February 2026

Verita AI is working with top-tier engineers on a cutting-edge project designing reinforcement learning environments that teach LLMs advanced AI/ML concepts. Your expertise would be valuable for shaping how next-generation models learn. The role: • Fully remote, contract • $150-$200/hour (based on expertise) + $500 take-home bonus • Minimum 4 hours daily overlap with PST (9am-5pm) • ~2 tasks per week, high autonomy Ideal for: • Graduates from top-tier engineering colleges or engineers from leading tech companies (FAANG+) […]

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The Wasserstein distance has emerged as a key metric to quantify distances between probability distributions, with applications in various fields, including machine learning, control theory, decision theory, and biological systems. Consequently, learning an unknown distribution with non-asymptotic and easy-to-compute error bounds in Wasserstein distance has become a fundamental problem in many fields. In this paper, we devise a novel algorithmic and theoretical framework to approximate an unknown probability distribution $mathbb{P}$ from a finite set of samples by an […]

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Offline reinforcement learning (RL) provides a compelling paradigm for training autonomous systems without the risks of online exploration, particularly in safety-critical domains. However, jointly achieving strong safety and performance from fixed datasets remains challenging. Existing safe offline RL methods often rely on soft constraints that allow violations, introduce excessive conservatism, or struggle to balance safety, reward optimization, and adherence to the data distribution. To address this, we propose Epigraph-Guided Flow Matching (EpiFlow), a framework that formulates safe offline […]

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— Recovery Lockout and the “Kindness UI” Spin — Introduction Workplaces continue to treat overwork as if it were a lack of management. If you are drowning, you need discipline. If your health is depleted, you need healing power. If you are broken, you need mindfulness. In some cases, it may be necessary to “push through”. Sometimes it’s “self-compassion” and “we’re here for you.” To do this, you take new leadership courses one after another. So was I. […]

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Algorithm-Based Fault Tolerance (ABFT) is widely adopted to detect silent data corruptions (SDCs) in matrix multiplication, a cornerstone operation in deep learning systems. However, existing threshold determination methods face critical challenges: analytical bounds are overly conservative, while probabilistic approaches like A-ABFT yield thresholds $160$–$4200times$ larger than actual rounding errors. We present V-ABFT, a variance-based adaptive threshold algorithm that achieves tighter error bounds by directly modeling the verification difference. By leveraging statistical variance estimation, V-ABFT reduces the threshold-to-actual-error ratio […]

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Semi-supervised learning (SSL) addresses the critical challenge of training accurate models when labeled data is scarce but unlabeled data is abundant. Graph-based SSL (GSSL) has emerged as a popular framework that captures data structure through graph representations. Classic graph SSL methods, such as Label Propagation and Label Spreading, aim to compute low-dimensional representations where points with the same labels are close in representation space. Although often effective, these methods can be suboptimal on data with complex label distributions. […]

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Graph Continual Learning (GCL) aims to solve the challenges of streaming graph data. However, current methods often depend on replay-based strategies, which raise concerns like memory limits and privacy issues, while also struggling to resolve the stability-plasticity dilemma. In this paper, we suggest that lightweight, task-specific modules can effectively guide the reasoning process of a fixed GNN backbone. Based on this idea, we propose Task-Aware Adaptive Modulation (TAAM). The key component of TAAM is its lightweight Neural Synapse […]

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This article is not a critique of Agile or Kanban as approaches, nor is it an attempt to prove that IT “does everything wrong.” I’m sharing observations from my own experience working with quality in manufacturing, energy, and later in IT products. This isn’t about terms and tools, but about how systems thinking is often lost when complex management models are simplified into rituals. If your processes are already well established and your systems are stable – great. […]

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