Orionnnn/hub-stats huggingface dataset:
EmbodiedGen: Towards a Generative 3D World Engine for Embodied
  Intelligence
Authors: name: Wang Xinjie, name: Liu Liu, name: Cao Yu, name: Wu Ruiqi, name: Qin Wenkang, name: Wang Dehui, name: Sui Wei, name: Su Zhizhong
Published: 2025-06-12 11:43:50

Constructing a physically realistic and accurately scaled simulated 3D world
is crucial for the training and evaluation of embodied intelligence tasks. The
diversity, realism, low cost accessibility and affordability of 3D data assets
are critical for achieving generalization and scalability in embodied AI.
However, most current embodied intelligence tasks still rely heavily on
traditional 3D computer graphics assets manually created and annotated, which
suffer from high production costs and limited realism. These limitations
significantly hinder the scalability of data driven approaches. We present
EmbodiedGen, a foundational platform for interactive 3D world generation. It
enables the scalable generation of high-quality, controllable and
photorealistic 3D assets with accurate physical properties and real-world scale
in the Unified Robotics Description Format (URDF) at low cost. These assets can
be directly imported into various physics simulation engines for fine-grained
physical control, supporting downstream tasks in training and evaluation.
EmbodiedGen is an easy-to-use, full-featured toolkit composed of six key
modules: Image-to-3D, Text-to-3D, Texture Generation, Articulated Object
Generation, Scene Generation and Layout Generation. EmbodiedGen generates
diverse and interactive 3D worlds composed of generative 3D assets, leveraging
generative AI to address the challenges of generalization and evaluation to the
needs of embodied intelligence related research. Code is available at
https://horizonrobotics.github.io/robot_lab/embodied_gen/index.html.
TeleMath: A Benchmark for Large Language Models in Telecom Mathematical
  Problem Solving
Authors: name: Vincenzo Colle, name: Mohamed Sana, name: Nicola Piovesan, name: Antonio De Domenico, name: Fadhel Ayed, name: Merouane Debbah
Published: 2025-06-12 13:04:18

The increasing adoption of artificial intelligence in telecommunications has
raised interest in the capability of Large Language Models (LLMs) to address
domain-specific, mathematically intensive tasks. Although recent advancements
have improved the performance of LLMs in general mathematical reasoning, their
effectiveness within specialized domains, such as signal processing, network
optimization, and performance analysis, remains largely unexplored. To address
this gap, we introduce TeleMath, the first benchmark dataset specifically
designed to evaluate LLM performance in solving mathematical problems with
numerical solutions in the telecommunications domain. Comprising 500
question-answer (QnA) pairs, TeleMath covers a wide spectrum of topics in the
telecommunications field. This paper outlines the proposed QnAs generation
pipeline, starting from a selected seed of problems crafted by Subject Matter
Experts. The evaluation of a wide range of open-source LLMs reveals that best
performance on TeleMath is achieved by recent models explicitly designed for
mathematical or logical reasoning. In contrast, general-purpose models, even
those with a large number of parameters, often struggle with these challenges.
We have released the dataset and the evaluation code to ease result
reproducibility and support future research.
TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to
  Evolving Research Corpora
Authors: name: Priyanka Kargupta, name: Nan Zhang, name: Yunyi Zhang, name: Rui Zhang, name: Prasenjit Mitra, name: Jiawei Han
Published: 2025-06-12 14:26:28

The rapid evolution of scientific fields introduces challenges in organizing
and retrieving scientific literature. While expert-curated taxonomies have
traditionally addressed this need, the process is time-consuming and expensive.
Furthermore, recent automatic taxonomy construction methods either (1)
over-rely on a specific corpus, sacrificing generalizability, or (2) depend
heavily on the general knowledge of large language models (LLMs) contained
within their pre-training datasets, often overlooking the dynamic nature of
evolving scientific domains. Additionally, these approaches fail to account for
the multi-faceted nature of scientific literature, where a single research
paper may contribute to multiple dimensions (e.g., methodology, new tasks,
evaluation metrics, benchmarks). To address these gaps, we propose TaxoAdapt, a
framework that dynamically adapts an LLM-generated taxonomy to a given corpus
across multiple dimensions. TaxoAdapt performs iterative hierarchical
classification, expanding both the taxonomy width and depth based on corpus'
topical distribution. We demonstrate its state-of-the-art performance across a
diverse set of computer science conferences over the years to showcase its
ability to structure and capture the evolution of scientific fields. As a
multidimensional method, TaxoAdapt generates taxonomies that are 26.51% more
granularity-preserving and 50.41% more coherent than the most competitive
baselines judged by LLMs.
Beyond True or False: Retrieval-Augmented Hierarchical Analysis of
  Nuanced Claims
Authors: name: Priyanka Kargupta, name: Runchu Tian, name: Jiawei Han
Published: 2025-06-12 14:17:45

