Tootfinder

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

Inside Out 🏢
内外 🏢
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Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Ruochen Ji, Lyu Tiangang
https://arxiv.org/abs/2506.06066

Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Mixed reality (MR) environments offer embodied spatial interaction, providing intuitive 3D manipulation capabilities that enhance the conceptual design process. Parametric modeling, a powerful and advanced architectural design method, enables the generation of complex, optimized geometries. However, its integration into MR environments remains limited due to precision constraints and unsuitable input modalities. Existing MR tools prioritize spatial interaction but lack the control and expressiv…

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
https://arxiv.org/abs/2506.08045

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in Agentic AI, these systems surpass traditional UAVs by exhibiting goal-driven behavior, contextual reasoning, and interactive autonomy. We provide a comprehensive foundation for understanding the architectural components and enabling technologies that distinguish…

Centrality Change Proneness: an Early Indicator of Microservice Architectural Degradation
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07690

Centrality Change Proneness: an Early Indicator of Microservice Architectural Degradation
Over the past decade, the wide adoption of Microservice Architecture has required the identification of various patterns and anti-patterns to prevent Microservice Architectural Degradation. Frequently, the systems are modelled as a network of connected services. Recently, the study of temporal networks has emerged as a way to describe and analyze evolving networks. Previous research has explored how software metrics such as size, complexity, and quality are related to microservice centrality in…

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
Or Tal, Felix Kreuk, Yossi Adi
https://arxiv.org/abs/2506.08570

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA) systems differ significantly across many dimensions, such as training datasets, modeling paradigms, and architectural choices. This diversity complicates efforts to evaluate models fairly and pinpoint which design choices most influence performance. While factors like data…

Deep Reinforcement Learning-Based RAN Slicing with Efficient Inter-Slice Isolation in Tactical Wireless Networks
Abderrahime Filali, Diala Naboulsi, Georges Kaddoum
https://arxiv.org/abs/2506.09039

Deep Reinforcement Learning-Based RAN Slicing with Efficient Inter-Slice Isolation in Tactical Wireless Networks
The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and the open RAN (O-RAN) paradigm, to unlock multiple architectural options and opportunities for a wide range of innovative applications. RAN slicing and the O-RAN paradigm are considered game changers in TNs, where the former makes it possible to tailor user services to users requirements, and the latter brings openness …

Inside Out 🏢
内外 🏢
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🎞️Lucky SHD 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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Series A, Episode 13 - Orac
CALLY: I'm sure.
[Scene: Gan is in a cabin. We see him listen to the following conversation over the intercom.]
BLAKE: [O.S.] Our only hope is if they have a supply on Aristo.
VILA: [O.S.] But if they don't?
https://blake.torpidity.net/m/113/60

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CoMemo: LVLMs Need Image Context with Image Memory
Shi Liu, Weijie Su, Xizhou Zhu, Wenhai Wang, Jifeng Dai
https://arxiv.org/abs/2506.06279 https://…

CoMemo: LVLMs Need Image Context with Image Memory
Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preser…

Leveraging Network Methods for Hub-like Microservice Detection
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07683

Leveraging Network Methods for Hub-like Microservice Detection
Context: Microservice Architecture is a popular architectural paradigm that facilitates flexibility by decomposing applications into small, independently deployable services. Catalogs of architectural anti-patterns have been proposed to highlight the negative aspects of flawed microservice design. In particular, the Hub-like anti-pattern lacks an unambiguous definition and detection method. Aim: In this work, we aim to find a robust detection approach for the Hub-like microservice anti-pattern …

I just saw the images of the DHS "rennovation" of the old St. Elizabeths in DC as the new DHS HQ.
The architectural renditions suggest that they will be turning it into something rather different than its historical shape (but the drawings i saw didn't look too bad - at least they didn't turn it into a Frank Gehry monstrosity with leaky roofs, dead-end hallways, and external stairs that require people to step open crevices.)
Given DHS head Noem's history, I …

This https://arxiv.org/abs/2502.19679 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDL_…

