Orionnnn/hub-stats huggingface dataset:
TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language
  Modeling
Authors: name: Yuancheng Wang, name: Dekun Chen, name: Xueyao Zhang, name: Junan Zhang, name: Jiaqi Li, name: Zhizheng Wu
Published: 2025-08-22 20:45:03

Speech tokenizers serve as foundational components for speech language
models, yet current designs exhibit several limitations, including: 1)
dependence on multi-layer residual vector quantization structures or high frame
rates, 2) reliance on auxiliary pre-trained models for semantic distillation,
and 3) requirements for complex two-stage training processes. In this work, we
introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a
novel approach designed to overcome these challenges. TaDiCodec employs
end-to-end optimization for quantization and reconstruction through a diffusion
autoencoder, while integrating text guidance into the diffusion decoder to
enhance reconstruction quality and achieve optimal compression. TaDiCodec
achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of
0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining
superior performance on critical speech generation evaluation metrics such as
Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS).
Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and
obviating the need for auxiliary pre-trained models. We also validate the
compatibility of TaDiCodec in language model based zero-shot text-to-speech
with both autoregressive modeling and masked generative modeling, demonstrating
its effectiveness and efficiency for speech language modeling, as well as a
significantly small reconstruction-generation gap. We will open source our code
and model checkpoints. Audio samples are are available at
https:/tadicodec.github.io/. We release code and model checkpoints at
https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.
Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance
  for Text-to-Image Generation
Authors: name: Yaqi Li, name: Peng Chen, name: Mingyang Han, name: Bu Pi, name: Haoxiang Shi, name: Runzhou Zhao, name: Yang Yao, name: Xuan Zhang, name: Jun Song
Published: 2025-08-25 13:53:02

Despite the promising progress of recent autoregressive models in
text-to-image (T2I) generation, their ability to handle multi-attribute and
ambiguous prompts remains limited. To address these limitations, existing works
have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and
employed reinforcement learning (RL) to improve reasoning capabilities.
However, most models provide reward signals only at the end of the generation
stage. This monolithic final-only guidance makes it difficult to identify which
stages contribute positively to the final outcome and may lead to suboptimal
policies. To tackle this issue, we propose a Visual-Chain of Guidance
(Visual-CoG) paradigm consisting of three stages: semantic reasoning, process
refining, and outcome evaluation, with stage-aware rewards providing immediate
guidance throughout the image generation pipeline. We further construct a
visual cognition benchmark, VisCog-Bench, which comprises four subtasks to
evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on
GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%,
5%, and 19%, respectively, demonstrating the superior performance of the
proposed Visual-CoG. We will release all the resources soon.
Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
Authors: name: Khaoula Chehbouni, name: Mohammed Haddou, name: Jackie Chi Kit Cheung, name: Golnoosh Farnadi
Published: 2025-08-25 14:43:10

Evaluating natural language generation (NLG) systems remains a core challenge
of natural language processing (NLP), further complicated by the rise of large
language models (LLMs) that aims to be general-purpose. Recently, large
language models as judges (LLJs) have emerged as a promising alternative to
traditional metrics, but their validity remains underexplored. This position
paper argues that the current enthusiasm around LLJs may be premature, as their
adoption has outpaced rigorous scrutiny of their reliability and validity as
evaluators. Drawing on measurement theory from the social sciences, we identify
and critically assess four core assumptions underlying the use of LLJs: their
ability to act as proxies for human judgment, their capabilities as evaluators,
their scalability, and their cost-effectiveness. We examine how each of these
assumptions may be challenged by the inherent limitations of LLMs, LLJs, or
current practices in NLG evaluation. To ground our analysis, we explore three
applications of LLJs: text summarization, data annotation, and safety
alignment. Finally, we highlight the need for more responsible evaluation
practices in LLJs evaluation, to ensure that their growing role in the field
supports, rather than undermines, progress in NLG.
SpotEdit: Evaluating Visually-Guided Image Editing Methods
Authors: name: Sara Ghazanfari, name: Wei-An Lin, name: Haitong Tian, name: Ersin Yumer
Published: 2025-08-25 16:08:57

