pwc-archive/papers-with-abstracts huggingface dataset:
PeerGuard: Defending Multi-Agent Systems Against Backdoor Attacks Through Mutual Reasoning
Authors: Falong Fan, Xi Li
Date: 2025-05-16 00:00:00
Tasks: Management

Multi-agent systems leverage advanced AI models as autonomous agents that interact, cooperate, or compete to complete complex tasks across applications such as robotics and traffic management. Despite their growing importance, safety in multi-agent systems remains largely underexplored, with most research focusing on single AI models rather than interacting agents. This work investigates backdoor vulnerabilities in multi-agent systems and proposes a defense mechanism based on agent interactions. By leveraging reasoning abilities, each agent evaluates responses from others to detect illogical reasoning processes, which indicate poisoned agents. Experiments on LLM-based multi-agent systems, including ChatGPT series and Llama 3, demonstrate the effectiveness of the proposed method, achieving high accuracy in identifying poisoned agents while minimizing false positives on clean agents. We believe this work provides insights into multi-agent system safety and contributes to the development of robust, trustworthy AI interactions.
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents
Authors: Ayesha Amjad, Saurav Sthapit, Tahir Qasim Syed
Date: 2025-05-16 00:00:00
Tasks: Form, Language Modeling, Language Modelling, Large Language Model, Optical Character Recognition (OCR), Reinforcement Learning (RL)

Extracting alphanumeric data from form-like documents such as invoices, purchase orders, bills, and financial documents is often performed via vision (OCR) and learning algorithms or monolithic pipelines with limited potential for systemic improvements. We propose an agentic AI system that leverages Large Language Model (LLM) agents and a reinforcement learning (RL) driver agent to automate consistent, self-improving extraction under LLM inference uncertainty. Our work highlights the limitations of monolithic LLM-based extraction and introduces a modular, multi-agent framework with task-specific prompts and an RL policy of rewards and penalties to guide a meta-prompting agent to learn from past errors and improve prompt-based actor agents. This self-corrective adaptive system handles diverse documents, file formats, layouts, and LLMs, aiming to automate accurate information extraction without the need for human intervention. Results as reported on two benchmark datasets of SOIRE, and CORD, are promising for the agentic AI framework.
Assessing Collective Reasoning in Multi-Agent LLMs via Hidden Profile Tasks
Authors: YuXuan Li, Aoi Naito, Hirokazu Shirado
Date: 2025-05-15 00:00:00
Tasks: Diagnostic

Multi-agent systems built on large language models (LLMs) promise enhanced problem-solving through distributed information integration, but also risk replicating collective reasoning failures observed in human groups. Yet, no theory-grounded benchmark exists to systematically evaluate such failures. In this paper, we introduce the Hidden Profile paradigm from social psychology as a diagnostic testbed for multi-agent LLM systems. By distributing critical information asymmetrically across agents, the paradigm reveals how inter-agent dynamics support or hinder collective reasoning. We first formalize the paradigm for multi-agent decision-making under distributed knowledge and instantiate it as a benchmark with nine tasks spanning diverse scenarios, including adaptations from prior human studies. We then conduct experiments with GPT-4.1 and five other leading LLMs, including reasoning-enhanced variants, showing that multi-agent systems across all models fail to match the accuracy of single agents given complete information. While agents' collective performance is broadly comparable to that of human groups, nuanced behavioral differences emerge, such as increased sensitivity to social desirability. Finally, we demonstrate the paradigm's diagnostic utility by exploring a cooperation-contradiction trade-off in multi-agent LLM systems. We find that while cooperative agents are prone to over-coordination in collective settings, increased contradiction impairs group convergence. This work contributes a reproducible framework for evaluating multi-agent LLM systems and motivates future research on artificial collective intelligence and human-AI interaction.
Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach
Authors: Umberto Cappellazzo, Minsu Kim, Stavros Petridis, Daniele Falavigna, Alessio Brutti
Date: 2025-05-20 00:00:00
Tasks: Audio-Visual Speech Recognition, Mixture-of-Experts, speech-recognition, Speech Recognition, Visual Speech Recognition

Audio-Visual Speech Recognition (AVSR) enhances robustness in noisy environments by integrating visual cues. While recent advances integrate Large Language Models (LLMs) into AVSR, their high computational cost hinders deployment in resource-constrained settings. To address this, we propose Llama-SMoP, an efficient Multimodal LLM that employs a Sparse Mixture of Projectors (SMoP) module to scale model capacity without increasing inference costs. By incorporating sparsely-gated mixture-of-experts (MoE) projectors, Llama-SMoP enables the use of smaller LLMs while maintaining strong performance. We explore three SMoP configurations and show that Llama-SMoP DEDR (Disjoint-Experts, Disjoint-Routers), which uses modality-specific routers and experts, achieves superior performance on ASR, VSR, and AVSR tasks. Ablation studies confirm its effectiveness in expert activation, scalability, and noise robustness.
RETRO: REthinking Tactile Representation Learning with Material PriOrs
Authors: Weihao Xia, Chenliang Zhou, Cengiz Oztireli
Date: 2025-05-20 00:00:00
Tasks: Representation Learning

