technocracy

The proliferation of behavioral data in digital retail has not been matched by equally rigorous frameworks for converting that data into customer intelligence that practitioners can act on. This paper addresses that gap by introducing RFM-B, a behavioral segmentation framework that extends the classical recency–frequency–monetary (RFM) model with four additional indicators derivable from standard e-commerce event logs: conversion rate (CVR), category breadth, average order value (AOV), and brand diversity. Applied to 4,635,837 interaction events from 64,204 purchasing customers […]

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arXiv:2604.15402v1 Announce Type: new Abstract: Classical symbolic protocol verification under Dolev–Yao uses binary attacker knowledge (known/unknown). This abstraction misses cumulative side-channel settings, where repeated noisy observations progressively improve attacker knowledge. We model this process with a graded attacker view (mu_Kin[0,1]), product T-norm leak updates, and finite-grid explicit-state execution in Modified Murphi. The method is optimised with exact concept-lattice attribute reducts and exposes threshold-driven safe-to-fail transitions that are not represented in corresponding binary runs under the same bounded assumptions. […]

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Writing a research paper is brutal. Even after the experiments are done, a researcher still faces weeks of translating messy lab notes, scattered results tables, and half-formed ideas into a polished, logically coherent manuscript formatted precisely to a conference’s specifications. For many fresh researchers, that translation work is where papers go to die. A team at Google Cloud AI Research propose ‘PaperOrchestra‘, a multi-agent system that autonomously converts unstructured pre-writing materials — a rough idea summary and raw […]

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arXiv:2602.18579v1 Announce Type: new Abstract: Developers often extract methods to improve readability, understanding, and reuse, while inlining keeps logic in one block. Prior work based on static metrics has not shown clear differences between these practices, and the human side of comprehension and navigation remains underexplored. We investigate Inline Method vs. Extract Method refactorings using a dynamic approach: eye tracking while participants read and solve tasks. We analyze key code areas and compare visual effort and reading behavior […]

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arXiv:2602.06063v1 Announce Type: new Abstract: We present the first end-to-end deployment of the Gemma3 family of large language and vision models on a tiled edge dataflow architecture (AMD Ryzen AI NPU). Our work introduces a set of hardware-aware techniques. For prefill, we introduce an efficient dequantization engine, optimize tiled matrix multiplication kernels, and propose FlowQKV, a chunked, pipelined attention mechanism. For decoding, we introduce FusedDQP, which fuses dequantization and projection into a single kernel, and FlowKV, which re-structures […]

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Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to manipulate model behavior by injecting poisoned data with hidden triggers. These attacks can lead to severe consequences, especially in critical applications such as autonomous driving, healthcare, and finance. Detecting and mitigating backdoor attacks is crucial across the lifespan of model’s phases, including pre-training, in-training, and post-training. In this paper, we propose Pre-Training Backdoor Mitigation for Federated Learning (FL-PBM), a […]

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