Tootfinder

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
Prathamesh Devadiga, Yashmitha Shailesh
https://arxiv.org/abs/2508.11338

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
We introduce RegimeNAS, a novel differentiable architecture search framework specifically designed to enhance cryptocurrency trading performance by explicitly integrating market regime awareness. Addressing the limitations of static deep learning models in highly dynamic financial environments, RegimeNAS features three core innovations: (1) a theoretically grounded Bayesian search space optimizing architectures with provable convergence properties; (2) specialized, dynamically activated neural …

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
Alba Maria Marmol-Romero, Flor Miriam Plaza-del-Arco, Arturo Montejo-Raez
https://arxiv.org/abs/2509.14797

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has be…

H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Shicheng Ye, Chao Yu, Kaiqiang Ke, Chengdong Xu, Yinqi Wei
https://arxiv.org/abs/2509.12810 https://

H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Large language model (LLM)-based agents have shown strong potential in multi-task scenarios, owing to their ability to transfer knowledge across diverse tasks. However, existing approaches often treat prior experiences and knowledge as monolithic units, leading to inefficient and coarse-grained knowledge transfer. In this work, we propose a novel hierarchical memory architecture that enables fine-grained knowledge transfer by decoupling high-level planning memory from low-level execution memory…

Timbre-Adaptive Transcription: A Lightweight Architecture with Associative Memory for Dynamic Instrument Separation
Ruigang Li, Yongxu Zhu
https://arxiv.org/abs/2509.12712 https…

Timbre-Adaptive Transcription: A Lightweight Architecture with Associative Memory for Dynamic Instrument Separation
Existing multi-timbre transcription models struggle with generalization beyond pre-trained instruments and rigid source-count constraints. We address these limitations with a lightweight deep clustering solution featuring: 1) a timbre-agnostic backbone achieving state-of-the-art performance with only half the parameters of comparable models, and 2) a novel associative memory mechanism that mimics human auditory cognition to dynamically encode unseen timbres via attention-based clustering. Our b…

🌳 ‘It has a heroic, Roman quality’: how Arkansas’s timber university building could revolutionise architecture
https://www.theguardian.com/artanddesign/2025/aug/26/it-has-a-heroic-rom…

MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances
Sujatha Kotte, Vangala Govindakrishnan Saipradeep, Vidushi Walia, Dhandapani Nandagopal, Thomas Joseph, Naveen Sivadasan, Bhagat Singh Lali
https://arxiv.org/abs/2509.10502…

MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances
Motivation: Accurate classification of mitotic figures into normal and atypical types is crucial for tumor prognostication in digital pathology. However, developing robust deep learning models for this task is challenging due to the subtle morphological differences, as well as significant class and hardness imbalances in real-world histopathology datasets. Methods: We propose a novel deep learning approach based on a ResNet backbone with specialized classification heads. Our architecture unique…

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- HARMONIC: A Content-Centric Cognitive Robotic Architecture
Oruganti, Nirenburg, McShane, English, Roberts, Arndt, Gonzalez, Seo, Sentis

Entanglement architecture of beyond-Landau quantum criticality
Menghan Song, Ting-Tung Wang, Liuke Lyu, William Witczak-Krempa, Zi Yang Meng
https://arxiv.org/abs/2509.09983 htt…

Entanglement architecture of beyond-Landau quantum criticality
Quantum critical points beyond the Landau paradigm exhibit fractionalized excitations and emergent gauge fields. Here, we use entanglement microscopy--full tomography of the reduced density matrix of small subregions and subsequent extraction of their quantum correlations--to resolve the entanglement architecture near such exotic critical points. We focus on genuine multipartite entanglement (GME). Through unbiased quantum Monte Carlo sampling of RDMs across conventional O(2)/O(3) Wilson-Fisher…

High-Fidelity Single-Shot Readout and Selective Nuclear Spin Control for a Spin-1/2 Quantum Register in Diamond
Prithvi Gundlapalli, Philipp J. Vetter, Genko Genov, Michael Olney-Fraser, Peng Wang, Matthias M. M\"uller, Katharina Senkalla, Fedor Jelezko
https://arxiv.org/abs/2510.09164

