pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
Authors: Nikolas Evkarpidi, Elena Tutubalina
Date: 2025-06-11 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Large Language Model, Question Answering, RAG, Retrieval-augmented Generation, Text-to-Code Generation, Text to SQL, Text-To-SQL

This paper presents a system developed for SemEval 2025 Task 8: Question Answering (QA) over tabular data. Our approach integrates several key components: text-to-SQL and text-to-code generation modules, a self-correction mechanism, and a retrieval-augmented generation (RAG). Additionally, it includes an end-to-end (E2E) module, all orchestrated by a large language model (LLM). Through ablation studies, we analyzed the effects of different parts of our pipeline and identified the challenges that are still present in this field. During the evaluation phase of the competition, our solution achieved an accuracy of 80%, resulting in a top-13 ranking among the 38 participating teams. Our pipeline demonstrates a significant improvement in accuracy for open-source models and achieves a performance comparable to proprietary LLMs in QA tasks over tables. The code is available at GitHub repository.
CoRT: Code-integrated Reasoning within Thinking
Authors: Chengpeng Li, Zhengyang Tang, Ziniu Li, Mingfeng Xue, Keqin Bao, Tian Ding, Ruoyu Sun, Benyou Wang, Xiang Wang, Junyang Lin, Dayiheng Liu
Date: 2025-06-11 00:00:00
Tasks: Mathematical Reasoning

Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or inaccurate when handling complex mathematical operations. Addressing these limitations through computational tools (e.g., computation libraries and symbolic solvers) is promising, but it introduces a technical challenge: Code Interpreter (CI) brings external knowledge beyond the model's internal text representations, thus the direct combination is not efficient. This paper introduces CoRT, a post-training framework for teaching LRMs to leverage CI effectively and efficiently. As a first step, we address the data scarcity issue by synthesizing code-integrated reasoning data through Hint-Engineering, which strategically inserts different hints at appropriate positions to optimize LRM-CI interaction. We manually create 30 high-quality samples, upon which we post-train models ranging from 1.5B to 32B parameters, with supervised fine-tuning, rejection fine-tuning and reinforcement learning. Our experimental results demonstrate that Hint-Engineering models achieve 4\% and 8\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging mathematical reasoning datasets. Furthermore, Hint-Engineering models use about 30\% fewer tokens for the 32B model and 50\% fewer tokens for the 1.5B model compared with the natural language models. The models and code are available at https://github.com/ChengpengLi1003/CoRT.
Kelvin kalu
Authors: Kelvin kalu
Date: 2025-06-12 00:00:00
Proceeding: AI-driven adaptive learning platforms hold great promise for transforming education. This study highlights their potential in increasing student performance and engagement. Future work will focus on broader deployments, real-time feedback mechanisms, and ethical AI design in education. 2025 6
Tasks: Marketing

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Early Life
my name is ifeanyi kalu kelvin I was bourn in a family of MR and MRS kalu gave birth to me on (28 February 2009) in a city of Ilorin kwara state Nigeria i went to brighter and unique kiddies model school nursery school i graduated at Basic 5 and went to GDJSS ODO OKUN and after my junior waec

Career
I was introduce to 2 in 1 business (network marketing and online business ) know as FHG this is this is an organization he has grown into a skilled freelancer who works on Fiverr, up work, kwork and other freelance platforms with a range of projects like (social medial marketing and digital marketing specialized in social medial promotion, creation of google knowledge panel, game development and lots more
Kolawole Emmanuel Victor
Authors: Kolawole Emmmanuel Victor
Date: 2025-06-12 00:00:00
Proceeding: 12/06 2025 6
Tasks: Marketing

Early Life
I am Kolawole Emmanuel Victor, i was born on September 14 2010, son of Mr. and Mrs. Kolawole. He is a young boy from Ilorin Kwara state. He began his education life at one of the prominent schools in Ilorin named Ajoke primary school He left Ajoke at primary two and attended Seat of Wisdom nursery and primary school, he graduated from primary to secondary school in the year 2020. He got admission to Model Secondary School.

