Orionnnn/hub-stats huggingface dataset:
Change State Space Models for Remote Sensing Change Detection
Authors: name: Elman Ghazaei, name: Erchan Aptoula
Published: 2025-04-15 11:25:10

Despite their frequent use for change detection, both ConvNets and Vision
transformers (ViT) exhibit well-known limitations, namely the former struggle
to model long-range dependencies while the latter are computationally
inefficient, rendering them challenging to train on large-scale datasets.
Vision Mamba, an architecture based on State Space Models has emerged as an
alternative addressing the aforementioned deficiencies and has been already
applied to remote sensing change detection, though mostly as a feature
extracting backbone. In this article the Change State Space Model is
introduced, that has been specifically designed for change detection by
focusing on the relevant changes between bi-temporal images, effectively
filtering out irrelevant information. By concentrating solely on the changed
features, the number of network parameters is reduced, enhancing significantly
computational efficiency while maintaining high detection performance and
robustness against input degradation. The proposed model has been evaluated via
three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based
counterparts at a fraction of their computational complexity. The
implementation will be made available at https://github.com/Elman295/CSSM upon
acceptance.
Multimodal Long Video Modeling Based on Temporal Dynamic Context
Authors: name: Haoran Hao, name: Jiaming Han, name: Yiyuan Zhang, name: Xiangyu Yue
Published: 2025-04-14 17:34:06

Recent advances in Large Language Models (LLMs) have led to significant
breakthroughs in video understanding. However, existing models still struggle
with long video processing due to the context length constraint of LLMs and the
vast amount of information within the video. Although some recent methods are
designed for long video understanding, they often lose crucial information
during token compression and struggle with additional modality like audio. In
this work, we propose a dynamic long video encoding method utilizing the
temporal relationship between frames, named Temporal Dynamic Context (TDC).
Firstly, we segment the video into semantically consistent scenes based on
inter-frame similarities, then encode each frame into tokens using visual-audio
encoders. Secondly, we propose a novel temporal context compressor to reduce
the number of tokens within each segment. Specifically, we employ a query-based
Transformer to aggregate video, audio, and instruction text tokens into a
limited set of temporal context tokens. Finally, we feed the static frame
tokens and the temporal context tokens into the LLM for video understanding.
Furthermore, to handle extremely long videos, we propose a training-free
chain-of-thought strategy that progressively extracts answers from multiple
video segments. These intermediate answers serve as part of the reasoning
process and contribute to the final answer. We conduct extensive experiments on
general video understanding and audio-video understanding benchmarks, where our
method demonstrates strong performance. The code and models are available at
https://github.com/Hoar012/TDC-Video.
LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews
Authors: name: Sukannya Purkayastha, name: Zhuang Li, name: Anne Lauscher, name: Lizhen Qu, name: Iryna Gurevych
Published: 2025-04-15 10:07:33

Peer review is a cornerstone of quality control in scientific publishing.
With the increasing workload, the unintended use of `quick' heuristics,
referred to as lazy thinking, has emerged as a recurring issue compromising
review quality. Automated methods to detect such heuristics can help improve
the peer-reviewing process. However, there is limited NLP research on this
issue, and no real-world dataset exists to support the development of detection
tools. This work introduces LazyReview, a dataset of peer-review sentences
annotated with fine-grained lazy thinking categories. Our analysis reveals that
Large Language Models (LLMs) struggle to detect these instances in a zero-shot
setting. However, instruction-based fine-tuning on our dataset significantly
boosts performance by 10-20 performance points, highlighting the importance of
high-quality training data. Furthermore, a controlled experiment demonstrates
that reviews revised with lazy thinking feedback are more comprehensive and
actionable than those written without such feedback. We will release our
dataset and the enhanced guidelines that can be used to train junior reviewers
in the community. (Code available here:
https://github.com/UKPLab/arxiv2025-lazy-review)
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and
  Verifiable Mathematical Dataset for Advancing Reasoning
Authors: name: Zhiwei He, name: Tian Liang, name: Jiahao Xu, name: Qiuzhi Liu, name: Xingyu Chen, name: Yue Wang, name: Linfeng Song, name: Dian Yu, name: Zhenwen Liang, name: Wenxuan Wang, name: Zhuosheng Zhang, name: Rui Wang, name: Zhaopeng Tu, name: Haitao Mi, name: Dong Yu
Published: 2025-04-15 17:59:51

