pwc-archive/papers-with-abstracts huggingface dataset:
Zak-OTFS with Spread Carrier Waveforms
Authors: Nishant Mehrotra, Sandesh Rao Mattu, Robert Calderbank
Date: 2025-05-12 00:00:00

Zak-OTFS (orthogonal time frequency space) modulation is a communication framework that parameterizes the wireless channel in the delay-Doppler (DD) domain, where the parameters map directly to physical attributes of the scatterers that comprise the scattering environment. As a consequence, the channel can be efficiently acquired and equalized. The Zak-OTFS carrier is a pulse in the DD domain, and the Zak transform converts it to a pulse train modulated by a tone (pulsone) in the time domain. The pulsone waveform is localized rather than spread, and it suffers from high peak-to-average power ratio (PAPR). We describe how to transform the orthonormal basis of Zak-OTFS pulsones into an orthonormal basis of spread carrier waveforms with low PAPR (only $6.58$ dB) that support communication in the presence of mobility and delay spread. This transformation is realized by a unitary transform based on the discrete affine Fourier transform. Unlike other spread modulations that achieve low PAPR by spreading information across a wider bandwidth (thus reducing the spectral efficiency), the proposed spread carrier-based Zak-OTFS achieves full spectral efficiency like pulsone-based Zak-OTFS, with $5.6$ dB lower PAPR per basis element. We demonstrate uncoded bit error rate (BER) similar to pulsone-based Zak-OTFS, and improved BER performance over competing methods based on OFDM and OTFS in high mobility & delay spread environments.
Beyond Diagonal RIS Design for Parameter Estimation With and Without Eavesdropping
Authors: Özlem Tuğfe Demir, Sinan Gezici
Date: 2025-05-09 00:00:00
Tasks: parameter estimation

In this letter, we investigate the transmission of a complex-valued parameter vector from a transmitter to an intended receiver, considering both the presence and absence of an eavesdropper. The direct links from the transmitter to both the intended receiver and the eavesdropper are assumed to be blocked, and communications occur solely through cascaded channels facilitated by a beyond-diagonal reconfigurable intelligent surface (BD-RIS). While previous research has considered this system under conventional (diagonal) RIS assistance, we extend the setup to incorporate BD-RIS and quantify the resulting improvement in estimation performance at the intended receiver. This performance is measured by the trace of the Fisher information matrix (FIM), or equivalently, the average Fisher information, while simultaneously limiting the estimation capability of the eavesdropper. We propose solutions and algorithms for optimizing the BD-RIS response matrix and demonstrate their effectiveness. Numerical results reveal that the BD-RIS provides a significant enhancement in estimation quality compared to conventional diagonal RIS architectures.
Continuous-Time Control Synthesis for Multiple Quadrotors under Signal Temporal Logic Specifications
Authors: Yating Yuan
Date: 2025-05-12 00:00:00

Ensuring continuous-time control of multiple quadrotors in constrained environments under signal temporal logic (STL) specifications is challenging due to nonlinear dynamics, safety constraints, and disturbances. This letter proposes a two-stage framework to address this challenge. First, exponentially decaying tracking error bounds are derived with multidimensional geometric control gains obtained via differential evolution. These bounds are less conservative, while the resulting tracking errors exhibit smaller oscillations and improved transient performance. Second, leveraging the time-varying bounds, a mixed-integer convex programming (MICP) formulation generates piecewise B\'ezier reference trajectories that satisfy STL and velocity limits, while ensuring inter-agent safety through convex-hull properties. Simulation results demonstrate that the proposed approach enables formally verifiable multi-agent coordination in constrained environments, with provable tracking guarantees under bounded disturbances.
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution
Authors: Yufei Lin, Chengwei Ye, Huanzhen Zhang, Kangsheng Wang, Linuo Xu, Shuyan Liu, Zeyu Zhang
Date: 2025-05-08 00:00:00
Tasks: Multi-agent Reinforcement Learning

Sparse reward environments pose significant challenges in reinforcement learning, especially within multi-agent systems (MAS) where feedback is delayed and shared across agents, leading to suboptimal learning. We propose Collaborative Multi-dimensional Course Learning (CCL), a novel curriculum learning framework that addresses this by (1) refining intermediate tasks for individual agents, (2) using a variational evolutionary algorithm to generate informative subtasks, and (3) co-evolving agents with their environment to enhance training stability. Experiments on five cooperative tasks in the MPE and Hide-and-Seek environments show that CCL outperforms existing methods in sparse reward settings.
Conceptual Logical Foundations of Artificial Social Intelligence
Authors: Eric Werner
Date: 2025-05-07 00:00:00

