Efficient task scheduling in large-scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality-of-service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL-MADRL) framework for task scheduling in heterogeneous distributed systems. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a lightweight actor-critic architecture implemented using […]

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arXiv:2604.04947v1 Announce Type: new Abstract: With the rapid proliferation of online sports journalism, extracting meaningful pre-game and post-game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news articles covering 800 matches across four major sports: Cricket, Soccer, Basketball, and Baseball. To ensure contextual relevance, we employ a two-step validation pipeline […]

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arXiv:2602.12300v1 Announce Type: new Abstract: We propose a new method, that we coined the “morphism-trick”, to integrate custom implementations of vector-Jacobian products in automatic differentiation softwares, applicable to a wide range of semiring-based computations. Our approach leads to efficient and semiring-agnostic implementations of the backward pass of dynamic programming algorithms. For the particular case of finite-state methods, we introduce an algorithm that computes and differentiates the $oplus$-sum of all paths’ weight of a finite-state automaton. Results show that, […]

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arXiv:2601.06262v1 Announce Type: cross Abstract: Community detection is a fundamental task in data analysis. Block models form a standard approach to partition nodes according to a graph model, facilitating the analysis and interpretation of the network structure. By grouping nodes with similar connection patterns, they enable the identification of a wide variety of underlying structures. The degree-corrected block model (DCBM) is an established model that accounts for the heterogeneity of node degrees. However, existing inference methods for the […]

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Accurate electricity price forecasting (EPF) is essential for operational planning, trading, and flexible asset scheduling in liberalised power systems, yet remains challenging due to volatility, heavy-tailed spikes, and frequent regime shifts. While deep learning (DL) has been increasingly adopted in EPF to capture complex and nonlinear price dynamics, several important gaps persist: (i) limited attention to multi-day horizons beyond day-ahead forecasting, (ii) insufficient exploration of state-of-the-art (SOTA) time series DL models, and (iii) a predominant reliance on aggregated […]

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arXiv:2602.10261v1 Announce Type: cross Abstract: Kernel methods have been extensively utilized in machine learning for classification and prediction tasks due to their ability to capture complex non-linear data patterns. However, single kernel approaches are inherently limited, as they rely on a single type of kernel function (e.g., Gaussian kernel), which may be insufficient to fully represent the heterogeneity or multifaceted nature of real-world data. Multiple kernel learning (MKL) addresses these limitations by constructing composite kernels from simpler ones […]

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APIs are the backbone of modern enterprises, connecting services, data, and business logic across microservices and cloud environments. Traditional perimeter-based security is no longer enough, as remote work, cloud adoption, and constant machine-to-machine communication blur internal and external boundaries. Zero Trust shifts the security model from “trust but verify” to “never trust, always verify,” treating every API request as potentially malicious. By enforcing strong identity verification through OAuth2, OpenID Connect, and mTLS, and applying granular authorization with attribute-based […]

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What happened in 2025 was this: the economics of code production were turned upside down. Instead of being very hard, time-consuming, and expensive to generate code, it became effectively free and instant. Lines of code went from being treasured, reused, cared for and carefully curated, to being disposable and regenerable, practically overnight. — Charity Majors, AI demands more engineering discipline. Not less Tags: charity-majors, ai-assisted-programming, generative-ai, ai, llms

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Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. The 17,300-view AI Engineer Singapore talk that quietly killed half my MLOps job I watched Merve Noyan’s “Your Agent Can Now Train Models” talk three times this week. It went up on the AI Engineer channel three days ago, hit 17,300 views in 72 hours, and now sits as the second-most-watched talk on the entire @aiDotEngineer feed — beaten only by the “CI/CD Is Dead” pitch […]

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