pwc-archive/papers-with-abstracts huggingface dataset:
Second SIGIR Workshop on Simulations for Information Access (Sim4IA 2025)
Authors: Philipp Schaer, Christin Katharina Kreutz, Krisztian Balog, Timo Breuer, Andreas Konstantin Kruff
Date: 2025-05-16 00:00:00

Simulations in information access (IA) have recently gained interest, as shown by various tutorials and workshops around that topic. Simulations can be key contributors to central IA research and evaluation questions, especially around interactive settings when real users are unavailable, or their participation is impossible due to ethical reasons. In addition, simulations in IA can help contribute to a better understanding of users, reduce complexity of evaluation experiments, and improve reproducibility. Building on recent developments in methods and toolkits, the second iteration of our Sim4IA workshop aims to again bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.
Terminators: Terms of Service Parsing and Auditing Agents
Authors: Maruf Ahmed Mridul, Inwon Kang, Oshani Seneviratne
Date: 2025-05-16 00:00:00
Tasks: Term Extraction

Terms of Service (ToS) documents are often lengthy and written in complex legal language, making them difficult for users to read and understand. To address this challenge, we propose Terminators, a modular agentic framework that leverages large language models (LLMs) to parse and audit ToS documents. Rather than treating ToS understanding as a black-box summarization problem, Terminators breaks the task down to three interpretable steps: term extraction, verification, and accountability planning. We demonstrate the effectiveness of our method on the OpenAI ToS using GPT-4o, highlighting strategies to minimize hallucinations and maximize auditability. Our results suggest that structured, agent-based LLM workflows can enhance both the usability and enforceability of complex legal documents. By translating opaque terms into actionable, verifiable components, Terminators promotes ethical use of web content by enabling greater transparency, empowering users to understand their digital rights, and supporting automated policy audits for regulatory or civic oversight.
Comparing Lexical and Semantic Vector Search Methods When Classifying Medical Documents
Authors: Lee Harris, Philippe De Wilde, James Bentham
Date: 2025-05-16 00:00:00
Tasks: Information Retrieval

Classification is a common AI problem, and vector search is a typical solution. This transforms a given body of text into a numerical representation, known as an embedding, and modern improvements to vector search focus on optimising speed and predictive accuracy. This is often achieved through neural methods that aim to learn language semantics. However, our results suggest that these are not always the best solution. Our task was to classify rigidly-structured medical documents according to their content, and we found that using off-the-shelf semantic vector search produced slightly worse predictive accuracy than creating a bespoke lexical vector search model, and that it required significantly more time to execute. These findings suggest that traditional methods deserve to be contenders in the information retrieval toolkit, despite the prevalence and success of neural models.
GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation
Authors: Ahmad Bin Rabiah, Julian McAuley
Date: 2025-05-15 00:00:00
Tasks: graph construction, Recommendation Systems

Graph-based recommendation systems are effective at modeling collaborative patterns but often suffer from two limitations: overreliance on low-pass filtering, which suppresses user-specific signals, and omission of sequential dynamics in graph construction. We introduce GSPRec, a graph spectral model that integrates temporal transitions through sequentially-informed graph construction and applies frequency-aware filtering in the spectral domain. GSPRec encodes item transitions via multi-hop diffusion to enable the use of symmetric Laplacians for spectral processing. To capture user preferences, we design a dual-filtering mechanism: a Gaussian bandpass filter to extract mid-frequency, user-level patterns, and a low-pass filter to retain global trends. Extensive experiments on four public datasets show that GSPRec consistently outperforms baselines, with an average improvement of 6.77% in NDCG@10. Ablation studies show the complementary benefits of both sequential graph augmentation and bandpass filtering.
TARGET: Benchmarking Table Retrieval for Generative Tasks
Authors: Xingyu Ji, Parker Glenn, Aditya G. Parameswaran, Madelon Hulsebos
Date: 2025-05-14 00:00:00
Tasks: Benchmarking, Representation Learning, Retrieval, Retrieval-augmented Generation, Table Retrieval, Text to SQL, Text-To-SQL

