Tootfinder

Shares of Hong Kong-listed Chinese chipmaker InnoScience closed up 31% on August 1, after Nvidia named the company as a supplier for its 800V HVDC architecture (Felix Tam/Bloomberg)
https://www.bloomberg.com/news/articles/2025-08…

(1/3) Please join me today on Zoom at 12pm ET / 11am CT / 10am MT / 9am PT for "Beyond the Algorithmic Feed," a free MediaEd Club webinar for the Media Education Lab:
https://mediaeducationlab.com/index.php/events/beyond-algorithmic-feed
While som…

A new efficient RPKI Design
Haya Schulmann, Niklas Vogel
https://arxiv.org/abs/2507.01465 https://arxiv.org/pdf/2507.01465

A new efficient RPKI Design
Resource Public Key Infrastructure (RPKI) is a critical security mechanism for BGP, but the complexity of its architecture is a growing concern as its adoption scales. Current RPKI design heavily reuses legacy PKI components, such as X.509 EE-certificates, ASN.1 encoding, and XML-based repository protocols, all these introduce excessive cryptographic validation, redundant metadata, and inefficiencies in both storage and processing. We show that these design choices, although based on establishe…

MIRROR: Cognitive Inner Monologue Between Conversational Turns for Persistent Reflection and Reasoning in Conversational LLMs
Nicole Hsing
https://arxiv.org/abs/2506.00430

MIRROR: Cognitive Inner Monologue Between Conversational Turns for Persistent Reflection and Reasoning in Conversational LLMs
Human intelligence relies on inner monologue to process complex information through simultaneous reflection, memory retrieval, and response formulation. We introduce MIRROR (Modular Internal Reasoning, Reflection, Orchestration, and Response), a cognitive architecture that systematically implements these parallel reasoning capabilities in large language models. MIRROR operates as a unified system with two distinct functional layers: the Thinker and the Talker. The Thinker encompasses: (1) the I…

Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture
Bochen Han, Songmao Zhang
https://arxiv.org/abs/2507.01701 https://

Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture
In this paper, we propose to incorporate the blackboard architecture into LLM multi-agent systems (MASs) so that (1) agents with various roles can share all the information and others' messages during the whole problem-solving process, (2) agents that will take actions are selected based on the current content of the blackboard, and (3) the selection and execution round is repeated until a consensus is reached on the blackboard. We develop the first implementation of this proposal and conduct e…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- A Benchmarking Platform for DDR4 Memory Performance in Data-Center-Class FPGAs
Andrea Galimberti, Gabriele Montanaro, Andrea Motta, Federico Proverbio, Davide Zoni

2D PZT MEMS Resonant Scanner Using a Three-Mask Process
Mehrdad Khodapanahandeh, Parviz Zolfaghari, Hakan Urey
https://arxiv.org/abs/2505.24566 https://

2D PZT MEMS Resonant Scanner Using a Three-Mask Process
This work presents the design, simulation, fabrication, and characterization of a novel architectural compact two-dimensional (2D) resonant MEMS scanning mirror actuated by thin-film lead zirconate titanate (PZT). The device employs an innovative mechanically coupled dual-axis architecture fabricated using a three-mask process on an SOI-PZT deposited wafer, significantly reducing system complexity while achieving high performance. The scanner integrates a 1 $\times$ 1.4 mm oval mirror within a …

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Modern Base Station Architecture: Enabling Passive Beamforming with Beyond Diagonal RISs
Mahmoud Raeisi, Hui Chen, Henk Wymeersch, Ertugrul Basar

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Cha...
Youssef Maklad, Fares Wael, Wael Elsersy, Ali Hamdi

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-bas...
Rachmad Vidya Wicaksana Putra, Muhammad Shafique

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
Romulo B. da Silva, C\'assio M. Oishi, Diego Passos, J. Nathan Kutz
https://arxiv.org/abs/2507.22303

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
We present $\textbf{CS-SHRED}$, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder ($\textbf{SHRED}$) to reconstruct spatiotemporal dynamics from incomplete, compressed, or corrupted data. Our approach introduces two key innovations. First, by incorporating CS techniques into the $\textbf{SHRED}$ architecture, our method leverages a batch-based forward framework with $\ell_1$ regularization to robustly recover signals even in scenarios wi…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
Aotao Wang, Haikuo Shao, Shaobo Ma, Zhongfeng Wang