Career
Kolawole Emmanuel Victor started his career life in 2025. He gain an opportunity into a great business and also a 2in1 business [Neolife].He became a freelancer and a network marketer, he is now a freelancer on Fiverr, Kwork and Upwork, and other freelancing platforms with many completed projects ( Social Media marketer, Programming projects, Digital Marketing ) but sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, You tube Automation and Telegram promotion.
Fake news detection: Taxonomy and comparative study
Authors: Faramarz Farhangian, Rafael M.O. Cruz, George D.C. Cavalcanti
Date: 2024-03-01 00:00:00
Proceeding: Information Fusion 2024 3
Tasks: Fake News Detection

The proliferation of social networks has presented a significant challenge in combating the pervasive issue of fake news within modern societies. Due to the large amount of information and news produced daily in text, audio, and video, the validation and verification of this information have become crucial tasks. Leveraging advancements in artificial intelligence, distinguishing between fake news and factual information through automatic fake news detection systems has become more feasible. Automatic fake news detection has been explored from diverse perspectives, employing various feature extraction and classification models. Nonetheless, empirical evaluations, categorization, and comparisons of existing techniques for handling this problem remain limited. In this paper, we revisit the definitions and perspectives of fake news and propose an updated taxonomy for the field based on multiple criteria: (1) Type of features used in fake news detection; (2) Fake news detection perspectives; (3) Feature representation methods; and (4) Classification approaches. Moreover, we conduct an extensive empirical study to evaluate several feature representation techniques and classification approaches based on accuracy and computational cost. Our experimental results demonstrate that the optimal feature extraction techniques vary depending on the characteristics of the dataset. Notably, context-dependent models based on transformer models consistently exhibit superior performance. Additionally, employing transformer models as feature extraction methods, rather than solely fine-tuning the network for the downstream task, improves overall performance. Through extensive error analysis, we identify that a combination of feature representation methods and classification algorithms, including classical ones, offer complementary aspects and should be considered for achieving better generalization performance while maintaining a relatively low computational cost.
LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Authors: Yilin Zhuang, Karthik Duraisamy
Date: 2025-06-10 00:00:00
Tasks: Uncertainty Quantification, Weather Forecasting

Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representation, and a transformer-based diffusion model produces sequential latent updates with arbitrary hour initialization. The model incorporates Geometric Rotary Position Embedding (GeoRoPE) to account for the Earth's spherical geometry, a dual-stream attention mechanism for efficient conditioning, and sinusoidal temporal embeddings to capture seasonal patterns. LaDCast achieves deterministic and probabilistic skill close to that of the European Centre for Medium-Range Forecast IFS-ENS, without any explicit perturbations. Notably, LaDCast demonstrates superior performance in tracking rare extreme events such as cyclones, capturing their trajectories more accurately than established models. By operating in latent space, LaDCast reduces storage and compute by orders of magnitude, demonstrating a practical path toward forecasting at kilometer-scale resolution in real time. We open-source our code and models and provide the training and evaluation pipelines at: https://github.com/tonyzyl/ladcast.
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Authors: Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, Mehrdad Farajtabar
Date: 2025-06-07 00:00:00
Tasks: Math

Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established math and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from contamination and does not provide insights into the reasoning traces. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs think. Through extensive experiments, we show that LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having remaining token budget. By comparing LRMs with their standard LLM counterparts under same inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models outperform LRMs, (2) medium-complexity tasks where LRMs demonstrates advantage, and (3) high-complexity tasks where both models face complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across scales. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models' computational behavior, shedding light on their strengths, limitations, and raising questions about their reasoning capabilities.
Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation
Authors: Maher Marwani, Georges Kaddoum
Date: 2024-06-03 00:00:00
Proceeding: IEEE Transactions on Machine Learning in Communications and Networking 2024 6
Tasks: Graph Neural Network, Management