Visually-guided image editing, where edits are conditioned on both visual
cues and textual prompts, has emerged as a powerful paradigm for fine-grained,
controllable content generation. Although recent generative models have shown
remarkable capabilities, existing evaluations remain simple and insufficiently
representative of real-world editing challenges. We present SpotEdit, a
comprehensive benchmark designed to systematically assess visually-guided image
editing methods across diverse diffusion, autoregressive, and hybrid generative
models, uncovering substantial performance disparities. To address a critical
yet underexplored challenge, our benchmark includes a dedicated component on
hallucination, highlighting how leading models, such as GPT-4o, often
hallucinate the existence of a visual cue and erroneously perform the editing
task. Our code and benchmark are publicly released at
https://github.com/SaraGhazanfari/SpotEdit.
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Authors: name: Zirui Tang, name: Boyu Niu, name: Xuanhe Zhou, name: Boxiu Li, name: Wei Zhou, name: Jiannan Wang, name: Guoliang Li, name: Xinyi Zhang, name: Fan Wu
Published: 2025-08-25 16:48:51

Semi-structured tables, widely used in real-world applications (e.g.,
financial reports, medical records, transactional orders), often involve
flexible and complex layouts (e.g., hierarchical headers and merged cells).
These tables generally rely on human analysts to interpret table layouts and
answer relevant natural language questions, which is costly and inefficient. To
automate the procedure, existing methods face significant challenges. First,
methods like NL2SQL require converting semi-structured tables into structured
ones, which often causes substantial information loss. Second, methods like
NL2Code and multi-modal LLM QA struggle to understand the complex layouts of
semi-structured tables and cannot accurately answer corresponding questions. To
this end, we propose ST-Raptor, a tree-based framework for semi-structured
table question answering using large language models. First, we introduce the
Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures
complex semi-structured table layouts, along with an effective algorithm for
constructing the tree. Second, we define a set of basic tree operations to
guide LLMs in executing common QA tasks. Given a user question, ST-Raptor
decomposes it into simpler sub-questions, generates corresponding tree
operation pipelines, and conducts operation-table alignment for accurate
pipeline execution. Third, we incorporate a two-stage verification mechanism:
forward validation checks the correctness of execution steps, while backward
validation evaluates answer reliability by reconstructing queries from
predicted answers. To benchmark the performance, we present SSTQA, a dataset of
764 questions over 102 real-world semi-structured tables. Experiments show that
ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code
is available at https://github.com/weAIDB/ST-Raptor.
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility,
  Reasoning, and Efficiency
Authors: name: Weiyun Wang, name: Zhangwei Gao, name: Lixin Gu, name: Hengjun Pu, name: Long Cui, name: Xingguang Wei, name: Zhaoyang Liu, name: Linglin Jing, name: Shenglong Ye, name: Jie Shao, name: Zhaokai Wang, name: Zhe Chen, name: Hongjie Zhang, name: Ganlin Yang, name: Haomin Wang, name: Qi Wei, name: Jinhui Yin, name: Wenhao Li, name: Erfei Cui, name: Guanzhou Chen, name: Zichen Ding, name: Changyao Tian, name: Zhenyu Wu, name: Jingjing Xie, name: Zehao Li, name: Bowen Yang, name: Yuchen Duan, name: Xuehui Wang, name: Songze Li, name: Xiangyu Zhao, name: Haodong Duan, name: Nianchen Deng, name: Bin Fu, name: Yinan He, name: Yi Wang, name: Conghui He, name: Botian Shi, name: Junjun He, name: Yingtong Xiong, name: Han Lv, name: Lijun Wu, name: Wenqi Shao, name: Kaipeng Zhang, name: Huipeng Deng, name: Biqing Qi, name: Jiaye Ge, name: Qipeng Guo, name: Wenwei Zhang, name: Wanli Ouyang, name: Limin Wang, name: Min Dou, name: Xizhou Zhu, name: Tong Lu, name: Dahua Lin, name: Jifeng Dai, name: Bowen Zhou, name: Weijie Su, name: Kai Chen, name: Yu Qiao, name: Wenhai Wang, name: Gen Luo
Published: 2025-08-25 17:58:17

