technocracy

arXiv:2601.05375v1 Announce Type: new Abstract: Modern commercial ground vehicles are increasingly equipped with multiple operational modalities (e.g., human driving, advanced driver assistance, remote tele-operation, full autonomy). These often rely on heterogeneous sensing infrastructures and distinct routing algorithms, which can yield misaligned perceptions of the traffic environment and route preferences. While such technologies accelerate the transition toward increasingly intelligent transportation networks, their current deployment fails to avoid challenges associated with selfish routing behavior, in which drivers or automated agents […]

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arXiv:2604.18781v1 Announce Type: new Abstract: Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice. Existing generative super-resolution (SR) methods produce visually compelling isotropic volumes but often introduce anatomical hallucinations, systematic volumetric overestimation, and structural distortions that compromise downstream quantitative analysis and diagnostic safety. To address this, we propose CAHAL (Clinically Applicable resolution enHAncement for Low-resolution MRI scans), a hallucination-robust, physics-informed resolution enhancement framework that operates directly in the […]

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arXiv:2601.20015v1 Announce Type: new Abstract: DBTuneSuite is a suite of experiments on four widely deployed free database systems to test their performance under various query/upsert loads and under various tuning options. The suite provides: (i) scripts to generate data and to install and run tests, making it expandable to other tests and systems; (ii) suggestions of which systems work best for which query types; and (iii) quantitative evidence that tuning options widely used in practice can behave very […]

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arXiv:2402.05187v3 Announce Type: replace Abstract: Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD’s full potential is limited, with the majority of research focusing on a particular mirror map — namely, the negative entropy — which gives rise to the renowned Natural Policy Gradient […]

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The rapid proliferation of the Internet of Things (IoT) has positioned the Message Queuing Telemetry Transport (MQTT) protocol as a fundamental communication standard for large-scale, resource-constrained systems. Despite its lightweight design and scalability advantages, modern MQTT deployments operate under increasingly complex conditions characterized by intensive security enforcement, dynamic traffic patterns, and widespread use of wildcard subscriptions. These factors introduce tightly coupled challenges related to system performance, congestion, and security, which are often addressed independently in existing literature. This […]

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Text classification in low-resource languages has become increasingly important due to the rapid growth of user-generated digital content. While multitask learning has long been studied in NLP, the use of LLMs for multitask text classification in low-resource languages such as Bengali remains underexplored. Although LLMs are inherently multilingual and multitasking, their effectiveness in structured multitask classification settings for Bengali has not been systematically evaluated. In this work, we investigate how LLMs can be leveraged for multitask Bengali text […]

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arXiv:2603.28888v1 Announce Type: new Abstract: Semantic anomalies-context-dependent hazards that pixel-level detectors cannot reason about-pose a critical safety risk in autonomous driving. We propose a emph{semantic observer layer}: a quantized vision-language model (VLM) running at 1–2,Hz alongside the primary AV control loop, monitoring for semantic edge cases, and triggering fail-safe handoffs when detected. Using Nvidia Cosmos-Reason1-7B with NVFP4 quantization and FlashAttention2, we achieve ~500 ms inference a ~50x speedup over the unoptimized FP16 baseline (no quantization, standard PyTorch attention) […]

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