pwc-archive/papers-with-abstracts huggingface dataset:
SQUiD: Synthesizing Relational Databases from Unstructured Text
Authors: Mushtari Sadia, Zhenning Yang, Yunming Xiao, Ang Chen, Amrita Roy Chowdhury
Date: 2025-05-25 00:00:00
Tasks: Management

Relational databases are central to modern data management, yet most data exists in unstructured forms like text documents. To bridge this gap, we leverage large language models (LLMs) to automatically synthesize a relational database by generating its schema and populating its tables from raw text. We introduce SQUiD, a novel neurosymbolic framework that decomposes this task into four stages, each with specialized techniques. Our experiments show that SQUiD consistently outperforms baselines across diverse datasets.
SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases
Authors: AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh, Mona Shahamat Naderi, Heshaam Faili
Date: 2025-05-23 00:00:00
Tasks: Text to SQL, Text-To-SQL

Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a critical component in Text-to-SQL systems, reducing prompt size for models with narrow context windows and sharpening model focus even when the entire schema fits. We present a zero-shot, training-free schema linking approach that first constructs a schema graph based on foreign key relations, then uses a single prompt to Gemini 2.5 Flash to extract source and destination tables from the user query, followed by applying classical path-finding algorithms and post-processing to identify the optimal sequence of tables and columns that should be joined, enabling the LLM to generate more accurate SQL queries. Despite being simple, cost-effective, and highly scalable, our method achieves state-of-the-art results on the BIRD benchmark, outperforming previous specialized, fine-tuned, and complex multi-step LLM-based approaches. We conduct detailed ablation studies to examine the precision-recall trade-off in our framework. Additionally, we evaluate the execution accuracy of our schema filtering method compared to other approaches across various model sizes.
Improved Algorithms for Overlapping and Robust Clustering of Edge-Colored Hypergraphs: An LP-Based Combinatorial Approach
Authors: Changyeol Lee, Yongho Shin, Hyung-Chan An
Date: 2025-05-23 00:00:00
Tasks: Clustering, Computational Efficiency

Clustering is a fundamental task in both machine learning and data mining. Among various methods, edge-colored clustering (ECC) has emerged as a useful approach for handling categorical data. Given a hypergraph with (hyper)edges labeled by colors, ECC aims to assign vertex colors to minimize the number of edges where the vertex color differs from the edge's color. However, traditional ECC has inherent limitations, as it enforces a nonoverlapping and exhaustive clustering. To tackle these limitations, three versions of ECC have been studied: Local ECC and Global ECC, which allow overlapping clusters, and Robust ECC, which accounts for vertex outliers. For these problems, both linear programming (LP) rounding algorithms and greedy combinatorial algorithms have been proposed. While these LP-rounding algorithms provide high-quality solutions, they demand substantial computation time; the greedy algorithms, on the other hand, run very fast but often compromise solution quality. In this paper, we present an algorithmic framework that combines the strengths of LP with the computational efficiency of combinatorial algorithms. Both experimental and theoretical analyses show that our algorithms efficiently produce high-quality solutions for all three problems: Local, Global, and Robust ECC. We complement our algorithmic contributions with complexity-theoretic inapproximability results and integrality gap bounds, which suggest that significant theoretical improvements are unlikely. Our results also answer two open questions previously raised in the literature.
Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data
Authors: Marco Brandizi, Carlos Bobed, Luca Garulli, Arné de Klerk, Keywan Hassani-Pak
Date: 2025-05-23 00:00:00
Tasks: Knowledge Graphs, Management

