February 2026

Let me set the scene. You’ve shipped three .NET applications in the last two years. Each one has a DbContext, a generic repository, probably a Unit of Work wrapper. Maybe you even went full CQRS on the last one because you’d watched enough conference talks to feel guilty about not doing it. And every single time, you wrote the same scaffolding. Different entity names. Different connection string. Same bones. At some point you stop and think — why […]

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Graph classification is dominated by permutation-invariant graph neural networks. We revisit this problem from a different perspective: can small language models (SLMs) act as graph classifiers when graphs are serialized as text? Unlike GNNs, sequence-based transformers do not encode permutation invariance by construction, raising a fundamental question about structural stability under node relabeling.We provide the first systematic study of permutation robustness in small graph-as-text models. We introduce an evaluation protocol based on Flip Rate and KL-to-Mean divergence to […]

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Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which is susceptible to being rewritten during generative purification. To address this, we propose WaterVIB, a theoretically grounded framework that reformulates the encoder as an information sieve via the Variational Information Bottleneck. Instead of overfitting to fragile cover details, our approach forces the model […]

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This paper deals with a system of G-stochastic differential equations with jumps, driven by G-Brownian motion and G-Lévy process. By using the Burkholdr-Davis-Gundy inequalities, we prove a moment estimate and the temporal Hölder regularity of the solution, under the Linear growth and the global Lipschitz conditions of the coefficients with respect to the state variable uniformly in the time variable. Moreover, different stability properties are proved. Some examples like Black-Scholes market driven by G-Brownian motion are employed in […]

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Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across multiple time scales. However, many existing multi-scale IMTS methods use resampling to obtain the coarse series, which can alter the original timestamps and disrupt the sampling pattern information. To address the challenge, we propose ReIMTS, a Recursive multi-scale modeling approach for Irregular Multivariate […]

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Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems. This sensitivity stems from the non-stationarity of RL: rollouts are generated by an evolving policy, and learning is shaped by exploration and reward feedback, unlike supervised fine-tuning (SFT) with fixed trajectories. As a result, prior work often relies on manual curation or simple heuristic filters (e.g., accuracy), which can admit incorrect or […]

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Recognizing the substantial computational cost of backpropagation (BP), non-BP methods have emerged as attractive alternatives for efficient learning on emerging neuromorphic systems. However, existing non-BP approaches still face critical challenges in efficiency and scalability. Inspired by neural representations and dynamic mechanisms in the brain, we propose a perturbation-based approach called LOw-rank Cluster Orthogonal (LOCO) weight modification. We find that low-rank is an inherent property of perturbation-based algorithms. Under this condition, the orthogonality constraint limits the variance of the […]

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Agentic Engineering Patterns > Sometimes it’s useful to have a coding agent give you a structured walkthrough of a codebase. Maybe it’s existing code you need to get up to speed on, maybe it’s your own code that you’ve forgotten the details of, or maybe you vibe coded the whole thing and need to understand how it actually works. Frontier models with the right agent harness can construct a detailed walkthrough to help you understand how code works. […]

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Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR) filtering operation governed by both feedforward weights and recurrent poles. While, in principle, all parameters including pole locations can be optimized via backpropagation through time (BPTT), such joint learning incurs substantial computational overhead and is often impractical for applications with limited training data. Echo state networks (ESNs) mitigate this limitation by fixing the recurrent dynamics and training only […]

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