pwc-archive/papers-with-abstracts huggingface dataset:
Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality
Authors: Xueguang Ma, Luyu Gao, Shengyao Zhuang, Jiaqi Samantha Zhan, Jamie Callan, Jimmy Lin
Date: 2025-05-05 00:00:00
Tasks: Retrieval

Recent advancements in large language models (LLMs) have driven interest in billion-scale retrieval models with strong generalization across retrieval tasks and languages. Additionally, progress in large vision-language models has created new opportunities for multimodal retrieval. In response, we have updated the Tevatron toolkit, introducing a unified pipeline that enables researchers to explore retriever models at different scales, across multiple languages, and with various modalities. This demo paper highlights the toolkit's key features, bridging academia and industry by supporting efficient training, inference, and evaluation of neural retrievers. We showcase a unified dense retriever achieving strong multilingual and multimodal effectiveness, and conduct a cross-modality zero-shot study to demonstrate its research potential. Alongside, we release OmniEmbed, to the best of our knowledge, the first embedding model that unifies text, image document, video, and audio retrieval, serving as a baseline for future research.
Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise
Authors: Vinay Kanakeri, Aritra Mitra
Date: 2025-04-25 00:00:00

We consider the problem of system identification of partially observed linear time-invariant (LTI) systems. Given input-output data, we provide non-asymptotic guarantees for identifying the system parameters under general heavy-tailed noise processes. Unlike previous works that assume Gaussian or sub-Gaussian noise, we consider significantly broader noise distributions that are required to admit only up to the second moment. For this setting, we leverage tools from robust statistics to propose a novel system identification algorithm that exploits the idea of boosting. Despite the much weaker noise assumptions, we show that our proposed algorithm achieves sample complexity bounds that nearly match those derived under sub-Gaussian noise. In particular, we establish that our bounds retain a logarithmic dependence on the prescribed failure probability. Interestingly, we show that such bounds can be achieved by requiring just a finite fourth moment on the excitatory input process.
Optimal Control for Network of Coupled Oscillators
Authors: Adnan Tahirovic
Date: 2025-04-25 00:00:00

This paper presents a nonlinear control framework for steering networks of coupled oscillators toward desired phase-locked configurations. Inspired by brain dynamics, where structured phase differences support cognitive functions, the focus is on achieving synchronization patterns beyond global coherence. The Kuramoto model, expressed in phase-difference coordinates, is used to describe the system dynamics. The control problem is formulated within the State-Dependent Riccati Equation (SDRE) framework, enabling the design of feedback laws through state-dependent factorisation. The unconstrained control formulation serves as a principled starting point for developing more general approaches that incorporate coupling constraints and actuation limits. Numerical simulations demonstrate that the proposed approach achieves robust phase-locking in both heterogeneous and large-scale oscillator networks, highlighting its potential applications in neuroscience, robotics, and distributed systems.
Energy Security and Resilience: Reviewing Concepts and Advancing Planning Perspectives for Transforming Integrated Energy Systems
Authors: Richard Schmitz, Franziska Flachsbarth, Leonie Sara Plaga, Martin Braun, Philipp Härtel
Date: 2025-04-25 00:00:00
Tasks: Decision Making

Recent events, including the pandemic, geopolitical conflicts, supply chain disruptions, and climate change impacts, have exposed the critical need to ensure energy security and resilience in energy systems. We review existing definitions and interrelations between energy security and resilience, conceptualising these terms in the context of energy system transformations. We introduce a classification of disturbances into shock events and slow burn processes to highlight key challenges to energy system resilience. Examples illustrate their distinct impacts on technical, economic, and environmental system performance over time. We compile relevant recourse options across resilience capacity levels and system planning horizons to address these challenges, emphasising actionable strategies for an increasingly integrated energy system. Finally, we propose policy recommendations to integrate shock events and slow burn processes into future energy system planning, enabling forward-looking decision-making and system design to analyse and mitigate potential disruptions.
Hierarchical Uncertainty-Aware Graph Neural Network
Authors: Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim
Date: 2025-04-28 00:00:00
Tasks: Diversity, Graph Neural Network, Representation Learning