Linked Data and labelled property graphs (LPG) are two data management approaches with complementary strengths and weaknesses, making their integration beneficial for sharing datasets and supporting software ecosystems. In this paper, we introduce rdf2pg, an extensible framework for mapping RDF data to semantically equivalent LPG formats and data-bases. Utilising this framework, we perform a comparative analysis of three popular graph databases - Virtuoso, Neo4j, and ArcadeDB - and the well-known graph query languages SPARQL, Cypher, and Gremlin. Our qualitative and quantitative as-sessments underline the strengths and limitations of these graph database technologies. Additionally, we highlight the potential of rdf2pg as a versatile tool for enabling polyglot access to knowledge graphs, aligning with established standards of Linked Data and the Semantic Web.
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects
Authors: Jipeng Zhang, Haolin Yang, Kehao Miao, Ruiyuan Zhang, Renjie Pi, Jiahui Gao, Xiaofang Zhou
Date: 2025-05-22 00:00:00
Tasks: Text to SQL, Text-To-SQL

Recent text-to-SQL models have achieved strong performance, but their effectiveness remains largely confined to SQLite due to dataset limitations. However, real-world applications require SQL generation across multiple dialects with varying syntax and specialized features, which remains a challenge for current models. The main obstacle in building a dialect-aware model lies in acquiring high-quality dialect-specific data. Data generated purely through static prompting - without validating SQLs via execution - tends to be noisy and unreliable. Moreover, the lack of real execution environments in the training loop prevents models from grounding their predictions in executable semantics, limiting generalization despite surface-level improvements from data filtering. This work introduces ExeSQL, a text-to-SQL framework with execution-driven, agentic bootstrapping. The method consists of iterative query generation, execution-based filtering (e.g., rejection sampling), and preference-based training, enabling the model to adapt to new SQL dialects through verifiable, feedback-guided learning. Experiments show that ExeSQL bridges the dialect gap in text-to-SQL, achieving average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQL, MySQL, and Oracle, respectively, across multiple datasets of varying difficulty.
NEXT-EVAL: Next Evaluation of Traditional and LLM Web Data Record Extraction
Authors: Soyeon Kim, Namhee Kim, Yeonwoo Jeong
Date: 2025-05-21 00:00:00
Tasks: Benchmarking, Hallucination, Large Language Model

Effective evaluation of web data record extraction methods is crucial, yet hampered by static, domain-specific benchmarks and opaque scoring practices. This makes fair comparison between traditional algorithmic techniques, which rely on structural heuristics, and Large Language Model (LLM)-based approaches, offering zero-shot extraction across diverse layouts, particularly challenging. To overcome these limitations, we introduce a concrete evaluation framework. Our framework systematically generates evaluation datasets from arbitrary MHTML snapshots, annotates XPath-based supervision labels, and employs structure-aware metrics for consistent scoring, specifically preventing text hallucination and allowing only for the assessment of positional hallucination. It also incorporates preprocessing strategies to optimize input for LLMs while preserving DOM semantics: HTML slimming, Hierarchical JSON, and Flat JSON. Additionally, we created a publicly available synthetic dataset by transforming DOM structures and modifying content. We benchmark deterministic heuristic algorithms and off-the-shelf LLMs across these multiple input formats. Our benchmarking shows that Flat JSON input enables LLMs to achieve superior extraction accuracy (F1 score of 0.9567) and minimal hallucination compared to other input formats like Slimmed HTML and Hierarchical JSON. We establish a standardized foundation for rigorous benchmarking, paving the way for the next principled advancements in web data record extraction.
Optimizing edge AI models on HPC systems with the edge in the loop
Authors: Marcel Aach, Cyril Blanc, Andreas Lintermann, Kurt De Grave
Date: 2025-05-26 00:00:00
Tasks: Hardware Aware Neural Architecture Search, Knowledge Distillation, Neural Architecture Search, Quantization

