Tootfinder

Residuality Theory: A Rebellious Take on Building Systems That Actually Survive
#resiliency

Interested in how #architecture can be understood sociologically?
In this podcast hosted by Alon Schwartz, I'm discussing how architecture can integrate human needs and usage processes in workplaces
So much fun to rise to the deep and challenging questions Alon threw at me

space design with Kerstin sailer- Sociology and Architecture
עיצוב מרחב עם אלון שוורץ · Episode

Supporting architecture evaluation for ATAM scenarios with LLMs
Rafael Capilla, J. Andr\'es D\'iaz-Pace, Yamid Ram\'irez, Jennifer P\'erez, Vanessa Rodr\'iguez-Horcajo
https://arxiv.org/abs/2506.00150

Supporting architecture evaluation for ATAM scenarios with LLMs
Architecture evaluation methods have long been used to evaluate software designs. Several evaluation methods have been proposed and used to analyze tradeoffs between different quality attributes. Having competing qualities leads to conflicts for selecting which quality-attribute scenarios are the most suitable ones that an architecture should tackle and for prioritizing the scenarios required by the stakeholders. In this context, architecture evaluation is carried out manually, often involving …

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Zhentan Zheng
https://arxiv.org/abs/2507.01927 https://arxiv.org/pdf/250…

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Deep neural networks have achieved remarkable results in computer vision tasks. In the early days, Convolutional Neural Networks (CNNs) were the mainstream architecture. In recent years, Vision Transformers (ViTs) have become increasingly popular. In addition, exploring applications of multi-layer perceptrons (MLPs) has provided new perspectives for research into vision model architectures. In this paper, we present evMLP accompanied by a simple event-driven local update mechanism. The proposed…

Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation
Nikolai Sergeev
https://arxiv.org/abs/2508.00017 https://

Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation
We present Generative Logic (GL), a deterministic architecture that begins from user-supplied axiomatic definitions -- written in a minimalist Mathematical Programming Language (MPL) -- and systematically explores their deductive neighborhood. Definitions are compiled into a distributed grid of simple Logic Blocks (LBs) that exchange messages; any time several expressions unify under an inference rule, a new fact is emitted with full provenance to its sources, yielding replayable, auditable pro…

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
Shereef Helal, Alberto Garcia-Ortiz, Lennart Bamberg
https://arxiv.org/abs/2506.01166

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
Leveraging high degrees of unstructured sparsity is a promising approach to enhance the efficiency of deep neural network DNN accelerators - particularly important for emerging Edge-AI applications. We introduce VUSA, a systolic-array architecture that virtually grows based on the present sparsity to perform larger matrix multiplications with the same number of physical multiply-accumulate MAC units. The proposed architecture achieves saving by 37% and 68% in area and power efficiency, respecti…

A Full-Stack Platform Architecture for Self-Organised Social Coordination
Matthew Scott, Jeremy Pitt
https://arxiv.org/abs/2507.01239 https://

A Full-Stack Platform Architecture for Self-Organised Social Coordination
To mitigate the restrictive centralising and monopolistic tendencies of platformisation, we aim to empower local communities by democratising platforms for self-organised social coordination. Our approach is to develop an open-source, full-stack architecture for platform development that supports ease of distribution and cloning, generativity, and a variety of hosting options. The architecture consists of a meta-platform that is used to instantiate a base platform with supporting libraries for …

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
Liang Geng
https://arxiv.org/abs/2506.00570

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
With the rapid penetration of artificial intelligence across industries and scenarios, a key challenge in building the next-generation intelligent core lies in effectively integrating the language understanding capabilities of foundation models with domain-specific knowledge bases in complex real-world applications. This paper proposes a multimodal cognition and embodied decision-making brain system, ``Wenlu", designed to enable secure fusion of private knowledge and public models, unified proc…

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…

AI-Governed Agent Architecture for Web-Trustworthy Tokenization of Alternative Assets
Ailiya Borjigin, Wei Zhou, Cong He
https://arxiv.org/abs/2507.00096 h…

AI-Governed Agent Architecture for Web-Trustworthy Tokenization of Alternative Assets
Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web. However, ensuring trustworthiness in web-based tokenized ecosystems poses significant challenges, from verifying off-chain asset data to enforcing regulatory compliance. This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets. In the proposed architecture, autono…

A framework for 3D interaction techniques
Pablo Figueroa, Mark Green, Benjamin Watson
https://arxiv.org/abs/2507.02254 https://arxiv.…

A framework for 3D interaction techniques
This paper presents a software architecture for 3D interaction techniques (ITs) and an object oriented, toolkit-independent framework that implements such architecture. ITs are composed of basic filters connected in a dataflow, where virtual input devices and objects in the scene are sources of information. An execution model defines the general flow of information between filters. This framework has been designed to be extensible: new information types, new input devices, new execution models,…

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Wenqi Jia, Ying Huang, Jian Xu, Zhewen Hu, Sian Jin, Jiannan Tian, Yuede Ji, Miao Yin
https://arxiv.org/abs/2507.01224

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often reach petabyte to exabyte scales. However, managing these vast data volumes introduces significant I/O and network bottlenecks, limiting practical performance and scalability. While cutting-edge lossy compression frameworks powered by deep neural networks (DNNs) …

