pwc-archive/papers-with-abstracts huggingface dataset:
Samuel adeyemi
Authors: Samuel adeyemi
Date: 2013-06-12 00:00:00
Proceeding: june 12 2013 6
Tasks: Marketing

My Name is adeyemi samuel damilare is young guy in the town of eruda, kwara State Nigeria and name of he parents is Mr. & Mrs. adeyemi. He went to hope international Nursery and secondary school in stadium road, when he is in pry 1 he left hope international Nursery and secondary school and went to seat of wisdom international Nursery and primary school,when he is in primary 5 he left seat of wisdom international school and went to ecwa secondary school to do is common entrance there
when he was in secondary school, he was introduced to 2 in 1 business (network marketing and Online business),Since joining the organization, he has grown into a skilled freelancer who works on Fiverr, Upwork, kwork and other freelance platforms with a range of projects (such as Social Media marketer and Digital Marketing| AND MANY MORE), though he occasionally specializes in Digital Marketing services such as Google knowledge panel, Tiktok promotion, build killer Shopify store and Generate Unlimited Sales for shopify store| AND MANY MORE
Domain Knowledge Integrated CNN-xLSTM-xAtt Network with Multi Stream Feature Fusion for Cuffless Blood Pressure Estimation from Photoplethysmography Signals
Authors: Md Shoaib Akhter Rafi, Md Kamrul Hasan
Date: 2025-05-13 00:00:00
Proceeding: 08/15 2025 5
Tasks: Blood pressure estimation, Photoplethysmography (PPG)

Estimating blood pressure (BP) from photoplethysmography (PPG) signals is challenging due to signal variability and noise, as well as the complex relationship between PPG and BP, which requires sophisticated algorithms and personalization to achieve high accuracy. This paper introduces a novel deep learning (DL)-based framework, which includes a CNN-xLSTM-xAtt-based deep neural network for extracting multi-domain features, and a multi-stream feature fusion network (MSFN) with a signal enhancer for BP estimation from PPG signals. The performance of this framework is further enhanced by incorporating domain knowledge into the DL network via an ML-based loss function. The data preprocessing step incorporates a novel signal selector that accepts signals with peak height and distance variance within a specified range. To ensure robust feature learning from the peaks, a convolution-based peak enhancement (CPE) method has been employed in this work. For feature extraction, the refined signals are processed using two parallel networks, enabling complementary feature learning from both the uni-dimensional signal and image domains. The uni-dimensional path of the proposed framework, containing two parallel networks–ConvLSTM and xLSTM–facilitate both short- and long-term temporal dependencies. Concurrently, the image-based network extracts morphological and spectral features from three different image modalities, utilizing the benefits of pretrained networks. To ensure the framework focuses on the most important source in this multi-stream network, a novel multi-stream spatial- and cross-attention network (M-SCAN) is proposed. Finally, combining both type of features by a CNN-xLSTM-based multi stream fusion network (MSFN) provides an estimation of BP. During the training of the DL network, a domain knowledge-integrated dynamic quantitative embedding supervision-based tuning (D-QuEST) is also proposed as a supervision loss for the model. The performance of the proposed framework has been tested on two publicly available datasets suggesting 0.05% MSE, 1.40% MAE, and 99.36% PCC on the small and 0.06% MSE, 1.58% MAE, and 99.09% PCC on the large dataset. The proposed framework outperforms existing state-of-the-art methods by a significant margin, even with high subject diversity. This demonstrates its potential for accurate BP estimation solely from PPG signals with diverse variations.
Colors Matter: AI-Driven Exploration of Human Feature Colors
Authors: Rama Alyoubi, Taif Alharbi, Albatul Alghamdi, Yara Alshehri, Elham Alghamdi
Date: 2025-05-20 00:00:00
Tasks: Classification, Face Detection

This study presents a robust framework that leverages advanced imaging techniques and machine learning for feature extraction and classification of key human attributes-namely skin tone, hair color, iris color, and vein-based undertones. The system employs a multi-stage pipeline involving face detection, region segmentation, and dominant color extraction to isolate and analyze these features. Techniques such as X-means clustering, alongside perceptually uniform distance metrics like Delta E (CIEDE2000), are applied within both LAB and HSV color spaces to enhance the accuracy of color differentiation. For classification, the dominant tones of the skin, hair, and iris are extracted and matched to a custom tone scale, while vein analysis from wrist images enables undertone classification into "Warm" or "Cool" based on LAB differences. Each module uses targeted segmentation and color space transformations to ensure perceptual precision. The system achieves up to 80% accuracy in tone classification using the Delta E-HSV method with Gaussian blur, demonstrating reliable performance across varied lighting and image conditions. This work highlights the potential of AI-powered color analysis and feature extraction for delivering inclusive, precise, and nuanced classification, supporting applications in beauty technology, digital personalization, and visual analytics.
Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications
Authors: Dikshit Chauhan, Bapi Dutta, Indu Bala, Niki van Stein, Thomas Bäck, Anupam Yadav
Date: 2025-05-21 00:00:00
Tasks: Evolutionary Algorithms, Natural Language Understanding, Prompt Engineering, Survey