We introduce InternVL 3.5, a new family of open-source multimodal models that
significantly advances versatility, reasoning capability, and inference
efficiency along the InternVL series. A key innovation is the Cascade
Reinforcement Learning (Cascade RL) framework, which enhances reasoning through
a two-stage process: offline RL for stable convergence and online RL for
refined alignment. This coarse-to-fine training strategy leads to substantial
improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To
optimize efficiency, we propose a Visual Resolution Router (ViR) that
dynamically adjusts the resolution of visual tokens without compromising
performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD)
strategy separates the vision encoder and language model across different GPUs,
effectively balancing computational load. These contributions collectively
enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning
performance and a 4.05times inference speedup compared to its predecessor,
i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as
GUI interaction and embodied agency. Notably, our largest model, i.e.,
InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs
across general multimodal, reasoning, text, and agentic tasks -- narrowing the
performance gap with leading commercial models like GPT-5. All models and code
are publicly released.
UQ: Assessing Language Models on Unsolved Questions
Authors: name: Fan Nie, name: Ken Ziyu Liu, name: Zihao Wang, name: Rui Sun, name: Wei Liu, name: Weijia Shi, name: Huaxiu Yao, name: Linjun Zhang, name: Andrew Y. Ng, name: James Zou, name: Sanmi Koyejo, name: Yejin Choi, name: Percy Liang, name: Niklas Muennighoff
Published: 2025-08-25 01:07:59

Benchmarks shape progress in AI research. A useful benchmark should be both
difficult and realistic: questions should challenge frontier models while also
reflecting real-world usage. Yet, current paradigms face a difficulty-realism
tension: exam-style benchmarks are often made artificially difficult with
limited real-world value, while benchmarks based on real user interaction often
skew toward easy, high-frequency problems. In this work, we explore a radically
different paradigm: assessing models on unsolved questions. Rather than a
static benchmark scored once, we curate unsolved questions and evaluate models
asynchronously over time with validator-assisted screening and community
verification. We introduce UQ, a testbed of 500 challenging, diverse questions
sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi
and history, probing capabilities including reasoning, factuality, and
browsing. UQ is difficult and realistic by construction: unsolved questions are
often hard and naturally arise when humans seek answers, thus solving them
yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset
and its collection pipeline combining rule-based filters, LLM judges, and human
review to ensure question quality (e.g., well-defined and difficult); (2)
UQ-Validators, compound validation strategies that leverage the
generator-validator gap to provide evaluation signals and pre-screen candidate
solutions for human review; and (3) UQ-Platform, an open platform where experts
collectively verify questions and solutions. The top model passes UQ-validation
on only 15% of questions, and preliminary human verification has already
identified correct answers among those that passed. UQ charts a path for
evaluating frontier models on real-world, open-ended challenges, where success
pushes the frontier of human knowledge. We release UQ at
https://uq.stanford.edu.
PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent
  LLMs
Authors: name: Zhilin Zhang, name: Xiang Zhang, name: Jiaqi Wei, name: Yiwei Xu, name: Chenyu You
Published: 2025-08-24 02:25:45