High-Fidelity Single-Shot Readout and Selective Nuclear Spin Control for a Spin-1/2 Quantum Register in Diamond
Quantum networks offer a way to overcome the size and complexity limitations of single quantum devices by linking multiple nodes into a scalable architecture. Group-IV color centers in diamond, paired with long-lived nuclear spins, have emerged as promising building blocks demonstrating proof-of-concept experiments such as blind quantum computing and quantum-enhanced sensing. However, realizing a large-scale electro-nuclear register remains a major challenge. Here we establish the germanium-vac…

Regular Expression Indexing for Log Analysis. Extended Version
Ling Zhang, Shaleen Deep, Jignesh M. Patel, Karthikeyan Sankaralingam
https://arxiv.org/abs/2510.10348 https://

Regular Expression Indexing for Log Analysis. Extended Version
In this paper, we present the design and architecture of REI, a novel system for indexing log data for regular expression queries. Our main contribution is an $n$-gram-based indexing strategy and an efficient storage mechanism that results in a speedup of up to 14x compared to state-of-the-art regex processing engines that do not use indexing, using only 2.1% of extra space. We perform a detailed study that analyzes the space usage of the index and the improvement in workload execution time, un…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- ControlPULPlet: A Flexible Real-time Multi-core RISC-V Controller for 2.5D Systems-in-package
Alessandro Ottaviano, Robert Balas, Tim Fischer, Thomas Benz, Andrea Bartolini, Luca Benini

Cortex-Synth: Differentiable Topology-Aware 3D Skeleton Synthesis with Hierarchical Graph Attention
Mohamed Zayaan S
https://arxiv.org/abs/2509.06705 https://

Cortex-Synth: Differentiable Topology-Aware 3D Skeleton Synthesis with Hierarchical Graph Attention
We present Cortex Synth, a novel end-to-end differentiable framework for joint 3D skeleton geometry and topology synthesis from single 2D images. Our architecture introduces three key innovations: (1) A hierarchical graph attention mechanism with multi-scale skeletal refinement, (2) Differentiable spectral topology optimization via Laplacian eigen decomposition, and (3) Adversarial geometric consistency training for pose structure alignment. The framework integrates four synergistic modules: a …

The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis
Eoin O'Doherty, Nicole Weinrauch, Andrew Talone, Uri Klempner, Xiaoyuan Yi, Xing Xie, Yi Zeng
https://arxiv.org/abs/2509.10297

The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis
Artificial intelligence (AI) is advancing at a pace that raises urgent questions about how to align machine decision-making with human moral values. This working paper investigates how leading AI systems prioritize moral outcomes and what this reveals about the prospects for human-AI symbiosis. We address two central questions: (1) What moral values do state-of-the-art large language models (LLMs) implicitly favour when confronted with dilemmas? (2) How do differences in model architecture, cul…

Singularities as Solitons? Quantum Vacuum Architecture of Black Holes
Eren Erberk Erkul
https://arxiv.org/abs/2510.06428 https://arxiv.org/pdf/2510.06428…

Singularities as Solitons? Quantum Vacuum Architecture of Black Holes
We propose that black holes are \emph{soliton-esque} objects, where gravitational collapse is balanced by quantum vacuum dispersion, modeled via \(R+αR^{2}\) gravity. Classical singularities are replaced by oscillating, finite-radius cores, thereby evading static no-go theorems. The event horizon is replaced by the \textit{Lamarina}, a surface of maximum redshift whose surface geometry yields Hawking-like radiation with corrections. The Raychaudhuri equations impose a Dyson-type ceiling on the…

A Brief Note on Cryptographic Pseudonyms for Anonymous Credentials
Ren\'e Mayrhofer, Anja Lehmann, abhi shelat
https://arxiv.org/abs/2510.05419 https://

