pwc-archive/papers-with-abstracts huggingface dataset:
Low-Overhead Channel Estimation Framework for Beyond Diagonal Reconfigurable Intelligent Surface Assisted Multi-User MIMO Communication
Authors: Rui Wang, Shuowen Zhang, Bruno Clerckx, Liang Liu
Date: 2025-04-15 00:00:00

Beyond diagonal reconfigurable intelligent surface (BD-RIS) refers to a family of RIS architectures characterized by scattering matrices not limited to being diagonal and enables higher wave manipulation flexibility and large performance gains over conventional (diagonal) RIS. To achieve those promising gains, accurate channel state information (CSI) needs to be acquired in BD-RIS assisted communication systems. However, the number of coefficients in the cascaded channels to be estimated in BD-RIS assisted systems is significantly larger than that in conventional RIS assisted systems, because the channels associated with the off-diagonal elements of the scattering matrix have to be estimated as well. Surprisingly, for the first time in the literature, this paper rigorously shows that the uplink channel estimation overhead in BD-RIS assisted systems is actually of the same order as that in the conventional RIS assisted systems. This amazing result stems from a key observation: for each user antenna, its cascaded channel matrix associated with one reference BD-RIS element is a scaled version of that associated with any other BD-RIS element due to the common RIS-base station (BS) channel. In other words, the number of independent unknown variables is far less than it would seem at first glance. Building upon this property, this paper manages to characterize the minimum overhead to perfectly estimate all the channels in the ideal case without noise at the BS, and propose a twophase estimation framework for the practical case with noise at the BS. Numerical results demonstrate outstanding channel estimation overhead reduction over existing schemes in BD-RIS assisted systems.
VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification
Authors: Lucas Heublein, Simon Kocher, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott
Date: 2025-04-14 00:00:00
Tasks: Data Augmentation, Data Compression, Disentanglement

Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reducing dependency on centralized infrastructure and ensuring high model performance. In the context of global navigation satellite system (GNSS) applications, the primary objective is to accurately monitor and classify interferences that degrade system performance in distributed environments, thereby enhancing situational awareness. To achieve this, machine learning (ML) models can be deployed on low-resource devices, ensuring minimal communication latency and preserving data privacy. The key challenge is to compress ML models while maintaining high classification accuracy. In this paper, we propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences. We demonstrate that the disentanglement approach can be leveraged for both data compression and data augmentation by interpolating the lower-dimensional latent representations of signal power. To validate our approach, we evaluate three VAE variants - vanilla, factorized, and conditional generative - on four distinct datasets, including two collected in controlled indoor environments and two real-world highway datasets. Additionally, we conduct extensive hyperparameter searches to optimize performance. Our proposed VAE achieves a data compression rate ranging from 512 to 8,192 and achieves an accuracy up to 99.92%.
Frequency Hopping Waveform Design for Secure Integrated Sensing and Communications
Authors: Ali Khandan Boroujeni, Giuseppe Thadeu Freitas de Abreu, Stefan Köpsell, Ghazal Bagheri, Kuranage Roche Rayan Ranasinghe, Rafael F. Schaefer
Date: 2025-04-14 00:00:00
Tasks: ISAC

We introduce a comprehensive approach to enhance the security, privacy, and sensing capabilities of integrated sensing and communications (ISAC) systems by leveraging random frequency agility (RFA) and random pulse repetition interval (PRI) agility (RPA) techniques. The combination of these techniques, which we refer to collectively as random frequency and PRI agility (RFPA), with channel reciprocity-based key generation (CRKG) obfuscates both Doppler frequency and PRIs, significantly hindering the chances that passive adversaries can successfully estimate radar parameters. In addition, a hybrid information embedding method integrating amplitude shift keying (ASK), phase shift keying (PSK), index modulation (IM), and spatial modulation (SM) is incorporated to increase the achievable bit rate of the system significantly. Next, a sparse-matched filter receiver design is proposed to efficiently decode the embedded information with a low bit error rate (BER). Finally, a novel RFPA-based secret generation scheme using CRKG ensures secure code creation without a coordinating authority. The improved range and velocity estimation and reduced clutter effects achieved with the method are demonstrated via the evaluation of the ambiguity function (AF) of the proposed waveforms.
Finite-Precision Conjugate Gradient Method for Massive MIMO Detection
Authors: Yiming Fang, Li Chen, Changsheng You, Dingzhu Wen, Pengcheng Zhu
Date: 2025-04-14 00:00:00