Architectural Vulnerability and Reliability Challenges in AI Text Annotation: A Survey-Inspired Framework with Independent Probability Assessment
Large Language Models, despite their power, have a fundamental architectural vulnerability stemming from their causal transformer design -- order sensitivity. This architectural constraint may distorts classification outcomes when prompt elements like label options are reordered, revealing a theoretical gap between accuracy metrics and true model reliability. The paper conceptualizes this vulnerability through the lens of survey methodology, where respondent biases parallel LLM positional depen…

Leveraging machine learning features for linear optical interferometer control
Sergei S. Kuzmin, Ivan V. Dyakonov, Stanislav S. Straupe
https://arxiv.org/abs/2505.24032

Leveraging machine learning features for linear optical interferometer control
We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a trainin…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
https://…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping fo…

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Robin Geens, Marian Verhelst
https://arxiv.org/abs/2506.02523 https://

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, improves the efficiency of large language models by projecting query, key, and value tensors into a compact latent space. This architectural change reduces the KV-cache size and significantly lowers memory bandwidth demands, particularly in the autoregressive decode phase. This letter presents the first hardware-centric analysis of MLA, comparing it to conventional Multi-Head Attention (MHA) and evaluating its implications for accele…

This https://arxiv.org/abs/2502.03503 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Analyzing limits for in-context learning
We examine limits of in-context learning (ICL) in transformer models trained from scratch, focusing on function approximation tasks as a controlled setting to uncover fundamental behaviors. While we show empirically that transformer models can generalize, approximating unseen classes of polynomial (non linear) functions, they cannot generalize beyond certain values. We provide both empirical and mathematical arguments explaining that these limitations stem from architectural components, namely …

Modern applications require search capabilities that go beyond basic text matching. They must be fast, accurate, personalised and context-aware. At this year's Berlin Buzzwords, Saurabh Singh will demonstrate how OpenSearch’s latest AI/ML enhancements and engine improvements enable organisations to build intelligent, scalable search experiences that meet these evolving needs.
Learn more:

From Search to Insight: Leveraging OpenSearch for Scalable, AI-Driven Search Experiences
Observability, log analytics, GenAI systems, and RAG pipelines must query massive volumes of semantic embeddings to retrieve relevant content instantly. Today’s search systems often fall short in handling high-dimensional vector data and similarity search. OpenSearch 3.0 brings significant architectural improvements to address these challenges. Integrating Apache Lucene 10 and JVM 21, the platform delivers 20% faster queries than its 2.x predecessor and 10× the throughput of 1.x versions. Ne…

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 …

Series D, Episode 01 - Rescue
DORIAN: Exactly.
AVON: You really are insane, aren't you?
DORIAN: By now I probably would be.
AVON: If it wasn't for this mysterious room.
DORIAN: And what it contains.
SOOLIN: [Enters] And what might that be, Dorian?
https://blake.torpidity.net/m/401/467…

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Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

This https://arxiv.org/abs/2504.13865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

A Survey on (M)LLM-Based GUI Agents
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM-based GUI Agents, systematically analyzing their architectural foundations, technical components, and evaluation methodologies. We identify and analyze f…

My Bartlett School of Architecture colleague Nina Vollenbroker is organising a session on 'Environments of Disability' at this year's AHRA conference in Liverpool (22-24 Nov 2025), based on the premise that architectural processes are central producers of disability
Abstracts are due 2nd June, call for papers and more info:

This https://arxiv.org/abs/2505.07802 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Improving Trajectory Stitching with Flow Models
Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to re…

Took this bad boy to Madrid. ❤️🇪🇸
#trains #publictransport #Spain

This https://arxiv.org/abs/2505.08992 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Dataflow & Tiling Strategies in Edge-AI FPGA Accelerators: A Comprehensive Literature Review
Edge-AI applications demand high-throughput, low-latency inference on FPGAs under tight resource and power constraints. This survey provides a comprehensive review of two key architectural decisions for FPGA-based neural network accelerators: (i) the dataflow (the order and manner in which data is moved and reused on chip), and (ii) the tiling/blocking strategy (how large tensors are partitioned to fit on-chip). We first present a broadened taxonomy of canonical dataflow styles: Weight-Stationa…