A Brief Note on Cryptographic Pseudonyms for Anonymous Credentials
This paper describes pseudonyms for the upcoming European Identity Wallet (EUDIW) architecture from both a cryptographic and an implementation perspective. Its main goal is to provide technical insights into the achievable properties and cryptographic realizations. In particular, we (1) outline the security and privacy requirements of EUDI pseudonyms as the basis for building consensus on the cross-country decision maker level; (2) sketch an abstract cryptographic protocol that fulfills these r…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Generative Artificial Intelligence for Beamforming in Low-Altitude Economy
Geng Sun, Jia Qi, Chuang Zhang, Xuejie Liu, Jiacheng Wang, Dusit Niyato, Yuanwei Liu, Dong In Kim

Advancing the Concept Maturity Level of the Servicing Architecture for Habitable Worlds Observer
Jonathan W. Arenberg
https://arxiv.org/abs/2510.03214 https://

Advancing the Concept Maturity Level of the Servicing Architecture for Habitable Worlds Observer
This paper advances the concept maturity level (CML) of the Habitable Worlds Observatory (HWO) servicing architecture. Since servicing has occurred on other missions, this paper argues that the current CML is 2. To advance to CML 3, option spaces must be established for trade studies. We introduce the three space ages and the argument that we are on the cusp of a new revolutionary era. Servicing is only part of that coming change and the other elements of the future space age are introduced. Th…

Distributed AI Agents for Cognitive Underwater Robot Autonomy
Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Yvan R. Petillot
https://arxiv.org/abs/2507.23735 https://

Distributed AI Agents for Cognitive Underwater Robot Autonomy
Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework to enable advanced cognitive capabilities in Autonomous Underwater Vehicles. UROSA decentralises cognition into specialised AI agents respons…

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Aleksandra Osowska-Kurczab, Klaudia Nazarko, Mateusz Marzec, Lidia Wojciechowska, Eli\v{s}ka Kreme\v{n}ov\'a
https://arxiv.org/abs/2508.03702

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) managing a highly dynamic product catalogue. This paper presents a unified content-based recommendation system deployed at Allegro.com, the largest e-commerce platform of European origin. The system is built on a prevalent Two Tower retrieval framework, rep…

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture
Anderson de Lima Luiz, Shubham Vijay Kurlekar, Munir Georges
https://arxiv.org/abs/2508.17814

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture
This work elaborates on a High performance computing (HPC) architecture based on Simple Linux Utility for Resource Management (SLURM) [1] for deploying heterogeneous Large Language Models (LLMs) into a scalable inference engine. Dynamic resource scheduling and seamless integration of containerized microservices have been leveraged herein to manage CPU, GPU, and memory allocations efficiently in multi-node clusters. Extensive experiments, using Llama 3.2 (1B and 3B parameters) [2] and Llama 3.1 …

Large-scale spatial variable gene atlas for spatial transcriptomics
Jiawen Chen, Jinwei Zhang, Dongshen Peng, Yutong Song, Aitong Ruan, Yun Li, Didong Li
https://arxiv.org/abs/2510.07653

Large-scale spatial variable gene atlas for spatial transcriptomics
Spatial variable genes (SVGs) reveal critical information about tissue architecture, cellular interactions, and disease microenvironments. As spatial transcriptomics (ST) technologies proliferate, accurately identifying SVGs across diverse platforms, tissue types, and disease contexts has become both a major opportunity and a significant computational challenge. Here, we present a comprehensive benchmarking study of 20 state-of-the-art SVG detection methods using human slides from STimage-1K4M,…

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…

Edge dependent Josephson Diode effect in WTe$_{2}$-Based Josephson junction
Guo-Liang Guo, Xiao-Hong Pan, Hao Dong, Xin Liu
https://arxiv.org/abs/2508.21357 https://

Edge dependent Josephson Diode effect in WTe$_{2}$-Based Josephson junction
The Josephson diode effect (JDE), a nonreciprocal supercurrent, is a cornerstone for future dissipationless electronics, yet achieving high efficiency in a simple device architecture remains a significant challenge. Here, we theoretically investigate the JDE in a junction based on monolayer 1T'-WTe$_2$. We first establish that different edge terminations of a WTe$_2$ nanoribbon lead to diverse electronic band structures, some of which host asymmetric edge states even with crystallographically e…

