pwc-archive/papers-with-abstracts huggingface dataset:
Novel Approach to Dual-Channel Estimation in Integrated Sensing and Communications for 6G
Authors: Alejandro Castilla, Saúl Fenollosa, Monika Drozdowska, Alejandro Lopez-Escudero, Sergio Micò-Rosa, Narcis Cardona
Date: 2025-07-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) design is crucial for 6G and harmonizes environmental data sensing with communication, emphasizing the need to understand and model these elements. This paper delves into dual-channel models for ISAC, employing channel extraction techniques to validate and enhance accuracy. Focusing on millimeter wave (mmWave) radars, it explores the extraction of the bistatic sensing channel from monostatic measurements and subsequent communication channel estimation. The proposed methods involve interference extraction, module and phase correlation analyses, chirp clustering, and auto-clutter reduction. A comprehensive set-up in an anechoic chamber with controlled scenarios evaluates the proposed techniques, demonstrating successful channel extraction and validation through Root Mean Square Delay Spread (RMS DS), Power Delay Profile (PDP), and Angle of Arrival (AoA) analysis. Comparison with Ray-Tracing (RT) simulations confirms the effectiveness of the proposed approach, presenting an innovative stride towards fully integrated sensing and communication in future networks.
Draw an Ugly Person An Exploration of Generative AIs Perceptions of Ugliness
Authors: Garyoung Kim, Huisung Kwon, Seoju Yun, Yu-Won Youn
Date: 2025-07-16 00:00:00
Tasks: Large Language Model

Generative AI does not only replicate human creativity but also reproduces deep-seated cultural biases, making it crucial to critically examine how concepts like ugliness are understood and expressed by these tools. This study investigates how four different generative AI models understand and express ugliness through text and image and explores the biases embedded within these representations. We extracted 13 adjectives associated with ugliness through iterative prompting of a large language model and generated 624 images across four AI models and three prompts. Demographic and socioeconomic attributes within the images were independently coded and thematically analyzed. Our findings show that AI models disproportionately associate ugliness with old white male figures, reflecting entrenched social biases as well as paradoxical biases, where efforts to avoid stereotypical depictions of marginalized groups inadvertently result in the disproportionate projection of negative attributes onto majority groups. Qualitative analysis further reveals that, despite supposed attempts to frame ugliness within social contexts, conventional physical markers such as asymmetry and aging persist as central visual motifs. These findings demonstrate that despite attempts to create more equal representations, generative AI continues to perpetuate inherited and paradoxical biases, underscoring the critical work being done to create ethical AI training paradigms and advance methodologies for more inclusive AI development.
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Authors: Xinyu Tang, Zhihao Lv, Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Zujie Wen, Zhiqiang Zhang, Jun Zhou
Date: 2025-07-17 00:00:00
Tasks: Cross-Lingual Transfer, In-Context Learning

Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-scarce scenarios. While cross-task in-context learning offers a direct solution for transferring knowledge across tasks, it still faces critical challenges in terms of robustness, scalability, and efficiency. In this paper, we investigate whether cross-task transfer can be achieved via latent space steering without parameter updates or input expansion. Through an analysis of activation patterns in the latent space of LLMs, we observe that the enhanced activations induced by in-context examples have consistent patterns across different tasks. Inspired by these findings, we propose CAST, a novel Cross-task Activation Steering Transfer framework that enables effective transfer by manipulating the model's internal activation states. Our approach first selects influential and diverse samples from high-resource tasks, then utilizes their contrastive representation-enhanced activations to adapt LLMs to low-resource tasks. Extensive experiments across both cross-domain and cross-lingual transfer settings show that our method outperforms competitive baselines and demonstrates superior scalability and lower computational costs.
AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research
Authors: Yilun Zhao, Weiyuan Chen, Zhijian Xu, Manasi Patwardhan, Yixin Liu, Chengye Wang, Lovekesh Vig, Arman Cohan
Date: 2025-07-17 00:00:00