Tactile perception is profoundly influenced by the surface properties of objects in contact. However, despite their crucial role in shaping tactile experiences, these material characteristics have been largely neglected in existing tactile representation learning methods. Most approaches primarily focus on aligning tactile data with visual or textual information, overlooking the richness of tactile feedback that comes from understanding the materials' inherent properties. In this work, we address this gap by revisiting the tactile representation learning framework and incorporating material-aware priors into the learning process. These priors, which represent pre-learned characteristics specific to different materials, allow tactile models to better capture and generalize the nuances of surface texture. Our method enables more accurate, contextually rich tactile feedback across diverse materials and textures, improving performance in real-world applications such as robotics, haptic feedback systems, and material editing.
ReactDiff: Latent Diffusion for Facial Reaction Generation
Authors: Jiaming Li, Sheng Wang, Xin Wang, Yitao Zhu, Honglin Xiong, Zixu Zhuang, Qian Wang
Date: 2025-05-20 00:00:00
Tasks: Decoder, Diversity

Given the audio-visual clip of the speaker, facial reaction generation aims to predict the listener's facial reactions. The challenge lies in capturing the relevance between video and audio while balancing appropriateness, realism, and diversity. While prior works have mostly focused on uni-modal inputs or simplified reaction mappings, recent approaches such as PerFRDiff have explored multi-modal inputs and the one-to-many nature of appropriate reaction mappings. In this work, we propose the Facial Reaction Diffusion (ReactDiff) framework that uniquely integrates a Multi-Modality Transformer with conditional diffusion in the latent space for enhanced reaction generation. Unlike existing methods, ReactDiff leverages intra- and inter-class attention for fine-grained multi-modal interaction, while the latent diffusion process between the encoder and decoder enables diverse yet contextually appropriate outputs. Experimental results demonstrate that ReactDiff significantly outperforms existing approaches, achieving a facial reaction correlation of 0.26 and diversity score of 0.094 while maintaining competitive realism. The code is open-sourced at \href{https://github.com/Hunan-Tiger/ReactDiff}{github}.
Algorithms for Nonlinear Mixed-Integer Location Estimation
Authors: Ophir Uziel, Efi Fogel, Dan Halperin, Sivan Toledo
Date: 2025-05-19 00:00:00

For three decades, carrier-phase observations have been used to obtain the most accurate location estimates using global navigation satellite systems (GNSS). These estimates are computed by minimizing a nonlinear mixed-integer least-squares problem. Existing algorithms linearize the problem, orthogonally project it to eliminate real variables, and then solve the integer least-square problem. There is now considerable interest in developing similar localization techniques for terrestrial and indoor settings. We show that algorithms that linearize first fail in these settings and we propose several algorithms for computing the estimates. Some of our algorithms are elimination algorithms that start by eliminating the non-linear terms in the constraints; others construct a geometric arrangement that allows us to efficiently enumerate integer solutions (in polynomial time). We focus on simplified localization problems in which the measurements are range (distance) measurements and carrier phase range measurements, with no nuisance parameters. The simplified problem allows us to focus on the core question of untangling the nonlinearity and the integer nature of some parameters. We show using simulations that the new algorithms are effective at close ranges at which the linearize-first approach fails.
Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
Authors: Ivan Bioli, Carlo Marcati, Giancarlo Sangalli
Date: 2025-05-16 00:00:00

Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solving linear systems involving the Gramian matrix. While matrix-free NGD methods based on the conjugate gradient (CG) method avoid explicit matrix inversion, the ill-conditioning of the Gramian significantly slows the convergence of CG. In this work, we extend matrix-free NGD to broader classes of problems than previously considered and propose the use of Randomized Nystr\"om preconditioning to accelerate convergence of the inner CG solver. The resulting algorithm demonstrates substantial performance improvements over existing NGD-based methods on a range of PDE problems discretized using neural networks.
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
Authors: Luca Pellegrini, Massimiliano Ghiotto, Edoardo Centofanti, Luca Franco Pavarino
Date: 2025-05-20 00:00:00