Multi-agent systems built upon large language models (LLMs) have demonstrated
remarkable capabilities in tackling complex compositional tasks. In this work,
we apply this paradigm to the paper-to-poster generation problem, a practical
yet time-consuming process faced by researchers preparing for conferences.
While recent approaches have attempted to automate this task, most neglect core
design and aesthetic principles, resulting in posters that require substantial
manual refinement. To address these design limitations, we propose PosterGen, a
multi-agent framework that mirrors the workflow of professional poster
designers. It consists of four collaborative specialized agents: (1) Parser and
Curator agents extract content from the paper and organize storyboard; (2)
Layout agent maps the content into a coherent spatial layout; (3) Stylist
agents apply visual design elements such as color and typography; and (4)
Renderer composes the final poster. Together, these agents produce posters that
are both semantically grounded and visually appealing. To evaluate design
quality, we introduce a vision-language model (VLM)-based rubric that measures
layout balance, readability, and aesthetic coherence. Experimental results show
that PosterGen consistently matches in content fidelity, and significantly
outperforms existing methods in visual designs, generating posters that are
presentation-ready with minimal human refinements.
Distilled-3DGS:Distilled 3D Gaussian Splatting
Authors: name: Lintao Xiang, name: Xinkai Chen, name: Jianhuang Lai, name: Guangcong Wang
Published: 2025-08-19 17:59:26

3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view
synthesis (NVS). However, it suffers from a significant drawback: achieving
high-fidelity rendering typically necessitates a large number of 3D Gaussians,
resulting in substantial memory consumption and storage requirements. To
address this challenge, we propose the first knowledge distillation framework
for 3DGS, featuring various teacher models, including vanilla 3DGS,
noise-augmented variants, and dropout-regularized versions. The outputs of
these teachers are aggregated to guide the optimization of a lightweight
student model. To distill the hidden geometric structure, we propose a
structural similarity loss to boost the consistency of spatial geometric
distributions between the student and teacher model. Through comprehensive
quantitative and qualitative evaluations across diverse datasets, the proposed
Distilled-3DGS, a simple yet effective framework without bells and whistles,
achieves promising rendering results in both rendering quality and storage
efficiency compared to state-of-the-art methods. Project page:
https://distilled3dgs.github.io . Code:
https://github.com/lt-xiang/Distilled-3DGS .
RotaTouille: Rotation Equivariant Deep Learning for Contours
Authors: name: Odin Hoff Gardaa, name: Nello Blaser
Published: 2025-08-22 13:05:55

Contours or closed planar curves are common in many domains. For example,
they appear as object boundaries in computer vision, isolines in meteorology,
and the orbits of rotating machinery. In many cases when learning from contour
data, planar rotations of the input will result in correspondingly rotated
outputs. It is therefore desirable that deep learning models be rotationally
equivariant. In addition, contours are typically represented as an ordered
sequence of edge points, where the choice of starting point is arbitrary. It is
therefore also desirable for deep learning methods to be equivariant under
cyclic shifts. We present RotaTouille, a deep learning framework for learning
from contour data that achieves both rotation and cyclic shift equivariance
through complex-valued circular convolution. We further introduce and
characterize equivariant non-linearities, coarsening layers, and global pooling
layers to obtain invariant representations for downstream tasks. Finally, we
demonstrate the effectiveness of RotaTouille through experiments in shape
classification, reconstruction, and contour regression.
Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing
Authors: name: Feng-Lin Liu, name: Shi-Yang Li, name: Yan-Pei Cao, name: Hongbo Fu, name: Lin Gao
Published: 2025-08-19 12:57:31

