Tootfinder

Series B, Episode 06 - Trial
LYE: I joined up to see some action.
PAR: More fool you.
LYE: You've been on campaigns, Par.
PAR: I have.
LYE: Done a lot of killing.
PAR: Enough. More. [Bercol and Rontane make their way around the corridor.]
https://blake.torpidity.net/m/206/6

TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge
Zhirui Huang, Rui Ma, Shijie Cao, Ran Shu, Ian Wang, Ting Cao, Chixiao Chen, Yongqiang Xiong
https://arxiv.org/abs/2509.13765

TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge
Ternary quantization has emerged as a powerful technique for reducing both computational and memory footprint of large language models (LLM), enabling efficient real-time inference deployment without significantly compromising model accuracy. Conventional LLM inference platforms (e.g GPUs) cannot capitalize on its benefits, as they (i) lack native support for ternary arithmetic and memory specialization and (ii) remain severely under-utilized in low-batch, real-time scenarios. In this work, we …

Liquid Crystal-Based RIS Loss-Trade-Off Analysis
Bowu Wang, Mohamadreza Delbari, Robin Neuder, Alejandro Jim\'enez-S\'aez, Vahid Jamali
https://arxiv.org/abs/2508.11489 …

Liquid Crystal-Based RIS Loss-Trade-Off Analysis
Liquid crystal (LC) technology has emerged as a promising solution for large reconfigurable intelligent surfaces (RISs) at millimeter wave (mmWave) bands, offering advantages such as low power consumption, scalability, and continuously tunable phase shifts. For LC-RIS based on the delay-line architecture, i.e., with dedicated phase shifters, there exists a trade-off between the maximum achievable phase-shift range and the corresponding insertion loss, which has not been studied for LC-RIS-assis…

I am very happy to announce that our big architecture paper for the Digidow project on distributed digital identity systems with biometric authentication for physical interaction is now online on arXiv: https://arxiv.org/abs/2508.10185.
While it can't have all the details, it summarizes the main de…

An Architecture for Distributed Digital Identities in the Physical World
Digital identities are increasingly important for mediating not only digital but also physical service transactions. Managing such identities through centralized providers can cause both availability and privacy concerns: single points of failure and control are ideal targets for global attacks on technical, organizational, or legal fronts. We design, analyze, and build a distributed digital identity architecture for physical world transactions in common scenarios like unlocking doors, public t…

Chinese ModernBERT with Whole-Word Masking
Zeyu Zhao, Ningtao Wang, Xing Fu, Yu Cheng
https://arxiv.org/abs/2510.12285 https://arxiv.org/pdf/2510.12285

Chinese ModernBERT with Whole-Word Masking
Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) a hardware-aware 32k BPE vocabulary tailored to frequent Chinese affixes/compounds, lowering the embedding budget; (ii) whole-word …

At Dreamforce 2025, Salesforce unveils Agentforce 360, a unified agentic AI stack to connect humans, AI agents, and more, and positions Slack as an "agentic OS" (Larry Dignan/Constellation Research)
https://www.constellationr.com/blog-news/i

Salesforce makes its Agentforce 360 case to be your AI agent platform
Salesforce announced a series of product updates, a process intelligence acquisition and an Agentforce 360 architecture designed to make the case that it should orchestrate first and third party AI agents. At Dreamforce 2025, Salesforce looked to differentiate itself from what's becoming a crowded agentic AI platform space. The cloud giants are looking to go horizontal with stacks to build, deploy and optimize AI agents. SaaS vendors are expanding into managing AI agents beyond their verticals.…

Instant prediction of relaxation in moir\'{e} superlattices using neural networks
Aleksei V. Belonovskii, Elizaveta I. Girshova, Erkki L\"ahderanta, Mikhail Kaliteevski
https://arxiv.org/abs/2509.13147

Instant prediction of relaxation in moiré superlattices using neural networks
The relaxation of moiré superlattices in twisted bilayers of transition metal dichalcogenides (TMDs) has been modeled using a set of neural-network-based approaches. We implemented and compared several architectures, including (i) an interpolator combined with an autoencoder, (ii) an interpolator combined with a decoder, (iii) a direct generator mapping input parameters to displacement fields, and (iv) a physics-informed neural network (PINN). Among these, the direct generator architecture dem…

Finally approaching #MuniHac, where I will talk about teaching Functional Software Architecture on Sunday.
https://munihac.de/2025.html

