Tootfinder

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Morteza Ziabakhsh, Kiyan Rezaee, Sadegh Eskandari, Seyed Amir Hossein Tabatabaei, Mohammad M. Ghassemi
https://arxiv.org/abs/2508.07486

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Modern software systems are increasingly shifting from monolithic architectures to microservices to enhance scalability, maintainability, and deployment flexibility. Existing microservice extraction methods typically rely on hard clustering, assigning each software component to a single microservice. This approach often increases inter-service coupling and reduces intra-service cohesion. We propose Mo2oM (Monolithic to Overlapping Microservices), a framework that formulates microservice extract…

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel Deschaud
https://arxiv.org/abs/2510.06876

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D pro…

Let's Simply Count: Quantifying Distributional Similarity Between Activities in Event Data
Henrik Kirchmann, Stephan A. Fahrenkrog-Petersen, Xixi Lu, Matthias Weidlich
https://arxiv.org/abs/2509.09440

Let's Simply Count: Quantifying Distributional Similarity Between Activities in Event Data
To obtain insights from event data, advanced process mining methods assess the similarity of activities to incorporate their semantic relations into the analysis. Here, distributional similarity that captures similarity from activity co-occurrences is commonly employed. However, existing work for distributional similarity in process mining adopt neural network-based approaches as developed for natural language processing, e.g., word2vec and autoencoders. While these approaches have been shown t…

Introspection in Learned Semantic Scene Graph Localisation
Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini
https://arxiv.org/abs/2510.07053 https://

Introspection in Learned Semantic Scene Graph Localisation
This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. …

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
Vibhor Agrawal, Fay Wang, Rishi Puri
https://arxiv.org/abs/2508.05647 https://

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
We present a novel graph neural network (GNN) architecture for retrieval-augmented generation (RAG) that leverages query-aware attention mechanisms and learned scoring heads to improve retrieval accuracy on complex, multi-hop questions. Unlike traditional dense retrieval methods that treat documents as independent entities, our approach constructs per-episode knowledge graphs that capture both sequential and semantic relationships between text chunks. We introduce an Enhanced Graph Attention Ne…

ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection
Xuanhao Luo, Shivesh Madan Nath Jha, Akruti Sinha, Zhizhen Li, Yuchen Liu
https://arxiv.org/abs/2509.05936

ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection
Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled data and significant human effort. To address these challenges, we propose ALPHA, the first Active Learning Pipeline for Human-free log Analysis. ALPHA integrates semantic embedding, clustering-based repre…

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Semantic Network Analysis of Achievement Standards in Physics of 2022 Revised Curriculum
Jibeom Seo, Jun Cho, Sukyung Han, Meesoon Ha
https://arxiv.org/abs/2508.02864 https://…

Semantic Network Analysis of Achievement Standards in Physics of 2022 Revised Curriculum
We investigate semantic networks of achievement standards for physics subjects in the 2022 revised curriculum to derive information embedded in the curriculum. We extract each subject's keywords with node strength and random-walk betweenness, detect communities of physics terms by the optimized greedy algorithm, and find the connectivity of physics subjects using bipartite networks. The network analysis reveals three remarkable results: First, keywords are about scientific thinking and practice…

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks
Yu Liu, Boxiang He, Fanggang Wang
https://arxiv.org/abs/2509.20835 https://arx…

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks
This paper proposes a novel and flexible security-aware semantic-driven integrated sensing and communication (ISAC) framework, namely security semantic ISAC (SS-ISAC). Inspired by the positive impact of the adversarial attack, a pair of pluggable encryption and decryption modules is designed in the proposed SS-ISAC framework. The encryption module is installed after the semantic transmitter, adopting a trainable adversarial residual network (ARN) to create the adversarial attack. Correspondingl…

Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin Fa{\ss}bender, Christian Hubschneider, J. Marius Z\"ollner
https://arxiv.org/abs/2509.04816

Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like traffic scene perception. While ensemble methods are commonly used to quantify uncertainty by combining multiple models, a mixture of experts (MoE) offers an efficient alternative by leveraging a gating network to dynamically weight expert predictions based on the input. Building on the promising use of MoEs for sema…