The capacity for complex mathematical reasoning is a key benchmark for
artificial intelligence. While reinforcement learning (RL) applied to LLMs
shows promise, progress is significantly hindered by the lack of large-scale
training data that is sufficiently challenging, possesses verifiable answer
formats suitable for RL, and is free from contamination with evaluation
benchmarks. To address these limitations, we introduce DeepMath-103K, a new,
large-scale dataset comprising approximately 103K mathematical problems,
specifically designed to train advanced reasoning models via RL. DeepMath-103K
is curated through a rigorous pipeline involving source analysis, stringent
decontamination against numerous benchmarks, and filtering for high difficulty
(primarily Levels 5-9), significantly exceeding existing open resources in
challenge. Each problem includes a verifiable final answer, enabling rule-based
RL, and three distinct R1-generated solutions suitable for diverse training
paradigms like supervised fine-tuning or distillation. Spanning a wide range of
mathematical topics, DeepMath-103K promotes the development of generalizable
reasoning. We demonstrate that models trained on DeepMath-103K achieve
significant improvements on challenging mathematical benchmarks, validating its
effectiveness. We release DeepMath-103K publicly to facilitate community
progress in building more capable AI reasoning systems:
https://github.com/zwhe99/DeepMath.
D^2iT: Dynamic Diffusion Transformer for Accurate Image Generation
Authors: name: Weinan Jia, name: Mengqi Huang, name: Nan Chen, name: Lei Zhang, name: Zhendong Mao
Published: 2025-04-13 06:33:28

Diffusion models are widely recognized for their ability to generate
high-fidelity images. Despite the excellent performance and scalability of the
Diffusion Transformer (DiT) architecture, it applies fixed compression across
different image regions during the diffusion process, disregarding the
naturally varying information densities present in these regions. However,
large compression leads to limited local realism, while small compression
increases computational complexity and compromises global consistency,
ultimately impacting the quality of generated images. To address these
limitations, we propose dynamically compressing different image regions by
recognizing the importance of different regions, and introduce a novel
two-stage framework designed to enhance the effectiveness and efficiency of
image generation: (1) Dynamic VAE (DVAE) at first stage employs a hierarchical
encoder to encode different image regions at different downsampling rates,
tailored to their specific information densities, thereby providing more
accurate and natural latent codes for the diffusion process. (2) Dynamic
Diffusion Transformer (D^2iT) at second stage generates images by predicting
multi-grained noise, consisting of coarse-grained (less latent code in smooth
regions) and fine-grained (more latent codes in detailed regions), through an
novel combination of the Dynamic Grain Transformer and the Dynamic Content
Transformer. The strategy of combining rough prediction of noise with detailed
regions correction achieves a unification of global consistency and local
realism. Comprehensive experiments on various generation tasks validate the
effectiveness of our approach. Code will be released at
https://github.com/jiawn-creator/Dynamic-DiT.
AI-University: An LLM-based platform for instructional alignment to
  scientific classrooms
Authors: name: Mostafa Faghih Shojaei, name: Rahul Gulati, name: Benjamin A. Jasperson, name: Shangshang Wang, name: Simone Cimolato, name: Dangli Cao, name: Willie Neiswanger, name: Krishna Garikipati
Published: 2025-04-11 01:26:34

We introduce AI University (AI-U), a flexible framework for AI-driven course
content delivery that adapts to instructors' teaching styles. At its core, AI-U
fine-tunes a large language model (LLM) with retrieval-augmented generation
(RAG) to generate instructor-aligned responses from lecture videos, notes, and
textbooks. Using a graduate-level finite-element-method (FEM) course as a case
study, we present a scalable pipeline to systematically construct training
data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and
optimize its responses through RAG-based synthesis. Our evaluation - combining
cosine similarity, LLM-based assessment, and expert review - demonstrates
strong alignment with course materials. We also have developed a prototype web
application, available at https://my-ai-university.com, that enhances
traceability by linking AI-generated responses to specific sections of the
relevant course material and time-stamped instances of the open-access video
lectures. Our expert model is found to have greater cosine similarity with a
reference on 86% of test cases. An LLM judge also found our expert model to
outperform the base Llama 3.2 model approximately four times out of five. AI-U
offers a scalable approach to AI-assisted education, paving the way for broader
adoption in higher education. Here, our framework has been presented in the
setting of a class on FEM - a subject that is central to training PhD and
Master students in engineering science. However, this setting is a particular
instance of a broader context: fine-tuning LLMs to research content in science.
Summarization of Multimodal Presentations with Vision-Language Models:
  Study of the Effect of Modalities and Structure
Authors: name: Théo Gigant, name: Camille Guinaudeau, name: Frédéric Dufaux
Published: 2025-04-14 09:55:01