The Paradigm of Massive Wireless Human Sensing: Concept, Architecture and Challenges
Mauro De Sanctis
https://arxiv.org/abs/2508.09756 https://arxiv.org/pd…

The Paradigm of Massive Wireless Human Sensing: Concept, Architecture and Challenges
This article is a position paper which introduces the paradigm of ``Massive Wireless Human Sensing'', i.e. an infrastructure for wireless human sensing based on a plethora of heterogeneous wireless communication signals. More specifically, we aim to exploit signal diversity in the time, frequency, and space domains using opportunistically both device-free and device-based wireless sensing approaches, with the objective of enhancing human sensing capabilities in terms of accuracy and service ava…

Series B, Episode 12 - The Keeper
VILA: What? I don't ...
GOLA: [To henchman] and Master of the Duel - you!
TARA: It is written he will kill you.
GOLA: You called him back, didn't you?
TARA: Yes I did.
GOLA: Why?
https://blake.torpidity.net/m/212/479 B7B…

NuGraph2 with Context--Aware Inputs: Physics--Inspired Improvements in Semantic Segmentation
Vitor F. Grizzi, Margaret Voetberg, Giuseppe Cerati, Hadi Meidani
https://arxiv.org/abs/2509.10684

NuGraph2 with Context--Aware Inputs: Physics--Inspired Improvements in Semantic Segmentation
Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geomet…

I-DCCRN-VAE: An Improved Deep Representation Learning Framework for Complex VAE-based Single-channel Speech Enhancement
Jiatong Li, Simon Doclo
https://arxiv.org/abs/2510.12485 …

I-DCCRN-VAE: An Improved Deep Representation Learning Framework for Complex VAE-based Single-channel Speech Enhancement
Recently, a complex variational autoencoder (VAE)-based single-channel speech enhancement system based on the DCCRN architecture has been proposed. In this system, a noise suppression VAE (NSVAE) learns to extract clean speech representations from noisy speech using pretrained clean speech and noise VAEs with skip connections. In this paper, we improve DCCRN-VAE by incorporating three key modifications: 1) removing the skip connections in the pretrained VAEs to encourage more informative speech…

A Method for Constructing Quasi-Random Peaked Quantum Circuits
O. G. Udalov
https://arxiv.org/abs/2508.07491 https://arxiv.org/pdf/2508.07491

A Method for Constructing Quasi-Random Peaked Quantum Circuits
An algorithm is proposed for constructing quasi-random "peaked" quantum circuits, i.e., circuits whose final qubit state exhibits a high probability concentration on a specific computational basis state. These circuits consist of random gates arranged in a brick-wall architecture. While the multiqubit state in the middle of the circuit can exhibit significant entanglement, the final state is, with high probability, a predetermined pure bitstring. A technique is introduced to obscure the final b…

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
Wei Shan Lee, I Hang Kwok, Kam Ian Leong, Chi Kiu Althina Chau, Kei Chon Sio
https://arxiv.org/abs/2508.10718

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
Accurate prediction of electronic band structures in two-dimensional materials remains a fundamental challenge, with existing methods struggling to balance computational efficiency and physical accuracy. We present the Symmetry-Constrained Multi-Scale Physics-Informed Neural Network (SCMS-PINN) v35, which directly learns graphene band structures while rigorously enforcing crystallographic symmetries through a multi-head architecture. Our approach introduces three specialized ResNet-6 pathways -…

Consistent Updates for Scalable Microservices
Devora Chait-Roth, Kedar S. Namjoshi, Thomas Wies
https://arxiv.org/abs/2508.04829 https://arxiv.org/pdf/2508…

Consistent Updates for Scalable Microservices
Online services are commonly implemented with a scalable microservice architecture, where isomorphic worker processes service client requests, recording persistent state in a backend data store. To maintain service, any modifications to the service functionality must be made on the fly -- i.e., as the service continues to process client requests -- but doing so is challenging. The central difficulty is that of avoiding potential inconsistencies caused by ''mixed mode'' operation, where workers …

Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
Bouthaina Slika, Fadi Dornaika, Fares Bougourzi, Karim Hammoudi
https://arxiv.org/abs/2510.06887

Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity. Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel enc…

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
https://arxiv.org/abs/2508.09996

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative analysis of three attention mechanisms (i.e., baseline multi-head attention, causal attention, and sparse attention) integrated with Convolutional Neural Networks (CNNs) for radio frequency (RF) signal classification. It proposes a novel CNN-Transformer hybrid architecture that leverages different attention patterns to…