The data landscape is rich with structured data, often of high value to organizations, driving important applications in data analysis and machine learning. Recent progress in representation learning and generative models for such data has led to the development of natural language interfaces to structured data, including those leveraging text-to-SQL. Contextualizing interactions, either through conversational interfaces or agentic components, in structured data through retrieval-augmented generation can provide substantial benefits in the form of freshness, accuracy, and comprehensiveness of answers. The key question is: how do we retrieve the right table(s) for the analytical query or task at hand? To this end, we introduce TARGET: a benchmark for evaluating TAble Retrieval for GEnerative Tasks. With TARGET we analyze the retrieval performance of different retrievers in isolation, as well as their impact on downstream tasks. We find that dense embedding-based retrievers far outperform a BM25 baseline which is less effective than it is for retrieval over unstructured text. We also surface the sensitivity of retrievers across various metadata (e.g., missing table titles), and demonstrate a stark variation of retrieval performance across datasets and tasks. TARGET is available at https://target-benchmark.github.io.
Beyond Time: Cross-Dimensional Frequency Supervision for Time Series Forecasting
Authors: Tianyi Shi, Zhu Meng, Yue Chen, Siyang Zheng, Fei Su, Jin Huang, Changrui Ren, Zhicheng Zhao
Date: 2025-05-16 00:00:00
Tasks: Time Series, Time Series Forecasting

Time series forecasting plays a crucial role in various fields, and the methods based on frequency domain analysis have become an important branch. However, most existing studies focus on the design of elaborate model architectures and are often tailored for limited datasets, still lacking universality. Besides, the assumption of independent and identically distributed (IID) data also contradicts the strong correlation of the time domain labels. To address these issues, abandoning time domain supervision, we propose a purely frequency domain supervision approach named cross-dimensional frequency (X-Freq) loss. Specifically, based on a statistical phenomenon, we first prove that the information entropy of the time series is higher than its spectral entropy, which implies higher certainty in frequency domain and thus can provide better supervision. Secondly, the Fourier Transform and the Wavelet Transform are applied to the time dimension and the channel dimension of the time series respectively, to capture the long-term and short-term frequency variations as well as the spatial configuration features. Thirdly, the loss between predictions and targets is uniformly computed in the frequency domain. Moreover, we plug-and-play incorporate X-Freq into multiple advanced forecasting models and compare on 14 real-world datasets. The experimental results demonstrate that, without making any modification to the original architectures or hyperparameters, X-Freq can improve the forecasting performance by an average of 3.3% on long-term forecasting datasets and 27.7% on short-term ones, showcasing superior generality and practicality. The code will be released publicly.
A Vision for Auto Research with LLM Agents
Authors: Chengwei Liu, Chong Wang, Jiayue Cao, Jingquan Ge, Kun Wang, Lvye Zhang, Ming-Ming Cheng, Penghai Zhao, Tianlin Li, Xiaojun Jia, Xiang Li, Xinfeng Li, Yang Liu, Yebo Feng, Yihao Huang, Yijia Xu, Yuqiang Sun, Zhenhong Zhou, Zhengzi Xu
Date: 2025-04-26 00:00:00

This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes.
Deciphering the AI Economy: A Mathematical Model Perspective
Authors: Davit Gondauri
Date: 2025-05-17 00:00:00
Tasks: model

The economy in the modern world is greatly influenced by artificial intelligence (AI). This paper aims to determine the impact of AI quantitative relationships on the country's economic parameters, including GDP per Capita. Historical data analysis is used in the research. A new mathematical algorithm for the magnitude of a technological level and AI factors vector has been developed. The study calculated the economic effect of AI on GDP per Capita. As a result of the analysis, it was revealed that there is a positive Pearson correlation between growth. On AI and GDP per Capita, that is, to increase GDP per Capita by 1%, an average increase of 23.9% in AI is required.
Endogenous clustering and analogy-based expectation equilibrium
Authors: Philippe Jehiel, Giacomo Weber
Date: 2025-05-19 00:00:00
Tasks: Clustering, Prediction

Normal-form two-player games are categorized by players into K analogy classes so as to minimize the prediction error about the behavior of the opponent. This results in Clustered Analogy-Based Expectation Equilibria in which strategies are analogy-based expectation equilibria given the analogy partitions and analogy partitions minimize the prediction errors given the strategies. We distinguish between environments with self-repelling analogy partitions in which some mixing over partitions is required and environments with self-attractive partitions in which several analogy partitions can arise, thereby suggesting new channels of belief heterogeneity and equilibrium multiplicity. Various economic applications are discussed.
Counterfactual Query Rewriting to Use Historical Relevance Feedback
Authors: Jüri Keller, Maik Fröbe, Gijs Hendriksen, Daria Alexander, Martin Potthast, Matthias Hagen, Philipp Schaer
Date: 2025-02-06 00:00:00
Tasks: counterfactual, Retrieval