Recent video editing methods achieve attractive results in style transfer or
appearance modification. However, editing the structural content of 3D scenes
in videos remains challenging, particularly when dealing with significant
viewpoint changes, such as large camera rotations or zooms. Key challenges
include generating novel view content that remains consistent with the original
video, preserving unedited regions, and translating sparse 2D inputs into
realistic 3D video outputs. To address these issues, we propose Sketch3DVE, a
sketch-based 3D-aware video editing method to enable detailed local
manipulation of videos with significant viewpoint changes. To solve the
challenge posed by sparse inputs, we employ image editing methods to generate
edited results for the first frame, which are then propagated to the remaining
frames of the video. We utilize sketching as an interaction tool for precise
geometry control, while other mask-based image editing methods are also
supported. To handle viewpoint changes, we perform a detailed analysis and
manipulation of the 3D information in the video. Specifically, we utilize a
dense stereo method to estimate a point cloud and the camera parameters of the
input video. We then propose a point cloud editing approach that uses depth
maps to represent the 3D geometry of newly edited components, aligning them
effectively with the original 3D scene. To seamlessly merge the newly edited
content with the original video while preserving the features of unedited
regions, we introduce a 3D-aware mask propagation strategy and employ a video
diffusion model to produce realistic edited videos. Extensive experiments
demonstrate the superiority of Sketch3DVE in video editing. Homepage and code:
http://http://geometrylearning.com/Sketch3DVE/
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated
  Chain-of-Thought-based Reinforced Fine-Tuning
Authors: name: Wenqiao Zhu, name: Ji Liu, name: Rongjuncheng Zhang, name: Haipang Wu, name: Yulun Zhang
Published: 2025-08-21 00:20:47

Reasoning capability plays a significantly critical role in the the broad
applications of Large Language Models (LLMs). To enhance the reasoning
performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning
approaches have been proposed to address the limited generalization capability
of LLMs trained solely via Supervised Fine-Tuning (SFT). Despite their
effectiveness, two major limitations hinder the advancement of LLMs. First,
vanilla RL-based approaches ignore annotated Chain-of-Thought (CoT) and
incorporate unstable reasoning path sampling, which typically results in model
collapse, unstable training process, and suboptimal performance. Second,
existing SFT approaches generally overemphasize the annotated CoT, potentially
leading to performance degradation due to insufficient exploitation of
potential CoT. In this paper, we propose a Contrastive learning with annotated
CoT-based Reinforced Fine-Tuning approach, i.e., , to enhance the
reasoning performance of LLMs while addressing the aforementioned limitations.
Specifically, we propose learning a representation for each CoT. Based on this
representation, we design novel contrastive signals to guide the fine-tuning
process. Our approach not only fully exploits the available annotated CoT but
also stabilizes the fine-tuning procedure by incorporating an additional
unsupervised learning signal. We conduct comprehensive experiments and in-depth
analysis with three baseline approaches, two foundation models, and two
datasets to demonstrate significant advantages of  in terms of
robustness, performance (up to 10.15\%), and efficiency (up to 30.62\%). Code
is available at https://github.com/WNQzhu/CARFT.
Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose
  Inverse Kinematics
Authors: name: Yuchen Yang, name: Linfeng Dong, name: Wei Wang, name: Zhihang Zhong, name: Xiao Sun
Published: 2025-08-19 06:53:57

In 3D human pose and shape estimation, SMPLify remains a robust baseline that
solves inverse kinematics (IK) through iterative optimization. However, its
high computational cost limits its practicality. Recent advances across domains
have shown that replacing iterative optimization with data-driven neural
networks can achieve significant runtime improvements without sacrificing
accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural
framework that replaces the iterative fitting process in SMPLify with a
single-pass regression model. The design of our framework targets two core
challenges in neural IK: data construction and generalization. To enable
effective training, we propose a temporal sampling strategy that constructs
initialization-target pairs from sequential frames. To improve generalization
across diverse motions and unseen poses, we propose a human-centric
normalization scheme and residual learning to narrow the solution space.
Learnable SMPLify supports both sequential inference and plug-in
post-processing to refine existing image-based estimators. Extensive
experiments demonstrate that our method establishes itself as a practical and
simple baseline: it achieves nearly 200x faster runtime compared to SMPLify,
generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic
manner when used as a plug-in tool on LucidAction. The code is available at
https://github.com/Charrrrrlie/Learnable-SMPLify.