Joint Neural SDF Reconstruction and Semantic Segmentation for CAD Models
Shen Fan, Przemyslaw Musialski
https://arxiv.org/abs/2510.03837 https://arxiv.org/…

Joint Neural SDF Reconstruction and Semantic Segmentation for CAD Models
We propose a simple, data-efficient pipeline that augments an implicit reconstruction network based on neural SDF-based CAD parts with a part-segmentation head trained under PartField-generated supervision. Unlike methods tied to fixed taxonomies, our model accepts meshes with any number of parts and produces coherent, geometry-aligned labels in a single pass. We evaluate on randomly sampled CAD meshes from the ABC dataset with intentionally varied part cardinalities, including over-segmented s…

From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Ke Xue, Rongfei Fan, Lixin, Dawei Zhao, Chao Zhu, Han Hu
https://arxiv.org/abs/2509.22425

From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Audio-visual speech separation aims to isolate each speaker's clean voice from mixtures by leveraging visual cues such as lip movements and facial features. While visual information provides complementary semantic guidance, existing methods often underexploit its potential by relying on static visual representations. In this paper, we propose CSFNet, a Coarse-to-Separate-Fine Network that introduces a recursive semantic enhancement paradigm for more effective separation. CSFNet operates in two …

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Yu Li, Zulong Chen, Wenjian Xu, Hong Wen, Yipeng Yu, Man Lung Yiu, Yuyu Yin
https://arxiv.org/abs/2508.13676

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural h…

Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks
Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez, Syed A. Zaidi
https://arxiv.org/abs/2509.22668 https:…

Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks
6G wireless networks aim to exploit semantic awareness to optimize radio resources. By optimizing the transmission through the lens of the desired goal, the energy consumption of transmissions can also be reduced, and the latency can be improved. To that end, this paper investigates a paradigm in which the capabilities of generative AI (GenAI) on the edge are harnessed for network optimization. In particular, we investigate an Unmanned Aerial Vehicle (UAV) handover framework that takes advantag…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.02048 https://ar…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Research in semantic communication has garnered considerable attention, particularly in the area of image transmission, where joint source-channel coding (JSCC)-based neural network (NN) modules are frequently employed. However, these systems often experience performance degradation over time due to an outdated knowledge base, highlighting the need for periodic updates. To address this challenge in the context of training JSCC modules for image transmission, we propose a federated learning (FL)…

Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design
Zhouxiang Zhao, Ran Yi, Yihan Cang, Boyang Jin, Zhaohui Yang, Mingzhe Chen, Chongwen Huang, Zhaoyang Zhang
https://arxiv.org/abs/2509.19791

Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design
This letter addresses the energy efficiency issue in unmanned aerial vehicle (UAV)-assisted autonomous systems. We propose a framework for an agentic artificial intelligence (AI)-powered low-altitude semantic wireless network, that intelligently orchestrates a sense-communicate-decide-control workflow. A system-wide energy consumption minimization problem is formulated to enhance mission endurance. This problem holistically optimizes key operational variables, including UAV's location, semantic…

SDC-Net: A Domain Adaptation Framework with Semantic-Dynamic Consistency for Cross-Subject EEG Emotion Recognition
Jiahao Tang, Youjun Li, Xiangting Fan, Yangxuan Zheng, Siyuan Lu, Xueping Li, Peng Fang, Chenxi Li, Zi-Gang Huang
https://arxiv.org/abs/2507.17524

SDC-Net: A Domain Adaptation Framework with Semantic-Dynamic Consistency for Cross-Subject EEG Emotion Recognition
Electroencephalography(EEG) based emotion recognition holds great promise for affective brain-computer interfaces (aBCIs), yet practical deployment remains challenging due to substantial inter-subject variability and the lack of labeled data in target domains. To overcome these limitations, we present a novel unsupervised Semantic-Dynamic Consistency domain adaptation network for fully label-free cross-subject EEG emotion recognition. First, we introduce a Same-Subject Same-Trial Mixup strategy…

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
https://arxiv.org/abs/2509.01583

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator. Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential…

Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
Andrew Yarovoi, Christopher R. Valenta
https://arxiv.org/abs/2508.20135 https://

Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
In this case study, we present a data-efficient point cloud segmentation pipeline and training framework for robust segmentation of unimproved roads and seven other classes. Our method employs a two-stage training framework: first, a projection-based convolutional neural network is pre-trained on a mixture of public urban datasets and a small, curated in-domain dataset; then, a lightweight prediction head is fine-tuned exclusively on in-domain data. Along the way, we explore the application of …

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng, Bin Chen, Xiaodong Cui, Lianwei Wu, Keke Tang
https://arxiv.org/abs/2507.13415

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. However, they overlook the semantic enhancement effects of large multimodal models and pay little attention to the emotional features of news. In addition, people find that fake news is more inclined to contain negative emotions than real ones. Therefore, we propose a novel Semantic Enhancement and Emotional Reasoning (SE…

Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada, Lexin Li, Linjun Zhang
https://arxiv.org/abs/2509.11316 https://arxiv.org/…

Contrastive Network Representation Learning
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional, and sparse networks that lack node or edge covariates, we propose a novel contrastive learning-based statistic…

Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper
Gehui Chen, Guan'an Wang, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2509.04957 https://

Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper
Recent Video-to-Audio (V2A) generation relies on extracting semantic and temporal features from video to condition generative models. Training these models from scratch is resource intensive. Consequently, leveraging foundation models (FMs) has gained traction due to their cross-modal knowledge transfer and generalization capabilities. One prior work has explored fine-tuning a lightweight mapper network to connect a pre-trained visual encoder with a text-to-audio generation model for V2A. Inspi…

Knowledge-Defined and Twin-Assisted Network Management for 6G
Tu\u{g}\c{c}e Bilen, Mehmet \"Ozdem
https://arxiv.org/abs/2509.23398 https://arxiv.org/p…

Knowledge-Defined and Twin-Assisted Network Management for 6G
The increasing complexity, dynamism, and heterogeneity of 6G networks demand management systems that can reason proactively and generalize beyond pre-defined cases. In this paper, we propose a modular, knowledge-defined architecture that integrates Digital Twin models with semantic reasoning and zero-shot learning to enable autonomous decision-making for previously unseen network scenarios. Real-time data streams are used to maintain synchronized virtual replicas of the physical network, which …

LLMs Between the Nodes: Community Discovery Beyond Vectors
Ekta Gujral, Apurva Sinha
https://arxiv.org/abs/2507.22955 https://arxiv.org/pdf/2507.22955

LLMs Between the Nodes: Community Discovery Beyond Vectors
Community detection in social network graphs plays a vital role in uncovering group dynamics, influence pathways, and the spread of information. Traditional methods focus primarily on graph structural properties, but recent advancements in Large Language Models (LLMs) open up new avenues for integrating semantic and contextual information into this task. In this paper, we present a detailed investigation into how various LLM-based approaches perform in identifying communities within social grap…

Deadline-Aware Bandwidth Allocation for Semantic Generative Communication with Diffusion Models
Jinhyuk Choi, Jihong Park, Seungeun Oh, Seong-Lyun Kim
https://arxiv.org/abs/2508.12701

Deadline-Aware Bandwidth Allocation for Semantic Generative Communication with Diffusion Models
The importance of Radio Access Network (RAN) in support Artificial Intelligence (AI) application services has grown significantly, underscoring the need for an integrated approach that considers not only network efficiency but also AI performance. In this paper we focus on a semantic generative communication (SGC) framework for image inpainting application. Specifically, the transmitter sends semantic information, i.e., semantic masks and textual descriptions, while the receiver utilizes a cond…

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Chunlei Wu, Hongfang Liu, Jason Flannick, Mark A. Musen, Andrew I. Su, Lawrence Hunter, Thomas M. Powers, Cathy H. Wu
https://arxiv.org/abs/2509.22424

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Knowledge graphs, collectively as a knowledge network, have become critical tools for knowledge discovery in computable and explainable knowledge systems. Due to the semantic and structural complexities of biomedical data, these knowledge graphs need to enable dynamic reasoning over large evolving graphs and support fit-for-purpose abstraction, while establishing standards, preserving provenance and enforcing policy constraints for actionable discovery. A recent meeting of leading scientists di…