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…

Urban Snaps II 📸
城市抓拍 II 📸
📷 Pentax MX
🎞️Fujifilm Neopan SS, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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I wrote some iPhone apps in Objective C in the early days, it was hard, I stopped about 15 years ago. Now I’m building a native Swift app with a voice based interface using Claude to write the code, and a Claude-flow agent swarm to create plans and architecture and more complex implementation steps. It’s fun, it works on my phone, and all I have to do is say what I want in steps small enough that I can tell I got it. Follow along at

Trump announced that he intends to build a $200 million, 90,000-square-foot ballroom where the East Wing of the White House now stands.
The project, designed by the traditionalist architect James McCrery II, would be built in the style of Gilded Age neoclassicism,
complete with arched windows, chandeliers and Corinthian columns.
(Many architectural preservationists have already expressed their concerns about the ballroom addition.)
Of course, it looks to be closer to t…

Opinion | Trump’s Gilded Age White House Ballroom Classicism
While the president is right to try to reclaim meaning in American architecture, Gilded Age neoclassicism is not the answer.

Data Agent: A Holistic Architecture for Orchestrating Data AI Ecosystems
Zhaoyan Sun, Jiayi Wang, Xinyang Zhao, Jiachi Wang, Guoliang Li
https://arxiv.org/abs/2507.01599

Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems
Traditional Data+AI systems utilize data-driven techniques to optimize performance, but they rely heavily on human experts to orchestrate system pipelines, enabling them to adapt to changes in data, queries, tasks, and environments. For instance, while there are numerous data science tools available, developing a pipeline planning system to coordinate these tools remains challenging. This difficulty arises because existing Data+AI systems have limited capabilities in semantic understanding, rea…

Subband Architecture Aided Selective Fixed-Filter Active Noise Control
Hong-Cheng Liang, Man-Wai Mak, Kong Aik Lee
https://arxiv.org/abs/2508.00603 https://

Subband Architecture Aided Selective Fixed-Filter Active Noise Control
The feedforward selective fixed-filter method selects the most suitable pre-trained control filter based on the spectral features of the detected reference signal, effectively avoiding slow convergence in conventional adaptive algorithms. However, it can only handle limited types of noises, and the performance degrades when the input noise exhibits non-uniform power spectral density. To address these limitations, this paper devises a novel selective fixed-filter scheme based on a delayless subb…

A Tree-guided CNN for image super-resolution
Chunwei Tian, Mingjian Song, Xiaopeng Fan, Xiangtao Zheng, Bob Zhang, David Zhang
https://arxiv.org/abs/2506.02585

A Tree-guided CNN for image super-resolution
Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not easy to find effect of important layers in a single network architecture to decrease performance of super-resolution. In this paper, we design a tree-guided CNN for image super-resolution (TSRNet). It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchica…

AWS deleted my 10-year account and all data without warning
https://www.seuros.com/blog/aws-deleted-my-10-year-account-without-warning/
- The Architecture That Should Have Protected Me -
Small reminder that there is no 100% protection in the…

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

Even if you're not #LDS #Mormon. Even if you're not #Utahn, if you or someone you know has been in or associated with this the

Deep Dive 2: The Boy Scouts of America
Podcast Episode · Architecture of Abuse · 06/20/2025 · 52m

TubuleTracker: a high-fidelity shareware software to quantify angiogenesis architecture and maturity
Danish Mahmood, Stephanie Buczkowski, Sahaj Shah, Autumn Anthony, Rohini Desetty, Carlo R Bartoli
https://arxiv.org/abs/2507.02024

TubuleTracker: a high-fidelity shareware software to quantify angiogenesis architecture and maturity
Background: In vitro endothelial cell culture is widely used to study angiogenesis. Histomicrographic images of cell networks are often analyzed manually, a process that is time-consuming and subjective. Automated tools like ImageJ (NIH) can assist, but are often slow and inaccurate. Additionally, as endothelial networks grow more complex, traditional architectural metrics may not fully reflect network maturity. To address these limitations, we developed tubuleTracker, a software tool that quan…

I actually really like neoclassical #architecture, but I enjoyed this interesting essay by Jeffery Tyler Syck in favor of diversity of style.
https://lawliberty.org/the-promise-of-

A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems
Amra Ramic, Stefan Kugele
https://arxiv.org/abs/2506.01595 https://

A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems
Background: Due to their diversity, complexity, and above all importance, safety-critical and dependable systems must be developed with special diligence. Criticality increases as these systems likely contain artificial intelligence (AI) components known for their uncertainty. As software and reference architectures form the backbone of any successful system, including safety-critical dependable systems with learning-enabled components, choosing the suitable architecture that guarantees safety …

Galileo Project Observatory Class System Architecture
Phillip Bridgham, Alex Delacroix, Laura Domine, Andriy Fedorenko, Ezra Kelderman, Sarah Little, Abraham Loeb, Robert Lundstrom, Eric Masson, Andrew Mead, Michael W Prior, Matthew Szenher, Foteini Vervelidou, Wesley Andres Watters
https://arxiv.org/abs/2506.00125

Galileo Project Observatory Class System Architecture
Scientific investigation of Unidentified Anomalous Phenomena (UAP) is limited by poor data quality and a lack of transparency. Existing data are often fragmented, uncalibrated, and missing critical metadata. To address these limitations, the authors present the Observatory Class Integrated Computing Platform (OCICP), a system designed for the systematic and scientific study of UAPs. OCICP employs multiple sensors to collect and analyze data on aerial phenomena. The OCICP system consists of two …