Vision-Language Models (VLMs) can process visual and textual information in
multiple formats: texts, images, interleaved texts and images, or even
hour-long videos. In this work, we conduct fine-grained quantitative and
qualitative analyses of automatic summarization of multimodal presentations
using VLMs with various representations as input. From these experiments, we
suggest cost-effective strategies for generating summaries from text-heavy
multimodal documents under different input-length budgets using VLMs. We show
that slides extracted from the video stream can be beneficially used as input
against the raw video, and that a structured representation from interleaved
slides and transcript provides the best performance. Finally, we reflect and
comment on the nature of cross-modal interactions in multimodal presentations
and share suggestions to improve the capabilities of VLMs to understand
documents of this nature.
Genius: A Generalizable and Purely Unsupervised Self-Training Framework
  For Advanced Reasoning
Authors: name: Fangzhi Xu, name: Hang Yan, name: Chang Ma, name: Haiteng Zhao, name: Qiushi Sun, name: Kanzhi Cheng, name: Junxian He, name: Jun Liu, name: Zhiyong Wu
Published: 2025-04-11 16:26:23

Advancing LLM reasoning skills has captivated wide interest. However, current
post-training techniques rely heavily on supervisory signals, such as outcome
supervision or auxiliary reward models, which face the problem of scalability
and high annotation costs. This motivates us to enhance LLM reasoning without
the need for external supervision. We introduce a generalizable and purely
unsupervised self-training framework, named Genius. Without external auxiliary,
Genius requires to seek the optimal response sequence in a stepwise manner and
optimize the LLM. To explore the potential steps and exploit the optimal ones,
Genius introduces a stepwise foresight re-sampling strategy to sample and
estimate the step value by simulating future outcomes. Further, we recognize
that the unsupervised setting inevitably induces the intrinsic noise and
uncertainty. To provide a robust optimization, we propose an
advantage-calibrated optimization (ACO) loss function to mitigate estimation
inconsistencies. Combining these techniques together, Genius provides an
advanced initial step towards self-improve LLM reasoning with general queries
and without supervision, revolutionizing reasoning scaling laws given the vast
availability of general queries. The code will be released at
https://github.com/xufangzhi/Genius.
ReZero: Enhancing LLM search ability by trying one-more-time
Authors: name: Alan Dao, name: Thinh Le
Published: 2025-04-15 09:18:21

Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM)
performance on knowledge-intensive tasks but depends heavily on initial search
query quality. Current methods, often using Reinforcement Learning (RL),
typically focus on query formulation or reasoning over results, without
explicitly encouraging persistence after a failed search. We introduce ReZero
(Retry-Zero), a novel RL framework that directly rewards the act of retrying a
search query following an initial unsuccessful attempt. This incentivizes the
LLM to explore alternative queries rather than prematurely halting. ReZero
demonstrates significant improvement, achieving 46.88% accuracy compared to a
25% baseline. By rewarding persistence, ReZero enhances LLM robustness in
complex information-seeking scenarios where initial queries may prove
insufficient.
Efficient Generative Model Training via Embedded Representation Warmup
Authors: name: Deyuan Liu, name: Peng Sun, name: Xufeng Li, name: Tao Lin
Published: 2025-04-14 12:43:17