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
Peter Wauyo, Dalia Bwiza, Alain Murara, Edwin Mugume, Eric Umuhoza
https://arxiv.org/abs/2510.02165

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
This research introduces a multimodal system designed to detect fraud and fare evasion in public transportation by analyzing closed circuit television (CCTV) and audio data. The proposed solution uses the Vision Transformer for Video (ViViT) model for video feature extraction and the Audio Spectrogram Transformer (AST) for audio analysis. The system implements a Tensor Fusion Network (TFN) architecture that explicitly models unimodal and bimodal interactions through a 2-fold Cartesian product. …

Fluid Antenna Port Prediction based on Large Language Models
Yali Zhang, Haifan Yin, Weidong Li, Emil Bjornson, Merouane Debbah
https://arxiv.org/abs/2509.01121 https://

Fluid Antenna Port Prediction based on Large Language Models
This study seeks to utilize large language models (LLMs) to forecast the moving ports of fluid antenna (FA). By repositioning the antenna to the locations identified by our proposed model, we intend to address the mobility challenges faced by user equipment (UE). To the best of our knowledge, this paper introduces, for the first time, the application of LLMs in the prediction of FA ports, presenting a novel model termed Port-LLM. The architecture of our model is based on the pre-trained GPT-2 f…

One Model, Two Minds: A Context-Gated Graph Learner that Recreates Human Biases
Shalima Binta Manir, Tim Oates
https://arxiv.org/abs/2509.08705 https://arx…

One Model, Two Minds: A Context-Gated Graph Learner that Recreates Human Biases
We introduce a novel Theory of Mind (ToM) framework inspired by dual-process theories from cognitive science, integrating a fast, habitual graph-based reasoning system (System 1), implemented via graph convolutional networks (GCNs), and a slower, context-sensitive meta-adaptive learning system (System 2), driven by meta-learning techniques. Our model dynamically balances intuitive and deliberative reasoning through a learned context gate mechanism. We validate our architecture on canonical fals…

Optical Fiber-Waveguide Hybrid Architecture for Mid-Infrared Ring Lasers
Tinghui An, T Toney Fernandez, Yuchen Wang, Yiguang Jiang, Benjamin Johnston, Solenn Cozic, Marcel Poulain, Thipahine Rault, Long Zhang, Alex Fuerbach
https://arxiv.org/abs/2509.03965

Optical Fiber-Waveguide Hybrid Architecture for Mid-Infrared Ring Lasers
We report a mid-infrared ring laser via fiber-to-waveguide hybrid integration enabled by directional couplers fabricated in fluoride glasses. Directional couplers were inscribed into fluoride glass using femtosecond laser direct writing, forming low-loss waveguides with a high positive refractive index change. The waveguides were optimized for efficient guidance at a wavelength of 2.7 {\textmu}m. Directional couplers with systematically varied coupling gap and interaction lengths were fabricate…

A small and interesting architecture for early fault-tolerant quantum computers
Jacob S. Nelson, Andrew J. Landahl, Andrew D. Baczewski
https://arxiv.org/abs/2507.20387 https://…

A small and interesting architecture for early fault-tolerant quantum computers
We present an architecture for early fault-tolerant quantum computers based on the smallest interesting colour code (Earl Campbell, 2016). It realizes a universal logical gate set consisting of single-qubit measurements and preparations in the X and Z bases, single- qubit Hadamard (H) gates, and three-qubit controlled-controlled-Z (CCZ) gates. State teleportations between [[4, 2, 2]] (2D) and [[8, 3, 2]] (3D) error-detecting color codes allow one to make use of the respective transversal H and …

Control and Navigation of a 2-D Electric Rocket
Andr\'e Fonte, Pedro Santos, Paulo Oliveira
https://arxiv.org/abs/2509.19970 https://arxiv.org/pdf/2509…

