technocracy

Bayesian Inference Explained Like You’re Guessing Who Ate Your Leftovers Let me tell you about the worst Wednesday of my life. I’m sitting at my desk. My left sock is wet for reasons I still don’t understand. My dog is losing his mind at a squirrel outside. And I’m staring at a Wikipedia page about “Bayesian inference” that reads like it was written by someone who actively hates the concept of fun. You’ve been doing Bayesian inference your whole life. […]

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New York, United States, March 3rd, 2026/Chainwire/–Threshold Network, the decentralized blockchain protocol behind tBTC, has introduced an update to its decentralized application featuring an all-in-one Unified Bitcoin App that enables users to route Bitcoin across major chains through a single interface. This new unified routing interface brings minting, redeeming, bridging, tracking, and native BTC swaps into a single application: The Threshold App. Users can now move Bitcoin across ecosystems through a coordinated system, rather than stitching together multiple […]

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arXiv:2601.08950v1 Announce Type: new Abstract: In educational applications, LLMs exhibit several fundamental pedagogical limitations, such as their tendency to reveal solutions rather than support dialogic learning. We introduce ConvoLearn (https://huggingface.co/datasets/masharma/convolearn ), a dataset grounded in knowledge building theory that operationalizes six core pedagogical dimensions: cognitive engagement, formative assessment, accountability, cultural responsiveness, metacognition, and power dynamics. We construct a semi-synthetic dataset of 1250 tutor-student dialogues (20 turns each) in middle school Earth Science through controlled interactions between human teachers […]

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arXiv:2601.17855v1 Announce Type: cross Abstract: Load balancing-the allocation of work across parallel resources to reduce delay, energy and cost-is a pervasive challenge in science and engineering, from large-scale simulation and data processing to cloud and manufacturing operations. Motivated by the emerging bottleneck in large language model (LLM) serving, we study a particularly stringent regime of load balancing that arises in barrier-synchronized, stateful systems: work cannot be freely migrated and progress is gated by the slowest participant at each […]

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Every day, billions of users scroll through personalized feeds on TikTok, Instagram, and YouTube, each experiencing a uniquely tailored stream of content. Behind this seemingly effortless personalization lies one of the most challenging problems in modern machine learning: how do you build recommendation systems that adapt to user interests in real-time, learning from every interaction within the same scrolling session? The engineering required to make this work, processing billions of events per second, updating models every few hours, […]

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arXiv:2602.21446v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified framework that combines the statistical guarantees of conformal prediction with the computational efficiency of HDC. For this framework, we propose two complementary variations. First, the set-valued formulation provides finite-sample, distribution-free coverage guarantees. […]

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arXiv:2602.22250v1 Announce Type: new Abstract: The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in business communication platforms. Our proposed solution for mail gateway and endpoint deception detection operates in a privacy-protective manner while handling the performance requirements of network and mobile security systems. The MobileBERT teacher receives fine-tuning before its transformation into a BiLSTM model with multi-head attention which maintains semantic discrimination only with 4.5 million parameters. The hybrid […]

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arXiv:2601.05264v1 Announce Type: new Abstract: This article provides a comprehensive systematic literature review of academic studies, industrial applications, and real-world deployments from 2018 to 2025, providing a practical guide and detailed overview of modern Retrieval-Augmented Generation (RAG) architectures. RAG offers a modular approach for integrating external knowledge without increasing the capacity of the model as LLM systems expand. Research and engineering practices have been fragmented as a result of the increasing diversity of RAG methodologies, which encompasses a […]

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arXiv:2604.01262v1 Announce Type: new Abstract: Traditional Online Public Access Catalogues (OPACs) are becoming less effective due to the rapid growth of scholarly literature. Conventional search methods, such as keyword indexing and Boolean queries, often fail to support efficient knowledge discovery. This paper proposes a Smart OPAC framework that transforms traditional OPACs into intelligent discovery systems using artificial intelligence and knowledge graph techniques. The framework enables semantic search, thematic filtering, and knowledge graph-based visualization to enhance user interaction and […]

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