Diffusion models excel at generating high-dimensional data but fall short in
training efficiency and representation quality compared to self-supervised
methods. We identify a key bottleneck: the underutilization of high-quality,
semantically rich representations during training notably slows down
convergence. Our systematic analysis reveals a critical representation
processing region -- primarily in the early layers -- where semantic and
structural pattern learning takes place before generation can occur. To address
this, we propose Embedded Representation Warmup (ERW), a plug-and-play
framework where in the first stage we get the ERW module serves as a warmup
that initializes the early layers of the diffusion model with high-quality,
pretrained representations. This warmup minimizes the burden of learning
representations from scratch, thereby accelerating convergence and boosting
performance. Our theoretical analysis demonstrates that ERW's efficacy depends
on its precise integration into specific neural network layers -- termed the
representation processing region -- where the model primarily processes and
transforms feature representations for later generation. We further establish
that ERW not only accelerates training convergence but also enhances
representation quality: empirically, our method achieves a 40times
acceleration in training speed compared to REPA, the current state-of-the-art
methods. Code is available at https://github.com/LINs-lab/ERW.
A Minimalist Approach to LLM Reasoning: from Rejection Sampling to
  Reinforce
Authors: name: Wei Xiong, name: Jiarui Yao, name: Yuhui Xu, name: Bo Pang, name: Lei Wang, name: Doyen Sahoo, name: Junnan Li, name: Nan Jiang, name: Tong Zhang, name: Caiming Xiong, name: Hanze Dong
Published: 2025-04-15 16:15:02

Reinforcement learning (RL) has become a prevailing approach for fine-tuning
large language models (LLMs) on complex reasoning tasks. Among recent methods,
GRPO stands out for its empirical success in training models such as
DeepSeek-R1, yet the sources of its effectiveness remain poorly understood. In
this work, we revisit GRPO from a reinforce-like algorithm perspective and
analyze its core components. Surprisingly, we find that a simple rejection
sampling baseline, RAFT, which trains only on positively rewarded samples,
yields competitive performance than GRPO and PPO. Our ablation studies reveal
that GRPO's main advantage arises from discarding prompts with entirely
incorrect responses, rather than from its reward normalization. Motivated by
this insight, we propose Reinforce-Rej, a minimal extension of policy gradient
that filters both entirely incorrect and entirely correct samples.
Reinforce-Rej improves KL efficiency and stability, serving as a lightweight
yet effective alternative to more complex RL algorithms. We advocate RAFT as a
robust and interpretable baseline, and suggest that future advances should
focus on more principled designs for incorporating negative samples, rather
than relying on them indiscriminately. Our findings provide guidance for future
work in reward-based LLM post-training.
The Scalability of Simplicity: Empirical Analysis of Vision-Language
  Learning with a Single Transformer
Authors: name: Weixian Lei, name: Jiacong Wang, name: Haochen Wang, name: Xiangtai Li, name: Jun Hao Liew, name: Jiashi Feng, name: Zilong Huang
Published: 2025-04-14 17:50:20

This paper introduces SAIL, a single transformer unified multimodal large
language model (MLLM) that integrates raw pixel encoding and language decoding
within a singular architecture. Unlike existing modular MLLMs, which rely on a
pre-trained vision transformer (ViT), SAIL eliminates the need for a separate
vision encoder, presenting a more minimalist architecture design. Instead of
introducing novel architectural components, SAIL adapts mix-attention
mechanisms and multimodal positional encodings to better align with the
distinct characteristics of visual and textual modalities. We systematically
compare SAIL's properties-including scalability, cross-modal information flow
patterns, and visual representation capabilities-with those of modular MLLMs.
By scaling both training data and model size, SAIL achieves performance
comparable to modular MLLMs. Notably, the removal of pretrained ViT components
enhances SAIL's scalability and results in significantly different cross-modal
information flow patterns. Moreover, SAIL demonstrates strong visual
representation capabilities, achieving results on par with ViT-22B in vision
tasks such as semantic segmentation. Code and models are available at
https://github.com/bytedance/SAIL.
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of
  Complex Videos in the Wild
Authors: name: Henghui Ding, name: Chang Liu, name: Nikhila Ravi, name: Shuting He, name: Yunchao Wei, name: Song Bai, name: Philip Torr, name: Kehuan Song, name: Xinglin Xie, name: Kexin Zhang, name: Licheng Jiao, name: Lingling Li, name: Shuyuan Yang, name: Xuqiang Cao, name: Linnan Zhao, name: Jiaxuan Zhao, name: Fang Liu, name: Mengjiao Wang, name: Junpei Zhang, name: Xu Liu, name: Yuting Yang, name: Mengru Ma, name: Hao Fang, name: Runmin Cong, name: Xiankai Lu, name: Zhiyang Che, name: Wei Zhan, name: Tianming Liang, name: Haichao Jiang, name: Wei-Shi Zheng, name: Jian-Fang Hu, name: Haobo Yuan, name: Xiangtai Li, name: Tao Zhang, name: Lu Qi, name: Ming-Hsuan Yang
Published: 2025-04-15 16:02:47