Artificial intelligence and machine learning models deployed on edge devices, e.g., for quality control in Additive Manufacturing (AM), are frequently small in size. Such models usually have to deliver highly accurate results within a short time frame. Methods that are commonly employed in literature start out with larger trained models and try to reduce their memory and latency footprint by structural pruning, knowledge distillation, or quantization. It is, however, also possible to leverage hardware-aware Neural Architecture Search (NAS), an approach that seeks to systematically explore the architecture space to find optimized configurations. In this study, a hardware-aware NAS workflow is introduced that couples an edge device located in Belgium with a powerful High-Performance Computing system in Germany, to train possible architecture candidates as fast as possible while performing real-time latency measurements on the target hardware. The approach is verified on a use case in the AM domain, based on the open RAISE-LPBF dataset, achieving ~8.8 times faster inference speed while simultaneously enhancing model quality by a factor of ~1.35, compared to a human-designed baseline.
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
Authors: Wenqing Zhou, Yuxuan Yan, Qianqian Yang
Date: 2025-05-26 00:00:00
Tasks: Answer Generation, Knowledge Graphs, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) has emerged as a promising approach to enhance the capabilities of language models by integrating external knowledge. Due to the diversity of data sources and the constraints of memory and computing resources, real-world data is often scattered in multiple devices. Conventional RAGs that store massive amounts of scattered data centrally face increasing privacy concerns and high computational costs. Additionally, RAG in a central node raises latency issues when searching over a large-scale knowledge base. To address these challenges, we propose a distributed Knowledge Graph-based RAG approach, referred to as DGRAG, in an edge-cloud system, where each edge device maintains a local knowledge base without the need to share it with the cloud, instead sharing only summaries of its knowledge. Specifically, DGRAG has two main phases. In the Distributed Knowledge Construction phase, DGRAG organizes local knowledge using knowledge graphs, generating subgraph summaries and storing them in a summary database in the cloud as information sharing. In the Collaborative Retrieval and Generation phase, DGRAG first performs knowledge retrieval and answer generation locally, and a gate mechanism determines whether the query is beyond the scope of local knowledge or processing capabilities. For queries that exceed the local knowledge scope, the cloud retrieves knowledge from the most relevant edges based on the summaries and generates a more precise answer. Experimental results demonstrate the effectiveness of the proposed DGRAG approach in significantly improving the quality of question-answering tasks over baseline approaches.
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
Authors: Junming Liu, Yanting Gao, Siyuan Meng, Yifei Sun, Aoqi Wu, Yufei Jin, YiRong Chen, Ding Wang, Guosun Zeng
Date: 2025-05-26 00:00:00
Tasks: Data-free Knowledge Distillation, Federated Learning, image-classification, Image Classification, Knowledge Distillation, Mixture-of-Experts, Synthetic Data Generation

Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise to inconsistent representations and divergent optimization dynamics across clients, ultimately hindering robust global performance. To transcend these challenges, we propose Mosaic, a novel data-free knowledge distillation framework tailored for heterogeneous distributed environments. Mosaic first trains local generative models to approximate each client's personalized distribution, enabling synthetic data generation that safeguards privacy through strict separation from real data. Subsequently, Mosaic forms a Mixture-of-Experts (MoE) from client models based on their specialized knowledge, and distills it into a global model using the generated data. To further enhance the MoE architecture, Mosaic integrates expert predictions via a lightweight meta model trained on a few representative prototypes. Extensive experiments on standard image classification benchmarks demonstrate that Mosaic consistently outperforms state-of-the-art approaches under both model and data heterogeneity. The source code has been published at https://github.com/Wings-Of-Disaster/Mosaic.
Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs
Authors: Hao Kang, Qingru Zhang, Han Cai, Weiyuan Xu, Tushar Krishna, Yilun Du, Tsachy Weissman
Date: 2025-05-26 00:00:00
Tasks: Code Generation, Recommendation Systems