Control and Navigation of a 2-D Electric Rocket
This work addresses the control and navigation of a simulated two-dimensional electric rocket. The model provides a simplified framework that neglects actuator dynamics and aerodynamic effects while capturing the complexities of underactuation and state coupling. Trajectory tracking is achieved through a modularized and layered control architecture, with employement of a Linear Quadratic Regulator (LQR) and Lyapunov theory. Full-state estimation is achieved through Kalman filtering techniques, …

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debugging
Jinwei Tang, Jiayin Qin, Nuo Xu, Pragnya Sudershan Nalla, Yu Cao, Yang, Zhao, Caiwen Ding

VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture
Sandaru Fernando, Imasha Jayarathne, Sithumini Abeysekara, Shanuja Sithamparanthan, Thushari Silva, Deshan Jayawardana
https://arxiv.org/abs/2509.22218

VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture
Data visualization is essential for interpreting complex datasets, yet traditional tools often require technical expertise, limiting accessibility. VizGen is an AI-assisted graph generation system that empowers users to create meaningful visualizations using natural language. Leveraging advanced NLP and LLMs like Claude 3.7 Sonnet and Gemini 2.0 Flash, it translates user queries into SQL and recommends suitable graph types. Built on a multi-agent architecture, VizGen handles SQL generation, gra…

Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific from Climate Reanalysis Dataset
Duc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia, Duc-Hai Nguyen, Minh-Hoa Tien, Xuan-Truong Ngo, Quang-Trung Luu, Quang-Lap Luu, Tai-Hung Nguyen, Thanh T. N. Nguyen, Chanh Kieu
https://arxiv.org/abs/2510.06118

Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific from Climate Reanalysis Dataset
This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from climate reanalysis datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) dataset during the 1980-2020 period, we demonstrate that ResNet can reasonably rep…

AudioRWKV: Efficient and Stable Bidirectional RWKV for Audio Pattern Recognition
Jiayu Xiong, Jun Xue, Jianlong Kwan, Jing Wang
https://arxiv.org/abs/2509.02167 https://

AudioRWKV: Efficient and Stable Bidirectional RWKV for Audio Pattern Recognition
Recently, Transformers (e.g., Audio Spectrogram Transformers, AST) and state-space models (e.g., Audio Mamba, AuM) have achieved remarkable progress in audio modeling. However, the O(L^2) computational complexity of the Transformer architecture hinders efficient long-sequence processing, while the Mamba architecture tends to become unstable when scaling parameters and data. To address these challenges, this paper proposes AudioRWKV (A-RWKV), a highly efficient and stable architecture for audio …

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Chirag Seth, Utkarsh Singh
https://arxiv.org/abs/2508.04623 https://

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Text-to-SQL translation enables non-expert users to query relational databases using natural language, with applications in education and business intelligence. This study evaluates three lightweight transformer models - T5-Small, BART-Small, and GPT-2 - on the Spider dataset, focusing on low-resource settings. We developed a reusable, model-agnostic pipeline that tailors schema formatting to each model's architecture, training them across 1000 to 5000 iterations and evaluating on 1000 test sam…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Performance Comparison of HTTP/3 and HTTP/2 with Proxy Integration
Fan Liu, Behrooz Farkiani, John Dehart, Jyoti Parwatikar, Patrick Crowley

PyG 2.0: Scalable Learning on Real World Graphs
Matthias Fey, Jinu Sunil, Akihiro Nitta, Rishi Puri, Manan Shah, Bla\v{z} Stojanovi\v{c}, Ramona Bendias, Alexandria Barghi, Vid Kocijan, Zecheng Zhang, Xinwei He, Jan Eric Lenssen, Jure Leskovec
https://arxiv.org/abs/2507.16991

PyG 2.0: Scalable Learning on Real World Graphs
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities. We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimization…

The Digital Life of Parisian Parks: Multifunctionality and Urban Context Uncovered by Mobile Application Traffic
Andr\'e Felipe Zanella, Linus W. Dietz, Sanja \v{S}\'cepanovi\'c, Ke Zhou, Zbigniew Smoreda, Daniele Quercia
https://arxiv.org/abs/2508.15516