Beat and Downbeat Tracking in Performance MIDI Using an End-to-End Transformer Architecture
Sebastian Murgul, Michael Heizmann
https://arxiv.org/abs/2507.00466

Beat and Downbeat Tracking in Performance MIDI Using an End-to-End Transformer Architecture
Beat tracking in musical performance MIDI is a challenging and important task for notation-level music transcription and rhythmical analysis, yet existing methods primarily focus on audio-based approaches. This paper proposes an end-to-end transformer-based model for beat and downbeat tracking in performance MIDI, leveraging an encoder-decoder architecture for sequence-to-sequence translation of MIDI input to beat annotations. Our approach introduces novel data preprocessing techniques, includi…

CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection
David Ortiz-Perez, Manuel Benavent-Lledo, Javier Rodriguez-Juan, Jose Garcia-Rodriguez, David Tom\'as
https://arxiv.org/abs/2506.01890

CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection
Early detection of cognitive disorders such as Alzheimer's disease is critical for enabling timely clinical intervention and improving patient outcomes. In this work, we introduce CogniAlign, a multimodal architecture for Alzheimer's detection that integrates audio and textual modalities, two non-intrusive sources of information that offer complementary insights into cognitive health. Unlike prior approaches that fuse modalities at a coarse level, CogniAlign leverages a word-level temporal alig…

This https://arxiv.org/abs/2501.18564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce SAM2Act, a multi-view robotic transformer-based policy that leverages multi-reso…

GLiDRE: Generalist Lightweight model for Document-level Relation Extraction
Robin Armingaud, Romaric Besan\c{c}on
https://arxiv.org/abs/2508.00757 https://…

GLiDRE: Generalist Lightweight model for Document-level Relation Extraction
Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to the need to model complex interactions between entities across sentences. Current approaches, largely based on the ATLOP architecture, are commonly evaluated on benchmarks like DocRED and Re-DocRED. However, their performance in zero-shot or few-shot settings remains largely underexplored due to the task's complexity. Recently, the GLiNER model has s…

Improving compiler support for SIMD offload using Arm Streaming SVE
Mohamed Husain Noor Mohamed, Adarsh Patil, Latchesar Ionkov, Eric Van Hensbergen
https://arxiv.org/abs/2506.02233

Improving compiler support for SIMD offload using Arm Streaming SVE
The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adja…

E2ATST: A Temporal-Spatial Optimized Energy-Efficient Architecture for Training Spiking Transformer
Yunhao Ma, Yanyu Lin, Mingjing Li, Puli Quan, Chenlin Zhou, Wenyue Zhang, Zhiwei Zhong, Wanyi Jia, Xueke Zhu, Qingyan Meng, Huihui Zhou, Fengwei An
https://arxiv.org/abs/2508.00475

E2ATST: A Temporal-Spatial Optimized Energy-Efficient Architecture for Training Spiking Transformer
(1) Pengcheng Laboratory, (2) Southern University of Science and Technology, (3) Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, (4) University of Chinese Academy of Sciences

“We need to go beyond legislation to a cultural change, where disability is treated as an identity to be celebrated.”
Brilliant article in the FT on housing design for disability and the aesthetics of accessibility
https://www.ft.com/content/05309cf9-96d9-460…

The aesthetics of accessibility: design for disability that’s dynamic not dreary
Architecture is at a fertile inflection point. But while housebuilding goals, sustainability and retrofitting are dominating the agenda, why does accessibility too often remain an afterthought?

DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation
Qitao Qin, Yucong Luo, Zhibo Chu
https://arxiv.org/abs/2507.01383

DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation
Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial attacks. Significant research has been conducted on targeted attacks in FedRec systems, motivated by commercial and social influence considerations. However, much of this work has largely overlooked the differential robustness of recommendation models. Moreover, our empirical findings indicate that existing targeted att…

Extremely Large-Scale Movable Antenna-Enabled Multiuser Communications: Modeling and Optimization
Min Fu, Lipeng Zhu, Rui Zhang
https://arxiv.org/abs/2506.02735

Extremely Large-Scale Movable Antenna-Enabled Multiuser Communications: Modeling and Optimization
Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser syste…

Composable OS Kernel Architectures for Autonomous Intelligence
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2508.00604 https://arxiv.org/pdf/2508.00…

Composable OS Kernel Architectures for Autonomous Intelligence
As intelligent systems permeate edge devices, cloud infrastructure, and embedded real-time environments, this research proposes a new OS kernel architecture for intelligent systems, transforming kernels from static resource managers to adaptive, AI-integrated platforms. Key contributions include: (1) treating Loadable Kernel Modules (LKMs) as AI-oriented computation units for fast sensory and cognitive processing in kernel space; (2) expanding the Linux kernel into an AI-native environment with…

Isogeometric contact analysis in subsea umbilical and power cables
Tianjiao Dai (School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Hubei Key Laboratory of Naval Architecture and Ocean Engineering Hydrodynamics, HUST, Wuhan, China), Shuo Yang (Zoomlion Heavy Industry Science and Technology Co., Ltd., China), Xing Jin (China Offshore Engineering and Technology Co., Ltd., China), Svein S{\ae}vik (Department of Marine Technology, Norwegian U…

Isogeometric contact analysis in subsea umbilical and power cables
Subsea umbilical and power cables contain a large number of contact interfaces between different geometries and materials. These complex interactions rise significant challenges for accurately considering contact surface properties by using traditional analytical solutions or finite element methods. These properties have been identified as the most sensitive parameters when performing the numerical simulation for stress analysis. Therefore, it is essential to apply a novel approach for contact …