We introduce AbGen, the first benchmark designed to evaluate the capabilities of LLMs in designing ablation studies for scientific research. AbGen consists of 1,500 expert-annotated examples derived from 807 NLP papers. In this benchmark, LLMs are tasked with generating detailed ablation study designs for a specified module or process based on the given research context. Our evaluation of leading LLMs, such as DeepSeek-R1-0528 and o4-mini, highlights a significant performance gap between these models and human experts in terms of the importance, faithfulness, and soundness of the ablation study designs. Moreover, we demonstrate that current automated evaluation methods are not reliable for our task, as they show a significant discrepancy when compared to human assessment. To better investigate this, we develop AbGen-Eval, a meta-evaluation benchmark designed to assess the reliability of commonly used automated evaluation systems in measuring LLM performance on our task. We investigate various LLM-as-Judge systems on AbGen-Eval, providing insights for future research on developing more effective and reliable LLM-based evaluation systems for complex scientific tasks.
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?
Authors: Antoni Zajko, Katarzyna Woźnica
Date: 2025-07-16 00:00:00
Tasks: Hyperparameter Optimization, Meta-Learning, Metric Learning, Representation Learning

Effectively representing heterogeneous tabular datasets for meta-learning purposes is still an open problem. Previous approaches rely on representations that are intended to be universal. This paper proposes two novel methods for tabular representation learning tailored to a specific meta-task - warm-starting Bayesian Hyperparameter Optimization. Both follow the specific requirement formulated by ourselves that enforces representations to capture the properties of landmarkers. The first approach involves deep metric learning, while the second one is based on landmarkers reconstruction. We evaluate the proposed encoders in two ways. Next to the gain in the target meta-task, we also use the degree of fulfillment of the proposed requirement as the evaluation metric. Experiments demonstrate that while the proposed encoders can effectively learn representations aligned with landmarkers, they may not directly translate to significant performance gains in the meta-task of HPO warm-starting.
Imbalanced Regression Pipeline Recommendation
Authors: Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Date: 2025-07-16 00:00:00
Tasks: AutoML, Meta-Learning, regression

Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target values. A common alternative is to employ balancing algorithms in preprocessing to address dataset imbalance. However, due to the variety of resampling methods and learning models, determining the optimal solution requires testing many combinations. Furthermore, the learning model, dataset, and evaluation metric affect the best strategies. This work proposes the Meta-learning for Imbalanced Regression (Meta-IR) framework, which diverges from existing literature by training meta-classifiers to recommend the best pipeline composed of the resampling strategy and learning model per task in a zero-shot fashion. The meta-classifiers are trained using a set of meta-features to learn how to map the meta-features to the classes indicating the best pipeline. We propose two formulations: Independent and Chained. Independent trains the meta-classifiers to separately indicate the best learning algorithm and resampling strategy. Chained involves a sequential procedure where the output of one meta-classifier is used as input for another to model intrinsic relationship factors. The Chained scenario showed superior performance, suggesting a relationship between the learning algorithm and the resampling strategy per task. Compared with AutoML frameworks, Meta-IR obtained better results. Moreover, compared with baselines of six learning algorithms and six resampling algorithms plus no resampling, totaling 42 (6 X 7) configurations, Meta-IR outperformed all of them. The code, data, and further information of the experiments can be found on GitHub: https://github.com/JusciAvelino/Meta-IR.
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Authors: Ruofan Hu, Dongyu Zhang, Huayi Zhang, Elke Rundensteiner
Date: 2025-07-16 00:00:00
Tasks: Learning with noisy labels, Meta-Learning

Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, this approach heavily depends on the availability of a clean labeled meta-dataset, which is difficult to obtain in practice. In this work, we thus tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset. Our approach leverages the data itself while bypassing the need for labels. Building on the insight that clean samples effectively preserve the consistency of related data structures across the last hidden and the final layer, whereas noisy samples disrupt this consistency, we design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU). CLID-MU leverages the alignment of data structures across these diverse feature spaces to evaluate model performance and use this alignment to guide training. Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods. The code is released at https://github.com/ruofanhu/CLID-MU.
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
Authors: Yuehua Song, Yong Gao
Date: 2025-07-15 00:00:00
Tasks: Drug Discovery, Graph Learning, Inductive Learning, Transductive Learning