The Digital Life of Parisian Parks: Multifunctionality and Urban Context Uncovered by Mobile Application Traffic
Landscape architecture typically considers urban parks through the lens of form and function. While past research on equitable access has focused mainly on form, studies of functions have been constrained by limited scale and coarse measurement. Existing efforts have partially quantified functions through small-scale surveys and movement data (e.g., GPS) or general usage records (e.g., CDR), but have not captured the activities and motivations underlying park visits. As a result, our understand…

Looking to Learn: Token-wise Dynamic Gating for Low-Resource Vision-Language Modelling
Bianca-Mihaela Ganescu, Suchir Salhan, Andrew Caines, Paula Buttery
https://arxiv.org/abs/2510.08470

Looking to Learn: Token-wise Dynamic Gating for Low-Resource Vision-Language Modelling
Training vision-language models on cognitively-plausible amounts of data requires rethinking how models integrate multimodal information. Within the constraints of the Vision track for the BabyLM Challenge 2025, we propose a lightweight decoder-based architecture with (1) token-wise dynamic gating for adaptive fusion of linguistic and visual cues, (2) feature modulation and channel attention to maximise the utility of limited visual information and (3) auxiliary contrastive objectives for visua…

Distributed fault-tolerant quantum memories over a 2xL array of qubit modules
Edwin Tham, Min Ye, Ilia Khait, John Gamble, Nicolas Delfosse
https://arxiv.org/abs/2508.01879 http…

Distributed fault-tolerant quantum memories over a 2xL array of qubit modules
We propose an architecture for a quantum memory distributed over a $2 \times L$ array of modules equipped with a cyclic shift implemented via flying qubits. The logical information is distributed across the first row of $L$ modules and quantum error correction is executed using ancilla modules on the second row equipped with a cyclic shift. This work proves that quantum LDPC codes such as BB codes can maintain their performance in a distributed setting while using solely one simple connector: a…

ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
Ge Yan, Jiyue Zhu, Yuquan Deng, Shiqi Yang, Ri-Zhao Qiu, Xuxin Cheng, Marius Memmel, Ranjay Krishna, Ankit Goyal, Xiaolong Wang, Dieter Fox
https://arxiv.org/abs/2509.01819

ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
This paper introduces ManiFlow, a visuomotor imitation learning policy for general robot manipulation that generates precise, high-dimensional actions conditioned on diverse visual, language and proprioceptive inputs. We leverage flow matching with consistency training to enable high-quality dexterous action generation in just 1-2 inference steps. To handle diverse input modalities efficiently, we propose DiT-X, a diffusion transformer architecture with adaptive cross-attention and AdaLN-Zero c…

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…

WARBERT: A Hierarchical BERT-based Model for Web API Recommendation
Zishuo Xu, Yuhong Gu, Dezhong Yao
https://arxiv.org/abs/2509.23175 https://arxiv.org/pd…

WARBERT: A Hierarchical BERT-based Model for Web API Recommendation
With the emergence of Web 2.0 and microservices architecture, the number of Web APIs has increased dramatically, further intensifying the demand for efficient Web API recommendation. Existing solutions typically fall into two categories: recommendation-type methods, which treat each API as a label for classification, and match-type methods, which focus on matching mashups through API retrieval. However, three critical challenges persist: 1) the semantic ambiguities in comparing API and mashup d…

Lyte Quorum: Off-Chain Ready Smart Contract Hosted with Choice
Hao Hao, Dahlia Malkhi, Maofan Yin, Lizan Zhou
https://arxiv.org/abs/2509.23448 https://arxi…

Lyte Quorum: Off-Chain Ready Smart Contract Hosted with Choice
This paper introduces Lyquor, a decentralized platform that reimagines blockchain infrastructure through a service-centric model where nodes selectively host smart contracts (called Lyquids) while preserving global composability. We present three key innovations: (1) Fate-Constrained Ordering (FCO), which decouples consensus from execution to enable selective hosting without sacrificing Layer-1 grade composability; (2) Direct Memory Architecture (DMA), which eliminates state access bottlenecks …