Claims made by individuals or entities are oftentimes nuanced and cannot be
clearly labeled as entirely "true" or "false" -- as is frequently the case with
scientific and political claims. However, a claim (e.g., "vaccine A is better
than vaccine B") can be dissected into its integral aspects and sub-aspects
(e.g., efficacy, safety, distribution), which are individually easier to
validate. This enables a more comprehensive, structured response that provides
a well-rounded perspective on a given problem while also allowing the reader to
prioritize specific angles of interest within the claim (e.g., safety towards
children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based
framework for automatically constructing a hierarchy of aspects typically
considered when addressing a claim and enriching them with corpus-specific
perspectives. This structure hierarchically partitions an input corpus to
retrieve relevant segments, which assist in discovering new sub-aspects.
Moreover, these segments enable the discovery of varying perspectives towards
an aspect of the claim (e.g., support, neutral, or oppose) and their respective
prevalence (e.g., "how many biomedical papers believe vaccine A is more
transportable than B?"). We apply ClaimSpect to a wide variety of real-world
scientific and political claims featured in our constructed dataset, showcasing
its robustness and accuracy in deconstructing a nuanced claim and representing
perspectives within a corpus. Through real-world case studies and human
evaluation, we validate its effectiveness over multiple baselines.
Discrete Audio Tokens: More Than a Survey!
Authors: name: Pooneh Mousavi, name: Gallil Maimon, name: Adel Moumen, name: Darius Petermann, name: Jiatong Shi, name: Haibin Wu, name: Haici Yang, name: Anastasia Kuznetsova, name: Artem Ploujnikov, name: Ricard Marxer, name: Bhuvana Ramabhadran, name: Benjamin Elizalde, name: Loren Lugosch, name: Jinyu Li, name: Cem Subakan, name: Phil Woodland, name: Minje Kim, name: Hung-yi Lee, name: Shinji Watanabe, name: Yossi Adi, name: Mirco Ravanelli
Published: 2025-06-12 01:35:43

Discrete audio tokens are compact representations that aim to preserve
perceptual quality, phonetic content, and speaker characteristics while
enabling efficient storage and inference, as well as competitive performance
across diverse downstream tasks.They provide a practical alternative to
continuous features, enabling the integration of speech and audio into modern
large language models (LLMs). As interest in token-based audio processing
grows, various tokenization methods have emerged, and several surveys have
reviewed the latest progress in the field. However, existing studies often
focus on specific domains or tasks and lack a unified comparison across various
benchmarks. This paper presents a systematic review and benchmark of discrete
audio tokenizers, covering three domains: speech, music, and general audio. We
propose a taxonomy of tokenization approaches based on encoder-decoder,
quantization techniques, training paradigm, streamability, and application
domains. We evaluate tokenizers on multiple benchmarks for reconstruction,
downstream performance, and acoustic language modeling, and analyze trade-offs
through controlled ablation studies. Our findings highlight key limitations,
practical considerations, and open challenges, providing insight and guidance
for future research in this rapidly evolving area. For more information,
including our main results and tokenizer database, please refer to our website:
https://poonehmousavi.github.io/dates-website/.
Fine-Grained Perturbation Guidance via Attention Head Selection
Authors: name: Donghoon Ahn, name: Jiwon Kang, name: Sanghyun Lee, name: Minjae Kim, name: Jaewon Min, name: Wooseok Jang, name: Saungwu Lee, name: Sayak Paul, name: Susung Hong, name: Seungryong Kim
Published: 2025-06-12 17:59:51