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
Wuyang Zhang, Yexin Tian, Xiandong Meng, Mengjie Wang, Junliang Du
https://arxiv.org/abs/2508.14427

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
This paper addresses the problems of missing reasoning chains and insufficient entity-level semantic understanding in large language models when dealing with tasks that require structured knowledge. It proposes a fine-tuning algorithm framework based on knowledge graph injection. The method builds on pretrained language models and introduces structured graph information for auxiliary learning. A graph neural network is used to encode entities and their relations, constructing a graph-based sema…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/10]:
- SINF: Semantic Neural Network Inference with Semantic Subgraphs
A. Q. M. Sazzad Sayyed, Francesco Restuccia

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Wenzheng Kong, Wenyi Zhang
https://arxiv.org/abs/2507.12850 https://

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Joint source-channel coding (JSCC) is an effective approach for semantic communication. However, current JSCC methods are difficult to integrate with existing communication network architectures, where application and network providers are typically different entities. Recently, a novel paradigm termed Split DeepJSCC has been under consideration to address this challenge. Split DeepJSCC employs a bit-level interface that enables separate design of source and channel codes, ensuring compatibilit…

Hybrid Semantic-Complementary Transmission for High-Fidelity Image Reconstruction
Hyelin Nam, Jihong Park, Jinho Choi, Seong-Lyun Kim
https://arxiv.org/abs/2507.17196 https://…

Hybrid Semantic-Complementary Transmission for High-Fidelity Image Reconstruction
Recent advances in semantic communication (SC) have introduced neural network (NN)-based transceivers that convey semantic representation (SR) of signals such as images. However, these NNs are trained over diverse image distributions and thus often fail to reconstruct fine-grained image-specific details. To overcome this limited reconstruction fidelity, we propose an extended SC framework, hybrid semantic communication (HSC), which supplements SR with complementary representation (CR) capturing…

Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph
Mengyang Cao, Frank F. Yang, Yi Jin, Yijun Yan
https://arxiv.org/abs/2508.14059 https://

Graph Neural Network for Product Recommendation on the Amazon Co-purchase Graph
Identifying relevant information among massive volumes of data is a challenge for modern recommendation systems. Graph Neural Networks (GNNs) have demonstrated significant potential by utilizing structural and semantic relationships through graph-based learning. This study assessed the abilities of four GNN architectures, LightGCN, GraphSAGE, GAT, and PinSAGE, on the Amazon Product Co-purchase Network under link prediction settings. We examined practical trade-offs between architectures, model …

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Youssef Maklad, Fares Wael, Ali Hamdi, Wael Elsersy, Khaled Shaban
https://arxiv.org/abs/2508.14300

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Traditional protocol fuzzing techniques, such as those employed by AFL-based systems, often lack effectiveness due to a limited semantic understanding of complex protocol grammars and rigid seed mutation strategies. Recent works, such as ChatAFL, have integrated Large Language Models (LLMs) to guide protocol fuzzing and address these limitations, pushing protocol fuzzers to wider exploration of the protocol state space. But ChatAFL still faces issues like unreliable output, LLM hallucinations, …

U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
Bohan Huang, Qianyun Bao, Haoyuan Ma
https://arxiv.org/abs/2509.22444 https://

U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
Medical image segmentation faces significant challenges in preserving fine-grained details and precise boundaries due to complex anatomical structures and pathological regions. These challenges primarily stem from two key limitations of conventional U-Net architectures: (1) their simple skip connections ignore the encoder-decoder semantic gap between various features, and (2) they lack the capability for multi-scale feature extraction in deep layers. To address these challenges, we propose the …

Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion
Yu Zhang, Baotong Tian, Zhiyao Duan
https://arxiv.org/abs/2507.14534 https://

Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion
Zero-shot online voice conversion (VC) holds significant promise for real-time communications and entertainment. However, current VC models struggle to preserve semantic fidelity under real-time constraints, deliver natural-sounding conversions, and adapt effectively to unseen speaker characteristics. To address these challenges, we introduce Conan, a chunkwise online zero-shot voice conversion model that preserves the content of the source while matching the voice timbre and styles of referenc…

Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati, Paolo Di Lorenzo
https://arxiv.org/abs/2507.17835

Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutual understanding due to the emergence of semantic channel noise. This undermines the receiver's ability to interpret transmitted representations, thereby reducing overall system performance. To address this issue, we propose the Parseval Frame Equalizer (PFE), a zero-shot, frame-based semantic channel equalizer that a…

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
Natallia Kokash, Bernard de Bono, Tom Gillespie
https://arxiv.org/abs/2509.15780 https://

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
We are developing infrastructure to support researchers in mapping data related to the peripheral nervous system and other physiological systems, with an emphasis on their relevance to the organs under investigation. The nervous system, a complex network of nerves and ganglia, plays a critical role in coordinating and transmitting signals throughout the body. To aid in this, we have created ApiNATOMY, a framework for the topological and semantic representation of multiscale physiological circui…

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Jiaqi Ma, Guo-Sen Xie, Fang Zhao, Zechao Li
https://arxiv.org/abs/2508.16159 https://

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures. However, this identical network design results in over-semantic homogenization. To address this, we propose a novel homologous but heterogeneous network. By treating support-query pairs as dual perspectives, we introduce heterogeneous visual aggregation (HA) modules to enhance complementa…

Prediction-Powered Communication with Distortion Guarantees
Matteo Zecchin, Unnikrishnan Kunnath Ganesan, Giuseppe Durisi, Petar Popovski, Osvaldo Simeone
https://arxiv.org/abs/2509.24373

Prediction-Powered Communication with Distortion Guarantees
The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscape is motivating a rethinking of classical Shannon information theory that emphasizes semantic and task-oriented paradigms. In this paper, we study a prediction-powered communication setting, in which devices, equipped with artificial intelligence (AI)-based predictors, communicate under zero-delay constraints with str…

Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission
Chen Zhu, Siyun Liang, Zhouxiang Zhao, Jianrong Bao, Zhaohui Yang, Zhaoyang Zhang, Dusit Niyato
https://arxiv.org/abs/2507.11913

Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission
Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on le…

AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR
Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Abhiram Gorle, Muhammad Ali Jamshed, John M. Cioffi
https://arxiv.org/abs/2507.19124

AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR
Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the low-rank channels that inevitably occur when there are more active users than there are degrees of spatial freedom, effectively often the number of antennas. The presented approach uses optimal nonlinear transceivers, equivalently generalized decision-feedback…

Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Le Zhang, Fuping Wu, Arun Thirunavukarasu, Kevin Bronik, Thomas Nichols, Bartlomiej W. Papiez
https://arxiv.org/abs/2508.18186

Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especially in medical imaging. In contrast, coarse annotations are quicker, cheaper, and easier to produce, even by non-experts. In this paper, we propose to use coarse drawings from both positive (target) and negative (background) classes in the image, even with noisy pixels, to train a convolutional neural network (CNN) for …

Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems
Diego Gosmar, Deborah A. Dahl
https://arxiv.org/abs/2509.14956 https://arxiv.o…

Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems
This paper proposes a novel architectural framework aimed at enhancing security and reliability in multi-agent systems (MAS). A central component of this framework is a network of Sentinel Agents, functioning as a distributed security layer that integrates techniques such as semantic analysis via large language models (LLMs), behavioral analytics, retrieval-augmented verification, and cross-agent anomaly detection. Such agents can potentially oversee inter-agent communications, identify potenti…

E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion
Minghui Wu, Zhen Gao
https://arxiv.org/abs/2509.19312 https://arxi…

E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion
Massive multiple-input multiple-output (MIMO) promises high spectral efficiency but also leads to high-dimensional downlink channel state information (CSI), which complicates real-time channel acquisition and precoding. To address this, we propose an end-to-end (E2E) uplink-downlink CSI fusion precoding network that jointly models downlink CSI reference signal (CSI-RS) design, CSI feedback, and base-station (BS) precoding within a single E2E neural architecture. Concretely, a projection network…

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper
https://arxiv.org/abs/2508.21529

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these descriptors are often patch-based, and so struggle to represent the fine features often present in micrographs; they also struggle with the large image sizes present in materials and biological image analysis. In this work, we train a convolutional neural network to upsample lo…

LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
Haozhe Jia, Wenshuo Chen, Yuqi Lin, Yang Yang, Lei Wang, Mang Ning, Bowen Tian, Songning Lai, Nanqian Jia, Yifan Chen, Yutao Yue
https://arxiv.org/abs/2509.25304

LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model
While current diffusion-based models, typically built on U-Net architectures, have shown promising results on the text-to-motion generation task, they still suffer from semantic misalignment and kinematic artifacts. Through analysis, we identify severe gradient attenuation in the deep layers of the network as a key bottleneck, leading to insufficient learning of high-level features. To address this issue, we propose \textbf{LUMA} (\textit{\textbf{L}ow-dimension \textbf{U}nified \textbf{M}otion …

BERTector: Intrusion Detection Based on Joint-Dataset Learning
Haoyang Hu, Xun Huang, Chenyu Wu, Shiwen Liu, Zhichao Lian, Shuangquan Zhang
https://arxiv.org/abs/2508.10327 http…

BERTector: Intrusion Detection Based on Joint-Dataset Learning
Intrusion detection systems (IDS) are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based on BERT. BERTector integrates three key components: NSS-Tokenizer for traffic-aware semantic tokenization, supervised fine-tuning with a hybrid dataset, and low-rank adaptation (LoRA) for efficient t…

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Yongye Su, Zeya Zhang, Jane Kou, Cheng Ju, Shubhojeet Sarkar, Yamin Wang, Ji Liu, Shengbo Guo
https://arxiv.org/abs/2509.13603

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a framework of modernized Facebook Group Scoped Search by blending traditional keyword-based retrieval with embedding-based retrieval (EBR) to improve the search relevance and diversity of search results. Our system integrates semantic retrieval into the existing keyword search pipeline, enabling users to discover more…

Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks
Seyed Hossein Ahmadpanah
https://arxiv.org/abs/2508.09149 https:…

Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks
Next-generation automotive applications require vehicular edge computing (VEC), but current management systems are essentially fixed and reactive. They are suboptimal in extremely dynamic vehicular environments because they are constrained to static optimization objectives and base their decisions on the current network states. This paper presents a novel Semantic-Aware Proactive LLM Orchestration (SP-LLM) framework to address these issues. Our method transforms the traditional Digital Twin (DT…

TFANet: Three-Stage Image-Text Feature Alignment Network for Robust Referring Image Segmentation
Qianqi Lu, Yuxiang Xie, Jing Zhang, Shiwei Zou, Yan Chen, Xidao Luan
https://arxiv.org/abs/2509.13070

TFANet: Three-Stage Image-Text Feature Alignment Network for Robust Referring Image Segmentation
Referring Image Segmentation (RIS) is a task that segments image regions based on language expressions, requiring fine-grained alignment between two modalities. However, existing methods often struggle with multimodal misalignment and language semantic loss, especially in complex scenes containing multiple visually similar objects, where uniquely described targets are frequently mislocalized or incompletely segmented. To tackle these challenges, this paper proposes TFANet, a Three-stage Image-T…

Spiking Variational Graph Representation Inference for Video Summarization
Wenrui Li, Wei Han, Liang-Jian Deng, Ruiqin Xiong, Xiaopeng Fan
https://arxiv.org/abs/2508.15389 https…

Spiking Variational Graph Representation Inference for Video Summarization
With the rise of short video content, efficient video summarization techniques for extracting key information have become crucial. However, existing methods struggle to capture the global temporal dependencies and maintain the semantic coherence of video content. Additionally, these methods are also influenced by noise during multi-channel feature fusion. We propose a Spiking Variational Graph (SpiVG) Network, which enhances information density and reduces computational complexity. First, we de…

Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
Michal Szczepanski, Martyna Poreba, Karim Haroun
https://arxiv.org/abs/2509.14165 https://…

Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
Vision Transformers (ViTs) achieve state-of-the-art performance in semantic segmentation but are hindered by high computational and memory costs. To address this, we propose STEP (SuperToken and Early-Pruning), a hybrid token-reduction framework that combines dynamic patch merging and token pruning to enhance efficiency without significantly compromising accuracy. At the core of STEP is dCTS, a lightweight CNN-based policy network that enables flexible merging into superpatches. Encoder blocks …