Accurately predicting drug-target interactions (DTIs) is pivotal for advancing drug discovery and target validation techniques. While machine learning approaches including those that are based on Graph Neural Networks (GNN) have achieved notable success in DTI prediction, many of them have difficulties in effectively integrating the diverse features of drugs, targets and their interactions. To address this limitation, we introduce a novel framework to take advantage of the power of both transductive learning and inductive learning so that features at molecular level and drug-target interaction network level can be exploited. Within this framework is a GNN-based model called Graph-in-Graph (GiG) that represents graphs of drug and target molecular structures as meta-nodes in a drug-target interaction graph, enabling a detailed exploration of their intricate relationships. To evaluate the proposed model, we have compiled a special benchmark comprising drug SMILES, protein sequences, and their interaction data, which is interesting in its own right. Our experimental results demonstrate that the GiG model significantly outperforms existing approaches across all evaluation metrics, highlighting the benefits of integrating different learning paradigms and interaction data.
Data-Driven Meta-Analysis and Public-Dataset Evaluation for Sensor-Based Gait Age Estimation
Authors: Varun Velankar
Date: 2025-07-15 00:00:00
Tasks: Age Estimation, Sensor Fusion

Estimating a person's age from their gait has important applications in healthcare, security and human-computer interaction. In this work, we review fifty-nine studies involving over seventy-five thousand subjects recorded with video, wearable and radar sensors. We observe that convolutional neural networks produce an average error of about 4.2 years, inertial-sensor models about 4.5 years and multi-sensor fusion as low as 3.4 years, with notable differences between lab and real-world data. We then analyse sixty-three thousand eight hundred forty-six gait cycles from the OU-ISIR Large-Population dataset to quantify correlations between age and five key metrics: stride length, walking speed, step cadence, step-time variability and joint-angle entropy, with correlation coefficients of at least 0.27. Next, we fine-tune a ResNet34 model and apply Grad-CAM to reveal that the network attends to the knee and pelvic regions, consistent with known age-related gait changes. Finally, on a one hundred thousand sample subset of the VersatileGait database, we compare support vector machines, decision trees, random forests, multilayer perceptrons and convolutional neural networks, finding that deep networks achieve up to 96 percent accuracy while processing each sample in under 0.1 seconds. By combining a broad meta-analysis with new large-scale experiments and interpretable visualizations, we establish solid performance baselines and practical guidelines for reducing gait-age error below three years in real-world scenarios.
The AI Ethical Resonance Hypothesis: The Possibility of Discovering Moral Meta-Patterns in AI Systems
Authors: Tomasz Zgliczyński-Cuber
Date: 2025-07-13 00:00:00

This paper presents a theoretical framework for the AI ethical resonance hypothesis, which proposes that advanced AI systems with purposefully designed cognitive structures ("ethical resonators") may emerge with the ability to identify subtle moral patterns that are invisible to the human mind. The paper explores the possibility that by processing and synthesizing large amounts of ethical contexts, AI systems may discover moral meta-patterns that transcend cultural, historical, and individual biases, potentially leading to a deeper understanding of universal ethical foundations. The paper also examines a paradoxical aspect of the hypothesis, in which AI systems could potentially deepen our understanding of what we traditionally consider essentially human - our capacity for ethical reflection.
FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Authors: Gal Beniamini, Yuval Dor, Alon Vinnikov, Shir Granot Peled, Or Weinstein, Or Sharir, Noam Wies, Tomer Nussbaum, Ido Ben Shaul, Tomer Zekharya, Yoav Levine, Shai Shalev-Shwartz, Amnon Shashua
Date: 2025-07-17 00:00:00
Tasks: Scheduling

Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems and push the boundaries of scientific understanding. To illuminate the limits of frontier model capabilities, we turn away from contrived competitive programming puzzles, and instead focus on real-life research problems. We construct FormulaOne, a benchmark that lies at the intersection of graph theory, logic, and algorithms, all well within the training distribution of frontier models. Our problems are incredibly demanding, requiring an array of reasoning steps. The dataset has three key properties. First, it is of commercial interest and relates to practical large-scale optimisation problems, such as those arising in routing, scheduling, and network design. Second, it is generated from the highly expressive framework of Monadic Second-Order (MSO) logic on graphs, paving the way toward automatic problem generation at scale; ideal for building RL environments. Third, many of our problems are intimately related to the frontier of theoretical computer science, and to central conjectures therein, such as the Strong Exponential Time Hypothesis (SETH). As such, any significant algorithmic progress on our dataset, beyond known results, could carry profound theoretical implications. Remarkably, state-of-the-art models like OpenAI's o3 fail entirely on FormulaOne, solving less than 1% of the questions, even when given 10 attempts and explanatory fewshot examples -- highlighting how far they remain from expert-level understanding in some domains. To support further research, we additionally curate FormulaOne-Warmup, offering a set of simpler tasks, from the same distribution. We release the full corpus along with a comprehensive evaluation framework.
Privacy-Preserving Fusion for Multi-Sensor Systems Under Multiple Packet Dropouts
Authors: Jie Huang, Jason J. R. Liu
Date: 2025-07-17 00:00:00
Tasks: Privacy Preserving, State Estimation