What makes a society possible at all? How is coordination and cooperation in social activity possible? What is the minimal mental architecture of a social agent? How is the information about the state of the world related to the agents intentions? How are the intentions of agents related? What role does communication play in this coordination process? This essay explores the conceptual and logical foundations of artificial social intelligence in the context of a society of multiple agents that communicate and cooperate to achieve some end. An attempt is made to provide an introduction to some of the key concepts, their formal definitions and their interrelationships. These include the notion of a changing social world of multiple agents. The logic of social intelligence goes beyond classical logic by linking information with strategic thought. A minimal architecture of social agents is presented. The agents have different dynamically changing, possible choices and abilities. The agents also have uncertainty, lacking perfect information about their physical state as well as their dynamic social state. The social state of an agent includes the intentional state of that agent, as well as, that agent's representation of the intentional states of other agents. Furthermore, it includes the evaluations agents make of their physical and social condition. Communication, semantic and pragmatic meaning and their relationship to intention and information states are investigated. The logic of agent abilities and intentions are motivated and formalized. The entropy of group strategic states is defined.
Intelligent Product 3.0: Decentralised AI Agents and Web3 Intelligence Standards
Authors: Alex C. Y. Wong, Duncan McFarlane, C. Ellarby, M. Lee, M. Kuok
Date: 2025-05-02 00:00:00

Twenty-five years ago, the specification of the Intelligent Product was established, envisaging real-time connectivity that not only enables products to gather accurate data about themselves but also allows them to assess and influence their own destiny. Early work by the Auto-ID project focused on creating a single, open-standard repository for storing and retrieving product information, laying a foundation for scalable connectivity. A decade later, the approach was revisited in light of low-cost RFID systems that promised a low-cost link between physical goods and networked information environments. Since then, advances in blockchain, Web3, and artificial intelligence have introduced unprecedented levels of resilience, consensus, and autonomy. By leveraging decentralised identity, blockchain-based product information and history, and intelligent AI-to-AI collaboration, this paper examines these developments and outlines a new specification for the Intelligent Product 3.0, illustrating how decentralised and AI-driven capabilities facilitate seamless interaction between physical AI and everyday products.
Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field
Authors: John Song, Lihao Zhang, Feng Ye, Haijian Sun
Date: 2025-05-06 00:00:00

Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly from lower-frequency bands due to severe free space path loss, minimal diffraction and specular reflection, and prominent scattering, making conventional channel modeling and pilot-based estimation approaches inefficient. In this work, we investigate the feasibility of applying radio radiance field (RRF) framework to the THz band. This method reconstructs a continuous RRF using visual-based geometry and sparse THz RF measurements, enabling efficient spatial channel state information (Spatial-CSI) modeling without dense sampling. We first build a fine simulated THz scenario, then we reconstruct the RRF and evaluate the performance in terms of both reconstruction quality and effectiveness in THz communication, showing that the reconstructed RRF captures key propagation paths with sparse training samples. Our findings demonstrate that RRF modeling remains effective in the THz regime and provides a promising direction for scalable, low-cost spatial channel reconstruction in future 6G networks.
ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks
Authors: Maria-Lamprini A. Bartsioka, Ioannis A. Bartsiokas, Panagiotis K. Gkonis, Dimitra I. Kaklamani, Iakovos S. Venieris
Date: 2025-05-05 00:00:00

Advanced fifth generation (5G) and beyond (B5G) communication networks have revolutionized wireless technologies, supporting ultra-high data rates, low latency, and massive connectivity. However, they also introduce vulnerabilities, particularly in decentralized Industrial Internet of Things (IIoT) environments. Traditional cryptographic methods struggle with scalability and complexity, leading researchers to explore Artificial Intelligence (AI)-driven physical layer techniques for secure communications. In this context, this paper focuses on the utilization of Machine and Deep Learning (ML/DL) techniques to tackle with the common problem of eavesdropping detection. To this end, a simulated industrial B5G heterogeneous wireless network is used to evaluate the performance of various ML/DL models, including Random Forests (RF), Deep Convolutional Neural Networks (DCNN), and Long Short-Term Memory (LSTM) networks. These models classify users as either legitimate or malicious ones based on channel state information (CSI), position data, and transmission power. According to the presented numerical results, DCNN and RF models achieve a detection accuracy approaching 100\% in identifying eavesdroppers with zero false alarms. In general, this work underlines the great potential of combining AI and Physical Layer Security (PLS) for next-generation wireless networks in order to address evolving security threats.
Program Semantic Inequivalence Game with Large Language Models
Authors: Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani
Date: 2025-05-02 00:00:00
Tasks: C++ code, Code Generation, Vulnerability Detection

Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics. Finding training examples to teach LLMs to solve these tasks can be challenging. In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game SInQ: a generator agent creates program variants that are semantically distinct, derived from a dataset of real-world programming tasks, while an evaluator agent has to identify input examples that cause the original programs and the generated variants to diverge in their behaviour, with the agents training each other semi-adversarially. We prove that this setup enables theoretically unlimited improvement through self-play in the limit of infinite computational resources. We evaluated our approach on multiple code generation and understanding benchmarks, including cross-language vulnerability detection (Lu et al., 2021), where our method improves vulnerability detection in C/C++ code despite being trained exclusively on Python code, and the challenging Python builtin identifier swap benchmark (Miceli-Barone et al., 2023), showing that whereas modern LLMs still struggle with this benchmark, our approach yields substantial improvements. We release the code needed to replicate the experiments, as well as the generated synthetic data, which can be used to fine-tune LLMs.
Optimal Trajectory Planning with Collision Avoidance for Autonomous Vehicle Maneuvering
Authors: Jason Zalev
Date: 2025-05-13 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Trajectory Planning

To perform autonomous driving maneuvers, such as parallel or perpendicular parking, a vehicle requires continual speed and steering adjustments to follow a generated path. In consequence, the path's quality is a limiting factor of the vehicle maneuver's performance. While most path planning approaches include finding a collision-free route, optimal trajectory planning involves solving the best transition from initial to final states, minimizing the action over all paths permitted by a kinematic model. Here we propose a novel method based on sequential convex optimization, which permits flexible and efficient optimal trajectory generation. The objective is to achieve the fastest time, shortest distance, and fewest number of path segments to satisfy motion requirements, while avoiding sensor blind-spots. In our approach, vehicle kinematics are represented by a discretized Dubins model. To avoid collisions, each waypoint is constrained by linear inequalities representing closest distance of obstacles to a polygon specifying the vehicle's extent. To promote smooth and valid trajectories, the solved kinematic state and control variables are constrained and regularized by penalty terms in the model's cost function, which enforces physical restrictions including limits for steering angle, acceleration and speed. In this paper, we analyze trajectories obtained for several parking scenarios. Results demonstrate efficient and collision-free motion generated by the proposed technique.
A spherical amplitude-phase formulation for 3-D adaptive line-of-sight (ALOS) guidance with USGES stability guarantees
Authors: Erlend M. Coates, Thor I. Fossen
Date: 2025-05-13 00:00:00
Tasks: valid

A recently proposed 3-D adaptive line-of-sight (ALOS) path-following algorithm addressed coupled motion dynamics of marine craft, aircraft, and uncrewed vehicles under environmental disturbances such as wind, waves, and ocean currents. Stability analysis established uniform semiglobal exponential stability (USGES) of the cross- and vertical-track errors using a body-velocity-based amplitude-phase representation of the North-East-Down (NED) kinematic differential equations. In this brief paper, we revisit the ALOS framework and introduce a novel spherical amplitude-phase representation. This formulation yields a more geometrically intuitive and physically observable description of the guidance errors and enables a significantly simplified stability proof. Unlike the previous model, which relied on a vertical crab angle derived from body-frame velocities, the new representation uses an alternative vertical crab angle and retains the USGES property. It also removes restrictive assumptions such as constant altitude/depth or zero horizontal crab angle, and remains valid for general 3-D maneuvers with nonzero roll, pitch, and flight-path angles.
Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
Authors: Francesco Prignoli, Ying Shuai Quan, Mohammad Jeddi, Jonas Sjöberg, Paolo Falcone
Date: 2025-05-09 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Model Predictive Control

This paper demonstrates the applicability of the safe model predictive control (SMPC) framework to autonomous driving scenarios, focusing on the design of adaptive cruise control (ACC) and automated lane-change systems. Building on the SMPC approach with priority-driven constraint softening -- which ensures the satisfaction of \emph{hard} constraints under external disturbances by selectively softening a predefined subset of adjustable constraints -- we show how the algorithm dynamically relaxes lower-priority, comfort-related constraints in response to unexpected disturbances while preserving critical safety requirements such as collision avoidance and lane-keeping. A learning-based algorithm approximating the time consuming SMPC is introduced to enable real-time execution. Simulations in real-world driving scenarios subject to unpredicted disturbances confirm that this prioritized softening mechanism consistently upholds stringent safety constraints, underscoring the effectiveness of the proposed method.
Formation Maneuver Control Based on the Augmented Laplacian Method
Authors: Xinzhe Zhou, Xuyang Wang, Xiaoming Duan, Yuzhu Bai, Jianping He
Date: 2025-05-09 00:00:00