The implementation of the conjugate gradient (CG) method for massive MIMO detection is computationally challenging, especially for a large number of users and correlated channels. In this paper, we propose a low computational complexity CG detection from a finite-precision perspective. First, we develop a finite-precision CG (FP-CG) detection to mitigate the computational bottleneck of each CG iteration and provide the attainable accuracy, convergence, and computational complexity analysis to reveal the impact of finite-precision arithmetic. A practical heuristic is presented to select suitable precisions. Then, to further reduce the number of iterations, we propose a joint finite-precision and block-Jacobi preconditioned CG (FP-BJ-CG) detection. The corresponding performance analysis is also provided. Finally, simulation results validate the theoretical insights and demonstrate the superiority of the proposed detection.
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Authors: Tomoyoshi Kimura, Xinlin Li, Osama Hanna, Yatong Chen, Yizhuo Chen, Denizhan Kara, Tianshi Wang, Jinyang Li, Xiaomin Ouyang, Shengzhong Liu, Mani Srivastava, Suhas Diggavi, Tarek Abdelzaher
Date: 2025-04-13 00:00:00
Tasks: cross-modal alignment, Self-Supervised Learning, Time Series

Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on the scale and quality of multimodal samples. These constraints significantly limit the performance of sensing intelligence in IoT applications, as the heterogeneity and the non-interpretability of time-series signals result in abundant unimodal data but scarce high-quality multimodal pairs. This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. InfoMAE achieves \textit{efficient cross-modal alignment} with \textit{limited data pairs} through a novel information theory-inspired formulation that simultaneously addresses distribution-level and instance-level alignment. Extensive experiments on two real-world IoT applications are performed to evaluate InfoMAE's pairing efficiency to bridge pretrained unimodal models into a cohesive joint multimodal model. InfoMAE enhances downstream multimodal tasks by over 60% with significantly improved multimodal pairing efficiency. It also improves unimodal task accuracy by an average of 22%.
Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
Authors: Zhixuan Pan, Shaowen Wang, Jian Li
Date: 2025-04-13 00:00:00

Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet principled explanations for their underlying mechanisms and several phenomena, such as scaling laws, hallucinations, and related behaviors, remain elusive. In this work, we revisit the classical relationship between compression and prediction, grounded in Kolmogorov complexity and Shannon information theory, to provide deeper insights into LLM behaviors. By leveraging the Kolmogorov Structure Function and interpreting LLM compression as a two-part coding process, we offer a detailed view of how LLMs acquire and store information across increasing model and data scales -- from pervasive syntactic patterns to progressively rarer knowledge elements. Motivated by this theoretical perspective and natural assumptions inspired by Heap's and Zipf's laws, we introduce a simplified yet representative hierarchical data-generation framework called the Syntax-Knowledge model. Under the Bayesian setting, we show that prediction and compression within this model naturally lead to diverse learning and scaling behaviors of LLMs. In particular, our theoretical analysis offers intuitive and principled explanations for both data and model scaling laws, the dynamics of knowledge acquisition during training and fine-tuning, factual knowledge hallucinations in LLMs. The experimental results validate our theoretical predictions.
Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
Authors: Osvaldo Simeone, Sangwoo Park, Matteo Zecchin
Date: 2025-04-12 00:00:00
Tasks: counterfactual, Decision Making, Diagnostic, Uncertainty Quantification

AI is poised to revolutionize telecommunication networks by boosting efficiency, automation, and decision-making. However, the black-box nature of most AI models introduces substantial risk, possibly deterring adoption by network operators. These risks are not addressed by the current prevailing deployment strategy, which typically follows a best-effort train-and-deploy paradigm. This paper reviews conformal calibration, a general framework that moves beyond the state of the art by adopting computationally lightweight, advanced statistical tools that offer formal reliability guarantees without requiring further training or fine-tuning. Conformal calibration encompasses pre-deployment calibration via uncertainty quantification or hyperparameter selection; online monitoring to detect and mitigate failures in real time; and counterfactual post-deployment performance analysis to address "what if" diagnostic questions after deployment. By weaving conformal calibration into the AI model lifecycle, network operators can establish confidence in black-box AI models as a dependable enabling technology for wireless systems.
A convergence law for continuous logic and continuous structures with finite domains
Authors: Vera Koponen
Date: 2025-04-11 00:00:00
Tasks: Relation