Our meetup was well-attended with two interesting talks:
📋 Sarah Julia Kriesch presented "Docker vs Podman" - a beginner-friendly introduction to container basics and key differences between these tools.
🔧 Philip Laine showcased "Spegel", an OCI Registry Mirror that leverages torrent technology for efficient container image distribution - saving traffic costs and safeguarding against registry outages.
1/2

Very nice article about LLM architecture, a bit too complicated for me but probably not for others..
https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison

The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design

@…
maybe interesting for you?
https://infosec.exchange/@srlabs/114777189749813576

This https://arxiv.org/abs/2505.23516 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance
We present the CASE framework, an open-source platform for adaptive, context-aware participatory research, and pandemic preparedness. CASE implements an event-driven architecture that enables dynamic survey workflows, allowing real-time adaptation based on participant responses, external data, temporal conditions, and evolving user states. The framework supports a broad range of research needs, from simple one-time questionnaires to complex longitudinal studies with advanced conditional logic. …

AI Accelerators for Large Language Model In-ference: Architecture Analysis and Scaling Strategies
Amit Sharma
https://arxiv.org/abs/2506.00008 https://

AI Accelerators for Large Language Model In-ference: Architecture Analysis and Scaling Strategies
The rapid growth of large-language models (LLMs) is driving a new wave of specialized hardware for inference. This paper presents the first workload-centric, cross-architectural performance study of commercial AI accelerators, spanning GPU-based chips, hybrid packages, and wafer-scale engines. We compare memory hierarchies, compute fabrics, and on-chip interconnects, and observe up to 3.7x performance variation across architectures as batch size and sequence length change. Four scaling techniqu…

Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
Hongfei Zhang, Kun Zhou, Ruizheng Wu, Jiangbo Lu
https://arxiv.org/abs/2508.00698 https://

Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
Image dehazing remains a challenging problem due to the spatially varying nature of haze in real-world scenes. While existing methods have demonstrated the promise of large-scale pretrained models for image dehazing, their architecture-specific designs hinder adaptability across diverse scenarios with different accuracy and efficiency requirements. In this work, we systematically investigate the generalization capability of pretrained depth representations-learned from millions of diverse image…

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster, Sandro Luigi Fiore
https://arxiv.org/abs/2505.24675

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments,…

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…

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
Ishika Singh, Ankit Goyal, Stan Birchfield, Dieter Fox, Animesh Garg, Valts Blukis
https://arxiv.org/abs/2506.01196

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware policies. We address the challenge of mapping natural language instructions and multi-view RGBD observations to quasi-static robot actions. 3D-aware robot policies achieve state-of-the-art performance on precise robot manipulation tasks, but struggle with generalization to unseen instructions, scenes, and objects. On …

Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts
Spencer Banasik
https://arxiv.org/abs/2506.01827 https…

Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts
As machine learning algorithms are shown to be an increasingly valuable tool, the demand for their access has grown accordingly. Oftentimes, it is infeasible to run inference with larger models without an accelerator, which may be unavailable in environments that have constraints such as energy consumption, security, or cost. To increase the availability of these models, we aim to im- prove the LLM inference speed on a CPU-only environment by modifying the cache architecture. To determine what …

High-Layer Attention Pruning with Rescaling
Songtao Liu, Peng Liu
https://arxiv.org/abs/2507.01900 https://arxiv.org/pdf/2507.01900…

High-Layer Attention Pruning with Rescaling
Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency. However, conventional training-free structured pruning methods often employ a heuristic metric that indiscriminately removes some attention heads across all pruning layers, without considering their positions within the network architecture. In this work, we propose a novel pruning algorithm that strategically prunes attention heads in the model's higher layers. Since t…

NVIDIA GPU Confidential Computing Demystified
Zhongshu Gu, Enriquillo Valdez, Salman Ahmed, Julian James Stephen, Michael Le, Hani Jamjoom, Shixuan Zhao, Zhiqiang Lin
https://arxiv.org/abs/2507.02770

NVIDIA GPU Confidential Computing Demystified
GPU Confidential Computing (GPU-CC) was introduced as part of the NVIDIA Hopper Architecture, extending the trust boundary beyond traditional CPU-based confidential computing. This innovation enables GPUs to securely process AI workloads, providing a robust and efficient solution for handling sensitive data. For end users, transitioning to GPU-CC mode is seamless, requiring no modifications to existing AI applications. However, this ease of adoption contrasts sharply with the complexity of the …

Urban Snaps 📸
城市抓拍 📸
📷 Pentax MX
🎞️Fujifilm Neopan SS, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Yiding Cao Geld mit PayPal.Me senden
Folgen Sie dem Link PayPal.Me/dingsterdingster und geben Sie den Betrag ein. Es ist bequem und sicher. Sie haben noch kein PayPal-Konto? Kein Problem.