When a retrieval system receives a query it has encountered before, previous relevance feedback, such as clicks or explicit judgments can help to improve retrieval results. However, the content of a previously relevant document may have changed, or the document might not be available anymore. Despite this evolved corpus, we counterfactually use these previously relevant documents as relevance signals. In this paper we proposed approaches to rewrite user queries and compare them against a system that directly uses the previous qrels for the ranking. We expand queries with terms extracted from the previously relevant documents or derive so-called keyqueries that rank the previously relevant documents to the top of the current corpus. Our evaluation in the CLEF LongEval scenario shows that rewriting queries with historical relevance feedback improves the retrieval effectiveness and even outperforms computationally expensive transformer-based approaches.
VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation
Authors: Yiting Wang, Guoheng Sun, Wanghao Ye, Gang Qu, Ang Li
Date: 2025-05-17 00:00:00
Tasks: Code Generation

Automating Register Transfer Level (RTL) code generation using Large Language Models (LLMs) offers substantial promise for streamlining digital circuit design and reducing human effort. However, current LLM-based approaches face significant challenges with training data scarcity, poor specification-code alignment, lack of verification mechanisms, and balancing generalization with specialization. Inspired by DeepSeek-R1, we introduce VeriReason, a framework integrating supervised fine-tuning with Guided Reward Proximal Optimization (GRPO) reinforcement learning for RTL generation. Using curated training examples and a feedback-driven reward model, VeriReason combines testbench evaluations with structural heuristics while embedding self-checking capabilities for autonomous error correction. On the VerilogEval Benchmark, VeriReason delivers significant improvements: achieving 83.1% functional correctness on the VerilogEval Machine benchmark, substantially outperforming both comparable-sized models and much larger commercial systems like GPT-4 Turbo. Additionally, our approach demonstrates up to a 2.8X increase in first-attempt functional correctness compared to baseline methods and exhibits robust generalization to unseen designs. To our knowledge, VeriReason represents the first system to successfully integrate explicit reasoning capabilities with reinforcement learning for Verilog generation, establishing a new state-of-the-art for automated RTL synthesis. The models and datasets are available at: https://huggingface.co/collections/AI4EDA-CASE Code is Available at: https://github.com/NellyW8/VeriReason
A discrete physics-informed training for projection-based reduced order models with neural networks
Authors: N. Sibuet, S. Ares de Parga, J. R. Bravo, R. Rossi
Date: 2025-03-31 00:00:00

This paper presents a physics-informed training framework for projection-based Reduced Order Models (ROMs). We extend the PROM-ANN architecture by complementing snapshot-based training with a FEM-based, discrete physics-informed residual loss, bridging the gap between traditional projection-based ROMs and physics-informed neural networks (PINNs). Unlike conventional PINNs that rely on analytical PDEs, our approach leverages FEM residuals to guide the learning of the ROM approximation manifold. Key contributions include: (1) a parameter-agnostic, discrete residual loss applicable to non-linear problems, (2) an architectural modification to PROM-ANN improving accuracy for fast-decaying singular values, and (3) an empirical study on the proposed physics informed training process for ROMs. The method is demonstrated on a non-linear hyperelasticity problem, simulating a rubber cantilever under multi-axial loads. The main accomplishment in regards to the proposed residual-based loss is its applicability on non-linear problems by interfacing with FEM software while maintaining reasonable training times. The modified PROM-ANN outperforms POD by orders of magnitude in snapshot reconstruction accuracy, while the original formulation is not able to learn a proper mapping for this use-case. Finally, the application of physics informed training in ANN-PROM modestly narrows the gap between data reconstruction and ROM accuracy, however it highlights the untapped potential of the proposed residual-driven optimization for future ROM development. This work underscores the critical role of FEM residuals in ROM construction and calls for further exploration on architectures beyond PROM-ANN.
For Overall Nighttime Visibility: Integrate Irregular Glow Removal With Glow-Aware Enhancement
Authors: Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang, Zhengguo Li
Date: 2024-09-23 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2024 9
Tasks: Flare Removal, Image Enhancement, Low-Light Image Enhancement, Zero-Shot Learning