We consider continuous relational structures with finite domain $[n] := \{1, \ldots, n\}$ and a many valued logic, $CLA$, with values in the unit interval and which uses continuous connectives and continuous aggregation functions. $CLA$ subsumes first-order logic on ``conventional'' finite structures. To each relation symbol $R$ and identity constraint $ic$ on a tuple the length of which matches the arity of $R$ we associate a continuous probability density function $\mu_R^{ic} : [0, 1] \to [0, \infty)$. We also consider a probability distribution on the set $\mathbf{W}_n$ of continuous structures with domain $[n]$ which is such that for every relation symbol $R$, identity constraint $ic$, and tuple $\bar{a}$ satisfying $ic$, the distribution of the value of $R(\bar{a})$ is given by $\mu_R^{ic}$, independently of the values for other relation symbols or other tuples. In this setting we prove that every formula in $CLA$ is asymptotically equivalent to a formula without any aggregation function. This is used to prove a convergence law for $CLA$ which reads as follows for formulas without free variables: If $\varphi \in CLA$ has no free variable and $I \subseteq [0, 1]$ is an interval, then there is $\alpha \in [0, 1]$ such that, as $n$ tends to infinity, the probability that the value of $\varphi$ is in $I$ tends to $\alpha$.
TextArena
Authors: Leon Guertler, Bobby Cheng, Simon Yu, Bo Liu, Leshem Choshen, Cheston Tan
Date: 2025-04-15 00:00:00
Tasks: text-based games

TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environments (including single-player, two-player, and multi-player setups) and allows for easy evaluation of model capabilities via an online-play system (against humans and other submitted models) with real-time TrueSkill scores. Traditional benchmarks rarely assess dynamic social skills such as negotiation, theory of mind, and deception, creating a gap that TextArena addresses. Designed with research, community and extensibility in mind, TextArena emphasizes ease of adding new games, adapting the framework, testing models, playing against the models, and training models. Detailed documentation of environments, games, leaderboard, and examples are available on https://github.com/LeonGuertler/TextArena and https://www.textarena.ai/.
Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots
Authors: Daniel T. Larsson, Dipankar Maity
Date: 2025-04-14 00:00:00

In this paper, we develop a systematic framework for the time-sequential compression of dynamic probabilistic occupancy grids. Our approach leverages ideas from signal compression theory to formulate an optimization problem that searches for a multi-resolution hierarchical encoder that balances the quality of the compressed map (distortion) with its description size, the latter of which relates to the bandwidth required to reliably transmit the map to other agents or to store map estimates in on-board memory. The resulting optimization problem allows for multi-resolution map compressions to be obtained that satisfy available communication or memory resources, and does not require knowledge of the occupancy map dynamics. We develop an algorithm to solve our problem, and demonstrate the utility of the proposed framework in simulation on both static (i.e., non-time varying) and dynamic (time-varying) occupancy maps.
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
Authors: Muhammad Al-Zafar Khan, Jamal Al-Karaki
Date: 2025-04-14 00:00:00
Tasks: Multi-agent Reinforcement Learning

In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) stochastic reaction-diffusion systems modeling molecular signaling, (2) neural-like electrochemical communication with Hebbian plasticity, and (3) a biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination.
PestMA: LLM-based Multi-Agent System for Informed Pest Management
Authors: Hongrui Shi, Shunbao Li, Zhipeng Yuan, Po Yang
Date: 2025-04-14 00:00:00
Tasks: Management

Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating decisions, highlighting the potential of LLM-based multi-agent systems to automate and enhance pest management processes.
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference
Authors: Yuta Matsui, Ryosuke Yamaki, Ryo Ueda, Seitaro Shinagawa, Tadahiro Taniguchi
Date: 2025-04-13 00:00:00
Tasks: Bayesian Inference, Image Captioning