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
E. A. Dzhivelikian, A. I. Panov
https://arxiv.org/abs/2510.03286

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neura…

Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates
Anoop Gavankar, Tanish Mittal, Joe Ninan, Shravan Hanasoge
https://arxiv.org/abs/2510.07034

Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates
Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stellar jitter. In this work, we develop a machine learning (ML) framework based on a Transformer architecture that aims to detect the presence…

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds
Stella Bitchebe, Oana Balmau
https://arxiv.org/abs/2508.03676 https://

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds
A large body of research has employed Machine Learning (ML) models to develop learned operating systems (OSes) and kernels. The latter dynamically adapts to the job load and dynamically adjusts resources (CPU, IO, memory, network bandwidth) allocation to respond to the actual user demand. What this work has in common is that it utilizes ML to improve kernel decisions. To this day, and to the best of our knowledge, no work has taken the opposite direction, i.e., using OS to improve ML. While som…

Worker Pool Design Pattern Explanation
https://coffeebytes.dev/en/software-architecture/worker-pool-design-pattern-explanation/

#ReleaseFriday — New version 3.1.0 of the recently talked about https://thi.ng/args package, a declarative & functional CLI argument parser & app framework. I updated the arg specifications to be fully self-desc…

Parametric Neural Amp Modeling with Active Learning
Florian Gr\"otschla, Longxiang Jiao, Luca A. Lanzend\"orfer, Roger Wattenhofer
https://arxiv.org/abs/2509.26564 htt…

Parametric Neural Amp Modeling with Active Learning
We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the mo…

This has got to be the most welcoming and beautiful public toilet facility I've ever seen. Look at the way it's lit - gorgeous!
https://www.archpaper.com/2025/08/toronto-to-the-loo-competition-public-restrooms/

Toronto’s To the Loo competition proves public restrooms can be beautiful too
the Toronto Public Space Committee, together with Cyan Station Architecture, have launched the To the Loo design competition to highlight the city’s shortage of public restrooms and to show that bathrooms can be an inspiring part of the public realm.

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…

An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels
Yubo Zhang, Jeremy Johnston, Xiaodong Wang
https://arxiv.org/abs/2510.02355 https://

An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels
We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels. The core is a deep EDN architecture with three modules: (i) an encoder NN, deployed at each user end, that compresses estimated downlink channels into low-dimensional latent vectors. The latent vector from each user is compressed and then fed back to the BS. (ii) a beamformer decoder NN at the BS that maps recovered latent vectors to beamformers, and (iii) a channel decoder NN at the B…

Mellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding
Nikita Pavlichenko, Iurii Nazarov, Ivan Dolgov, Ekaterina Garanina, Dmitry Ustalov, Ivan Bondyrev, Kseniia Lysaniuk, Evgeniia Vu, Kirill Chekmenev, Joseph Shtok, Yaroslav Golubev, Anton Semenkin, Uladzislau Sazanovich
https://arxiv.org/abs/2510…

Mellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding
We present the Mellum models family, open-weight code completion models designed for interactive use in JetBrains IDEs. Mellums have 4B parameters, adopt a Llama-style architecture, and are pre-trained on ~4T tokens of permissively licensed, multi-language code. Our studies show that (i) careful data curation and staged training significantly improve the model's quality, (ii) editor-critical capabilities such as context packing are necessary for high-quality suggestions, and (iii) a compact, ta…

i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agents
Anupam Purwar, Aditya Choudhary
https://arxiv.org/abs/2509.20971 https://arxiv.org/pd…

i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agents
We experiment with a low-latency, end-to-end voice-to-voice communication model to optimize it for real-time conversational applications. By analyzing components essential to voice to voice (V-2-V) system viz. automatic speech recognition (ASR), text-to-speech (TTS), and dialog management, our work analyzes how to reduce processing time while maintaining high-quality interactions to identify the levers for optimizing V-2-V system. Our work identifies that TTS component which generates life-like…

A Theory of Information, Variation, and Artificial Intelligence
Bijean Ghafouri
https://arxiv.org/abs/2508.19264 https://arxiv.org/pdf/2508.19264

A Theory of Information, Variation, and Artificial Intelligence
A growing body of empirical work suggests that the widespread adoption of generative AI produces a significant homogenizing effect on information, creativity, and cultural production. I first develop a novel theoretical framework to explain this phenomenon. I argue that a dynamic of AI-derivative epistemology, in which individuals increasingly defer to AI outputs, allows a centralized AI Prism to function, a technical mechanism whose architecture is designed to reduce variance and converge on t…

