pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Bistatic ISAC Systems for Low-Altitude Economy
Authors: Zhenkun Zhang, Yining Xu, Cunhua Pan, Hong Ren, Yiming Yu, Jiangzhou Wang
Date: 2025-06-22 00:00:00
Tasks: Computational Efficiency, Integrated sensing and communication, ISAC, Tensor Decomposition

The burgeoning low-altitude economy (LAE) necessitates integrated sensing and communication (ISAC) systems capable of high-accuracy multi-target localization and velocity estimation under hardware and coverage constraints inherent in conventional ISAC architectures. This paper addresses these challenges by proposing a cooperative bistatic ISAC framework within MIMO-OFDM cellular networks, enabling robust sensing services for LAE applications through standardized 5G New Radio (NR) infrastructure. We first develop a low-complexity parameter extraction algorithm employing CANDECOMP/PARAFAC (CP) tensor decomposition, which exploits the inherent Vandermonde structure in delay-related factor matrices to efficiently recover bistatic ranges, Doppler velocities, and angles-of-arrival (AoA) from multi-dimensional received signal tensors. To resolve data association ambiguity across distributed transmitter-receiver pairs and mitigate erroneous estimates, we further design a robust fusion scheme based on the minimum spanning tree (MST) method, enabling joint 3D position and velocity reconstruction. Comprehensive simulation results validate the framework's superiority in computational efficiency and sensing performance for low-altitude scenarios.
ISAC Network Planning: Sensing Coverage Analysis and 3-D BS Deployment Optimization
Authors: Kaitao Meng, Kawon Han, Christos Masouros, Lajos Hanzo
Date: 2025-06-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localization coverage, while guaranteeing the desired communication performance. In contrast to existing schemes optimized for a single target, an effective network-level approach has to ensure consistent localization accuracy throughout the entire service area. While employing time-of-flight (ToF) based localization, we first analyze the deployment problem from a localization-performance coverage perspective, aiming for minimizing the area Cramer-Rao Lower Bound (A-CRLB) to ensure uniformly high positioning accuracy across the service area. We prove that for a fixed number of BSs, uniformly scaling the service area by a factor \kappa increases the optimal A-CRLB in proportion to \kappa^{2\beta}, where \beta is the BS-to-target pathloss exponent. Based on this, we derive an approximate scaling law that links the achievable A-CRLB across the area of interest to the dimensionality of the sensing area. We also show that cooperative BSs extends the coverage but yields marginal A-CRLB improvement as the dimensionality of the sensing area grows.
Searching for a Hidden Markov Anomaly over Multiple Processes
Authors: Levli Citron, Kobi Cohen, Qing Zhao
Date: 2025-06-20 00:00:00
Tasks: Anomaly Detection

We address the problem of detecting an anomalous process among a large number of processes. At each time t, normal processes are in state zero (normal state), while the abnormal process may be in either state zero (normal state) or state one (abnormal state), with the states being hidden. The transition between states for the abnormal process is governed by a Markov chain over time. At each time step, observations can be drawn from a selected subset of processes. Each probed process generates an observation depending on its hidden state, either a typical distribution under state zero or an abnormal distribution under state one. The objective is to design a sequential search strategy that minimizes the expected detection time, subject to an error probability constraint. In contrast to prior works that assume i.i.d. observations, we address a new setting where anomalies evolve according to a hidden Markov model. To this end, we propose a novel algorithm, dubbed Anomaly Detection under Hidden Markov model (ADHM), which dynamically adapts the probing strategy based on accumulated statistical evidence and predictive belief updates over hidden states. ADHM effectively leverages temporal correlations to focus sensing resources on the most informative processes. The algorithm is supported by an asymptotic theoretical foundation, grounded in an oracle analysis that characterizes the fundamental limits of detection under the assumption of a known distribution of the hidden states. In addition, the algorithm demonstrates strong empirical performance, consistently outperforming existing methods in extensive simulations.
Neural Polar Decoders for DNA Data Storage
Authors: Ziv Aharoni, Henry D. Pfister
Date: 2025-06-20 00:00:00