Series B, Episode 03 - Weapon
ORAC: No. It was a priority one - for automatic relay to the senior officer present.
BLAKE: Orac, I want more information. I want to know everything there is to know about this man Coser; I want to know how he got out of the base; and I want to know what IMIPAK is.
https://blake.torpidi…

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ACE-Step: A Step Towards Music Generation Foundation Model
Junmin Gong, Sean Zhao, Sen Wang, Shengyuan Xu, Joe Guo
https://arxiv.org/abs/2506.00045 https:/…

ACE-Step: A Step Towards Music Generation Foundation Model
We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic architectural design. Current methods face inherent trade-offs between generation speed, musical coherence, and controllability. For example, LLM-based models (e.g. Yue, SongGen) excel at lyric alignment but suffer from slow inference and structural artifacts. Diffusion models (e.g. DiffRhythm), on the…

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Muhammad Qasim Ali, Aditya Sridhar, Shahbuland Matiana, Alex Wong, Mohammad Al-Sharman
https://arxiv.org/abs/2506.01182

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Humanoid robots have the potential to perform complex tasks in human centered environments but require robust predictive models to reason about the outcomes of their actions. We introduce Humanoid World Models (HWM) a family of lightweight open source video based models that forecast future egocentric observations conditioned on actions. We train two types of generative models Masked Transformers and FlowMatching on 100 hours of humanoid demonstrations. Additionally we explore architectural var…

This https://arxiv.org/abs/2505.22767 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
Large Language Models (LLMs) are typically analysed through architectural, behavioural, or training-data lenses. This article offers a theoretical and experiential re-framing: LLMs as dynamic instantiations of Collective human Knowledge (CK), where intelligence is evoked through dialogue rather than stored statically. Drawing on concepts from neuroscience and AI, and grounded in sustained interaction with ChatGPT-4, I examine emergent dialogue patterns, the implications of fine-tuning, and the …

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…

Series B, Episode 10 - Voice from the Past
JENNA: Complaining of some sort of noise.
AVON: A sort of tone, he said.
CALLY: Oscillating. It was there again just now.
AVON: As a part of his nightmare?
CALLY: Yes, I think so.
https://blake.torpidity.net/m/210/85

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Series B, Episode 10 - Voice from the Past
JENNA: Complaining of some sort of noise.
AVON: A sort of tone, he said.
CALLY: Oscillating. It was there again just now.
AVON: As a part of his nightmare?
CALLY: Yes, I think so.
https://blake.torpidity.net/m/210/85

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Data warehouses, lakes, lakehouses, and more – our choices significantly affect operational costs and development speed. Join Lars Albertsson at Berlin Buzzwords to explore how different data processing paradigms impact deployment, failure handling, and data quality. Learn strategies to minimise costs and latency, bridge between paradigms, and enhance development iteration and operational efficiency.
Learn more:

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

Visualizing Cloud-native Applications with KubeDiagrams
Philippe Merle, Fabio Petrillo
https://arxiv.org/abs/2505.22879 https://arxiv…

Visualizing Cloud-native Applications with KubeDiagrams
Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability. As the de facto orchestration standard, Kubernetes enables this abstraction, but its declarative configuration model makes the architectural understanding difficult. Developers, operators, and architects struggle to form accurate mental models from raw manifests, Helm charts, or cluster state descriptions. We introduce KubeDiagrams, an open-source tool that trans…

Alley Stories IV 🔣
巷弄故事 IV 🔣
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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Alley Stories IV 🔣
巷弄故事 IV 🔣
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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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.

Urban Discoveries 🏙️
都市发现 🏙️
📷 Pentax MX
🎞️Lucky SHD 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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Urban Discoveries 🏙️
都市发现 🏙️
📷 Pentax MX
🎞️Lucky SHD 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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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.