I-ETL: an interoperability-aware health (meta) data pipeline to enable federated analyses
Nelly Barret, Anna Bernasconi, Boris Bikbov, Pietro Pinoli
https://arxiv.org/abs/2509.22351

I-ETL: an interoperability-aware health (meta) data pipeline to enable federated analyses
Clinicians are interested in better understanding complex diseases, such as cancer or rare diseases, so they need to produce and exchange data to mutualize sources and join forces. To do so and ensure privacy, a natural way consists in using a decentralized architecture and Federated Learning algorithms. This ensures that data stays in the organization in which it has been collected, but requires data to be collected in similar settings and similar models. In practice, this is often not the cas…

Two-Timescale Learning for Pilot-Free ISAC Systems
Jian Xiao, Ji Wang, Qimei Cui, Lihua Li, Xingwang Li, Yingzhuang Liu, Tony Q. S. Quek
https://arxiv.org/abs/2508.17749 https:/…

Two-Timescale Learning for Pilot-Free ISAC Systems
A pilot-free integrated sensing and communication (ISAC) system is investigated, in which phase-modulated continuous wave (PMCW) and non-orthogonal multiple access (NOMA) waveforms are co-designed to achieve simultaneous target sensing and data transmission. To enhance effective data throughput (i.e., Goodput) in PMCW-NOMA ISAC systems, we propose a deep learning-based receiver architecture, termed two-timescale Transformer (T3former), which leverages a Transformer architecture to perform joint…

Series B, Episode 05 - Pressure Point
VERON: No!
GAN: It's all right, it's all right. We won't hurt you. [Hands her pill he has taken from a container.] Take this.
VERON: No.
GAN: Please.
VERON: [Swallows pill] Who are you?
https://blake.torpidity.net/m/205/255

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…

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
Felix Quinque, Alan Aboudib, Szymon Fonau, Rodrigo Lopez Portillo Alcocer, Brian McCrindle, Steffen Cruz
https://arxiv.org/abs/2507.17766

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
In August 2024, Bittensor's Subnet 9 (SN9) demonstrated that a distributed network of incentivized, permissionless actors could each pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, while surpassing established baselines. While that work validated blockchain-based decentralized pretraining as viable, it contained core issues: (i) every miner had to fit an entire model locally, and (ii) "winner-takes-all" rewards encouraged model hoarding. Here we introd…

Leveraging 3D Technologies for Hardware Security: Opportunities and Challenges
Peng Gu, Shuangchen Li, Dylan Stow, Russell Barnes, Liu Liu, Yuan Xie, Eren Kursshan
https://arxiv.org/abs/2508.19309

Leveraging 3D Technologies for Hardware Security: Opportunities and Challenges
3D die stacking and 2.5D interposer design are promising technologies to improve integration density, performance and cost. Current approaches face serious issues in dealing with emerging security challenges such as side channel attacks, hardware trojans, secure IC manufacturing and IP piracy. By utilizing intrinsic characteristics of 2.5D and 3D technologies, we propose novel opportunities in designing secure systems. We present: (i) a 3D architecture for shielding side-channel information; (i…

Laguerre geometry for optimization of gridshell with specified force distribution
Kohei Kabaki, Kentaro Hayakawa, Makoto Ohsaki
https://arxiv.org/abs/2508.15179 https://

Laguerre geometry for optimization of gridshell with specified force distribution
For the design of gridshells consisting of continuous beams in two directions and quadrilateral faces to cover a large space of architecture, it is important to arrange each row and column of beams along a planar curve and ensure planar faces for constructability and cross-sectional compatibility at joints. It is also important that the gridshell is in equilibrium mainly with axial forces against the design loads; i.e., bending deformation should be avoided. In this study, we first find a conti…

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Haoxue Wang, Keli Wen, Yuante Li, Qiancheng Qu, Xiangxu Mu, Xinjie Shen, Jiaqi Gao, Chenyang Chang, Chuhan Xie, San Yu Cheung, Zhuoyuan Hu, Xinyu Wang, Sirui Bi, Bi'an Du
https://arxiv.org/abs/2509.21507

QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with point-in-time correctness, evidence attribution, and integration into research workflows. To tackle this, We present QuantMind, an intelligent knowledge extraction and retrieval framework tailored to quantitative finance. QuantMind adopts a two-stage architecture: (i) a knowledge extraction stage that transforms heterog…