The proliferation of wireless technologies and the escalating performance requirements of wireless applications have led to diverse and dynamic wireless environments, presenting formidable challenges to existing radio resource management (RRM) frameworks. Researchers have proposed utilizing deep learning (DL) models to address these challenges to learn patterns from wireless data and leverage the extracted information to resolve multiple RRM tasks, such as channel allocation and power control. However, it is noteworthy that the majority of existing DL architectures are designed to operate on Euclidean data, thereby disregarding a substantial amount of information about the topological structure of wireless networks. As a result, the performance of DL models may be suboptimal when applied to wireless environments due to the failure to capture the network’s non-Euclidean geometry. This study presents a novel approach to address the challenge of power control and spectrum allocation in an N-link interference environment with shared channels, utilizing a graph neural network (GNN) based framework. In this type of wireless environment, the available bandwidth can be divided into blocks, offering greater flexibility in allocating bandwidth to communication links, but also requiring effective management of interference. One potential solution to mitigate the impact of interference is to control the transmission power of each link while ensuring the network’s data rate performance. Therefore, the power control and spectrum allocation problems are inherently coupled and should be solved jointly. The proposed GNN-based framework presents a promising avenue for tackling this complex challenge. Our experimental results demonstrate that our proposed approach yields significant improvements compared to other existing methods in terms of convergence, generalization, performance, and robustness, particularly in the context of an imperfect channel.
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Authors: Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Chunyu Miao, Dongyuan Li, Aiwei Liu, Yue Zhou, Yankai Chen, Weizhi Zhang, Yangning Li, Liancheng Fang, Renhe Jiang, Philip S. Yu
Date: 2025-06-11 00:00:00

Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership.
DeGLIF for Label Noise Robust Node Classification using GNNs
Authors: Pintu Kumar, Nandyala Hemachandra
Date: 2025-05-30 00:00:00
Tasks: Denoising, Node Classification

Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out Influence Function. DeGLIF uses a small set of clean data and the leave-one-out influence function to make label noise robust node-level prediction on graph data. Leave-one-out influence function approximates the change in the model parameters if a training point is removed from the training dataset. Recent advances propose a way to calculate the leave-one-out influence function for Graph Neural Networks (GNNs). We extend that recent work to estimate the change in validation loss, if a training node is removed from the training dataset. We use this estimate and a new theoretically motivated relabelling function to denoise the training dataset. We propose two DeGLIF variants to identify noisy nodes. Both these variants do not require any information about the noise model or the noise level in the dataset; DeGLIF also does not estimate these quantities. For one of these variants, we prove that the noisy points detected can indeed increase risk. We carry out detailed computational experiments on different datasets to show the effectiveness of DeGLIF. It achieves better accuracy than other baseline algorithms
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Authors: Jiajin Liu, Dongzhe Fan, Jiacheng Shen, Chuanhao Ji, Daochen Zha, Qiaoyu Tan
Date: 2025-06-12 00:00:00
Tasks: Graph Learning, In-Context Learning, Recommendation Systems

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality alignment in a pairwise manner while overlooking structural relationships across data points. Integrating multimodality with structured graph information (i.e., multimodal graphs, MMGs) is essential for real-world applications such as social networks, healthcare, and recommendation systems. Existing MMG learning methods fall into three paradigms based on how they leverage MLLMs: Encoder, Aligner, and Predictor. MLLM-as-Encoder focuses on enhancing graph neural networks (GNNs) via multimodal feature fusion; MLLM-as-Aligner aligns multimodal attributes in language or hidden space to enable LLM-based graph reasoning; MLLM-as-Predictor treats MLLMs as standalone reasoners with in-context learning or fine-tuning. Despite their advances, the MMG field lacks a unified benchmark to fairly evaluate across these approaches, making it unclear what progress has been made. To bridge this gap, we present Graph-MLLM, a comprehensive benchmark for multimodal graph learning by systematically evaluating these three paradigms across six datasets with different domains. Through extensive experiments, we observe that jointly considering the visual and textual attributes of the nodes benefits graph learning, even when using pre-trained text-to-image alignment models (e.g., CLIP) as encoders. We also find that converting visual attributes into textual descriptions further improves performance compared to directly using visual inputs. Moreover, we observe that fine-tuning MLLMs on specific MMGs can achieve state-of-the-art results in most scenarios, even without explicit graph structure information. We hope that our open-sourced library will facilitate rapid, equitable evaluation and inspire further innovative research in this field.
When Large Language Models are Reliable for Judging Empathic Communication
Authors: Aakriti Kumar, Nalin Poungpeth, Diyi Yang, Erina Farrell, Bruce Lambert, Matthew Groh
Date: 2025-06-11 00:00:00