Ionic models, described by systems of stiff ordinary differential equations, are fundamental tools for simulating the complex dynamics of excitable cells in both Computational Neuroscience and Cardiology. Approximating these models using Artificial Neural Networks poses significant challenges due to their inherent stiffness, multiscale nonlinearities, and the wide range of dynamical behaviors they exhibit, including multiple equilibrium points, limit cycles, and intricate interactions. While in previous studies the dynamics of the transmembrane potential has been predicted in low dimensionality settings, in the present study we extend these results by investigating whether Fourier Neural Operators can effectively learn the evolution of all the state variables within these dynamical systems in higher dimensions. We demonstrate the effectiveness of this approach by accurately learning the dynamics of three well-established ionic models with increasing dimensionality: the two-variable FitzHugh-Nagumo model, the four-variable Hodgkin-Huxley model, and the forty-one-variable O'Hara-Rudy model. To ensure the selection of near-optimal configurations for the Fourier Neural Operator, we conducted automatic hyperparameter tuning under two scenarios: an unconstrained setting, where the number of trainable parameters is not limited, and a constrained case with a fixed number of trainable parameters. Both constrained and unconstrained architectures achieve comparable results in terms of accuracy across all the models considered. However, the unconstrained architecture required approximately half the number of training epochs to achieve similar error levels, as evidenced by the loss function values recorded during training. These results underline the capabilities of Fourier Neural Operators to accurately capture complex multiscale dynamics, even in high-dimensional dynamical systems.
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Authors: Guoming Li, Jian Yang, Yifan Chen
Date: 2025-05-20 00:00:00
Tasks: Anomaly Detection, Graph Anomaly Detection, Node Classification, Stochastic Block Model

Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various graph-related tasks. Conventional methods adopt a graph-wise filtering paradigm, imposing a uniform filter across all nodes, yet recent findings suggest that this rigid paradigm struggles with heterophilic graphs. To overcome this, recent works have introduced node-wise filtering, which assigns distinct filters to individual nodes, offering enhanced adaptability. However, a fundamental gap remains: a comprehensive framework unifying these two strategies is still absent, limiting theoretical insights into the filtering paradigms. Moreover, through the lens of Contextual Stochastic Block Model, we reveal that a synthesis of graph-wise and node-wise filtering provides a sufficient solution for classification on graphs exhibiting both homophily and heterophily, suggesting the risk of excessive parameterization and potential overfitting with node-wise filtering. To address the limitations, this paper introduces Coarsening-guided Partition-wise Filtering (CPF). CPF innovates by performing filtering on node partitions. The method begins with structure-aware partition-wise filtering, which filters node partitions obtained via graph coarsening algorithms, and then performs feature-aware partition-wise filtering, refining node embeddings via filtering on clusters produced by $k$-means clustering over features. In-depth analysis is conducted for each phase of CPF, showing its superiority over other paradigms. Finally, benchmark node classification experiments, along with a real-world graph anomaly detection application, validate CPF's efficacy and practical utility.
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems
Authors: Zeynab Kaseb, Rahul Rane, Aleksandra Lekic, Matthias Moller, Amin Khodaei, Peter Palensky, Pedro P. Vergara
Date: 2025-05-19 00:00:00

This paper presents a proof-of-concept for integrating quantum hardware with real-time digital simulator (RTDS) to model and control modern power systems, including renewable energy resources. Power flow (PF) analysis and optimal power flow (OPF) studies are conducted using RTDS coupled with Fujitsu's CMOS Digital Annealer and D-Wave's Advantage quantum processors. The adiabatic quantum power flow (AQPF) and adiabatic quantum optimal power flow (AQOPF) algorithms are used to perform PF and OPF, respectively, on quantum and quantum-inspired hardware. The experiments are performed on the IEEE 9-bus test system and a modified version that includes solar and wind farms. The findings demonstrate that the AQPF and AQOPF algorithms can accurately perform PF and OPF, yielding results that closely match those of classical Newton-Raphson (NR) solvers while also exhibiting robust convergence. Furthermore, the integration of renewable energy sources (RES) within the AQOPF framework proves effective in maintaining system stability and performance, even under variable generation conditions. These findings highlight the potential of quantum computing to significantly enhance the modeling and control of future power grids, particularly in systems with high renewable energy penetration.
Approximation theory for 1-Lipschitz ResNets
Authors: Davide Murari, Takashi Furuya, Carola-Bibiane Schönlieb
Date: 2025-05-17 00:00:00