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
Siratish Sakpiboonchit
https://arxiv.org/abs/2509.08696 https://arxi…

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
This paper presents a method to accelerate the inference process of diffusion transformer (DiT)-based text-to-speech (TTS) models by applying a selective caching mechanism to transformer layers. Specifically, I integrate SmoothCache into the F5-TTS architecture, focusing on caching outputs of self-attention and feed-forward network layers to reduce redundant computations during the denoising process. A calibration phase is introduced to analyze L1 relative errors between timesteps, guiding the …

Transversal STAR architecture for megaquop-scale quantum simulation with neutral atoms
Refaat Ismail, I-Chi Chen, Chen Zhao, Ronen Weiss, Fangli Liu, Hengyun Zhou, Sheng-Tao Wang, Andrew Sornborger, Milan Kornja\v{c}a
https://arxiv.org/abs/2509.18294

One-third of Sun-like stars are born with misaligned planet-forming disks
Lauren I. Biddle (The University of Texas at Austin), Brendan P. Bowler (University of California Santa Barbara), Marvin Morgan (The University of Texas at Austin, University of California Santa Barbara), Quang H. Tran (Yale University), Ya-Lin Wu (Taiwan Normal University)
https://

One-third of Sun-like stars are born with misaligned planet-forming disks
Exoplanets are organized in a broad array of orbital configurations that reflect their formation along with billions of years of dynamical processing through gravitational interactions. This history is encoded in the angular momentum architecture of planetary systems--the relation between the rotational properties of the central star and the orbital geometry of planets. A primary observable is the alignment (or misalignment) between the rotational axis of the star and the orbital plane of its p…

Series C, Episode 09 - Sarcophagus
TARRANT: Wait a minute, Orac-
ORAC: Disconnect. [The glasses tumble off the tray which begins to swoop around in the air.]
DAYNA: I don't believe that.
VILA: Try and convince me, will you? Please?
https://blake.torpidity.net/m/309/228 …

Time-adaptive SympNets for separable Hamiltonian systems
Konrad Janik, Peter Benner
https://arxiv.org/abs/2509.16026 https://arxiv.org/pdf/2509.16026

Time-adaptive SympNets for separable Hamiltonian systems
Measurement data is often sampled irregularly i.e. not on equidistant time grids. This is also true for Hamiltonian systems. However, existing machine learning methods, which learn symplectic integrators, such as SympNets [20] and HénonNets [4] still require training data generated by fixed step sizes. To learn time-adaptive symplectic integrators, an extension to SympNets, which we call TSympNets, was introduced in [20]. We adapt the architecture of TSympNets and extend them to non-autonomous…

The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology
Sebastian Copei, Oliver Hohlfeld, Jens Kosiol
https://arxiv.org/abs/2508.16341

The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology
The technological landscape changes daily, making it nearly impossible for a single person to be aware of all trends or available tools that may or may not be suitable for their software project. This makes tool selection and architectural design decisions a complex problem, especially for large-scale software systems. To tackle this issue, we introduce CAPI, the Comprehensive Architecture Pattern Integration method that uses a diagnostic decision tree to suggest architectural patterns dependin…

KBest: Efficient Vector Search on Kunpeng CPU
Kaihao MA, Meiling Wang, Senkevich Oleg, Zijian LI, Daihao Xue, Dmitriy Malyshev, Yangming Lv, Shihai Xiao, Xiao Yan, Radionov Alexander, Weidi Zeng, Yuanzhan Gao, Zhiyu Zou, Yao xin, Liu Lin, Junhao Wu, Yiding Liu, Yaoyao Fu, Gongyi Wang, Gong Zhang, Fei Yi, Yingfan Liu
https://arxiv.org/abs/2…

KBest: Efficient Vector Search on Kunpeng CPU
Vector search, which returns the vectors most similar to a given query vector from a large vector dataset, underlies many important applications such as search, recommendation, and LLMs. To be economic, vector search needs to be efficient to reduce the resources required by a given query workload. However, existing vector search libraries (e.g., Faiss and DiskANN) are optimized for x86 CPU architectures (i.e., Intel and AMD CPUs) while Huawei Kunpeng CPUs are based on the ARM architecture and c…

Thinking While Listening: Simple Test Time Scaling For Audio Classification
Prateek Verma, Mert Pilanci
https://arxiv.org/abs/2509.19676 https://arxiv.org/…