Recent research on graph neural networks (GNNs) has explored mechanisms for capturing local uncertainty and exploiting graph hierarchies to mitigate data sparsity and leverage structural properties. However, the synergistic integration of these two approaches remains underexplored. This work introduces a novel architecture, the Hierarchical Uncertainty-Aware Graph Neural Network (HU-GNN), which unifies multi-scale representation learning, principled uncertainty estimation, and self-supervised embedding diversity within a single end-to-end framework. Specifically, HU-GNN adaptively forms node clusters and estimates uncertainty at multiple structural scales from individual nodes to higher levels. These uncertainty estimates guide a robust message-passing mechanism and attention weighting, effectively mitigating noise and adversarial perturbations while preserving predictive accuracy on semi-supervised classification tasks. We also offer key theoretical contributions, including a probabilistic formulation, rigorous uncertainty-calibration guarantees, and formal robustness bounds. Extensive experiments on standard benchmarks demonstrate that our model achieves state-of-the-art robustness and interpretability.
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization
Authors: Irshad A. Meer, Bruno Hörmann, Mustafa Ozger, Fabien Geyer, Alberto Viseras, Dominic Schupke, Cicek Cavdar
Date: 2025-04-25 00:00:00
Tasks: Management, Reinforcement Learning (RL)

The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black-box nature, which limits interpretability in the decision-making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.
Embedding based retrieval for long tail search queries in ecommerce
Authors: Akshay Kekuda, Yuyang Zhang, Arun Udayashankar
Date: 2025-05-03 00:00:00
Tasks: Large Language Model, Retrieval

In this abstract we present a series of optimizations we performed on the two-tower model architecture [14], training and evaluation datasets to implement semantic product search at Best Buy. Search queries on bestbuy.com follow the pareto distribution whereby a minority of them account for most searches. This leaves us with a long tail of search queries that have low frequency of issuance. The queries in the long tail suffer from very spare interaction signals. Our current work focuses on building a model to serve the long tail queries. We present a series of optimizations we have done to this model to maximize conversion for the purpose of retrieval from the catalog. The first optimization we present is using a large language model to improve the sparsity of conversion signals. The second optimization is pretraining an off-the-shelf transformer-based model on the Best Buy catalog data. The third optimization we present is on the finetuning front. We use query-to-query pairs in addition to query-to-product pairs and combining the above strategies for finetuning the model. We also demonstrate how merging the weights of these finetuned models improves the evaluation metrics. Finally, we provide a recipe for curating an evaluation dataset for continuous monitoring of model performance with human-in-the-loop evaluation. We found that adding this recall mechanism to our current term match-based recall improved conversion by 3% in an online A/B test.
A Generalised and Adaptable Reinforcement Learning Stopping Method
Authors: Reem Bin-Hezam, Mark Stevenson
Date: 2025-05-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), TAR

This paper presents a Technology Assisted Review (TAR) stopping approach based on Reinforcement Learning (RL). Previous such approaches offered limited control over stopping behaviour, such as fixing the target recall and tradeoff between preferring to maximise recall or cost. These limitations are overcome by introducing a novel RL environment, GRLStop, that allows a single model to be applied to multiple target recalls, balances the recall/cost tradeoff and integrates a classifier. Experiments were carried out on six benchmark datasets (CLEF e-Health datasets 2017-9, TREC Total Recall, TREC Legal and Reuters RCV1) at multiple target recall levels. Results showed that the proposed approach to be effective compared to multiple baselines in addition to offering greater flexibility.
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
Authors: Yuying Zhao, Xiaodong Yang, Huiyuan Chen, Xiran Fan, Yu Wang, Yiwei Cai, Tyler Derr
Date: 2025-05-03 00:00:00
Tasks: Collaborative Filtering, Data Augmentation, Fairness

Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item embeddings that are crucial for recommendations. However, the data often suffers from sparsity and imbalance issues: limited observations of user-item interactions can result in sub-optimal performance, and a predominance of interactions with popular items may introduce recommendation bias. To address these challenges, we employ Pretrained Language Models (PLMs) to enhance the interaction data with textual information, leading to a denser and more balanced dataset. Specifically, we propose a simple yet effective data augmentation method (SimAug) based on the textual similarity from PLMs, which can be seamlessly integrated to any systems as a lightweight, plug-and-play component in the pre-processing stage. Our experiments across nine datasets consistently demonstrate improvements in both utility and fairness when training with the augmented data generated by SimAug. The code is available at https://github.com/YuyingZhao/SimAug.
Renewable-Colocated Green Hydrogen Production: Optimal Scheduling and Profitability
Authors: Siying Li, Lang Tong, Timothy Mount, Kanchan Upadhyay, Harris Eisenhardt, Pradip Kumar
Date: 2025-04-25 00:00:00
Tasks: Scheduling