This report provides a comprehensive overview of the 4th Pixel-level Video
Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025.
It summarizes the challenge outcomes, participating methodologies, and future
research directions. The challenge features two tracks: MOSE, which focuses on
complex scene video object segmentation, and MeViS, which targets
motion-guided, language-based video segmentation. Both tracks introduce new,
more challenging datasets designed to better reflect real-world scenarios.
Through detailed evaluation and analysis, the challenge offers valuable
insights into the current state-of-the-art and emerging trends in complex video
segmentation. More information can be found on the workshop website:
https://pvuw.github.io/.
Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding
Authors: name: Tao Zhang, name: Xiangtai Li, name: Zilong Huang, name: Yanwei Li, name: Weixian Lei, name: Xueqing Deng, name: Shihao Chen, name: Shunping Ji, name: Jiashi Feng
Published: 2025-04-14 17:52:22

Multimodal Large Language Models (MLLMs) achieve remarkable performance for
fine-grained pixel-level understanding tasks. However, all the works rely
heavily on extra components, such as vision encoder (CLIP), segmentation
experts, leading to high system complexity and limiting model scaling. In this
work, our goal is to explore a highly simplified MLLM without introducing extra
components. Our work is motivated by the recent works on Single trAnsformer as
a unified vIsion-Language Model (SAIL) design, where these works jointly learn
vision tokens and text tokens in transformers. We present Pixel-SAIL, a single
transformer for pixel-wise MLLM tasks. In particular, we present three
technical improvements on the plain baseline. First, we design a learnable
upsampling module to refine visual token features. Secondly, we propose a novel
visual prompt injection strategy to enable the single transformer to understand
visual prompt inputs and benefit from the early fusion of visual prompt
embeddings and vision tokens. Thirdly, we introduce a vision expert
distillation strategy to efficiently enhance the single transformer's
fine-grained feature extraction capability. In addition, we have collected a
comprehensive pixel understanding benchmark (PerBench), using a manual check.
It includes three tasks: detailed object description, visual prompt-based
question answering, and visual-text referring segmentation. Extensive
experiments on four referring segmentation benchmarks, one visual prompt
benchmark, and our PerBench show that our Pixel-SAIL achieves comparable or
even better results with a much simpler pipeline. Code and model will be
released at https://github.com/magic-research/Sa2VA.
Efficient Process Reward Model Training via Active Learning
Authors: name: Keyu Duan, name: Zichen Liu, name: Xin Mao, name: Tianyu Pang, name: Changyu Chen, name: Qiguang Chen, name: Michael Qizhe Shieh, name: Longxu Dou
Published: 2025-04-14 14:53:56

Process Reward Models (PRMs) provide step-level supervision to large language
models (LLMs), but scaling up training data annotation remains challenging for
both humans and LLMs. To address this limitation, we propose an active learning
approach, ActPRM, which proactively selects the most uncertain samples for
training, substantially reducing labeling costs. During training, we use the
PRM to estimate uncertainty after the forward pass, retaining only highly
uncertain data. A capable yet costly reasoning model then labels this data.
Then we compute the loss with respect to the labels and update the PRM's
weights. We compare ActPRM vs. vanilla fine-tuning, on a pool-based active
learning setting, demonstrating that ActPRM reduces 50% annotation, but
achieving the comparable or even better performance. Beyond annotation
efficiency, we further advance the actively trained PRM by filtering over 1M+
math reasoning trajectories with ActPRM, retaining 60% of the data. A
subsequent training on this selected dataset yields a new state-of-the-art
(SOTA) PRM on ProcessBench (75.0%) and PRMBench (65.5%) compared with same
sized models.