1-Lipschitz neural networks are fundamental for generative modelling, inverse problems, and robust classifiers. In this paper, we focus on 1-Lipschitz residual networks (ResNets) based on explicit Euler steps of negative gradient flows and study their approximation capabilities. Leveraging the Restricted Stone-Weierstrass Theorem, we first show that these 1-Lipschitz ResNets are dense in the set of scalar 1-Lipschitz functions on any compact domain when width and depth are allowed to grow. We also show that these networks can exactly represent scalar piecewise affine 1-Lipschitz functions. We then prove a stronger statement: by inserting norm-constrained linear maps between the residual blocks, the same density holds when the hidden width is fixed. Because every layer obeys simple norm constraints, the resulting models can be trained with off-the-shelf optimisers. This paper provides the first universal approximation guarantees for 1-Lipschitz ResNets, laying a rigorous foundation for their practical use.
Policy Gradient with Second Order Momentum
Authors: Tianyu Sun
Date: 2025-05-16 00:00:00

We develop Policy Gradient with Second-Order Momentum (PG-SOM), a lightweight second-order optimisation scheme for reinforcement-learning policies. PG-SOM augments the classical REINFORCE update with two exponentially weighted statistics: a first-order gradient average and a diagonal approximation of the Hessian. By preconditioning the gradient with this curvature estimate, the method adaptively rescales each parameter, yielding faster and more stable ascent of the expected return. We provide a concise derivation, establish that the diagonal Hessian estimator is unbiased and positive-definite under mild regularity assumptions, and prove that the resulting update is a descent direction in expectation. Numerical experiments on standard control benchmarks show up to a 2.1x increase in sample efficiency and a substantial reduction in variance compared to first-order and Fisher-matrix baselines. These results indicate that even coarse second-order information can deliver significant practical gains while incurring only D memory overhead for a D-parameter policy. All code and reproducibility scripts will be made publicly available.
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
Authors: Kamal Singh, Sami Marouani, Ahmad Al Sheikh, Pham Tran Anh Quang, Amaury Habrard
Date: 2025-05-20 00:00:00
Tasks: Decision Making, Kolmogorov-Arnold Networks, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing. However, existing RL approaches often suffer from lack of interpretability and difficulty in extracting controller equations. In this paper, we propose the use of Kolmogorov-Arnold Networks (KAN) for interpretable RL in network control. We employ a PPO agent with a 1-layer actor KAN model and an MLP Critic network to learn load balancing policies that maximise throughput utility, minimize loss as well as delay. Our approach allows us to extract controller equations from the learned neural networks, providing insights into the decision-making process. We evaluate our approach using different reward functions demonstrating its effectiveness in improving network performance while providing interpretable policies.
Towards Efficient Multi-Scale Deformable Attention on NPU
Authors: Chenghuan Huang, Zhigeng Xu, Chong Sun, Chen Li, Ziyang Ma
Date: 2025-05-20 00:00:00

Multi-scale deformable attention (MSDA) is a flexible and powerful feature extraction mechanism for visual tasks, but its random-access grid sampling strategy poses significant optimization challenges, especially on domain-specific accelerators such as NPUs. In this work, we present a co-design approach that systematically rethinks memory access and computation strategies for MSDA on the Ascend NPU architecture. With this co-design approach, our implementation supports both efficient forward and backward computation, is fully adapted for training workloads, and incorporates a suite of hardware-aware optimizations. Extensive experiments show that our solution achieves up to $5.9\times$ (forward), $8.9\times$ (backward), and $7.3\times$ (end-to-end training) speedup over the grid sample-based baseline, and $1.9\times$, $2.4\times$, and $2.0\times$ acceleration over the latest vendor library, respectively.
RTL++: Graph-enhanced LLM for RTL Code Generation
Authors: Mohammad Akyash, Kimia Azar, Hadi Kamali
Date: 2025-05-11 00:00:00
Tasks: Code Generation

As hardware design complexity escalates, there is an urgent need for advanced automation in electronic design automation (EDA). Traditional register transfer level (RTL) design methods are manual, time-consuming, and prone to errors. While commercial (instruction-tuned) large language models (LLMs) shows promising performance for automation, they pose security and privacy concerns. Open-source models offer alternatives; however, they frequently fall short in quality/correctness, largely due to limited, high-quality RTL code data essential for effective training and generalization. This paper proposes RTL++, a first-of-its-kind LLM-assisted method for RTL code generation that utilizes graph representations of code structures to enhance the quality of generated code. By encoding RTL code into a textualized control flowgraphs (CFG) and data flow graphs (DFG), RTL++ captures the inherent hierarchy, dependencies, and relationships within the code. This structured graph-based approach enhances the context available to LLMs, enabling them to better understand and generate instructions. By focusing on data generation through graph representations, RTL++ addresses the limitations of previous approaches that rely solely on code and suffer from lack of diversity. Experimental results demonstrate that RTL++ outperforms state-of-the-art models fine-tuned for RTL generation, as evaluated using the VerilogEval benchmark's Pass@1/5/10 metric, as well as the RTLLM1.1 model, which highlight the effectiveness of graph-enhanced context in advancing the capabilities of LLM-assisted RTL code generation.