Thinking While Listening: Simple Test Time Scaling For Audio Classification
We propose a framework that enables neural models to "think while listening" to everyday sounds, thereby enhancing audio classification performance. Motivated by recent advances in the reasoning capabilities of large language models, we address two central questions: (i) how can thinking be incorporated into existing audio classification pipelines to enable reasoning in the category space and improve performance, and (ii) can a new architecture be designed from the ground up to support both thi…

Flexible and High-Performance Radio Access Networks for upcoming Sixth-Generation (6G) Systems
Peter Schefczik, Umar Toseef, Paolo Baracca, Ralf Klotsche, Torsten Dudda, Mai-Anh Phan, Lorenzo Miretti, David Ginthoer, Bin Han
https://arxiv.org/abs/2509.24417

Flexible and High-Performance Radio Access Networks for upcoming Sixth-Generation (6G) Systems
The collaborative research project 6G-ANNA develops concepts for the 6G radio access network (RAN) architecture and technology components. Previous RAN generations have become inherently more complex and reach their limits in handling foreseen future traffic demands with their diverse characteristics in an efficient manner, e.g., for the use-case of mobile eXtended Reality (XR) on a massive scale. One main objective of 6G is to regain both operational and energy efficiency, i.e., by simplificat…

RARO: Reliability-aware Conversion with Enhanced Read Performance for QLC SSDs
Yanyun Wang, Dingcui Yu, Yina Lv, Yunpeng Song, Yumiao Zhao, Liang Shi
https://arxiv.org/abs/2508.19530

RARO: Reliability-aware Conversion with Enhanced Read Performance for QLC SSDs
Quad-level cell (QLC) flash offers significant benefits in cost and capacity, but its limited reliability leads to frequent read retries, which severely degrade read performance. A common strategy in high-density flash storage is to program selected blocks in a low-density mode (SLC), sacrificing some capacity to achieve higher I/O performance. This hybrid storage architecture has been widely adopted in consumer-grade storage systems. However, existing hybrid storage schemes typically focus on …

Transient Stability-Driven Planning for the Optimal Sizing of Resilient AC/DC Hybrid Microgrids
Yi Wang, Goran Strbac
https://arxiv.org/abs/2507.17110 https://

Transient Stability-Driven Planning for the Optimal Sizing of Resilient AC/DC Hybrid Microgrids
This paper proposes a transient stability-driven planning framework for the optimal sizing problem of resilient AC/DC hybrid microgrids (HMGs) under different types of contingencies, capturing frequency and voltage stability requirements as well as the frequency-voltage coupling dynamics of AC/DC interlinking converters (ICs). The planning model is formulated into a defender-attacker-defender (DAD) architecture, which can be further merged into two levels, i.e., upper-level and low-level proble…

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
Akshay Priyadarshi, Eamonn Kerins
https://arxiv.org/abs/2509.24401

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
The Nancy Grace Roman Space Telescope (Roman) will unveil for the first time the full architecture of planetary systems across Galactic distances through the discovery of up to 200,000 cool and hot exoplanets using microlensing and transit detection methods. Roman's huge exoplanet haul, and Galactic reach, will require new methods to leverage the full exoplanet demographic content of the combined microlensing and transit samples, given the different sensitivity bias of the techniques to planet …

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
Omid Orang, Patricia O. Lucas, Gabriel I. F. Paiva, Petronio C. L. Silva, Felipe Augusto Rocha da Silva, Adriano Alonso Veloso, Frederico Gadelha Guimaraes
https://arxiv.org/abs/2507.17016

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and c…

Crosslisted article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
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- Quantized Neural Networks for Microcontrollers: A Comprehensive Review of Methods, Platforms, and...
Hamza A. Abushahla, Dara Varam, Ariel J. N. Panopio, Mohamed I. AlHajri

Secular Resonances in Planet-Hosting Binary Stars. I. General Theory
Nader Haghighipour, Michael Andrew
https://arxiv.org/abs/2507.17092 https://

Secular Resonances in Planet-Hosting Binary Stars. I. General Theory
Motivated by the diversity of circumstellar planets in binary stars and the strong effects of the secular resonances of Jupiter and Saturn on the formation and architecture of the inner solar system, we have launched an expansive project on studying the effects of secular resonances on the formation of terrestrial planets around a star of a moderately close binary. As the first phase of our project, we present here the general theory of secular resonances in dual-star systems where the primary …