Wireless sensor networks (WSNs) are critical components in modern cyber-physical systems, enabling efficient data collection and fusion through spatially distributed sensors. However, the inherent risks of eavesdropping and packet dropouts in such networks pose significant challenges to secure state estimation. In this paper, we address the privacy-preserving fusion estimation (PPFE) problem for multi-sensor systems under multiple packet dropouts and eavesdropping attacks. To mitigate these issues, we propose a distributed encoding-based privacy-preserving mechanism (PPM) within a control-theoretic framework, ensuring data privacy during transmission while maintaining the performance of legitimate state estimation. A centralized fusion filter is developed, accounting for the coupling effects of packet dropouts and the encoding-based PPM. Boundedness conditions for the legitimate user's estimation error covariance are derived via a modified algebraic Riccati equation. Additionally, by demonstrating the divergence of the eavesdropper's mean estimation error, the proposed PPFE algorithm's data confidentiality is rigorously analyzed. Simulation results for an Internet-based three-tank system validate the effectiveness of the proposed approach, highlighting its potential to enhance privacy without compromising estimation accuracy.
Power in Sharing Networks with a priori Unions
Authors: Michele Aleandri, Francesco Ciardiello, Andrea Di Liddo
Date: 2025-07-17 00:00:00

We introduce and analyze a novel family of power indices tailored for sharing networks in technological markets, where firms operate competitively within, but not across, distinct industrial sectors. In these settings, inter-firm collaboration structures emerge from formal technology licensing agreements. The proposed indices are defined over graphs with a priori unions and combine two key centrality measures - degree-based and rescaled eigenvector centrality - modulated by positive market coefficients that reflect sectoral dynamics. We first explore the monotonicity properties of these indices, highlighting their responsiveness to local changes in network structure. Interestingly, major economic actors exhibit structural stability when inter-sectoral technological spillovers are minimal. Building on these findings, we provide theoretical underpinnings by characterizing the indices as the Shapley values of a family of coherent and economically interpretable transferable utility (TU) games defined over such graphs. However, for a broad class of network structures, the core of these TU games is often empty, signaling inherent instability in technological sharing arrangements. Finally, we offer an axiomatic foundation for this family of indices, proving independence of the proposed axioms. This axiomatization extends naturally to exchange networks, even when stage-propagation coefficients are not positive.
Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks
Authors: Sean P. Maley, Carlos Gershenson, Stuart A. Kauffman
Date: 2025-07-17 00:00:00

There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition. We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled ``help''/``harm''), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals phase transitions in both connectivity and resilience. Our findings extend classical random threshold and Erd\H{o}s-R\'enyi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil.
GradNetOT: Learning Optimal Transport Maps with GradNets
Authors: Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
Date: 2025-07-17 00:00:00

Monotone gradient functions play a central role in solving the Monge formulation of the optimal transport problem, which arises in modern applications ranging from fluid dynamics to robot swarm control. When the transport cost is the squared Euclidean distance, Brenier's theorem guarantees that the unique optimal map is the gradient of a convex function, namely a monotone gradient map, and it satisfies a Monge-Amp\`ere equation. In [arXiv:2301.10862] [arXiv:2404.07361], we proposed Monotone Gradient Networks (mGradNets), neural networks that directly parameterize the space of monotone gradient maps. In this work, we leverage mGradNets to directly learn the optimal transport mapping by minimizing a training loss function defined using the Monge-Amp\`ere equation. We empirically show that the structural bias of mGradNets facilitates the learning of optimal transport maps and employ our method for a robot swarm control problem.