arXiv:2602.16814v1 Announce Type: new Abstract: The expansion of AI toward the edge increasingly exposes the cost and fragility of cen- tralised intelligence. Data transmission, latency, energy consumption, and dependence on large data centres create bottlenecks that scale poorly across heterogeneous, mobile, and resource-constrained environments. In this paper, we introduce Node Learning, a decen- tralised learning paradigm in which intelligence resides at individual edge nodes and expands through selective peer interaction. Nodes learn continuously from local data, maintain their […]

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This paper introduces a novel investigation into the additive structure of primes that transcends the classical framework of the Goldbach Conjecture. Standing on the shoulders of Christian Goldbach’s foundational insight regarding the sum of primes (1742) and Sophie Germain’s pioneering work on special prime pairs, our inquiry addresses a qualitatively distinct problem: the existence of three simultaneously prime numbers (p, q, r) satisfying the relation p = q + r − 1. Unlike classical Goldbach verifications—notably the monumental […]

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A typical Vertical Federated Learning (VFL) scenario involves several participants collaboratively training a machine learning model, where each party has different features for the same samples, with labels held exclusively by one party. Since labels contain sensitive information, VFL must ensure the privacy of labels. However, existing VFL-targeted label inference attacks are either limited to specific scenarios or require auxiliary data, rendering them impractical in real-world applications. We introduce a novel Label Enumeration Attack (LEA) that, for the […]

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In this month’s episode of Functional Futures, our guest is Erik Svedäng, a game designer who has created many board and video games. Among them is Else Heart.Break(), a puzzle video game with its own programming language. He is also the creator of Carp, a statically-typed lisp for real-time applications. In the episode, we talk about game design, game development, and how Carp enables developers to build performant games while keeping true to functional programming idioms. As always, […]

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In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. Our today’s guest is Syed Jafri, a Senior Software Engineer at Caribou – a company that helps car owners get quotes on refinancing and insurance. He managed to successfully pitch and introduce Haskell at Caribou. They are currently using it for some of the services […]

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This survey (re)introduces reinforcement learning methods to economists. The curse of dimensionality limits how far exact dynamic programming can be effectively applied, forcing us to rely on suitably "small" problems or our ability to convert "big" problems into smaller ones. While this reduction has been sufficient for many classical applications, a growing class of economic models resists such reduction. Reinforcement learning algorithms offer a natural, sample-based extension of dynamic programming, extending tractability to problems with high-dimensional states, continuous […]

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arXiv:2602.05861v1 Announce Type: cross Abstract: Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are widely used to learn from such data, counterfactual graph learning has emerged as a promising approach to improve model interpretability. Counterfactual explanation research focuses on identifying a counterfactual graph that is similar to the original but leads to different predictions. These explanations optimize two objectives simultaneously: the sparsity of changes in the counterfactual […]

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arXiv:2601.19967v1 Announce Type: new Abstract: Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this issue, unlearnable examples introduce imperceptible perturbations into data, preventing models from learning effectively. However, existing methods typically rely on deep neural networks as surrogate models for perturbation generation, resulting in significant computational costs. In this work, we propose Perturbation-Induced Linearization (PIL), a computationally efficient yet effective method that generates perturbations using only linear […]

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Neural operators have emerged as promising surrogate models for solving partial differential equations (PDEs), but struggle to generalise beyond training distributions and are often constrained to a fixed temporal discretisation. This work introduces a physics-informed training framework that addresses these limitations by decomposing PDEs using operator splitting methods, training separate neural operators to learn individual non-linear physical operators while approximating linear operators with fixed finite-difference convolutions. This modular mixture-of-experts architecture enables generalisation to novel physical regimes by explicitly […]

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