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…

High-order wavefront sensing and control for the Roman Coronagraph Instrument (CGI): architecture and measured performance
Eric Cady, Nicholas Bowman, Alexandra Z. Greenbaum, James G. Ingalls, Brian Kern, John Krist, David Marx, Ilya Poberezhskiy, A J Eldorado Riggs, Garreth Ruane, Byoung-Joon Seo, Fang Shi, Hanying Zhou
https://arxiv.org/…

High-order wavefront sensing and control for the Roman Coronagraph Instrument (CGI): architecture and measured performance
The Nancy Grace Roman Space Telescope (``Roman'') is a 2.4m space telescope scheduled for a 2026 launch. The Coronagraph Instrument (CGI) on Roman is a technology-demonstration instrument with a coronagraph and, for the first time in space, deformable mirrors and active wavefront control. This paper walks through the algorithmic and system-level architecture of the HOWFSC implementation for CGI, including the use of ground-in-the-loop (GITL) operations to support computationally-expensive opera…

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

FlexNGIA 2.0: Redesigning the Internet with Agentic AI - Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI
Mohamed Faten Zhani, Younes Korbi, Yamen Mkadem
https://arxiv.org/abs/2509.02124

FlexNGIA 2.0: Redesigning the Internet with Agentic AI - Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI
The escalating demands of immersive communications, alongside advances in network softwarization and AI-driven cognition and generative reasoning, create a pivotal opportunity to rethink and reshape the future Internet. In this context, we introduce in this paper, FlexNGIA 2.0, an Agentic AI-driven Internet architecture that leverages LLM-based AI agents to autonomously orchestrate, configure, and evolve the network. These agents can, at runtime, perceive, reason, coordinate among themselves to…

A Proposal for Yield Improvement with Power Tradeoffs in CMOS LNAs (English Version)
J. L. Gonz\'alez, J. C. Cruz, R. L. Moreno, D. V\'azquez
https://arxiv.org/abs/2508.20611

A Proposal for Yield Improvement with Power Tradeoffs in CMOS LNAs (English Version)
This paper studies an architecture with digitally controllable gain and power consumption to mitigate the impact of process variations on CMOS low-noise amplifiers (LNAs). A \SI{130}{nm}, \SI{1.2}{V} LNA implementing the proposed architecture is designed based on an analysis of variability in traditional LNAs under different bias currents and on the corresponding effects on the performance of a complete receiver. Two different adjustment strategies are evaluated, both of which are compatible wi…

Reconfigurable Optical Logic Gates and 2-Bit Decoder Using Multiport Directional Couplers
Noel Francisco Prado Bucaro, Yushan Hu, Satoshi Sunada, Takeo Maruyama
https://arxiv.org/abs/2509.20825

Reconfigurable Optical Logic Gates and 2-Bit Decoder Using Multiport Directional Couplers
We demonstrate a reconfigurable photonic platform capable of implementing both optical logic gates and a 2-bit decoder on the same device. The architecture is based on cascaded multiport directional couplers interleaved with thermo-optic phase shifters, enabling programmable $N \times N$ unitary transformations. Optimal heater voltages for phase control are determined through Bayesian optimization. The devices operate reliably across multiple wavelengths with 50 GHz spacing, suggesting potentia…

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
Xiaoxiao Li, An Zhu, Youhai Jiang, Fengjie Zhu
https://arxiv.org/abs/2508.14916 https://

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
This paper presents the architecture and performance of a novel Multilingual Automatic Speech Recognition (ASR) system developed by the Transsion Speech Team for Track 1 of the MLC-SLM 2025 Challenge. The proposed system comprises three key components: 1) a frozen Whisper-large-v3 based speech encoder, leveraging large-scale pretraining to ensure robust acoustic feature extraction; 2) a trainable adaptor module using Linear-ReLU-Linear transformation mechanisms to effectively align speech and t…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Man...
Ricardo Cannizzaro, Michael Groom, Jonathan Routley, Robert Osazuwa Ness, Lars Kunze