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- ECLYPSE: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum
Jacopo Massa, Valerio De Caro, Stefano Forti, Patrizio Dazzi, Davide Bacciu, Antonio Brogi

KLAN: Kuaishou Landing-page Adaptive Navigator
Fan Li, Chang Meng, Jiaqi Fu, Shuchang Liu, Jiashuo Zhang, Tianke Zhang, Xueliang Wang, Xiaoqiang Feng
https://arxiv.org/abs/2507.23459

KLAN: Kuaishou Landing-page Adaptive Navigator
Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in S…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Resilient-By-Design: A Resiliency Framework for Future Wireless Networks
Nurul Huda Mahmood, Sumudu Samarakoon, Pawani Porambage, Mehdi Bennis, Matti Latva-aho

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Image Captioning via Compact Bidirectional Architecture
Zijie Song, Yuanen Zhou, Zhenzhen Hu, Daqing Liu, Huixia Ben, Richang Hong, Meng Wang

Mix-of-Language-Experts Architecture for Multilingual Programming
Yifan Zong, Yuntian Deng, Pengyu Nie
https://arxiv.org/abs/2506.18923 https://

Mix-of-Language-Experts Architecture for Multilingual Programming
Large language models (LLMs) have demonstrated impressive capabilities in aiding developers with tasks like code comprehension, generation, and translation. Supporting multilingual programming -- i.e., coding tasks across multiple programming languages -- typically requires either (1) finetuning a single LLM across all programming languages, which is cost-efficient but sacrifices language-specific specialization and performance, or (2) finetuning separate LLMs for each programming language, whi…

Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
Ali \c{S}enol, Garima Agrawal, Huan Liu
https://arxiv.org/abs/2506.21443 https://arxiv.org/pdf/2506.21443 https://arxiv.org/html/2506.21443
arXiv:2506.21443v1 Announce Type: new
Abstract: Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)\-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk\-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)\-Enhanced LLM framework that integrates pretrained LLMs with structured, task\-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK\-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK\-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA\-based implementation achieves 98\% classification accuracy. Comparative studies against zero\-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high\-stakes NLP applications.
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Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- TrIM, Triangular Input Movement Systolic Array for Convolutional Neural Networks: Dataflow and An...
Cristian Sestito, Shady Agwa, Themis Prodromakis

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Man...
Ricardo Cannizzaro, Michael Groom, Jonathan Routley, Robert Osazuwa Ness, Lars Kunze

Efficient and Fault-Tolerant Memristive Neural Networks with In-Situ Training
Santlal Prajapat, Manobendra Nath Mondal, Susmita Sur-Kolay
https://arxiv.org/abs/2507.20193 https:…

Efficient and Fault-Tolerant Memristive Neural Networks with In-Situ Training
Neuromorphic architectures, which incorporate parallel and in-memory processing, are crucial for accelerating artificial neural network (ANN) computations. This work presents a novel memristor-based multi-layer neural network (memristive MLNN) architecture and an efficient in-situ training algorithm. The proposed design performs matrix-vector multiplications, outer products, and weight updates in constant time $\mathcal{O}(1)$, leveraging the inherent parallelism of memristive crossbars. Each s…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Calibrating DRAMPower Model for HPC: A Runtime Perspective from Real-Time Measurements
Shi, Abdelhamid, Ilsche, Chamazcoti, Evenblij, Gupta, Catthoor

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

Monolithic optoelectronic circuit design for on-chip terahertz applications
Kateryna Kusyak, Benedikt Schulte, Toru Matsuyama, Gunda Kipp, Hope M. Bretscher, Matthew W. Day, Guido Meier, Alexander M. Potts, James W. McIver
https://arxiv.org/abs/2507.21052

Monolithic optoelectronic circuit design for on-chip terahertz applications
We demonstrate a monolithic coplanar stripline platform for on-chip terahertz (THz) generation, transmission, and detection, addressing key challenges of mode purity, bandwidth, and referencing. Capacitive coupling of the photoconductive generator switch enforces pure odd-mode propagation, increases THz field strength, and extends the operational frequency range, achieving 0.05-1.4 THz. Our architecture enables fully monolithic fabrication with amorphous silicon switches, in situ field referenc…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Optimal and Near-Optimal Adaptive Vector Quantization
Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher, Shay Vargaftik

