pwc-archive/papers-with-abstracts huggingface dataset:
Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Authors: Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang
Date: 2025-06-11 00:00:00

Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that characterizes CoT reasoning through the dual lenses of sufficiency and necessity. Incorporating causal Probability of Sufficiency and Necessity allows us not only to determine which steps are logically sufficient or necessary to the prediction outcome, but also to quantify their actual influence on the final reasoning outcome under different intervention scenarios, thereby enabling the automated addition of missing steps and the pruning of redundant ones. Extensive experimental results on various mathematical and commonsense reasoning benchmarks confirm substantial improvements in reasoning efficiency and reduced token usage without sacrificing accuracy. Our work provides a promising direction for improving LLM reasoning performance and cost-effectiveness.
Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition
Authors: Panagiotis Kaliosis, John Pavlopoulos
Date: 2025-06-11 00:00:00
Tasks: Handwritten Text Recognition

Handwritten text recognition aims to convert visual input into machine-readable text, and it remains challenging due to the evolving and context-dependent nature of handwriting. Character sets change over time, and character frequency distributions shift across historical periods or regions, often causing models trained on broad, heterogeneous corpora to underperform on specific subsets. To tackle this, we propose a novel loss function that incorporates the Wasserstein distance between the character frequency distribution of the predicted text and a target distribution empirically derived from training data. By penalizing divergence from expected distributions, our approach enhances both accuracy and robustness under temporal and contextual intra-dataset shifts. Furthermore, we demonstrate that character distribution alignment can also improve existing models at inference time without requiring retraining by integrating it as a scoring function in a guided decoding scheme. Experimental results across multiple datasets and architectures confirm the effectiveness of our method in boosting generalization and performance. We open source our code at https://github.com/pkaliosis/fada.
Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Authors: Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen, Xi Ye
Date: 2025-06-11 00:00:00
Tasks: Re-Ranking, Retrieval

Recent work has identified retrieval heads (Wu et al., 2025b), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHEAD (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHEAD by aggregating attention scores with respect to the input query, using a handful of examples from real-world tasks (e.g., long-context QA). We further introduce QR- RETRIEVER, an efficient and effective retriever that uses the accumulated attention mass of QRHEAD as retrieval scores. We use QR- RETRIEVER for long-context reasoning by selecting the most relevant parts with the highest retrieval scores. On multi-hop reasoning tasks LongMemEval and CLIPPER, this yields over 10% performance gains over full context and outperforms strong dense retrievers. We also evaluate QRRETRIEVER as a re-ranker on the BEIR benchmark and find that it achieves strong zero-shot performance, outperforming other LLM-based re-rankers such as RankGPT. Further analysis shows that both the querycontext attention scoring and task selection are crucial for identifying QRHEAD with strong downstream utility. Overall, our work contributes a general-purpose retriever and offers interpretability insights into the long-context capabilities of LMs.
Foundation Models in Medical Imaging -- A Review and Outlook
Authors: Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu, Melis Erdal Cesur, Kevin Groot Lipman, Edwin D. de Jong, Hugo Horlings, Clárisa I. Sanchez, Cees G. M. Snoek, Lodewyk Wessels, Ritse Mann, Eric Marcus, Jonas Teuwen
Date: 2025-06-10 00:00:00
Tasks: Self-Supervised Learning

Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Authors: Shun Lei, Yaoxun Xu, Zhiwei Lin, Huaicheng Zhang, Wei Tan, Hangting Chen, Jianwei Yu, Yixuan Zhang, Chenyu Yang, Haina Zhu, Shuai Wang, Zhiyong Wu, Dong Yu
Date: 2025-06-09 00:00:00
Tasks: Instruction Following, Music Generation

Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing approaches still struggle with the complex composition of songs and the scarcity of high-quality data, leading to limitations in sound quality, musicality, instruction following, and vocal-instrument harmony. To address these challenges, we introduce LeVo, an LM-based framework consisting of LeLM and a music codec. LeLM is capable of parallelly modeling two types of tokens: mixed tokens, which represent the combined audio of vocals and accompaniment to achieve vocal-instrument harmony, and dual-track tokens, which separately encode vocals and accompaniment for high-quality song generation. It employs two decoder-only transformers and a modular extension training strategy to prevent interference between different token types. To further enhance musicality and instruction following, we introduce a multi-preference alignment method based on Direct Preference Optimization (DPO). This method handles diverse human preferences through a semi-automatic data construction process and DPO post-training. Experimental results demonstrate that LeVo consistently outperforms existing methods on both objective and subjective metrics. Ablation studies further justify the effectiveness of our designs. Audio examples are available at https://levo-demo.github.io/. Code is released at https://github.com/tencent-ailab/songgeneration.
HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class
Authors: James V. Roggeveen, Erik Y. Wang, Will Flintoft, Peter Donets, Lucy S. Nathwani, Nickholas Gutierrez, David Ettel, Anton Marius Graf, Siddharth Dandavate, Arjun Nageswaran, Raglan Ward, Ava Williamson, Anne Mykland, Kacper K. Migacz, Yijun Wang, Egemen Bostan, Duy Thuc Nguyen, Zhe He, Marc L. Descoteaux, Felix Yeung, Shida Liu, Jorge García Ponce, Luke Zhu, Yuyang Chen, Ekaterina S. Ivshina, Miguel Fernandez, Minjae Kim, Kennan Gumbs, Matthew Scott Tan, Russell Yang, Mai Hoang, David Brown, Isabella A. Silveira, Lavon Sykes, Ahmed Roman, William Fredenberg, Yiming Chen, Lucas Martin, Yixing Tang, Kelly Werker Smith, Hongyu Liao, Logan G. Wilson, Alexander Dazhen Cai, Andrea Elizabeth Biju, Michael P. Brenner
Date: 2025-05-17 00:00:00
Tasks: Math, Mathematical Problem-Solving, Mathematical Reasoning

Large language models (LLMs) have shown remarkable progress in mathematical problem-solving, but evaluation has largely focused on problems that have exact analytical solutions or involve formal proofs, often overlooking approximation-based problems ubiquitous in applied science and engineering. To fill this gap, we build on prior work and present HARDMath2, a dataset of 211 original problems covering the core topics in an introductory graduate applied math class, including boundary-layer analysis, WKB methods, asymptotic solutions of nonlinear partial differential equations, and the asymptotics of oscillatory integrals. This dataset was designed and verified by the students and instructors of a core graduate applied mathematics course at Harvard. We build the dataset through a novel collaborative environment that challenges students to write and refine difficult problems consistent with the class syllabus, peer-validate solutions, test different models, and automatically check LLM-generated solutions against their own answers and numerical ground truths. Evaluation results show that leading frontier models still struggle with many of the problems in the dataset, highlighting a gap in the mathematical reasoning skills of current LLMs. Importantly, students identified strategies to create increasingly difficult problems by interacting with the models and exploiting common failure modes. This back-and-forth with the models not only resulted in a richer and more challenging benchmark but also led to qualitative improvements in the students' understanding of the course material, which is increasingly important as we enter an age where state-of-the-art language models can solve many challenging problems across a wide domain of fields.
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
Authors: Yong Su, Jiahang Li, Simin An, Hengpeng Xu, Weilong Peng
Date: 2025-06-13 00:00:00
Proceeding: IEEE TII 2025 6
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Edge-computing, Federated Learning, Multiple Instance Learning, Personalized Federated Learning, Video Anomaly Detection, Weakly-supervised Learning, Weakly-supervised Video Anomaly Detection

Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level annotations. However, the inherent challenges of optimizing noisy representation with coarse-grained labels often result in substantial local model errors, which are exacerbated during federated aggregation, particularly in heterogeneous scenarios. To address these limitations, we propose a novel dual-detector framework incorporating adaptive dynamic recursive mapping, which significantly enhances local model accuracy and robustness against representation noise. Our framework integrates two complementary components: a channel-averaged anomaly detector and a channel-statistical anomaly detector, which interact through cross-detector adaptive decision parameters to enable iterative optimization and stable anomaly scoring across all instances. Furthermore, we introduce the scene similarity adaptive local aggregation algorithm, which dynamically aggregates and learns private models based on scene similarity, thereby enhancing generalization capabilities across diverse scenarios. Extensive experiments conducted on the NVIDIA Jetson AGX Xavier platform using the ShanghaiTech and UBnormal datasets demonstrate the superior performance of our approach in both centralized and federated settings. Notably, in federated environments, our method achieves remarkable improvements of 6.2% and 12.3% in AUC compared to state-of-the-art methods, underscoring its effectiveness in resource-constrained scenarios and its potential for real-world applications in distributed video surveillance systems.
Diffusion Subspace Clustering for Hyperspectral Images
Authors: Jiaxin Chen, Shujun Liu, Zhongbiao Zhang, Huajun Wang
Date: 2023-07-12 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023 7
Tasks: Clustering

Hyperspectral image (HSI) subspace clustering remains a challenging task due to the poor spatial and rich spectral resolutions of HSIs. Most of the existing HSI subspace clustering approaches just extract the spatial and spectral features, ignoring the intrinsic distribution information of data and leading to low accuracy of clustering generally. To solve this problem, this article presents a diffusion subspace clustering model (DiffSC) that learns distribution information of HSI data simultaneously through a diffusion module (DM). Specifically, due to the diffusion probabilistic model (DPM) learning raw object data distribution to generate data of the same distribution, which has received wide attention in generation tasks and outperforms other generative models significantly, we attempt to apply the DPM in the field of feature extraction. DiffSC performs distribution information extraction of HSIs by the DM and fuses them with spatial-spectral features extracted by deep subspace clustering for training jointly. Experiment outcomes demonstrate that intermediate activation of specific timestep in the inverse diffusion process captures latent distribution information of images effectively and improves the HSI clustering accuracy significantly. Since the DPM is simplified, it can be easily trained from scratch. We evaluate the presented DiffSC on five real HSI datasets, and the experiments indicate that DiffSC can obtain the most advanced clustering outcomes that notably outperform most existing HSI subspace clustering approaches.
Sunday rehannah
Authors: Sunday Rehannah
Date: 2025-06-14 00:00:00
Proceeding: ICML 2025 6
Tasks: Marketing

Early Life

Sunday rehannah mary who is born on / 9/ 2010/ June in ilorin, My parent are Mr. and Mrs. Sunday who are native of benue , but they live in ilorin kwara state, Nigeria. I went to mountain of joy School in ilorin, when i was in pry 5 i graduated from primary school and still continue with another Secondary School, Which is bishop smith memorial college . When i was in JSS3 class, i was introduced to a great business know as Network Marketing ([Neolife][1]) as she joined the business she has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but she sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. She has been doing this for the 1 year.

Career

Sunday rehannah start his career in the years 2025 , Because she join a great and awesome business opportunity call [2 in 1 business opportunity] Neolife. And she is also an expert in online business that he specialize on Digital marketing, social media marketing, Seo expert, Shopify marketing, dropshipping, promotion and so on. And she is working on some freelancer platform like [Upwork][3], [fiverr][4], [kwork][5], [PeoplePerHour][6], [Latium] [7]and so on
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture
Authors: GigaChat team, Mamedov Valentin, Evgenii Kosarev, Gregory Leleytner, Ilya Shchuckin, Valeriy Berezovskiy, Daniil Smirnov, Dmitry Kozlov, Sergei Averkiev, Lukyanenko Ivan, Aleksandr Proshunin, Ainur Israfilova, Ivan Baskov, Artem Chervyakov, Emil Shakirov, Mikhail Kolesov, Daria Khomich, Darya Latortseva, Sergei Porkhun, Yury Fedorov, Oleg Kutuzov, Polina Kudriavtseva, Sofiia Soldatova, Kolodin Egor, Stanislav Pyatkin, Dzmitry Menshykh, Grafov Sergei, Eldar Damirov, Karlov Vladimir, Ruslan Gaitukiev, Arkadiy Shatenov, Alena Fenogenova, Nikita Savushkin, Fedor Minkin
Date: 2025-06-11 00:00:00
Tasks: Language Modeling, Language Modelling, Mixture-of-Experts

Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open-source (https://huggingface.co/ai-sage), aiming to expand NLP research opportunities and support the development of industrial solutions for the Russian language.
INNOCENT FAVOUR PRAISE
Authors: INNOCENT FAVOUR PRAISE
Date: 2025-06-14 00:00:00
Proceeding: 06/14 2025 6
Tasks: Marketing

SHORT BIOGRAPHY
My Name is INNOCENT FAVOUR PRAISE I I'M FROM IMO STATE I WAS BORN 11/10/2009, I was also Born In Ilorin Kwara State, Also Born In The Family Of Mr And Miss INNOCENT, I'M Introduced Into A Business Called Fhg 2in1 Business As A Freelancer When I'm In SS1

EARLY LIFE
I'M INNOCENT FAVOUR PRAISE By Name Born In The Family Of Mr And Miss INNOCENT I Attend My Nusery Pry School At Ecwa Ilorin Kwara State Government School I'm Finished My Pry School There And I also Attend High Point Academy And DO MY JSS1 To Jss2 There, And Also Attend Shaik Abdul Kodri College in SSS1 At That Cased I was Introduced To A Business Called Fhg 2IN1 Business Opportunity By Mr Godday Isreal Great Sponsor And Started During Glory Be To God Almighty

CAREER
INNOCENT FAVOUR PRAISE Started Is Career In The Year Of 2025 Because He Joined The Great Awesome Business Opportunity Called 2IN1 Business Opportunity NEO LIFE Specialized In Network Marketing And Online Business With Some Freelancing Platform E.g Fiverr, Kwork, Upwork, Lattium , And More
A Deep Graph Neural Network-Based Mechanism for Social Recommendations
Authors: Z Guo，H Wang
Date: 2020-04-10 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2020 4
Tasks: Graph Neural Network, Recommendation Systems

Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.
TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG Decoding
Authors: Wei Zhao, Haodong Lu, Baocan Zhang, Xinwang Zheng, Wenfeng Wang, Haifeng Zhou
Date: 2025-06-15 00:00:00
Proceeding: Cognitive Neurodynamics 2025 6
Tasks: Brain Computer Interface, EEG, Eeg Decoding, Motor Imagery

Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain–computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures
Authors: Alexandru Valentin Patrascu, Ciprian-Mihai Ceausescu, and Bogdan Alexe
Date: 2025-01-31 00:00:00
Proceeding: Structural, Syntactic, and Statistical Pattern Recognition 2025 1
Tasks: Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Semantic Segmentation

We address the problem of medical image segmentation in the context of limited training data. Our approach builds on the capabilities of the Vision Transformer (ViT) and the recent Segmenter model, adapting them for the task of medical image segmentation. By leveraging Segmenter models pre-trained on moderately-sized datasets like ADE20K, we demonstrate their effectiveness when fine-tuned on smaller and scarce medical imaging datasets, specifically those for skin lesions and polyps. Employing our proposed training strategy, the adapted Segmenter model both matches and surpasses the current state-of-the-art on three key medical image datasets: ISIC2018 for skin lesions, and CVCClinicDB and ETIS-LaribPolypDB for polyps, while maintaining competitive performance on Kvasir-SEG and CVC ColonDB.