Recent guidance methods in diffusion models steer reverse sampling by
perturbing the model to construct an implicit weak model and guide generation
away from it. Among these approaches, attention perturbation has demonstrated
strong empirical performance in unconditional scenarios where classifier-free
guidance is not applicable. However, existing attention perturbation methods
lack principled approaches for determining where perturbations should be
applied, particularly in Diffusion Transformer (DiT) architectures where
quality-relevant computations are distributed across layers. In this paper, we
investigate the granularity of attention perturbations, ranging from the layer
level down to individual attention heads, and discover that specific heads
govern distinct visual concepts such as structure, style, and texture quality.
Building on this insight, we propose "HeadHunter", a systematic framework for
iteratively selecting attention heads that align with user-centric objectives,
enabling fine-grained control over generation quality and visual attributes. In
addition, we introduce SoftPAG, which linearly interpolates each selected
head's attention map toward an identity matrix, providing a continuous knob to
tune perturbation strength and suppress artifacts. Our approach not only
mitigates the oversmoothing issues of existing layer-level perturbation but
also enables targeted manipulation of specific visual styles through
compositional head selection. We validate our method on modern large-scale
DiT-based text-to-image models including Stable Diffusion 3 and FLUX.1,
demonstrating superior performance in both general quality enhancement and
style-specific guidance. Our work provides the first head-level analysis of
attention perturbation in diffusion models, uncovering interpretable
specialization within attention layers and enabling practical design of
effective perturbation strategies.
DreamActor-H1: High-Fidelity Human-Product Demonstration Video
  Generation via Motion-designed Diffusion Transformers
Authors: name: Lizhen Wang, name: Zhurong Xia, name: Tianshu Hu, name: Pengrui Wang, name: Pengfei Wang, name: Zerong Zheng, name: Ming Zhou
Published: 2025-06-12 10:58:23

In e-commerce and digital marketing, generating high-fidelity human-product
demonstration videos is important for effective product presentation. However,
most existing frameworks either fail to preserve the identities of both humans
and products or lack an understanding of human-product spatial relationships,
leading to unrealistic representations and unnatural interactions. To address
these challenges, we propose a Diffusion Transformer (DiT)-based framework. Our
method simultaneously preserves human identities and product-specific details,
such as logos and textures, by injecting paired human-product reference
information and utilizing an additional masked cross-attention mechanism. We
employ a 3D body mesh template and product bounding boxes to provide precise
motion guidance, enabling intuitive alignment of hand gestures with product
placements. Additionally, structured text encoding is used to incorporate
category-level semantics, enhancing 3D consistency during small rotational
changes across frames. Trained on a hybrid dataset with extensive data
augmentation strategies, our approach outperforms state-of-the-art techniques
in maintaining the identity integrity of both humans and products and
generating realistic demonstration motions. Project page:
https://submit2025-dream.github.io/DreamActor-H1/.
Attention, Please! Revisiting Attentive Probing for Masked Image
  Modeling
Authors: name: Bill Psomas, name: Dionysis Christopoulos, name: Eirini Baltzi, name: Ioannis Kakogeorgiou, name: Tilemachos Aravanis, name: Nikos Komodakis, name: Konstantinos Karantzalos, name: Yannis Avrithis, name: Giorgos Tolias
Published: 2025-06-11 21:10:26

As fine-tuning (FT) becomes increasingly impractical at scale, probing is
emerging as the preferred evaluation protocol for self-supervised learning
(SSL). Yet, the standard linear probing (LP) fails to adequately reflect the
potential of models trained with Masked Image Modeling (MIM), due to the
distributed nature of patch tokens. This motivates the need for attentive
probing, an alternative that uses attention to selectively aggregate
patch-level features. Despite its growing adoption, attentive probing remains
under-explored, with existing methods suffering from excessive parameterization
and poor computational efficiency.
  In this work, we revisit attentive probing through the lens of the
accuracy-efficiency trade-off. We conduct a systematic study of existing
methods, analyzing their mechanisms and benchmarking their performance. We
introduce efficient probing (EP), a multi-query cross-attention mechanism that
eliminates redundant projections, reduces the number of trainable parameters,
and achieves up to a 10times speed-up over conventional multi-head
attention. Despite its simplicity, EP outperforms LP and prior attentive
probing approaches across seven benchmarks, generalizes well beyond MIM to
diverse pre-training paradigms, produces interpretable attention maps, and
achieves strong gains in low-shot and layer-wise settings. Code available at
https://github.com/billpsomas/efficient-probing.
StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated
  Video Streams
Authors: name: Zike Wu, name: Qi Yan, name: Xuanyu Yi, name: Lele Wang, name: Renjie Liao
Published: 2025-06-10 14:52:36

Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams
is crucial for numerous real-world applications. However, existing methods
struggle to jointly address three key challenges: 1) processing uncalibrated
inputs in real time, 2) accurately modeling dynamic scene evolution, and 3)
maintaining long-term stability and computational efficiency. To this end, we
introduce StreamSplat, the first fully feed-forward framework that transforms
uncalibrated video streams of arbitrary length into dynamic 3D Gaussian
Splatting (3DGS) representations in an online manner, capable of recovering
scene dynamics from temporally local observations. We propose two key technical
innovations: a probabilistic sampling mechanism in the static encoder for 3DGS
position prediction, and a bidirectional deformation field in the dynamic
decoder that enables robust and efficient dynamic modeling. Extensive
experiments on static and dynamic benchmarks demonstrate that StreamSplat
consistently outperforms prior works in both reconstruction quality and dynamic
scene modeling, while uniquely supporting online reconstruction of arbitrarily
long video streams. Code and models are available at
https://github.com/nickwzk/StreamSplat.
Compound AI Systems Optimization: A Survey of Methods, Challenges, and
  Future Directions
Authors: name: Yu-Ang Lee, name: Guan-Ting Yi, name: Mei-Yi Liu, name: Jui-Chao Lu, name: Guan-Bo Yang, name: Yun-Nung Chen
Published: 2025-06-09 21:04:14

Recent advancements in large language models (LLMs) and AI systems have led
to a paradigm shift in the design and optimization of complex AI workflows. By
integrating multiple components, compound AI systems have become increasingly
adept at performing sophisticated tasks. However, as these systems grow in
complexity, new challenges arise in optimizing not only individual components
but also their interactions. While traditional optimization methods such as
supervised fine-tuning (SFT) and reinforcement learning (RL) remain
foundational, the rise of natural language feedback introduces promising new
approaches, especially for optimizing non-differentiable systems. This paper
provides a systematic review of recent progress in optimizing compound AI
systems, encompassing both numerical and language-based techniques. We
formalize the notion of compound AI system optimization, classify existing
methods along several key dimensions, and highlight open research challenges
and future directions in this rapidly evolving field. A list of surveyed papers
is publicly available at https://github.com/MiuLab/AISysOpt-Survey.
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal
  Gaussian Splatting
Authors: name: Ziyi Wang, name: Yanran Zhang, name: Jie Zhou, name: Jiwen Lu
Published: 2025-06-11 17:23:21

The scale diversity of point cloud data presents significant challenges in
developing unified representation learning techniques for 3D vision. Currently,
there are few unified 3D models, and no existing pre-training method is equally
effective for both object- and scene-level point clouds. In this paper, we
introduce UniPre3D, the first unified pre-training method that can be
seamlessly applied to point clouds of any scale and 3D models of any
architecture. Our approach predicts Gaussian primitives as the pre-training
task and employs differentiable Gaussian splatting to render images, enabling
precise pixel-level supervision and end-to-end optimization. To further
regulate the complexity of the pre-training task and direct the model's focus
toward geometric structures, we integrate 2D features from pre-trained image
models to incorporate well-established texture knowledge. We validate the
universal effectiveness of our proposed method through extensive experiments
across a variety of object- and scene-level tasks, using diverse point cloud
models as backbones. Code is available at https://github.com/wangzy22/UniPre3D.
LLM Unlearning Should Be Form-Independent
Authors: name: Xiaotian Ye, name: Mengqi Zhang, name: Shu Wu
Published: 2025-06-09 14:21:25