CI/CD #Pipeline #Architecture: Complete Guide to Building Robust CI and CD Pipelines
https://

Composable Building Blocks for Controllable and Transparent Interactive AI Systems
Sebe Vanbrabant, Gustavo Rovelo Ruiz, Davy Vanacken
https://arxiv.org/abs/2506.02262

Composable Building Blocks for Controllable and Transparent Interactive AI Systems
While the increased integration of AI technologies into interactive systems enables them to solve an equally increasing number of tasks, the black box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models. While this makes individual AI models clearer, the overarching system architecture remains opaque. To this end,…

Hybrid SLC-MLC RRAM Mixed-Signal Processing-in-Memory Architecture for Transformer Acceleration via Gradient Redistribution
Chang Eun Song, Priyansh Bhatnagar, Zihan Xia, Nam Sung Kim, Tajana Rosing, Mingu Kang
https://arxiv.org/abs/2506.00020

Hybrid SLC-MLC RRAM Mixed-Signal Processing-in-Memory Architecture for Transformer Acceleration via Gradient Redistribution
Transformers, while revolutionary, face challenges due to their demanding computational cost and large data movement. To address this, we propose HyFlexPIM, a novel mixed-signal processing-in-memory (PIM) accelerator for inference that flexibly utilizes both single-level cell (SLC) and multi-level cell (MLC) RRAM technologies to trade-off accuracy and efficiency. HyFlexPIM achieves efficient dual-mode operation by utilizing digital PIM for high-precision and write-intensive operations while ana…

How Fast Can Graph Computations Go on Fine-grained Parallel Architectures
Yuqing Wang, Charles Colley, Brian Wheatman, Jiya Su, David F. Gleich, Andrew A. Chien
https://arxiv.org/abs/2507.00949

How Fast Can Graph Computations Go on Fine-grained Parallel Architectures
Large-scale graph problems are of critical and growing importance and historically parallel architectures have provided little support. In the spirit of co-design, we explore the question, How fast can graph computing go on a fine-grained architecture? We explore the possibilities of an architecture optimized for fine-grained parallelism, natural programming, and the irregularity and skew found in real-world graphs. Using two graph benchmarks, PageRank (PR) and Breadth-First Search (BFS), we ev…

This https://arxiv.org/abs/2501.03836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis
Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
Hamidou Tembine, Issa Bamia, Massa NDong, Bakary Coulibaly, Oumar Issiaka Traore, Moussa Traore, Moussa Sanogo, Mamadou Eric Sangare, Salif Kante, Daryl Noupa Yongueng, Hafiz Tiomoko Ali, Malik Tiomoko, Frejus Laleye, Boualem Djehiche, Wesmanegda Elisee Dipama, Idris Baba Saje, Hammid Mohammed Ibrahim, Moumini Sanogo, Marie Coursel Nininahazwe, Abdul-Latif Siita, Haine Mhlongo, Teddy Nelvy Dieu Merci Kouka, Mariam Serine Jerid…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
While global linguistic diversity spans more than 7164 recognized languages, the current dominant architecture of machine intelligence remains fundamentally biased toward written text. This bias excludes over 700 million people particularly in rural and remote regions who are audio-literate. In this work, we introduce a fully textless, audio-to-audio machine intelligence framework designed to serve this underserved population, and all the people who prefer audio-efficiency. Our contributions in…

Model-Driven Deep Learning Enhanced Joint Beamforming and Mode Switching for RDARS-Aided MIMO Systems
Chengwang Ji, Kehui Li, Haiquan Lu, Qiaoyan Peng, Jintao Wang, Shaodan Ma
https://arxiv.org/abs/2508.00326

Model-Driven Deep Learning Enhanced Joint Beamforming and Mode Switching for RDARS-Aided MIMO Systems
Reconfigurable distributed antenna and reflecting surface (RDARS) is a promising architecture for future sixth-generation (6G) wireless networks. In particular, the dynamic working mode configuration for the RDARS-aided system brings an extra selection gain compared to the existing reconfigurable intelligent surface (RIS)-aided system and distributed antenna system (DAS). In this paper, we consider the RDARS-aided downlink multiple-input multiple-output (MIMO) system and aim to maximize the wei…

Cross-Attention Message-Passing Transformers for Code-Agnostic Decoding in 6G Networks
Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim, Jong-Seon No
https://arxiv.org/abs/2507.01038

Cross-Attention Message-Passing Transformers for Code-Agnostic Decoding in 6G Networks
Channel coding for 6G networks is expected to support a wide range of requirements arising from heterogeneous communication scenarios. These demands challenge traditional code-specific decoders, which lack the flexibility and scalability required for next-generation systems. To tackle this problem, we propose an AI-native foundation model for unified and code-agnostic decoding based on the transformer architecture. We first introduce a cross-attention message-passing transformer (CrossMPT). Cro…

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
Alexander Nikitas Dimopoulos, Joseph Grasso
https://arxiv.org/abs/2508.00822

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
This study analyzes semantic segmentation performance across heterogeneously labeled point-cloud datasets relevant to public safety applications, including pre-incident planning systems derived from lidar scans. Using NIST's Point Cloud City dataset (Enfield and Memphis collections), we investigate challenges in unifying differently labeled 3D data. Our methodology employs a graded schema with the KPConv architecture, evaluating performance through IoU metrics on safety-relevant features. Resul…

STELLA: Self-Evolving LLM Agent for Biomedical Research
Ruofan Jin, Zaixi Zhang, Mengdi Wang, Le Cong
https://arxiv.org/abs/2507.02004 https://

STELLA: Self-Evolving LLM Agent for Biomedical Research
The rapid growth of biomedical data, tools, and literature has created a fragmented research landscape that outpaces human expertise. While AI agents offer a solution, they typically rely on static, manually curated toolsets, limiting their ability to adapt and scale. Here, we introduce STELLA, a self-evolving AI agent designed to overcome these limitations. STELLA employs a multi-agent architecture that autonomously improves its own capabilities through two core mechanisms: an evolving Templat…