We study the optimal green hydrogen production and energy market participation of a renewable-colocated hydrogen producer (RCHP) that utilizes onsite renewable generation for both hydrogen production and grid services. Under deterministic and stochastic profit-maximization frameworks, we analyze RCHP's multiple market participation models and derive closed-form optimal scheduling policies that dynamically allocate renewable energy to hydrogen production and electricity export to the wholesale market. Analytical characterizations of the RCHP's operating profit and the optimal sizing of renewable and electrolyzer capacities are obtained. We use real-time renewable production and electricity price data from three independent system operators to assess the impacts of hydrogen market prices, renewable generation, and electricity prices on RCHP's profitability.
Multi-agents based User Values Mining for Recommendation
Authors: Lijian Chen, Wei Yuan, Tong Chen, Xiangyu Zhao, Nguyen Quoc Viet Hung, Hongzhi Yin
Date: 2025-05-02 00:00:00
Tasks: Hallucination, Recommendation Systems, Text Summarization

Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that fail to align with users' fundamental and long-term preferences. This is because they primarily focus on extracting shallow and short-term interests from user behavior data, which is inherently dynamic and challenging to model. Unlike these transient interests, user values are more stable and play a crucial role in shaping user behaviors, such as purchasing items and consuming content. Incorporating user values into recommender systems can help stabilize recommendation performance and ensure results better reflect users' latent preferences. However, acquiring user values is typically difficult and costly. To address this challenge, we leverage the strong language understanding, zero-shot inference, and generalization capabilities of Large Language Models (LLMs) to extract user values from users' historical interactions. Unfortunately, direct extraction using LLMs presents several challenges such as length constraints and hallucination. To overcome these issues, we propose ZOOM, a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction. In ZOOM, we apply text summarization techniques to condense item content while preserving essential meaning. To mitigate hallucinations, ZOOM introduces two specialized agent roles: evaluators and supervisors, to collaboratively generate accurate user values. Extensive experiments on two widely used recommendation datasets with two state-of-the-art recommendation models demonstrate the effectiveness and generalization of our framework in automatic user value mining and recommendation performance improvement.
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning
Authors: YuHan Liu, Lin Ning, Neo Wu, Karan Singhal, Philip Andrew Mansfield, Devora Berlowitz, Sushant Prakash, Bradley Green
Date: 2025-05-02 00:00:00
Tasks: Recommendation Systems, Representation Learning, Self-Supervised Learning

User sequence modeling is crucial for modern large-scale recommendation systems, as it enables the extraction of informative representations of users and items from their historical interactions. These user representations are widely used for a variety of downstream tasks to enhance users' online experience. A key challenge for learning these representations is the lack of labeled training data. While self-supervised learning (SSL) methods have emerged as a promising solution for learning representations from unlabeled data, many existing approaches rely on extensive negative sampling, which can be computationally expensive and may not always be feasible in real-world scenario. In this work, we propose an adaptation of Barlow Twins, a state-of-the-art SSL methods, to user sequence modeling by incorporating suitable augmentation methods. Our approach aims to mitigate the need for large negative sample batches, enabling effective representation learning with smaller batch sizes and limited labeled data. We evaluate our method on the MovieLens-1M, MovieLens-20M, and Yelp datasets, demonstrating that our method consistently outperforms the widely-used dual encoder model across three downstream tasks, achieving an 8%-20% improvement in accuracy. Our findings underscore the effectiveness of our approach in extracting valuable sequence-level information for user modeling, particularly in scenarios where labeled data is scarce and negative examples are limited.
Unifying Direct and Indirect Learning for Safe Control of Linear Systems
Authors: Amir Modares, Niyousha Ghiasi, Bahare Kiumarsi, Hamidreza Modares
Date: 2025-04-25 00:00:00
Tasks: Transfer Learning