Large language models (LLMs) have shown remarkable performance across diverse reasoning and generation tasks, and are increasingly deployed as agents in dynamic environments such as code generation and recommendation systems. However, many real-world applications, such as high-frequency trading and real-time competitive gaming, require decisions under strict latency constraints, where faster responses directly translate into higher rewards. Despite the importance of this latency quality trade off, it remains underexplored in the context of LLM based agents. In this work, we present the first systematic study of this trade off in real time decision making tasks. To support our investigation, we introduce two new benchmarks: HFTBench, a high frequency trading simulation, and StreetFighter, a competitive gaming platform. Our analysis reveals that optimal latency quality balance varies by task, and that sacrificing quality for lower latency can significantly enhance downstream performance. To address this, we propose FPX, an adaptive framework that dynamically selects model size and quantization level based on real time demands. Our method achieves the best performance on both benchmarks, improving win rate by up to 80% in Street Fighter and boosting daily yield by up to 26.52% in trading, underscoring the need for latency aware evaluation and deployment strategies for LLM based agents. These results demonstrate the critical importance of latency aware evaluation and deployment strategies for real world LLM based agents. Our benchmarks are available at Latency Sensitive Benchmarks.
PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning
Authors: Yisu Wang, Ruilong Wu, Xinjiao Li, Dirk Kutscher
Date: 2025-05-24 00:00:00

Large-scale deep neural networks (DNN) exhibit excellent performance for various tasks. As DNNs and datasets grow, distributed training becomes extremely time-consuming and demands larger clusters. A main bottleneck is the resulting gradient aggregation overhead. While gradient compression and sparse collective communication techniques are commonly employed to alleviate network load, many gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. This paper introduces PacTrain, a novel framework that accelerates distributed training by combining pruning with sparse gradient compression. Active pruning of the neural network makes the model weights and gradients sparse. By ensuring the global knowledge of the gradient sparsity among all distributed training workers, we can perform lightweight compression communication without harming accuracy. We show that the PacTrain compression scheme achieves a near-optimal compression strategy while remaining compatible with the all-reduce primitive. Experimental evaluations show that PacTrain improves training throughput by 1.25 to 8.72 times compared to state-of-the-art compression-enabled systems for representative vision and language models training tasks under bandwidth-constrained conditions.
Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models
Authors: Xuchen Pan, Yanxi Chen, Yushuo Chen, Yuchang Sun, Daoyuan Chen, WenHao Zhang, Yuexiang Xie, Yilun Huang, Yilei Zhang, Dawei Gao, Yaliang Li, Bolin Ding, Jingren Zhou
Date: 2025-05-23 00:00:00

Trinity-RFT is a general-purpose, flexible and scalable framework designed for reinforcement fine-tuning (RFT) of large language models. It is built with a decoupled design, consisting of (1) an RFT-core that unifies and generalizes synchronous/asynchronous, on-policy/off-policy, and online/offline modes of RFT, (2) seamless integration for agent-environment interaction with high efficiency and robustness, and (3) systematic data pipelines optimized for RFT. Trinity-RFT can be easily adapted for diverse application scenarios, and serves as a unified platform for exploring advanced reinforcement learning paradigms. This technical report outlines the vision, features, design and implementations of Trinity-RFT, accompanied by extensive examples demonstrating the utility and user-friendliness of the proposed framework.
IoT-Enabled Hemodynamic Surveillance System: AD8232 Bioelectric Signal Processing with ESP32
Authors: Hemalatha R J, Shubham Malhotra, Shivapanchakshari T G, Lokesh K, Dev Anand D, Samson Jebakumar S
Date: 2025-05-14 00:00:00
Tasks: Rhythm

