Diffusion Model
Authors: name: Qingyu Shi, name: Jinbin Bai, name: Zhuoran Zhao, name: Wenhao Chai, name: Kaidong Yu, name: Jianzong Wu, name: Shuangyong Song, name: Yunhai Tong, name: Xiangtai Li, name: Xuelong Li, name: Shuicheng Yan
Published: 2025-05-29 16:15:48

Unified generation models aim to handle diverse tasks across modalities --
such as text generation, image generation, and vision-language reasoning --
within a single architecture and decoding paradigm. Autoregressive unified
models suffer from slow inference due to sequential decoding, and
non-autoregressive unified models suffer from weak generalization due to
limited pretrained backbones. We introduce Muddit, a unified discrete diffusion
transformer that enables fast and parallel generation across both text and
image modalities. Unlike prior unified diffusion models trained from scratch,
Muddit integrates strong visual priors from a pretrained text-to-image backbone
with a lightweight text decoder, enabling flexible and high-quality multimodal
generation under a unified architecture. Empirical results show that Muddit
achieves competitive or superior performance compared to significantly larger
autoregressive models in both quality and efficiency. The work highlights the
potential of purely discrete diffusion, when equipped with strong visual
priors, as a scalable and effective backbone for unified generation.