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…

RoboBrain 2.0 Technical Report
BAAI RoboBrain Team, Mingyu Cao, Huajie Tan, Yuheng Ji, Minglan Lin, Zhiyu Li, Zhou Cao, Pengwei Wang, Enshen Zhou, Yi Han, Yingbo Tang, Xiangqi Xu, Wei Guo, Yaoxu Lyu, Yijie Xu, Jiayu Shi, Cheng Chi, Mengdi Zhao, Xiaoshuai Hao, Shanyu Rong, Zhengliang Cai, Bolun Zhang, Shuyi Zhang, Huaihai Lyu, Mengfei Du, Lingfeng Zhang, Xi Feng, Xiaodan Liu, Yance Jiao, Chenrui He, Mengsi Lyu, Zhuo Chen, Yulong Ao, Xue Sun, Zheqi He, Jingshu Zheng, Xi Yang, Donghai Shi…

RoboBrain 2.0 Technical Report
We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial…

Large Processor Chip Model
Kaiyan Chang, Mingzhi Chen, Yunji Chen, Zhirong Chen, Dongrui Fan, Junfeng Gong, Nan Guo, Yinhe Han, Qinfen Hao, Shuo Hou, Xuan Huang, Pengwei Jin, Changxin Ke, Cangyuan Li, Guangli Li, Huawei Li, Kuan Li, Naipeng Li, Shengwen Liang, Cheng Liu, Hongwei Liu, Jiahua Liu, Junliang Lv, Jianan Mu, Jin Qin, Bin Sun, Chenxi Wang, Duo Wang, Mingjun Wang, Ying Wang, Chenggang Wu, Peiyang Wu, Teng Wu, Xiao Xiao, Mengyao Xie, Chenwei Xiong, Ruiyuan Xu, Mingyu Yan, Xiaoc…

Large Processor Chip Model
Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on…

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

A Domain-specific Language and Architecture for Detecting Process Activities from Sensor Streams in IoT
Ronny Seiger, Daniel Locher, Marco Kaufmann, Aaron F. Kurz
https://arxiv.org/abs/2507.00686

A Domain-specific Language and Architecture for Detecting Process Activities from Sensor Streams in IoT
Modern Internet of Things (IoT) systems are equipped with a plethora of sensors providing real-time data about the current operations of their components, which is crucial for the systems' internal control systems and processes. However, these data are often too fine-grained to derive useful insights into the execution of the larger processes an IoT system might be part of. Process mining has developed advanced approaches for the analysis of business processes that may also be used in the conte…

Vision Transformers for Multi-Variable Climate Downscaling: Emulating Regional Climate Models with a Shared Encoder and Multi-Decoder Architecture
Fabio Merizzi, Harilaos Loukos
https://arxiv.org/abs/2506.22447

Vision Transformers for Multi-Variable Climate Downscaling: Emulating Regional Climate Models with a Shared Encoder and Multi-Decoder Architecture
Global Climate Models (GCMs) are critical for simulating large-scale climate dynamics, but their coarse spatial resolution limits their applicability in regional studies. Regional Climate Models (RCMs) refine this through dynamic downscaling, albeit at considerable computational cost and with limited flexibility. While deep learning has emerged as an efficient data-driven alternative, most existing studies have focused on single-variable models that downscale one variable at a time. This approa…

Multi-modal brain MRI synthesis based on SwinUNETR
Haowen Pang, Weiyan Guo, Chuyang Ye
https://arxiv.org/abs/2506.02467 https://arxiv…

Multi-modal brain MRI synthesis based on SwinUNETR
Multi-modal brain magnetic resonance imaging (MRI) plays a crucial role in clinical diagnostics by providing complementary information across different imaging modalities. However, a common challenge in clinical practice is missing MRI modalities. In this paper, we apply SwinUNETR to the synthesize of missing modalities in brain MRI. SwinUNETR is a novel neural network architecture designed for medical image analysis, integrating the strengths of Swin Transformer and convolutional neural networ…

This https://arxiv.org/abs/2503.07792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Efficient Neural Clause-Selection Reinforcement
Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principle…

ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
Junyu Wang, Tianrui Wang, Meng Ge, Longbiao Wang, Jianwu Dang
https://arxiv.org/abs/2507.02666

ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism often allocates a portion of attention weights to irrelevant information, potentially impairing the model's discriminative ability. To address this, we introduce a differential attention mechanism, which effectively mitigates ineffective attention allocation through the integration of dual-softmax operations and appro…

Adaptive Privacy-Preserving SSD
Na Young Ahn, Dong Hoon Lee
https://arxiv.org/abs/2506.02030 https://arxiv.org/pdf/2506.02030

Adaptive Privacy-Preserving SSD
Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels (PL0-PL3) that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning adjusts levels contextually, boosting privacy with negligible performance overhead and complexity.

