February 2026

There has been a growing interest in using neural networks, especially message-passing neural networks (MPNNs), to solve hard combinatorial optimization problems heuristically. However, existing learning-based approaches for hard combinatorial optimization tasks often rely on supervised training data, reinforcement learning, or gradient estimators, leading to significant computational overhead, unstable training, or a lack of provable performance guarantees. In contrast, classical approximation algorithms offer such performance guarantees under worst-case inputs but are non-differentiable and unable to adaptively exploit structural regularities […]

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In recent years, there has been growing interest in understanding neural architectures’ ability to learn to execute discrete algorithms, a line of work often referred to as neural algorithmic reasoning. The goal is to integrate algorithmic reasoning capabilities into larger neural pipelines. Many such architectures are based on (message-passing) graph neural networks (MPNNs), owing to their permutation equivariance and ability to deal with sparsity and variable-sized inputs. However, existing work is either largely empirical and lacks formal guarantees […]

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I’m learning the foundations of RL in alignment with the Alberta Plan for AI research and have been running through sets of experiments to both learn and experiment. To that end I spent the last month validating different methods for streaming deep RL on a non-stationary, adversarial dataset of real SSH honeypot observations. This work focuses on prediction and is in line with steps 1 & 2 of the Alberta Plan (Sutton, Bowling, & Pilarski 2022). After implementing […]

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The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment […]

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How are you, hacker? 🪐 What’s happening in tech today, February 13, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From OpenClaw After the Hype: A Real-World Test of a “Do-Anything” AI Assistant to AI Coding Tip 006 – Review Every Line Before You Commit, let’s dive right in. OpenClaw After the Hype: A Real-World Test of a “Do-Anything” AI Assistant By @navigatingnoise […]

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Hey Hackers! Welcome to the second-ever edition of HackerNoon Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype. This week, we’re excited to share three amazing projects that have proven their necessity and usefulness: Agent Observatory, Formonger, and Olio AI. Introducing the Projects of the Week! Agent Observatory https://hackernoon.com/agent-observatory-earns-a-56-proof-of-usefulness-score-by-making-ai-agents-observable-without-risk?embedable=true If you’re a backend or platform engineer, then Agent Observatory is the […]

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Imbalanced classification presents a formidable challenge in machine learning, particularly when tabular datasets are plagued by noise and overlapping class boundaries. From a geometric perspective, the core difficulty lies in the topological intrusion of the majority class into the minority manifold, which obscures the true decision boundary. Traditional undersampling techniques, such as Edited Nearest Neighbours (ENN), typically employ symmetric cleaning rules and uniform voting, failing to capture the local manifold structure and often inadvertently removing informative minority samples. […]

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Constrained Reinforcement Learning (CRL) aims to optimize decision-making policies under constraint conditions, making it highly applicable to safety-critical domains such as autonomous driving, robotics, and power grid management. However, existing robust CRL approaches predominantly focus on single-step perturbations and temporally independent adversarial models, lacking explicit modeling of robustness against temporally coupled perturbations. To tackle these challenges, we propose TCRL, a novel temporal-coupled adversarial training framework for robust constrained reinforcement learning (TCRL) in worst-case scenarios. First, TCRL introduces a […]

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