Synchronization errors, such as insertions and deletions, present a fundamental challenge in DNA-based data storage systems, arising from both synthesis and sequencing noise. These channels are often modeled as insertion-deletion-substitution (IDS) channels, for which designing maximum-likelihood decoders is computationally expensive. In this work, we propose a data-driven approach based on neural polar decoders (NPDs) to design low-complexity decoders for channels with synchronization errors. The proposed architecture enables decoding over IDS channels with reduced complexity $O(AN log N )$, where $A$ is a tunable parameter independent of the channel. NPDs require only sample access to the channel and can be trained without an explicit channel model. Additionally, NPDs provide mutual information (MI) estimates that can be used to optimize input distributions and code design. We demonstrate the effectiveness of NPDs on both synthetic deletion and IDS channels. For deletion channels, we show that NPDs achieve near-optimal decoding performance and accurate MI estimation, with significantly lower complexity than trellis-based decoders. We also provide numerical estimates of the channel capacity for the deletion channel. We extend our evaluation to realistic DNA storage settings, including channels with multiple noisy reads and real-world Nanopore sequencing data. Our results show that NPDs match or surpass the performance of existing methods while using significantly fewer parameters than the state-of-the-art. These findings highlight the promise of NPDs for robust and efficient decoding in DNA data storage systems.
Empowering Near-Field Communications in Low-Altitude Economy with LLM: Fundamentals, Potentials, Solutions, and Future Directions
Authors: Zhuo Xu, Tianyue Zheng, Linglong Dai
Date: 2025-06-20 00:00:00

The low-altitude economy (LAE) is gaining significant attention from academia and industry. Fortunately, LAE naturally aligns with near-field communications in extremely large-scale MIMO (XL-MIMO) systems. By leveraging near-field beamfocusing, LAE can precisely direct beam energy to unmanned aerial vehicles, while the additional distance dimension boosts overall spectrum efficiency. However, near-field communications in LAE still face several challenges, such as the increase in signal processing complexity and the necessity of distinguishing between far and near-field users. Inspired by the large language models (LLM) with powerful ability to handle complex problems, we apply LLM to solve challenges of near-field communications in LAE. The objective of this article is to provide a comprehensive analysis and discussion on LLM-empowered near-field communications in LAE. Specifically, we first introduce fundamentals of LLM and near-field communications, including the key advantages of LLM and key characteristics of near-field communications. Then, we reveal the opportunities and challenges of near-field communications in LAE. To address these challenges, we present a LLM-based scheme for near-field communications in LAE, and provide a case study which jointly distinguishes far and near-field users and designs multi-user precoding matrix. Finally, we outline and highlight several future research directions and open issues.
SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Authors: Tan Loc Nguyen, Tin T. Tran
Date: 2025-06-19 00:00:00
Tasks: Recommendation Systems

Recommender systems play a crucial role in enabling personalized content delivery amidst the challenges of information overload and human mobility. Although conventional methods often rely on interaction matrices or graph-based retrieval, recent approaches have sought to exploit contextual signals such as time and location. However, most existing models focus on node-level representation or isolated edge attributes, underutilizing the relational structure between interactions. We propose SEP-GCN, a novel graph-based recommendation framework that learns from pairs of contextually similar interaction edges, each representing a user-item check-in event. By identifying edge pairs that occur within similar temporal windows or geographic proximity, SEP-GCN augments the user-item graph with contextual similarity links. These links bridge distant but semantically related interactions, enabling improved long-range information propagation. The enriched graph is processed via an edge-aware convolutional mechanism that integrates contextual similarity into the message-passing process. This allows SEP-GCN to model user preferences more accurately and robustly, especially in sparse or dynamic environments. Experiments on benchmark data sets show that SEP-GCN consistently outperforms strong baselines in both predictive accuracy and robustness.
Competing Bandits in Matching Markets via Super Stability
Authors: Soumya Basu
Date: 2025-06-19 00:00:00