Large language models (LLMs) excel at generating empathic responses in text-based conversations. But, how reliably do they judge the nuances of empathic communication? We investigate this question by comparing how experts, crowdworkers, and LLMs annotate empathic communication across four evaluative frameworks drawn from psychology, natural language processing, and communications applied to 200 real-world conversations where one speaker shares a personal problem and the other offers support. Drawing on 3,150 expert annotations, 2,844 crowd annotations, and 3,150 LLM annotations, we assess inter-rater reliability between these three annotator groups. We find that expert agreement is high but varies across the frameworks' sub-components depending on their clarity, complexity, and subjectivity. We show that expert agreement offers a more informative benchmark for contextualizing LLM performance than standard classification metrics. Across all four frameworks, LLMs consistently approach this expert level benchmark and exceed the reliability of crowdworkers. These results demonstrate how LLMs, when validated on specific tasks with appropriate benchmarks, can support transparency and oversight in emotionally sensitive applications including their use as conversational companions.
SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Authors: Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
Date: 2025-06-03 00:00:00

This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping for objectives like energy, throughput, area, and accuracy becomes challenging. This paper introduces SENMap, flexible mapping software for efficiently mapping large SNN and ANN applications onto adaptable architectures. SENMap considers architectural, pretrained SNN and ANN realistic examples, and event rate-based parameters and is open-sourced along with SENSIM to aid flexible neuromorphic chip design before fabrication. Experimental results show SENMap enables 40 percent energy improvements for a baseline SENSIM operating in timestep asynchronous mode of operation. SENMap is designed in such a way that it facilitates mapping large spiking neural networks for future modifications as well.
The Iris File Extension
Authors: Ryan Erik Landvater, Michael David Olp, Mustafa Yousif, Ulysses Balis
Date: 2025-04-21 00:00:00
Tasks: whole slide images

A modern digital pathology vendor-agnostic binary slide format specifically targeting the unmet need of efficient real-time transfer and display has not yet been established. The growing adoption of digital pathology only intensifies the need for an intermediary digital slide format that emphasizes performance for use between slide servers and image management software. The DICOM standard is a well-established format widely used for the long-term storage of both images and associated critical metadata. However, it was inherently designed for radiology rather than digital pathology, a discipline that imposes a unique set of performance requirements due to high-speed multi-pyramidal rendering within whole slide viewer applications. Here we introduce the Iris file extension, a binary container specification explicitly designed for performance-oriented whole slide image viewer systems. The Iris file extension specification is explicit and straightforward, adding modern compression support, a dynamic structure with fully optional metadata features, computationally trivial deep file validation, corruption recovery capabilities, and slide annotations. In addition to the file specification document, we provide source code to allow for (de)serialization and validation of a binary stream against the standard. We also provide corresponding binary builds with C++, Python, and JavaScript language support. Finally, we provide full encoder and decoder implementation source code, as well as binary builds (part of the separate Iris Codec Community module), with language bindings for C++ and Python, allowing for easy integration with existing WSI solutions. We provide the Iris File Extension specification openly to the community in the form of a Creative Commons Attribution-No Derivative 4.0 International license.