Current low-light image enhancement (LLIE) techniques truly enhance luminance but have limited exploration on another harmful factor of nighttime visibility, the glow effects with multiple shapes in the real world. The presence of glow is inevitable due to widespread artificial light sources, and direct enhancement can cause further glow diffusion. In the pursuit of Overall Nighttime Visibility Enhancement (ONVE), we propose a physical model guided framework ONVE to derive a Nighttime Imaging Model with Near-Field Light Sources (NIM-NLS), whose APSF prior generator is validated efficiently in six categories of glow shapes. Guided by this physical-world model as domain knowledge, we subsequently develop an extensible Light-aware Blind Deconvolution Network (LBDN) to face the blind decomposition challenge on direct transmission map D and light source map G based on APSF. Then, an innovative Glow-guided Retinex-based progressive Enhancement module (GRE) is introduced as a further optimization on reflection R from D to harmonize the conflict of glow removal and brightness boost. Notably, ONVE is an unsupervised framework based on a zero-shot learning strategy and uses physical domain knowledge to form the overall pipeline and network. Empirical evaluations on multiple datasets validate the remarkable efficacy of the proposed ONVE in improving nighttime visibility and performance of high-level vision tasks.
PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks
Authors: Agrima Khanna, Fangqi Liu, Samir Gupta, Sophie Pavia, Ayan Mukhopadhyay, Abhishek Dubey
Date: 2025-01-04 00:00:00
Proceeding: International Conference on Distributed Computing and Networking 2025 1
Tasks: Scheduling

The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.
Introducing EG-IPT and ipt~: a novel electric guitar dataset and a new Max/MSP object for real-time classification of instrumental playing techniques
Authors: Marco Fiorini, Nicolas Brochec, Joakim Borg, Riccardo Pasini
Date: 2025-06-24 00:00:00
Proceeding: NIME 2025 6

This paper presents two key contributions to the real-time classification of Instrumental Playing Techniques (IPTs) in the context of NIME and human-machine interactive systems: the EG-IPT dataset and the ipt∼ Max/MSP object. The EG-IPT dataset, specifically designed for electric guitar, encompasses a broad range of IPTs captured across six distinct audio sources (five microphones and one direct input) and three pickup configurations. This diversity in recording conditions provides a robust foundation for training accurate models. We evaluate the dataset by employing a Convolutional Neural Network-based classifier (CNN), achieving state-of-the-art performance across a wide array of IPT classes, thereby validating the dataset's efficacy. The ipt∼ object is a new Max/MSP external enabling real-time classification of IPTs via pre-trained CNN models. While in this paper it's demonstrated with the EG-IPT dataset, the ipt∼ object is adaptable to models trained on various instruments. By integrating EG-IPT and ipt∼, we introduce a novel, end-to-end workflow that spans from data collection, model training to real-time classification and humancomputer interaction. This workflow exemplifies the entanglement of diverse components (data acquisition, machine learning, real-time processing, and interactive control) within a unified system, advancing the potential for dynamic, real-time music performance and human-computer interaction in the context of NIME.
Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making
Authors: Jacob Kleiman, Kevin Frank, Joseph Voyles, Sindy Campagna
Date: 2025-05-19 00:00:00
Tasks: Decision Making

Simulations, although powerful in accurately replicating real-world systems, often remain inaccessible to non-technical users due to their complexity. Conversely, large language models (LLMs) provide intuitive, language-based interactions but can lack the structured, causal understanding required to reliably model complex real-world dynamics. We introduce our simulation agent framework, a novel approach that integrates the strengths of both simulation models and LLMs. This framework helps empower users by leveraging the conversational capabilities of LLMs to interact seamlessly with sophisticated simulation systems, while simultaneously utilizing the simulations to ground the LLMs in accurate and structured representations of real-world phenomena. This integrated approach helps provide a robust and generalizable foundation for empirical validation and offers broad applicability across diverse domains.
How transfer learning is used in generative models for image classification: improved accuracy
Authors: Danial Ebrahimzadeh, Sarah S Sharif, Yaser M Banad
Date: 2024-12-09 00:00:00
Proceeding: Signal, Image and Video Processing 2024 12
Tasks: Classification, Generative Adversarial Network, image-classification, Image Classification, Transfer Learning

Recent breakthroughs in generative neural networks have paved the way for transformative capabilities, particularly in their capacity to generate novel data, notably in the realm of images. The integration of these models with the increasingly popular technique of transfer learning, designed for proficient feature extraction, holds the promise of enhancing overall performance. This paper delves into the exploration of employing generative models in conjunction with transfer learning methods for feature extraction, with a specific focus on image classification tasks. Our investigation aims to scrutinize the effectiveness of leveraging generative models alongside pre-trained models as feature extractors in the context of image classification. To the best of our knowledge, our investigation is the first to link transfer learning and generative models for a discriminative task under one roof. The proposed approach undergoes rigorous evaluation on two distinct datasets, employing specific metrics to gauge the model’s performance. The results exhibit a notable nearly 10% enhancement achieved through the integration of generative models, underscoring their potential for achieving heightened accuracy in image classification. These findings highlight significant advancements in image classification accuracy, surpassing the performance of conventional Artificial Neural Network (ANN) models.