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN
Abdelaziz Salama, Zeinab Nezami, Mohammed M. H. Qazzaz, Maryam Hafeez, Syed Ali Raza Zaidi
https://arxiv.org/abs/2507.21696

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN
The deployment of AI agents within legacy Radio Access Network (RAN) infrastructure poses significant safety and reliability challenges for future 6G networks. This paper presents a novel Edge AI framework for autonomous network optimisation in Open RAN environments, addressing these challenges through three core innovations: (1) a persona-based multi-tools architecture enabling distributed, context-aware decision-making; (2) proactive anomaly detection agent powered by traffic predictive tool;…

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- MoDA: Multi-modal Diffusion Architecture for Talking Head Generation
Li, Li, Lin, Qian, Yao, Jia, Wang, Chen, Wang

Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
[1/1]:
- Deep Reparameterization for Full Waveform Inversion: Architecture Benchmarking, Robust Inversion,...
Feng Liu, Yaxing Li, Rui Su, Jianping Huang, Lei Bai

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- No Redundancy, No Stall: Lightweight Streaming 3D Gaussian Splatting for Real-time Rendering
Linye Wei, Jiajun Tang, Fan Fei, Boxin Shi, Runsheng Wang, Meng Li

Game-Theoretic Gradient Control for Robust Neural Network Training
Maria Zaitseva, Ivan Tomilov, Natalia Gusarova
https://arxiv.org/abs/2507.19143 https://…

Game-Theoretic Gradient Control for Robust Neural Network Training
Feed-forward neural networks (FFNNs) are vulnerable to input noise, reducing prediction performance. Existing regularization methods like dropout often alter network architecture or overlook neuron interactions. This study aims to enhance FFNN noise robustness by modifying backpropagation, interpreted as a multi-agent game, and exploring controlled target variable noising. Our "gradient dropout" selectively nullifies hidden layer neuron gradients with probability 1 - p during backpropagation, w…

Weekend Reads
* A new RPKI design https://arxiv.org/abs/2507.01465
* Finding New IP KVMs https://www.runzero.com/blog/oob-p1-ip-kvm/

A new efficient RPKI Design
Resource Public Key Infrastructure (RPKI) is a critical security mechanism for BGP, but the complexity of its architecture is a growing concern as its adoption scales. Current RPKI design heavily reuses legacy PKI components, such as X.509 EE-certificates, ASN.1 encoding, and XML-based repository protocols, all these introduce excessive cryptographic validation, redundant metadata, and inefficiencies in both storage and processing. We show that these design choices, although based on establishe…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- RIDAS: A Multi-Agent Framework for AI-RAN with Representation- and Intention-Driven Agents
Kuiyuan Ding, Caili Guo, Yang Yang, Jianzhang Guo

Replaced article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- LightCurve MoE: A Dynamic Sparse Routing Mixture-of-Experts Architecture for Efficient Stellar Li...
Wang, Bai, Song, Xu, Han, Li, Hu, Yang, Liu

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- When Servers Meet Species: A Fab-to-Grave Lens on Computing's Biodiversity Impact
Tianyao Shi, Ritbik Kumar, Inez Hua, Yi Ding

In rejecting diplomacy and choosing war, not only in breach of international law but at the behest of a country pursuing annihilation in Gaza, the US has delivered a resounding blow to the architecture of global affairs. It has signalled that countries that negotiate (Iran) face stark consequences, which those which rush to own the bomb (North Korea) can avoid. The cost of this reckless act may not be fully felt or comprehended for decades.
https://www.theguardian.com/commentisfree/2025/jun/22/the-guardian-view-on-trump-bombing-iran-an-and-reckless-act?CMP=Share_iOSApp_Other

Supernova: Achieving More with Less in Transformer Architectures
Andrei-Valentin Tanase, Elena Pelican
https://arxiv.org/abs/2507.15773 https://

Supernova: Achieving More with Less in Transformer Architectures
We present Supernova, a 650M-parameter decoder-only transformer that demonstrates how careful architectural design and tokenization innovation can achieve the performance of larger models while maintaining computational efficiency. Our architecture combines Rotary Positional Embeddings (RoPE), Grouped Query Attention (GQA) with a 3:1 compression ratio, RMSNorm for computational efficiency, and SwiGLU activation functions. A critical innovation is our custom 128,000-vocabulary byte-level BPE tok…

Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising
Danilo Costarelli, Michele Piconi, Alessio Troiani
https://arxiv.org/abs/2507.14869 htt…

Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising
We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of parallel computing architecture in a natural and efficient manner. We compare the performance of the PCA method with the standard Gibbs sampler …

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning
Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri, Seyed Pooya Shariatpanahi

Moonshot's Kimi K2 uses a 1T-parameter MoE architecture with 32B active parameters and outperforms models like GPT-4.1 and DeepSeek-V3 on key benchmarks (Michael Nuñez/VentureBeat)
https://venturebeat.com/ai/moonshot-ais-kimi-k2-outperfor…

Moonshot AI’s Kimi K2 outperforms GPT-4 in key benchmarks — and it’s free
Chinese AI startup Moonshot releases open-source Kimi K2 model that outperforms OpenAI and Anthropic on coding tasks with breakthrough agentic capabilities and competitive pricing.

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions
Xiaopei Chen, Wen Wu, Liang Li, Fei Ji

Graphical user interface and architecture of an Android mobile application for a visual-to-auditory sensory substitution system #SightSense …

Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective
Hangjie Yuan, Weihua Chen, Jun Cen, Hu Yu, Jingyun Liang, Shuning Chang, Zhihui Lin, Tao Feng, Pengwei Liu, Jiazheng Xing, Hao Luo, Jiasheng Tang, Fan Wang, Yi Yang
https://arxiv.org/abs/2507.08801

Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective
Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive video generation. Existing autoregressive video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive latency due to next-token decoding. In this paper, we introduce Lumos-1, an autoregressive video generator that retains the LLM architecture with minimal architectural modifications. To inject s…

This https://arxiv.org/abs/2503.02585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

A Hypernetwork-Based Approach to KAN Representation of Audio Signals
Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-SpectralDistance of 1.29 and the highest Perceptual Evaluation of Speech Qua…

ZettaLith: An Architectural Exploration of Extreme-Scale AI Inference Acceleration
Kia Silverbrook
https://arxiv.org/abs/2507.02871 https://

ZettaLith: An Architectural Exploration of Extreme-Scale AI Inference Acceleration
The high computational cost and power consumption of current and anticipated AI systems present a major challenge for widespread deployment and further scaling. Current hardware approaches face fundamental efficiency limits. This paper introduces ZettaLith, a scalable computing architecture designed to reduce the cost and power of AI inference by over 1,000x compared to current GPU-based systems. Based on architectural analysis and technology projections, a single ZettaLith rack could potential…

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
Ravin Kumar
https://arxiv.org/abs/2507.14270 https://

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression. The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both computationally efficient and elegant. The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((α_i + \tanh(β_i x_i)) \cdot γ_i x_i) + δ$, where all parameter…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Wall-Proximity Matters: Understanding the Effect of Device Placement with Respect to the Wall for...
He Wang, Yunpeng Ge, Ivan Wang-Hei Ho

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference
Mo, Wang, Wei, Zeng, Cao, Ma, Jing, Cao, Xue, Yang, Yang

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
https://arxiv.org/abs/2507.07996 https://arxiv.org/pdf/2507.07996 https://arxiv.org/html/2507.07996
arXiv:2507.07996v1 Announce Type: new
Abstract: Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.
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Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability throu...
Fern\'andez-Becerra, Gonz\'alez-Santamarta, Guerrero-Higueras, Rodr\'iguez-Lera, Olivera…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Cyber-attack TTP analysis for EPES systems
Alexios Lekidis
https://

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
Adrien Benamira, Tristan Gu\'erand, Thomas Peyrin, Sayandeep Saha

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matri...
Jannis Sch\"onleber, Lukas Cavigelli, Matteo Perotti, Luca Benini, Renzo Andri

Design and Development of an Automated Contact Angle Tester (ACAT) for Surface Wettability Measurement
Connor Burgess, Kyle Douin, Amir Kordijazi
https://arxiv.org/abs/2507.12431 …

