Imbalanced node classification in graph neural networks (GNNs) happens when some labels are much more common than others, which causes the model to learn unfairly and perform badly on the less common classes. To solve this problem, we propose a Curriculum-Guided Feature Learning and Three-Stage Attention Network (CL3AN-GNN), a learning network that uses a three-step attention system (Engage, Enact, Embed) similar to how humans learn. The model begins by engaging with structurally simpler features, defined as (1) local […]

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When data is scarce or mistakes are costly, average-case metrics fall short. What a practitioner needs is a guarantee: with probability at least $1-δ$, the learned policy is $varepsilon$-close to optimal after $N$ episodes. This is the PAC promise, and between 2018 and 2025 the RL theory community made striking progress on when such promises can be kept. We survey that progress. Our organizing tool is the Coverage-Structure-Objective (CSO) framework, proposed here, which decomposes nearly every PAC sample […]

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We prove that Moore machines can be exactly realized as state-space models (SSMs), establishing a formal correspondence between symbolic automata and these continuous machine learning architectures. These Moore-SSMs preserve both the complete symbolic structure and input-output behavior of the original Moore machine, but operate in Euclidean space. With this correspondence, we compare the training of SSMs with both passive and active automata learning. In recovering automata from the SYNTCOMP benchmark, we show that SSMs require orders of magnitude […]

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Joe Rose, president at strategic technology provider JBS Dev, wants to cut through one of the myths of working with generative and agentic AI systems. “It’s a common misconception that your data has to be perfect before you do any of these types of workloads,” he explains. As a recent article in AI Fieldbook outlines, vendors and consultants – not surprisingly – suggest you need huge data lakes and multi-year data transformation programmes respectively. Executives are therefore scratching […]

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arXiv:2602.06172v1 Announce Type: new Abstract: Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. We propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that shifts […]

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Non-parameterized constructors are a code smell of an invalid object that will dangerously mutate. Incomplete objects cause lots of issues. TL;DR: Pass the essence to all your objects so they will not need to mutate. Problems 😔 Mutability Incomplete objects Concurrency inconsistencies between creation and essence setting. Setters Solutions 😃 Pass the object’s essence on creation Create objects with their immutable essence. Refactorings ⚙️ https://hackernoon.com/refactoring-remove-setters-codesmell?embedable=true https://hackernoon.com/refactoring-016-building-with-the-essence?embedable=true Examples 📚 Some persistence frameworks in statically-typed languages require an empty constructor. Sample Code 📖 Wrong […]

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arXiv:2602.02564v1 Announce Type: new Abstract: Data annotation is essential for supervised learning, yet producing accurate, unbiased, and scalable labels remains challenging as datasets grow in size and modality. Traditional human-centric pipelines are costly, slow, and prone to annotator variability, motivating reliability-aware automated annotation. We present AURA (Agentic AI for Unified Reliability Modeling and Annotation Aggregation), an agentic AI framework for large-scale, multi-modal data annotation. AURA coordinates multiple AI agents to generate and validate labels without requiring ground truth. […]

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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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