This paper proposes a novel formation maneuver control method for both 2-D and 3-D space, which enables the formation to translate, scale, and rotate with arbitrary orientation. The core innovation is the novel design of weights in the proposed augmented Laplacian matrix. Instead of using scalars, we represent weights as matrices, which are designed based on a specified rotation axis and allow the formation to perform rotation in 3-D space. To further improve the flexibility and scalability of the formation, the rotational axis adjustment approach and dynamic agent reconfiguration method are developed, allowing formations to rotate around arbitrary axes in 3-D space and new agents to join the formation. Theoretical analysis is provided to show that the proposed approach preserves the original configuration of the formation. The proposed method maintains the advantages of the complex Laplacian-based method, including reduced neighbor requirements and no reliance on generic or convex nominal configurations, while achieving arbitrary orientation rotations via a more simplified implementation. Simulations in both 2-D and 3-D space validate the effectiveness of the proposed method.
Pose Estimation for Intra-cardiac Echocardiography Catheter via AI-Based Anatomical Understanding
Authors: Jaeyoung Huh, Ankur Kapoor, Young-Ho Kim
Date: 2025-05-07 00:00:00
Tasks: Anatomy, Pose Estimation, Position

Intra-cardiac Echocardiography (ICE) plays a crucial role in Electrophysiology (EP) and Structural Heart Disease (SHD) interventions by providing high-resolution, real-time imaging of cardiac structures. However, existing navigation methods rely on electromagnetic (EM) tracking, which is susceptible to interference and position drift, or require manual adjustments based on operator expertise. To overcome these limitations, we propose a novel anatomy-aware pose estimation system that determines the ICE catheter position and orientation solely from ICE images, eliminating the need for external tracking sensors. Our approach leverages a Vision Transformer (ViT)-based deep learning model, which captures spatial relationships between ICE images and anatomical structures. The model is trained on a clinically acquired dataset of 851 subjects, including ICE images paired with position and orientation labels normalized to the left atrium (LA) mesh. ICE images are patchified into 16x16 embeddings and processed through a transformer network, where a [CLS] token independently predicts position and orientation via separate linear layers. The model is optimized using a Mean Squared Error (MSE) loss function, balancing positional and orientational accuracy. Experimental results demonstrate an average positional error of 9.48 mm and orientation errors of (16.13 deg, 8.98 deg, 10.47 deg) across x, y, and z axes, confirming the model accuracy. Qualitative assessments further validate alignment between predicted and target views within 3D cardiac meshes. This AI-driven system enhances procedural efficiency, reduces operator workload, and enables real-time ICE catheter localization for tracking-free procedures. The proposed method can function independently or complement existing mapping systems like CARTO, offering a transformative approach to ICE-guided interventions.
VIMPPI: Enhancing Model Predictive Path Integral Control with Variational Integration for Underactuated Systems
Authors: Igor Alentev, Lev Kozlov, Ivan Domrachev, Simeon Nedelchev, Jee-Hwan Ryu
Date: 2025-05-07 00:00:00

This paper presents VIMPPI, a novel control approach for underactuated double pendulum systems developed for the AI Olympics competition. We enhance the Model Predictive Path Integral framework by incorporating variational integration techniques, enabling longer planning horizons without additional computational cost. Operating at 500-700 Hz with control interpolation and disturbance detection mechanisms, VIMPPI substantially outperforms both baseline methods and alternative MPPI implementations
Learning 3D Persistent Embodied World Models
Authors: Siyuan Zhou, Yilun Du, Yuncong Yang, Lei Han, Peihao Chen, Dit-yan Yeung, Chuang Gan
Date: 2025-05-05 00:00:00

The ability to simulate the effects of future actions on the world is a crucial ability of intelligent embodied agents, enabling agents to anticipate the effects of their actions and make plans accordingly. While a large body of existing work has explored how to construct such world models using video models, they are often myopic in nature, without any memory of a scene not captured by currently observed images, preventing agents from making consistent long-horizon plans in complex environments where many parts of the scene are partially observed. We introduce a new persistent embodied world model with an explicit memory of previously generated content, enabling much more consistent long-horizon simulation. During generation time, our video diffusion model predicts RGB-D video of the future observations of the agent. This generation is then aggregated into a persistent 3D map of the environment. By conditioning the video model on this 3D spatial map, we illustrate how this enables video world models to faithfully simulate both seen and unseen parts of the world. Finally, we illustrate the efficacy of such a world model in downstream embodied applications, enabling effective planning and policy learning.