Design and Development of an Automated Contact Angle Tester (ACAT) for Surface Wettability Measurement
The Automated Contact Angle Tester (ACAT) is a fully integrated robotic work cell developed to automate the measurement of surface wettability on 3D-printed materials. Designed for precision, repeatability, and safety, ACAT addresses the limitations of manual contact angle testing by combining programmable robotics, precise liquid dispensing, and a modular software-hardware architecture. The system is composed of three core subsystems: (1) an electrical system including power, control, and safe…

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
https://arxiv.org/abs/2506.10347

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with S…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Towards Constraint-aware Learning for Resource Allocation in NFV Networks
Tianfu Wang, Long Yang, Chao Wang, Chuan Qin, Liwei Deng, Wei Wu, Junyang Wang, Li Shen, Hui Xiong

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- GainSight: Application-Guided Profiling for Composing Heterogeneous On-Chip Memories in AI Hardwa...
Li, Hung, Tan, Ho{\ss}feld, Jiajun, Liu, Yan, Wang, Wong, Tambe

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Anonymized Network Sensing Graph Challenge
Hayden Jananthan, et al.

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- LLM-Aided Testbench Generation and Bug Detection for Finite-State Machines
Jitendra Bhandari, Johann Knechtel, Ramesh Narayanaswamy, Siddharth Garg, Ramesh Karri

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Deterministic Scheduling of Periodic Messages for Low Latency in Cloud RAN
Dominique Barth, Ma\"el Guiraud, Yann Strozecki

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- ChatModel: Automating Reference Model Design and Verification with LLMs
Jianmin Ye, Tianyang Liu, Qi Tian, Shengchu Su, Zhe Jiang, Xi Wang

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- DiP: A Scalable, Energy-Efficient Systolic Array for Matrix Multiplication Acceleration
Ahmed J. Abdelmaksoud, Shady Agwa, Themis Prodromakis

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Enhancing Network Failure Mitigation with Performance-Aware Ranking
Namyar, Ghavidel, Crankshaw, Berger, Hsieh, Kandula, Govindan, Arzani

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Densify & Conquer: Densified, smaller base-stations can conquer the increasing carbon footprint p...
Agrim Gupta, Adel Heidari, Jiaming Jin, Dinesh Bharadia

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compre...
Shupeng Ning, Hanqing Zhu, Chenghao Feng, Jiaqi Gu, David Z. Pan, Ray T. Chen

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx Re...
Donato Francesco Falcone, et al.

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- IEEE 802.11be Wi-Fi 7: Feature Summary and Performance Evaluation
Xiaoqian Liu, Yuhan Dong, Yiqing Li, Yousi Lin, Ming Gan

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
Zhiyu Shao, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Per-Bank Bandwidth Regulation of Shared Last-Level Cache for Real-Time Systems
Connor Sullivan, Alex Manley, Mohammad Alian, Heechul Yun

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
Reconfigurable Stream Network Architecture
https://

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach
Bermudez, Farreras, Groshev, Trujillo, de la Bandera, Barco

A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration
Wei-Hsing Huang (Celine), Janak Sharda (Celine), Cheng-Jhih Shih (Celine), Yuyao Kong (Celine), Faaiq Waqar (Celine), Pin-Jun Chen (Celine), Yingyan (Celine), Lin, Shimeng Yu
https://arxiv.org/abs/2507.19142

A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration
Conventional large language models (LLMs) are equipped with dozens of GB to TB of model parameters, making inference highly energy-intensive and costly as all the weights need to be loaded to onboard processing elements during computation. Recently, the Mixture-of-Experts (MoE) architecture has emerged as an efficient alternative, promising efficient inference with less activated weights per token. Nevertheless, fine-grained MoE-based LLMs face several challenges: 1) Variable workloads during r…

A Fault-Tolerant Architecture for Urban and Rural Digital Connectivity: Synergizing SDWMN, Direct-to-Mobile Broadcasting, and Hybrid Cloud Streaming
Pavel Malinovskiy
https://arxiv.org/abs/2507.14205

A Fault-Tolerant Architecture for Urban and Rural Digital Connectivity: Synergizing SDWMN, Direct-to-Mobile Broadcasting, and Hybrid Cloud Streaming
We propose an integrated architecture combining Software-Defined Wireless Mesh Networks (SDWMN), Direct-to-Mobile (D2M) broadcasting, and Kafka-based hybrid cloud streaming to improve wireless network performance in both urban and rural settings. The approach addresses urban congestion and rural digital exclusion through traffic offloading, enhanced fault tolerance, and equitable resource allocation. We model urban congestion $ρ_u = λ_t / μ_c$ and rural coverage deficit $δ_r = 1 - C_r / C_{…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation
Hazem Taha, Ameer M. S. Abdelhadi