Large Language Model (LLM) unlearning aims to erase or suppress undesirable
knowledge within the model, offering promise for controlling harmful or private
information to prevent misuse. However, recent studies highlight its limited
efficacy in real-world scenarios, hindering practical adoption. In this study,
we identify a pervasive issue underlying many downstream failures: the
effectiveness of existing unlearning methods heavily depends on the form of
training samples and frequently fails to generalize to alternate expressions of
the same knowledge. We formally characterize this problem as Form-Dependent
Bias and systematically investigate its specific manifestation patterns across
various downstream tasks. To quantify its prevalence and support future
research, we introduce ORT, a novel benchmark designed to evaluate the
robustness of unlearning methods against variations in knowledge expression.
Results reveal that Form-Dependent Bias is both widespread and severe among
current techniques.
  We argue that LLM unlearning should be form-independent to address the
endless forms of downstream tasks encountered in real-world security-critical
scenarios. Towards this goal, we introduce Rank-one Concept Redirection (ROCR),
a novel training-free method, as a promising solution path. ROCR performs
unlearning by targeting the invariants in downstream tasks, specifically the
activated dangerous concepts. It is capable of modifying model parameters
within seconds to redirect the model's perception of a specific unlearning
target concept to another harmless concept. Extensive experiments demonstrate
that ROCR significantly improves unlearning effectiveness compared to
traditional methods while generating highly natural outputs.
Token Perturbation Guidance for Diffusion Models
Authors: name: Javad Rajabi, name: Soroush Mehraban, name: Seyedmorteza Sadat, name: Babak Taati
Published: 2025-06-10 21:25:46

Classifier-free guidance (CFG) has become an essential component of modern
diffusion models to enhance both generation quality and alignment with input
conditions. However, CFG requires specific training procedures and is limited
to conditional generation. To address these limitations, we propose Token
Perturbation Guidance (TPG), a novel method that applies perturbation matrices
directly to intermediate token representations within the diffusion network.
TPG employs a norm-preserving shuffling operation to provide effective and
stable guidance signals that improve generation quality without architectural
changes. As a result, TPG is training-free and agnostic to input conditions,
making it readily applicable to both conditional and unconditional generation.
We further analyze the guidance term provided by TPG and show that its effect
on sampling more closely resembles CFG compared to existing training-free
guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1
show that TPG achieves nearly a 2times improvement in FID for unconditional
generation over the SDXL baseline, while closely matching CFG in prompt
alignment. These results establish TPG as a general, condition-agnostic
guidance method that brings CFG-like benefits to a broader class of diffusion
models. The code is available at
https://github.com/TaatiTeam/Token-Perturbation-Guidance
PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a
  Unified Framework
Authors: name: SiXiang Chen, name: Jianyu Lai, name: Jialin Gao, name: Tian Ye, name: Haoyu Chen, name: Hengyu Shi, name: Shitong Shao, name: Yunlong Lin, name: Song Fei, name: Zhaohu Xing, name: Yeying Jin, name: Junfeng Luo, name: Xiaoming Wei, name: Lei Zhu
Published: 2025-06-12 14:28:12

Generating aesthetic posters is more challenging than simple design images:
it requires not only precise text rendering but also the seamless integration
of abstract artistic content, striking layouts, and overall stylistic harmony.
To address this, we propose PosterCraft, a unified framework that abandons
prior modular pipelines and rigid, predefined layouts, allowing the model to
freely explore coherent, visually compelling compositions. PosterCraft employs
a carefully designed, cascaded workflow to optimize the generation of
high-aesthetic posters: (i) large-scale text-rendering optimization on our
newly introduced Text-Render-2M dataset; (ii) region-aware supervised
fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via
best-of-n preference optimization; and (iv) joint vision-language feedback
refinement. Each stage is supported by a fully automated data-construction
pipeline tailored to its specific needs, enabling robust training without
complex architectural modifications. Evaluated on multiple experiments,
PosterCraft significantly outperforms open-source baselines in rendering
accuracy, layout coherence, and overall visual appeal-approaching the quality
of SOTA commercial systems. Our code, models, and datasets can be found in the
Project page: https://ephemeral182.github.io/PosterCraft