We study bandit learning in matching markets with two-sided reward uncertainty, extending prior research primarily focused on single-sided uncertainty. Leveraging the concept of `super-stability' from Irving (1994), we demonstrate the advantage of the Extended Gale-Shapley (GS) algorithm over the standard GS algorithm in achieving true stable matchings under incomplete information. By employing the Extended GS algorithm, our centralized algorithm attains a logarithmic pessimal stable regret dependent on an instance-dependent admissible gap parameter. This algorithm is further adapted to a decentralized setting with a constant regret increase. Finally, we establish a novel centralized instance-dependent lower bound for binary stable regret, elucidating the roles of the admissible gap and super-stable matching in characterizing the complexity of stable matching with bandit feedback.
Code Rate Optimization via Neural Polar Decoders
Authors: Ziv Aharoni, Bashar Huleihel, Henry D Pfister, Haim H Permuter
Date: 2025-06-18 00:00:00
Tasks: Capacity Estimation

This paper proposes a method to optimize communication code rates via the application of neural polar decoders (NPDs). Employing this approach enables simultaneous optimization of code rates over input distributions while providing a practical coding scheme within the framework of polar codes. The proposed approach is designed for scenarios where the channel model is unknown, treating the channel as a black box that produces output samples from input samples. We employ polar codes to achieve our objectives, using NPDs to estimate mutual information (MI) between the channel inputs and outputs, and optimize a parametric model of the input distribution. The methodology involves a two-phase process: a training phase and an inference phase. In the training phase, two steps are repeated interchangeably. First, the estimation step estimates the MI of the channel inputs and outputs via NPDs. Second, the improvement step optimizes the input distribution parameters to maximize the MI estimate obtained by the NPDs. In the inference phase, the optimized model is used to construct polar codes. This involves incorporating the Honda-Yamamoto (HY) scheme to accommodate the optimized input distributions and list decoding to enhance decoding performance. Experimental results on memoryless and finite-state channels (FSCs) demonstrate the effectiveness of our approach, particularly in cases where the channel's capacity-achieving input distribution is non-uniform. For these cases, we show significant improvements in MI and bit error rates (BERs) over those achieved by uniform and independent and identically distributed (i.i.d.) input distributions, validating our method for block lengths up to 1024. This scalable approach has potential applications in real-world communication systems, bridging theoretical capacity estimation and practical coding performance.
Spectral Contraction of Boundary-Weighted Filters on delta-Hyperbolic Graphs
Authors: Le Vu Anh, Mehmet Dik, Nguyen Viet Anh
Date: 2025-06-18 00:00:00

Hierarchical graphs often exhibit tree-like branching patterns, a structural property that challenges the design of traditional graph filters. We introduce a boundary-weighted operator that rescales each edge according to how far its endpoints drift toward the graph's Gromov boundary. Using Busemann functions on delta-hyperbolic networks, we prove a closed-form upper bound on the operator's spectral norm: every signal loses a curvature-controlled fraction of its energy at each pass. The result delivers a parameter-free, lightweight filter whose stability follows directly from geometric first principles, offering a new analytic tool for graph signal processing on data with dense or hidden hierarchical structure.
LLM Agent for Hyper-Parameter Optimization
Authors: Wanzhe Wang, Jianqiu Peng, Menghao Hu, Weihuang Zhong, Tong Zhang, Shuai Wang, Yixin Zhang, Mingjie Shao, Wanli Ni
Date: 2025-06-18 00:00:00
Tasks: Large Language Model

Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters tuning methods for warm-start particles swarm optimization with cross and mutation (WS-PSO-CM) algortihm for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication are primarily heuristic-based, exhibiting low levels of automation and unsatisfactory performance. In this paper, we design an large language model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and model context protocol (MCP) are applied. In particular, the LLM agent is first setup via a profile, which specifies the mission, background, and output format. Then, the LLM agent is driven by the prompt requirement, and iteratively invokes WS-PSO-CM algorithm for exploration. Finally, the LLM agent autonomously terminates the loop and returns a set of hyper-parameters. Our experiment results show that the minimal sum-rate achieved by hyper-parameters generated via our LLM agent is significantly higher than those by both human heuristics and random generation methods. This indicates that an LLM agent with PSO knowledge and WS-PSO-CM algorithm background is useful in finding high-performance hyper-parameters.
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Authors: Hong Qing Yu
Date: 2025-06-23 00:00:00
Tasks: Causal Inference, Decision Making, RAG, Retrieval-augmented Generation

Large language models excel at generating fluent text but frequently struggle with structured reasoning involving temporal constraints, causal relationships, and probabilistic reasoning. To address these limitations, we propose Temporal Causal Probabilistic Description Logic (T-CPDL), an integrated framework that extends traditional Description Logic with temporal interval operators, explicit causal relationships, and probabilistic annotations. We present two distinct variants of T-CPDL: one capturing qualitative temporal relationships through Allen's interval algebra, and another variant enriched with explicit timestamped causal assertions. Both variants share a unified logical structure, enabling complex reasoning tasks ranging from simple temporal ordering to nuanced probabilistic causation. Empirical evaluations on temporal reasoning and causal inference benchmarks confirm that T-CPDL substantially improves inference accuracy, interpretability, and confidence calibration of language model outputs. By delivering transparent reasoning paths and fine-grained temporal and causal semantics, T-CPDL significantly enhances the capability of language models to support robust, explainable, and trustworthy decision-making. This work also lays the groundwork for developing advanced Logic-Retrieval-Augmented Generation (Logic-RAG) frameworks, potentially boosting the reasoning capabilities and efficiency of knowledge graph-enhanced RAG systems.
Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Authors: Alexandros Evangelidis, Gricel Vázquez, Simos Gerasimou
Date: 2025-06-21 00:00:00
Tasks: Decision Making, Sequential Decision Making

Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement. This iterative procedure offers a balance between accuracy and efficiency, as refinement occurs only when necessary. Through a comprehensive empirical evaluation comprising diverse case studies and MDPs up to 1M states, we demonstrate significant performance improvements yielded by our approach compared to the leading probabilistic model checker PRISM (up to 2x), thus offering a very competitive solution for real-world policy synthesis tasks in larger MDPs.
Machine Learning Model Integration with Open World Temporal Logic for Process Automation
Authors: Dyuman Aditya, Colton Payne, Mario Leiva, Paulo Shakarian
Date: 2025-06-21 00:00:00

Recent advancements in Machine Learning (ML) have yielded powerful models capable of extracting structured information from diverse and complex data sources. However, a significant challenge lies in translating these perceptual or extractive outputs into actionable, reasoned decisions within complex operational workflows. To address these challenges, this paper introduces a novel approach that integrates the outputs from various machine learning models directly with the PyReason framework, an open-world temporal logic programming reasoning engine. PyReason's foundation in generalized annotated logic allows for the seamless incorporation of real-valued outputs (e.g., probabilities, confidence scores) from diverse ML models, treating them as truth intervals within its logical framework. Crucially, PyReason provides mechanisms, implemented in Python, to continuously poll ML model outputs, convert them into logical facts, and dynamically recompute the minimal model, ensuring real-tine adaptive decision-making. Furthermore, its native support for temporal reasoning, knowledge graph integration, and fully explainable interface traces enables sophisticated analysis over time-sensitive process data and existing organizational knowledge. By combining the strengths of perception and extraction from ML models with the logical deduction and transparency of PyReason, we aim to create a powerful system for automating complex processes. This integration finds utility across numerous domains, including manufacturing, healthcare, and business operations.
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving
Authors: Chuxue Cao, Mengze Li, Juntao Dai, Jinluan Yang, Zijian Zhao, Shengyu Zhang, Weijie Shi, Chengzhong Liu, Sirui Han, Yike Guo
Date: 2025-06-20 00:00:00
Tasks: Automated Theorem Proving, Diversity, Mathematical Reasoning

Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas. However, their effectiveness in complex mathematical reasoning involving multi-step FOL deductions is still under-researched. While LLMs perform competitively on established mathematical reasoning benchmarks, they struggle with multi-step FOL tasks, as demonstrated by Deepseek-Prover-V2-7B's low accuracy (4.2%) on our proposed theorem proving dataset. This issue arises from the limited exploration of diverse proof strategies and the potential for early reasoning mistakes to undermine entire proofs. To address these issues, we propose DREAM, a self-adaptive solution that enhances the Diversity and REAsonability of LLMs' generation strategies. DREAM incorporates an Axiom-Driven Strategy Diversification mechanism to promote varied strategic outcomes and a Sub-Proposition Error Feedback to help LLMs reflect on and correct their proofs. Our contributions include pioneering advancements in LLMs' mathematical reasoning through FOL theorem proving, introducing a novel inference stage solution that improves performance by 0.6% to 6.4%, and providing a curated dataset of 447 mathematical theorems in Lean 4 format for evaluation.
Approximation Fixpoint Theory with Refined Approximation Spaces
Authors: Linde Vanbesien, Bart Bogaerts, Marc Denecker
Date: 2025-06-19 00:00:00

Approximation Fixpoint Theory (AFT) is a powerful theory covering various semantics of non-monotonic reasoning formalisms in knowledge representation such as Logic Programming and Answer Set Programming. Many semantics of such non-monotonic formalisms can be characterized as suitable fixpoints of a non-monotonic operator on a suitable lattice. Instead of working on the original lattice, AFT operates on intervals in such lattice to approximate or construct the fixpoints of interest. While AFT has been applied successfully across a broad range of non-monotonic reasoning formalisms, it is confronted by its limitations in other, relatively simple, examples. In this paper, we overcome those limitations by extending consistent AFT to deal with approximations that are more refined than intervals. Therefore, we introduce a more general notion of approximation spaces, showcase the improved expressiveness and investigate relations between different approximation spaces.
Floating-Point Neural Networks Are Provably Robust Universal Approximators
Authors: Geonho Hwang, Wonyeol Lee, Yeachan Park, Sejun Park, Feras Saad
Date: 2025-06-19 00:00:00

The classical universal approximation (UA) theorem for neural networks establishes mild conditions under which a feedforward neural network can approximate a continuous function $f$ with arbitrary accuracy. A recent result shows that neural networks also enjoy a more general interval universal approximation (IUA) theorem, in the sense that the abstract interpretation semantics of the network using the interval domain can approximate the direct image map of $f$ (i.e., the result of applying $f$ to a set of inputs) with arbitrary accuracy. These theorems, however, rest on the unrealistic assumption that the neural network computes over infinitely precise real numbers, whereas their software implementations in practice compute over finite-precision floating-point numbers. An open question is whether the IUA theorem still holds in the floating-point setting. This paper introduces the first IUA theorem for floating-point neural networks that proves their remarkable ability to perfectly capture the direct image map of any rounded target function $f$, showing no limits exist on their expressiveness. Our IUA theorem in the floating-point setting exhibits material differences from the real-valued setting, which reflects the fundamental distinctions between these two computational models. This theorem also implies surprising corollaries, which include (i) the existence of provably robust floating-point neural networks; and (ii) the computational completeness of the class of straight-line programs that use only floating-point additions and multiplications for the class of all floating-point programs that halt.