We propose the Metropolis-Hastings Captioning Game (MHCG), a method to fuse knowledge of multiple vision-language models (VLMs) by learning from each other. Although existing methods that combine multiple models suffer from inference costs and architectural constraints, MHCG avoids these problems by performing decentralized Bayesian inference through a process resembling a language game. The knowledge fusion process establishes communication between two VLM agents alternately captioning images and learning from each other. We conduct two image-captioning experiments with two VLMs, each pre-trained on a different dataset. The first experiment demonstrates that MHCG achieves consistent improvement in reference-free evaluation metrics. The second experiment investigates how MHCG contributes to sharing VLMs' category-level vocabulary by observing the occurrence of the vocabulary in the generated captions.
Toward Super Agent System with Hybrid AI Routers
Authors: Yuhang Yao, Haixin Wang, Yibo Chen, Jiawen Wang, Min Chang Jordan Ren, Bosheng Ding, Salman Avestimehr, Chaoyang He
Date: 2025-04-11 00:00:00

AI Agents powered by Large Language Models are transforming the world through enormous applications. A super agent has the potential to fulfill diverse user needs, such as summarization, coding, and research, by accurately understanding user intent and leveraging the appropriate tools to solve tasks. However, to make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required to ensure high efficiency and low cost. This paper presents a design of the Super Agent System. Upon receiving a user prompt, the system first detects the intent of the user, then routes the request to specialized task agents with the necessary tools or automatically generates agentic workflows. In practice, most applications directly serve as AI assistants on edge devices such as phones and robots. As different language models vary in capability and cloud-based models often entail high computational costs, latency, and privacy concerns, we then explore the hybrid mode where the router dynamically selects between local and cloud models based on task complexity. Finally, we introduce the blueprint of an on-device super agent enhanced with cloud. With advances in multi-modality models and edge hardware, we envision that most computations can be handled locally, with cloud collaboration only as needed. Such architecture paves the way for super agents to be seamlessly integrated into everyday life in the near future.
Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis
Authors: Hao liu, Lijun He, Jiaxi Liang, Zhihan Ren, Fan Li
Date: 2025-04-15 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Dependency Parsing, Image-text matching, Sentiment Analysis, Text Matching

Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and determine their sentiment polarity. However, existing approaches often fall short in simultaneously addressing three core challenges: Sentiment Cue Perception (SCP), Multimodal Information Misalignment (MIM), and Semantic Noise Elimination (SNE). To overcome these limitations, we propose DASCO (\textbf{D}ependency Structure \textbf{A}ugmented \textbf{Sco}ping Framework), a fine-grained scope-oriented framework that enhances aspect-level sentiment reasoning by leveraging dependency parsing trees. First, we designed a multi-task pretraining strategy for MABSA on our base model, combining aspect-oriented enhancement, image-text matching, and aspect-level sentiment-sensitive cognition. This improved the model's perception of aspect terms and sentiment cues while achieving effective image-text alignment, addressing key challenges like SCP and MIM. Furthermore, we incorporate dependency trees as syntactic branch combining with semantic branch, guiding the model to selectively attend to critical contextual elements within a target-specific scope while effectively filtering out irrelevant noise for addressing SNE problem. Extensive experiments on two benchmark datasets across three subtasks demonstrate that DASCO achieves state-of-the-art performance in MABSA, with notable gains in JMASA (+3.1\% F1 and +5.4\% precision on Twitter2015).
Leveraging multimodal explanatory annotations for video interpretation with Modality Specific Dataset
Authors: Elisa Ancarani, Julie Tores, Lucile Sassatelli, Rémy Sun, Hui-Yin Wu, Frédéric Precioso
Date: 2025-04-15 00:00:00

We examine the impact of concept-informed supervision on multimodal video interpretation models using MOByGaze, a dataset containing human-annotated explanatory concepts. We introduce Concept Modality Specific Datasets (CMSDs), which consist of data subsets categorized by the modality (visual, textual, or audio) of annotated concepts. Models trained on CMSDs outperform those using traditional legacy training in both early and late fusion approaches. Notably, this approach enables late fusion models to achieve performance close to that of early fusion models. These findings underscore the importance of modality-specific annotations in developing robust, self-explainable video models and contribute to advancing interpretable multimodal learning in complex video analysis.