Integrating Large Language Models (LLMs) and Evolutionary Computation (EC) represents a promising avenue for advancing artificial intelligence by combining powerful natural language understanding with optimization and search capabilities. This manuscript explores the synergistic potential of LLMs and EC, reviewing their intersections, complementary strengths, and emerging applications. We identify key opportunities where EC can enhance LLM training, fine-tuning, prompt engineering, and architecture search, while LLMs can, in turn, aid in automating the design, analysis, and interpretation of ECs. The manuscript explores the synergistic integration of EC and LLMs, highlighting their bidirectional contributions to advancing artificial intelligence. It first examines how EC techniques enhance LLMs by optimizing key components such as prompt engineering, hyperparameter tuning, and architecture search, demonstrating how evolutionary methods automate and refine these processes. Secondly, the survey investigates how LLMs improve EC by automating metaheuristic design, tuning evolutionary algorithms, and generating adaptive heuristics, thereby increasing efficiency and scalability. Emerging co-evolutionary frameworks are discussed, showcasing applications across diverse fields while acknowledging challenges like computational costs, interpretability, and algorithmic convergence. The survey concludes by identifying open research questions and advocating for hybrid approaches that combine the strengths of EC and LLMs.
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
Authors: Hongli Zhou, Hui Huang, Ziqing Zhao, Lvyuan Han, Huicheng Wang, Kehai Chen, Muyun Yang, Wei Bao, Jian Dong, Bing Xu, Conghui Zhu, Hailong Cao, Tiejun Zhao
Date: 2025-05-21 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Large Language Model

The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concerns about their ability to accurately reflect authentic model capabilities. This paper provides a critical analysis of benchmark effectiveness, examining main-stream prominent LLM benchmarks using results from diverse models. We first propose a new framework for accurate and reliable estimations of item characteristics and model abilities. Specifically, we propose Pseudo-Siamese Network for Item Response Theory (PSN-IRT), an enhanced Item Response Theory framework that incorporates a rich set of item parameters within an IRT-grounded architecture. Based on PSN-IRT, we conduct extensive analysis which reveals significant and varied shortcomings in the measurement quality of current benchmarks. Furthermore, we demonstrate that leveraging PSN-IRT is able to construct smaller benchmarks while maintaining stronger alignment with human preference.
Can LLMs $\textit{understand}$ Math? -- Exploring the Pitfalls in Mathematical Reasoning
Authors: Tiasa Singha Roy, Aditeya Baral, Ayush Rajesh Jhaveri, Yusuf Baig
Date: 2025-05-21 00:00:00
Tasks: Math, Mathematical Reasoning

Large language models (LLMs) demonstrate considerable potential in various natural language tasks but face significant challenges in mathematical reasoning, particularly in executing precise, multi-step logic. However, current evaluation frameworks judge their performance solely based on accuracy, which only accounts for the final answer. This study explores these pitfalls by employing a novel evaluation framework. We propose an evaluation metric called the MAPLE score, which holistically quantifies reasoning misalignment by integrating error rates, redundancy, and validity.
ThinkRec: Thinking-based recommendation via LLM
Authors: Qihang Yu, Kairui Fu, Shengyu Zhang, Zheqi Lv, Fan Wu, Fei Wu
Date: 2025-05-21 00:00:00
Tasks: Text Generation

Recent advances in large language models (LLMs) have enabled more semantic-aware recommendations through natural language generation. Existing LLM for recommendation (LLM4Rec) methods mostly operate in a System 1-like manner, relying on superficial features to match similar items based on click history, rather than reasoning through deeper behavioral logic. This often leads to superficial and erroneous recommendations. Motivated by this, we propose ThinkRec, a thinking-based framework that shifts LLM4Rec from System 1 to System 2 (rational system). Technically, ThinkRec introduces a thinking activation mechanism that augments item metadata with keyword summarization and injects synthetic reasoning traces, guiding the model to form interpretable reasoning chains that consist of analyzing interaction histories, identifying user preferences, and making decisions based on target items. On top of this, we propose an instance-wise expert fusion mechanism to reduce the reasoning difficulty. By dynamically assigning weights to expert models based on users' latent features, ThinkRec adapts its reasoning path to individual users, thereby enhancing precision and personalization. Extensive experiments on real-world datasets demonstrate that ThinkRec significantly improves the accuracy and interpretability of recommendations. Our implementations are available in anonymous Github: https://anonymous.4open.science/r/ThinkRec_LLM.
Guided Policy Optimization under Partial Observability
Authors: Yueheng Li, Guangming Xie, Zongqing Lu
Date: 2025-05-21 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem. To address this, we introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner. The guider takes advantage of privileged information while ensuring alignment with the learner's policy that is primarily trained via imitation learning. We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches. Empirical evaluations show strong performance of GPO across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents
Authors: Yuqi Zhou, Sunhao Dai, Shuai Wang, Kaiwen Zhou, Qinqlin Jia, Junxu
Date: 2025-05-21 00:00:00
Tasks: Answer Generation, Reinforcement Learning (RL), Visual Grounding

Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object grounding and thereby achieving substantial performance gains. In this paper, we first conduct extensive analysis experiments of three key components of that training pipeline: input design, output evaluation, and policy update-each revealing distinct challenges arising from blindly applying general-purpose RL without adapting to GUI grounding tasks. Input design: Current templates encourage the model to generate chain-of-thought reasoning, but longer chains unexpectedly lead to worse grounding performance. Output evaluation: Reward functions based on hit signals or box area allow models to exploit box size, leading to reward hacking and poor localization quality. Policy update: Online RL tends to overfit easy examples due to biases in length and sample difficulty, leading to under-optimization on harder cases. To address these issues, we propose three targeted solutions. First, we adopt a Fast Thinking Template that encourages direct answer generation, reducing excessive reasoning during training. Second, we incorporate a box size constraint into the reward function to mitigate reward hacking. Third, we revise the RL objective by adjusting length normalization and adding a difficulty-aware scaling factor, enabling better optimization on hard samples. Our GUI-G1-3B, trained on 17K public samples with Qwen2.5-VL-3B-Instruct, achieves 90.3% accuracy on ScreenSpot and 37.1% on ScreenSpot-Pro. This surpasses all prior models of similar size and even outperforms the larger UI-TARS-7B, establishing a new state-of-the-art in GUI agent grounding. The project repository is available at https://github.com/Yuqi-Zhou/GUI-G1.
Neural Conditional Transport Maps
Authors: Carlos Rodriguez-Pardo, Leonardo Chiani, Emanuele Borgonovo, Massimo Tavoni
Date: 2025-05-21 00:00:00
Tasks: Sensitivity

We present a neural framework for learning conditional optimal transport (OT) maps between probability distributions. Our approach introduces a conditioning mechanism capable of processing both categorical and continuous conditioning variables simultaneously. At the core of our method lies a hypernetwork that generates transport layer parameters based on these inputs, creating adaptive mappings that outperform simpler conditioning methods. Comprehensive ablation studies demonstrate the superior performance of our method over baseline configurations. Furthermore, we showcase an application to global sensitivity analysis, offering high performance in computing OT-based sensitivity indices. This work advances the state-of-the-art in conditional optimal transport, enabling broader application of optimal transport principles to complex, high-dimensional domains such as generative modeling and black-box model explainability.
Lifelong Knowledge Editing requires Better Regularization
Authors: Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu, Ahmed Alaa, Thomas Hartvigsen, Gopala Anumanchipalli
Date: 2025-02-03 00:00:00
Tasks: knowledge editing

Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this paper, we formalize the popular locate-then-edit methods as a two-step fine-tuning process, allowing us to precisely identify the root cause of this degradation. We show that model degradation occurs due to (1) over-optimization of internal activations and (2) continuous norm-growth of edited matrices. To mitigate these issues, we introduce two regularization techniques: (1) Most-Probable Early Stopping (MPES) and (2) explicit Frobenius norm-constraint. We demonstrate that applying these simple yet effective regularization techniques at key points in the editing process can substantially mitigate model degradation. Combining these regularization methods enables scaling locate-then-edit methods to 10,000 edits while reducing editing time by 42-61%. These results show that targeted regularization is essential for lifelong knowledge editing.
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models
Authors: Yuchen Yan, Jin Jiang, Zhenbang Ren, Yijun Li, Xudong Cai, Yang Liu, Xin Xu, Mengdi Zhang, Jian Shao, Yongliang Shen, Jun Xiao, Yueting Zhuang
Date: 2025-05-21 00:00:00
Tasks: Benchmarking, Reinforcement Learning (RL)