This paper aims to learn safe controllers for uncertain discrete-time linear systems under disturbances while achieving the following two crucial goals: 1) integration of different sources of information (i.e., prior information in terms of physical knowledge and posterior information in terms of streaming data), and 2) unifying direct learning with indirect learning. These goals are achieved by representing a parametrized data-driven constrained matrix zonotope form of closed-loop systems that is conformant to prior knowledge. To this end, we first leverage collected data to characterize closed-loop systems by a matrix zonotope and then show that the explainability of these closed-loop systems by prior knowledge can be formalized by adding an equality conformity constraint, which refines the matrix zonotope obtained by data to a constrained matrix zonotope. The prior knowledge is further refined by conforming it to the set of models obtained from a novel zonotope-based system identifier. The source of data used for zonotope-based system identification can be different than the one used for closed-loop representation, allowing to perform transfer learning and online adaptation to new data. The parametrized closed-loop set of systems is then leveraged to directly learn a controller that robustly imposes safety on the closed-loop system. We consider both polytope and zonotope safe sets and provide set inclusion conditions using linear programming to impose safety through {\lambda}-contractivity. For polytope safe sets, a primal-dual optimization is developed to formalize a linear programming optimization that certifies the set inclusion. For zonotope safe sets, the constrained zonotope set of all next states is formed, and set inclusion is achieved by ensuring the inclusion of this constrained zonotope in a {\lambda}-scaled level set of the safe set.
Towards Explainable Temporal User Profiling with LLMs
Authors: Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher
Date: 2025-05-01 00:00:00
Tasks: Recommendation Systems

Accurately modeling user preferences is vital not only for improving recommendation performance but also for enhancing transparency in recommender systems. Conventional user profiling methods, such as averaging item embeddings, often overlook the evolving, nuanced nature of user interests, particularly the interplay between short-term and long-term preferences. In this work, we leverage large language models (LLMs) to generate natural language summaries of users' interaction histories, distinguishing recent behaviors from more persistent tendencies. Our framework not only models temporal user preferences but also produces natural language profiles that can be used to explain recommendations in an interpretable manner. These textual profiles are encoded via a pre-trained model, and an attention mechanism dynamically fuses the short-term and long-term embeddings into a comprehensive user representation. Beyond boosting recommendation accuracy over multiple baselines, our approach naturally supports explainability: the interpretable text summaries and attention weights can be exposed to end users, offering insights into why specific items are suggested. Experiments on real-world datasets underscore both the performance gains and the promise of generating clearer, more transparent justifications for content-based recommendations.
Neural Incremental Input-to-State Stable Control Lyapunov Functions for Unknown Continuous-time Systems
Authors: Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
Date: 2025-04-25 00:00:00

This work primarily focuses on synthesizing a controller that guarantees an unknown continuous-time system to be incrementally input-to-state stable ($\delta$-ISS). In this context, the notion of $\delta$-ISS control Lyapunov function ($\delta$-ISS-CLF) for the continuous-time system is introduced. Combined with the controller, the $\delta$-ISS-CLF guarantees that the system is incrementally stable. As the paper deals with unknown dynamical systems, the controller as well as the $\delta$-ISS-CLF are parametrized using neural networks. The data set used to train the neural networks is generated from the state space of the system by proper sampling. Now, to give a formal guarantee that the controller makes the system incrementally stable, we develop a validity condition by having some Lipschitz continuity assumptions and incorporate the condition into the training framework to ensure a provable correctness guarantee at the end of the training process. Finally, we demonstrate the effectiveness of the proposed approach through several case studies: a scalar system with a non-affine, non-polynomial structure, a one-link manipulator system, a nonlinear Moore-Greitzer model of a jet engine, and a rotating rigid spacecraft model.
Hypencoder: Hypernetworks for Information Retrieval
Authors: Julian Killingback, Hansi Zeng, Hamed Zamani
Date: 2025-02-07 00:00:00
Tasks: Information Retrieval, Instruction Following, Reranking, Retrieval

Existing information retrieval systems are largely constrained by their reliance on vector inner products to assess query-document relevance, which naturally limits the expressiveness of the relevance score they can produce. We propose a new paradigm; instead of representing a query as a vector, we use a small neural network that acts as a learned query-specific relevance function. This small neural network takes a document representation as input (in this work we use a single vector) and produces a scalar relevance score. To produce the small neural network we use a hypernetwork, a network that produces the weights of other networks, as our query encoder. We name this category of encoder models Hypencoders. Experiments on in-domain search tasks show that Hypencoders significantly outperform strong dense retrieval models and even surpass reranking models and retrieval models with an order of magnitude more parameters. To assess the extent of Hypencoders' capabilities, we evaluate on a set of hard retrieval tasks including tip-of-the-tongue and instruction-following retrieval tasks. On harder tasks, we find that the performance gap widens substantially compared to standard retrieval tasks. Furthermore, to demonstrate the practicality of our method, we implement an approximate search algorithm and show that our model is able to retrieve from a corpus of 8.8M documents in under 60 milliseconds.