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink
Tierui Gong, Chau Yuen, Chong Meng Samson See, M\'erouane Debbah, Lajos Hanzo
https://arxiv.org/abs/2506.01355 …

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink
Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution for evolving wireless receivers from the classical to the quantum domain. To further unleash their great potential in wireless communications, we propose a flexible architecture for Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) receivers in the multi-user uplink. Then the corresponding signal model of the RAQ-MIMO system is constructed by paving the way from quantum physics to classical wireless com…

iRonCub 3: The Jet-Powered Flying Humanoid Robot
Davide Gorbani, Hosameldin Awadalla Omer Mohamed, Giuseppe L'Erario, Gabriele Nava, Punith Reddy Vanteddu, Shabarish Purushothaman Pillai, Antonello Paolino, Fabio Bergonti, Saverio Taliani, Alessandro Croci, Nicholas James Tremaroli, Silvio Traversaro, Bruno Vittorio Trombetta, Daniele Pucci
https…

iRonCub 3: The Jet-Powered Flying Humanoid Robot
This article presents iRonCub 3, a jet-powered humanoid robot, and its first flight experiments. Unlike traditional aerial vehicles, iRonCub 3 aims to achieve flight using a full-body humanoid form, which poses unique challenges in control, estimation, and system integration. We highlight the robot's current mechanical and software architecture, including its propulsion system, control framework, and experimental infrastructure. The control and estimation framework is first validated in simulat…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Seeing Through the Fog: Empowering Mobile Devices to Expose and Mitigate RAN Buffer Effects on De...
Yuxin Liu, Tianyang Zhang, Kyle Jamieson, Yaxiong Xie

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Gaosheng Zhao, Dusit Niyato, Geng Sun, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2506.00417

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By learning latent dynamics, world models provide a sample-efficient framework that is especially valuable in data-constrained or safety-critical scenarios. In this paper, we present a comprehensive overview of world models, highlighting their architecture, training paradigms, and a…

Bi-SamplerZ: A Hardware-Efficient Gaussian Sampler Architecture for Quantum-Resistant Falcon Signatures
Binke Zhao, Ghada Alsuhi, Hani Saleh, Baker Mohammad
https://arxiv.org/abs/2505.24509

Bi-SamplerZ: A Hardware-Efficient Gaussian Sampler Architecture for Quantum-Resistant Falcon Signatures
FALCON is a standardized quantum-resistant digital signature scheme that offers advantages over other schemes, but features more complex signature generation process. This paper presents Bi-Samplerz, a fully hardware-implemented, high-efficiency dual-path discrete Gaussian sampler designed to accelerate Falcon signature generation. Observing that the SamplerZ subroutine is consistently invoked in pairs during each signature generation, we propose a dual-datapath architecture capable of generati…

This https://arxiv.org/abs/2505.24799 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond naïve upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages…

A Compact 16-bit S-box over Tower Field $\F_{(((2^2)^2)^2)^2}$ with High Security
Bahram Rashidi, Behrooz Khadem
https://arxiv.org/abs/2507.01423 https://

A Compact 16-bit S-box over Tower Field $\F_{(((2^2)^2)^2)^2}$ with High Security
This paper introduces a compact and secure 16-bit substitution box (S-box) designed over the composite field $\F_{(((2^2)^2)^2)^2}$, optimized for both hardware efficiency and cryptographic robustness. The proposed S-box decomposes operations into subfields, leveraging a tower field architecture. This enables significant hardware reduction through optimized field inversion and a low-cost affine transformation. Security evaluations confirm resilience against linear, differential, algebraic and D…

A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion
Maryem Fadili (VeDeCom, IRSEEM), Mohamed Anis Ghaoui (VeDeCom), Louis Lecrosnier (IRSEEM, ESIGELEC), Steve Pechberti (VeDeCom), Redouane Khemmar (IRSEEM, ESIGELEC, IRSEEM)
https://arxiv.org/abs/2507.02430

A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion
In autonomous driving, recent research has increasingly focused on collaborative perception based on deep learning to overcome the limitations of individual perception systems. Although these methods achieve high accuracy, they rely on high communication bandwidth and require unrestricted access to each agent's object detection model architecture and parameters. These constraints pose challenges real-world autonomous driving scenarios, where communication limitations and the need to safeguard p…

AREE-Based Decoupled Design of Hybrid Beamformers in mmWave XL-MIMO Systems
Jiazhe Li, Nicol\`o Decarli, Francesco Guidi, Heng Dong, Anna Guerra, Alessandro Bazzi, Zhuoming Li
https://arxiv.org/abs/2507.02802

AREE-Based Decoupled Design of Hybrid Beamformers in mmWave XL-MIMO Systems
Hybrid beamforming has been widely employed in mmWave communications such as vehicular-to-everything (V2X) scenarios, as a compromise between hardware complexity and spectral efficiency. However, the inherent coupling between analog and digital precoders in hybrid array architecture significantly limits the computational and spectral efficiency of existing algorithms. To address this issue, we propose an alternating residual error elimination (AREE) algorithm, which decomposes the hybrid beamfo…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem
Soysal, Koliogeorgi, Firtina, Ghiasi, Nadig, Mao, Oliveira, Liang, Zambaku, Sadrosadati, Mutlu
…

Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
Hao Song, Yiming Shen, Wenxuan Luo, Leixin Guo, Ting Chen, Jiashui Wang, Beibei Li, Xiaosong Zhang, Jiachi Chen
https://arxiv.org/abs/2506.02040

Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
The Model Context Protocol (MCP) is an emerging standard designed to enable seamless interaction between Large Language Model (LLM) applications and external tools or resources. Within a short period, thousands of MCP services have already been developed and deployed. However, the client-server integration architecture inherent in MCP may expand the attack surface against LLM Agent systems, introducing new vulnerabilities that allow attackers to exploit by designing malicious MCP servers. In th…