This dissertation proposes an electrocardiogram (ECG) tracking device that diagnoses cardiopulmonary problems using the Internet of Things (IoT) desired results. The initiative is built on the internet observing an electrocardiogram with the AD8232 heart rhythm sensor and the ESP32 expansion kit, using an on-premise connected device platform to transform sensing input into meaningful data. That subsequently supervises an ECG signal and delivers it to an intelligent phone via Wi-Fi for data analysis. That is the pace of the circulating. Assessing body temperature, pulse rate, and coronary arteries are vital measures to defend your health. The heartbeat rate may be measured in two ways: there are by palpating the pulse at the wrist or neck directly or other alternative by utilizing a cardiac sensor. Monitoring alcohol levels in cardiac patients is critical for measuring the influence of liquor on their health and the efficacy of therapy. It assists in recognizing the association between alcohol consumption and cardiac issues, rather than rhythm recorded in beats per minute (bpm). An IR transmitter/receiver pair (OLED) needs to stay compatible up near the sensor's knuckle current or voltage pulse. The detector's electrical output is evaluated by suitable electronic circuits to produce a visual clue (digital display). We must design a cost-effective, user-friendly, and efficient ECG monitoring system with contemporary technology for both persons imprisoned by disease or aging, as well as healthcare professionals. Microcontroller combined with software. A smartphone application is created to monitor the cardiovascular health of distant patients in real-time
SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment
Authors: Xiangyu Li, Jingqiang Chen
Date: 2025-05-26 00:00:00
Tasks: Articles, Citation Recommendation, Sentence

Citations are crucial in scientific research articles as they highlight the connection between the current study and prior work. However, this process is often time-consuming for researchers. In this study, we propose the SciRGC framework, which aims to automatically recommend citation articles and generate citation sentences for citation locations within articles. The framework addresses two key challenges in academic citation generation: 1) how to accurately identify the author's citation intent and find relevant citation papers, and 2) how to generate high-quality citation sentences that align with human preferences. We enhance citation recommendation accuracy in the citation article recommendation module by incorporating citation networks and sentiment intent, and generate reasoning-based citation sentences in the citation sentence generation module by using the original article abstract, local context, citation intent, and recommended articles as inputs. Additionally, we propose a new evaluation metric to fairly assess the quality of generated citation sentences. Through comparisons with baseline models and ablation experiments, the SciRGC framework not only improves the accuracy and relevance of citation recommendations but also ensures the appropriateness of the generated citation sentences in context, providing a valuable tool for interdisciplinary researchers.
A Domain Ontology for Modeling the Book of Purification in Islam
Authors: Hessa Alawwad
Date: 2025-05-23 00:00:00

This paper aims to address a gap in major Islamic topics by developing an ontology for the Book of Purification in Islam. Many authoritative Islamic texts begin with the Book of Purification, as it is essential for performing prayer (the second pillar of Islam after Shahadah, the profession of faith) and other religious duties such as Umrah and Hajj. The ontology development strategy followed six key steps: (1) domain identification, (2) knowledge acquisition, (3) conceptualization, (4) classification, (5) integration and implementation, and (6) ontology generation. This paper includes examples of the constructed tables and classifications. The focus is on the design and analysis phases, as technical implementation is beyond the scope of this study. However, an initial implementation is provided to illustrate the steps of the proposed strategy. The developed ontology ensures reusability by formally defining and encoding the key concepts, attributes, and relationships related to the Book of Purification. This structured representation is intended to support knowledge sharing and reuse.
BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
Authors: Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury, Hamed Alhoori
Date: 2025-05-22 00:00:00
Tasks: Benchmarking, RAG, Retrieval-augmented Generation

In scientific research, limitations refer to the shortcomings, constraints, or weaknesses within a study. Transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often a) underreport them in the paper text and b) use hedging strategies to satisfy editorial requirements at the cost of readers' clarity and confidence. This underreporting behavior, along with an explosion in the number of publications, has created a pressing need to automatically extract or generate such limitations from scholarly papers. In this direction, we present a complete architecture for the computational analysis of research limitations. Specifically, we create a dataset of limitations in ACL, NeurIPS, and PeerJ papers by extracting them from papers' text and integrating them with external reviews; we propose methods to automatically generate them using a novel Retrieval Augmented Generation (RAG) technique; we create a fine-grained evaluation framework for generated limitations; and we provide a meta-evaluation for the proposed evaluation techniques.