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Binary VPN Traffic Detection Using Wavelet Features and Machine Learning
Yasameen Sajid Razooqi, Adrian Pekar

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switchi...
Shou, Liu, Nie, Meng, Zhou, Jiang, Lv, Xu, Lu, Chen, Yu, Shen, Zhu, Jiang

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, Jian Cheng
https://arxiv.org/abs/2507.15300

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for mobile applications. Through in-depth analysis, we identify two major limitations in the conventional decoupled preprocessing-rendering dataflow adopted by existing accelerators: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different t…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Fast Graph Vector Search via Hardware Acceleration and Delayed-Synchronization Traversal
Wenqi Jiang, Hang Hu, Torsten Hoefler, Gustavo Alonso

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding
Wang, Shao, Nabeel, Roy, Mankali, Bhandari, Karri, Sinanoglu, Shafique, Knechtel

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Managemen...
Li, Madhukumar, Li, Liu, Teng, Wu, Wang, Yan, Simeonidou

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
CXLMemSim: A pure software simulated CXL.mem for performance characterization

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- A Survey on Semantic Communications in Internet of Vehicles
Sha Ye, Qiong Wu, Pingyi Fan, Qiang Fan

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- GPU Performance Portability needs Autotuning
Burkhard Ringlein, Thomas Parnell, Radu Stoica

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
EvalNet: A Practical Toolchain for Generation and Analysis of Extreme-Scale Interconnects

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation
Chenhui Deng, Yunsheng Bai, Haoxing Ren

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- XR Offloading Across Multiple Time Scales: The Roles of Power, Temperature, and Energy
Malandrino, Chukhno, Catania, Molinaro, Chiasserini

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols
Arnav Sheth, Ivaxi Sheth, Mario Fritz

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?
Ramesh Raskar, et al.

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
EvalNet: A Practical Toolchain for Generation and Analysis of Extreme-Scale Interconnects

Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
Jesmin Jahan Tithi, Hanjiang Wu, Avishaii Abuhatzera, Fabrizio Petrini
https://arxiv.org/abs/2506.15006

Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
The explosive growth of Large Language Models (LLMs) - such as GPT-4 with 1.8 trillion parameters - demands a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-out domain, and popular parallelism/optimization strategies used in LLMs. We introduce an…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Eywa: Automating Model Based Testing using LLMs
Mondal, Singha, Millstein, Varghese, Beckett, Kakarla

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- GNN-ACLP: Graph Neural Networks Based Analog Circuit Link Prediction
Pan, Zhou, Ma, Wang, Zhao, Li, Lin, Lio, Wang

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- A Time-Triggered Communication Method Based on Urgency-Based Scheduler in Time-Sensitive Networking
Feng Luo, Yunpeng Li, Zitong Wang, Yi Ren, Yingpeng Tong, Zhouping Zhang, Qin Liu

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- DiP: A Scalable, Energy-Efficient Systolic Array for Matrix Multiplication Acceleration
Ahmed J. Abdelmaksoud, Shady Agwa, Themis Prodromakis

Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization
Nan Li, Qi Sun, Lehan Wang, Xiaofei Xu, Jinri Huang, Chunhui Liu, Jing Gao, Yuhong Huang, Chih-Lin I
https://arxiv.org/abs/2507.08403

Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization
Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall incorporate AI from the onset to address its complexity and support ubiquitous AI applications. Based on our extensive mobile network operation and standardization experience from 2G to 5G, this paper explores the design and standardization principles of AI-Na…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new/
[1/1]:
YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithme...

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An, Alex Um, Advay Ratan, Yakun Sophia Shao

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAs
Ehsan Kabir, Austin R. J. Downey, Jason D. Bakos, David Andrews, Miaoqing Huang

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- FAMOUS: Flexible Accelerator for the Attention Mechanism of Transformer on UltraScale FPGAs
Ehsan Kabir, Md. Arafat Kabir, Austin R. J. Downey, Jason D. Bakos, David Andrews, Miaoqing Huang