[2025-06-03 Tue (UTC), 9 new articles found for cs.NI Networking and Internet Architecture]
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CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
Minghao Ning, Yufeng Yang, Keqi Shu, Shucheng Huang, Jiaming Zhong, Maryam Salehi, Mahdi Rahmani, Yukun Lu, Chen Sun, Aladdin Saleh, Ehsan Hashemi, Amir Khajepour
https://arxiv.org/abs/2507.02245

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
We present CoInfra, a large-scale cooperative infrastructure perception system and dataset designed to advance robust multi-agent perception under real-world and adverse weather conditions. The CoInfra system includes 14 fully synchronized sensor nodes, each equipped with dual RGB cameras and a LiDAR, deployed across a shared region and operating continuously to capture all traffic participants in real-time. A robust, delay-aware synchronization protocol and a scalable system architecture that …

This https://arxiv.org/abs/2407.20123 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Context-Enhanced CSI Tracking Using Koopman-Inspired Dual Autoencoders in Dynamic Wireless Environments
This paper introduces a novel framework for tracking and predicting Channel State Information (CSI) by leveraging Physics-Informed Autoencoders (PIAE) integrated with a learned Koopman operator. The proposed approach models CSI as a nonlinear dynamical system governed by both intrinsic channel behavior and exogenous contextual factors such as position, temperature, and atmospheric conditions. The architecture comprises dual autoencoders-one dedicated to CSI and another to contextual inputs-link…

Requirements for Active Assistance of Natural Questions in Software Architecture
Diogo Lemos, Ademar Aguiar, Neil B. Harrison
https://arxiv.org/abs/2506.23898

Requirements for Active Assistance of Natural Questions in Software Architecture
Natural questions are crucial to shaping key architectural decisions and preserving architectural knowledge. They arise organically during the architectural design process, often resulting from the existing architectural experience of the designer and the distinctive characteristics of the system being designed. However, natural questions are often mismanaged or ignored, which can lead to architectural drift, knowledge loss, inefficient resource use, or poor understandability of the system's ar…

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

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Anum Nawaz, Hafiz Humza Mahmood Ramzan, Xianjia Yu, Zhuo Zou, Tomi Westerlund
https://arxiv.org/abs/2506.02038

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment
Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabil…

[2025-06-04 Wed (UTC), 5 new articles found for cs.NI Networking and Internet Architecture]
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[2025-07-03 Thu (UTC), 5 new articles found for cs.NI Networking and Internet Architecture]
toXiv_bot_toot

Energy-Oriented Computing Architecture Simulator for SNN Training
Yunhao Ma (Pengcheng Laboratory, Southern University of Science and Technology), Wanyi Jia (Pengcheng Laboratory, Southern University of Science and Technology), Yanyu Lin (Pengcheng Laboratory), Wenjie Lin (Pengcheng Laboratory), Xueke Zhu (Pengcheng Laboratory), Huihui Zhou (Pengcheng Laboratory), Fengwei An (Southern University of Science and Technology)

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

[2025-07-04 Fri (UTC), 8 new articles found for cs.NI Networking and Internet Architecture]
toXiv_bot_toot

[2025-08-04 Mon (UTC), 1 new article found for cs.AR Hardware Architecture]
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Innovative Research on IoT Architecture and Robotic Operating Platforms: Applications of Large Language Models and Generative AI
Huiwen Han
https://arxiv.org/abs/2506.22477

Innovative Research on IoT Architecture and Robotic Operating Platforms: Applications of Large Language Models and Generative AI
This paper introduces an innovative design for robotic operating platforms, underpinned by a transformative Internet of Things (IoT) architecture, seamlessly integrating cutting-edge technologies such as large language models (LLMs), generative AI, edge computing, and 5G networks. The proposed platform aims to elevate the intelligence and autonomy of IoT systems and robotics, enabling them to make real-time decisions and adapt dynamically to changing environments. Through a series of compelling…

[2025-07-03 Thu (UTC), 2 new articles found for cs.AR Hardware Architecture]
toXiv_bot_toot

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang, Jiacheng Wang, Gang Sun, Dusit Niyato, Hongfang Yu, Zehui Xiong, Xianbin Wang, Xuemin Shen
https://arxiv.org/abs/2507.00672

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and multimodal data processing. This survey explores the integration of multi-LLMs (Large Language Models) to address this in edge computing, where multiple specialized LLMs collaborate to enhance task performance and adaptability …

[2025-07-04 Fri (UTC), 5 new articles found for cs.AR Hardware Architecture]
toXiv_bot_toot

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Antonio Ojea
https://arxiv.org/abs/2506.23628 https://

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Traditional Kubernetes networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure. This paper introduces Kubernetes Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations. KNDs integrate network resource management into Kubernetes' core by utilizing Dynamic Resource Allocation (DRA), Node Resource Interface (NRI) improvements, and upcoming OCI Runtime Specificati…

[2025-06-03 Tue (UTC), 9 new articles found for cs.AR Hardware Architecture]
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[2025-06-04 Wed (UTC), 6 new articles found for cs.AR Hardware Architecture]
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Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Qiufeng Li, Yiwen Liang, Weidong Cao
https://arxiv.org/abs/2506.02311

Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Compute-in-memory (CIM) architecture has been widely explored to address the von Neumann bottleneck in accelerating deep neural networks (DNNs). However, its reliability remains largely understudied, particularly in the emerging domain of floating-point (FP) CIM, which is crucial for speeding up high-precision inference and on device training. This paper introduces Unicorn-CIM, a framework to uncover the vulnerability and improve the resilience of high-precision CIM, built on static r…