Large reasoning models such as OpenAI o1 and DeepSeek-R1 have achieved remarkable performance in the domain of reasoning. A key component of their training is the incorporation of verifiable rewards within reinforcement learning (RL). However, existing reward benchmarks do not evaluate reference-based reward systems, leaving researchers with limited understanding of the accuracy of verifiers used in RL. In this paper, we introduce two benchmarks, VerifyBench and VerifyBench-Hard, designed to assess the performance of reference-based reward systems. These benchmarks are constructed through meticulous data collection and curation, followed by careful human annotation to ensure high quality. Current models still show considerable room for improvement on both VerifyBench and VerifyBench-Hard, especially smaller-scale models. Furthermore, we conduct a thorough and comprehensive analysis of evaluation results, offering insights for understanding and developing reference-based reward systems. Our proposed benchmarks serve as effective tools for guiding the development of verifier accuracy and the reasoning capabilities of models trained via RL in reasoning tasks.
Long-Form Information Alignment Evaluation Beyond Atomic Facts
Authors: Danna Zheng, Mirella Lapata, Jeff Z. Pan
Date: 2025-05-21 00:00:00
Tasks: Form, nlg evaluation

Information alignment evaluators are vital for various NLG evaluation tasks and trustworthy LLM deployment, reducing hallucinations and enhancing user trust. Current fine-grained methods, like FactScore, verify facts individually but neglect inter-fact dependencies, enabling subtle vulnerabilities. In this work, we introduce MontageLie, a challenging benchmark that constructs deceptive narratives by "montaging" truthful statements without introducing explicit hallucinations. We demonstrate that both coarse-grained LLM-based evaluators and current fine-grained frameworks are susceptible to this attack, with AUC-ROC scores falling below 65%. To enable more robust fine-grained evaluation, we propose DoveScore, a novel framework that jointly verifies factual accuracy and event-order consistency. By modeling inter-fact relationships, DoveScore outperforms existing fine-grained methods by over 8%, providing a more robust solution for long-form text alignment evaluation. Our code and datasets are available at https://github.com/dannalily/DoveScore.
Exploring the Innovation Opportunities for Pre-trained Models
Authors: Minjung Park, Jodi Forlizzi, John Zimmerman
Date: 2025-05-21 00:00:00

Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.
Large Language Models as Computable Approximations to Solomonoff Induction
Authors: Jun Wan, Lingrui Mei
Date: 2025-05-21 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, text-classification, Text Classification

The rapid advancement of large language models (LLMs) calls for a rigorous theoretical framework to explain their empirical success. While significant progress has been made in understanding LLM behaviors, existing theoretical frameworks remain fragmented in explaining emergent phenomena through a unified mathematical lens. We establish the first formal connection between LLM architectures and Algorithmic Information Theory (AIT) by proving two fundamental results: (1) the training process computationally approximates Solomonoff prior through loss minimization interpreted as program length optimization, and (2) next-token prediction implements approximate Solomonoff induction. We leverage AIT to provide a unified theoretical explanation for in-context learning, few-shot learning, and scaling laws. Furthermore, our theoretical insights lead to a principled method for few-shot example selection that prioritizes samples where models exhibit lower predictive confidence. We demonstrate through experiments on diverse text classification benchmarks that this strategy yields significant performance improvements, particularly for smaller model architectures, when compared to selecting high-confidence examples. Our framework bridges the gap between theoretical foundations and practical LLM behaviors, providing both explanatory power and actionable insights for future model development.
IA-T2I: Internet-Augmented Text-to-Image Generation
Authors: Chuanhao Li, Jianwen Sun, Yukang Feng, Mingliang Zhai, Yifan Chang, Kaipeng Zhang
Date: 2025-05-21 00:00:00
Tasks: Image Generation, Image Retrieval, Text to Image Generation, Text-to-Image Generation

Current text-to-image (T2I) generation models achieve promising results, but they fail on the scenarios where the knowledge implied in the text prompt is uncertain. For example, a T2I model released in February would struggle to generate a suitable poster for a movie premiering in April, because the character designs and styles are uncertain to the model. To solve this problem, we propose an Internet-Augmented text-to-image generation (IA-T2I) framework to compel T2I models clear about such uncertain knowledge by providing them with reference images. Specifically, an active retrieval module is designed to determine whether a reference image is needed based on the given text prompt; a hierarchical image selection module is introduced to find the most suitable image returned by an image search engine to enhance the T2I model; a self-reflection mechanism is presented to continuously evaluate and refine the generated image to ensure faithful alignment with the text prompt. To evaluate the proposed framework's performance, we collect a dataset named Img-Ref-T2I, where text prompts include three types of uncertain knowledge: (1) known but rare. (2) unknown. (3) ambiguous. Moreover, we carefully craft a complex prompt to guide GPT-4o in making preference evaluation, which has been shown to have an evaluation accuracy similar to that of human preference evaluation. Experimental results demonstrate the effectiveness of our framework, outperforming GPT-4o by about 30% in human evaluation.