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

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

Geolog-IA: Conversational System for Academic Theses
Micaela Fuel Pozo, Andrea Guatumillo Saltos, Yese\~na Tipan Llumiquinga, Kelly Lascano Aguirre, Marilyn Castillo Jara, Christian Mejia-Escobar
https://arxiv.org/abs/2510.02653

Geolog-IA: Conversational System for Academic Theses
This study presents the development of Geolog-IA, a novel conversational system based on artificial intelligence that responds naturally to questions about geology theses from the Central University of Ecuador. Our proposal uses the Llama 3.1 and Gemini 2.5 language models, which are complemented by a Retrieval Augmented Generation (RAG) architecture and an SQLite database. This strategy allows us to overcome problems such as hallucinations and outdated knowledge. The evaluation of Geolog-IA's …

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval
Yu Liu, Yanbing Liu, Fangfang Yuan, Cong Cao, Youbang Sun, Kun Peng, WeiZhuo Chen, Jianjun Li, Zhiyuan Ma
https://arxiv.org/abs/2508.16438

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval
Recent advances in large language models (LLMs) and dense retrievers have driven significant progress in retrieval-augmented generation (RAG). However, existing approaches face significant challenges in complex reasoning-oriented multi-hop retrieval tasks: 1) Ineffective reasoning-oriented planning: Prior methods struggle to generate robust multi-step plans for complex queries, as rule-based decomposers perform poorly on out-of-template questions. 2) Suboptimal reasoning-driven retrieval: Relat…

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
Zihao Huang, Yu Bao, Qiyang Min, Siyan Chen, Ran Guo, Hongzhi Huang, Defa Zhu, Yutao Zeng, Banggu Wu, Xun Zhou, Siyuan Qiao
https://arxiv.org/abs/2508.18756

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
While Mixture of Experts (MoE) models achieve remarkable efficiency by activating only subsets of parameters, they suffer from high memory access costs during inference. Memory-layer architectures offer an appealing alternative with very few memory access, but previous attempts like UltraMem have only matched the performance of 2-expert MoE models, falling significantly short of state-of-the-art 8-expert configurations. We present UltraMemV2, a redesigned memory-layer architecture that closes t…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performanc...
Shayne Wadle, Yanxin Zhang, Vikas Singh, Karthikeyan Sankaralingam

Convolutions are Competitive with Transformers for Encrypted Traffic Classification with Pre-training
Chungang Lin, Weiyao Zhang, Tianyu Zuo, Chao Zha, Yilong Jiang, Ruiqi Meng, Haitong Luo, Xuying Meng, Yujun Zhang
https://arxiv.org/abs/2508.02001

Convolutions are Competitive with Transformers for Encrypted Traffic Classification with Pre-training
Encrypted traffic classification is vital for modern network management and security. To reduce reliance on handcrafted features and labeled data, recent methods focus on learning generic representations through pre-training on large-scale unlabeled data. However, current pre-trained models face two limitations originating from the adopted Transformer architecture: (1) Limited model efficiency due to the self-attention mechanism with quadratic complexity; (2) Unstable traffic scalability to lon…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Information-acquiring von Neumann architecture of a computer: Functionality and subjectivity
Eiji Konishi

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…

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…

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Mohamed Manzour, Catherine M. Elias, Omar M. Shehata, Rub\'en Izquierdo, Miguel \'Angel Sotelo
https://arxiv.org/abs/2509.20218

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real-world deployments of lane-change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are oft…

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Mohamed Manzour, Catherine M. Elias, Omar M. Shehata, Rub\'en Izquierdo, Miguel \'Angel Sotelo
https://arxiv.org/abs/2509.20218

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real-world deployments of lane-change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are oft…

Chiplet-Based RISC-V SoC with Modular AI Acceleration
P. Ramkumar, S. S. Bharadwaj
https://arxiv.org/abs/2509.18355 https://arxiv.org/pdf/2509.18355…

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

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

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