January 2026

The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suffer from a fundamental capability gap: unlike frontier large models, they lack the inference-time strong generalization to work with complicated, unfamiliar codebases. We identify that the prevailing Task-Centric Learning (TCL) paradigm, which scales exposure across disparate repositories, fails to address this limitation. In response, we propose Repository-Centric Learning (RCL), a paradigm shift that prioritizes vertical repository […]

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I’ve been diving into the deep end of Reinforcement Learning and Generative Models lately, specifically trying to see if I could train a simple diffusion model from scratch using nothing but a reward signal. On paper, it sounded like a fun weekend experiment, but in reality, it was a 72-hour masterclass in frustration. By Sunday night, I was staring at a screen of pure static; every time I adjusted the hyperparameters, the model would either collapse into a […]

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Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which they are applied is left to the user, or else defined in general-purpose default pass sequences. While general-purpose sequences miss opportunities for optimisation which are particular to individual circuits, designing pass sequences bespoke to particular circuits requires exceptional knowledge about quantum circuit design and optimisation. Here we propose and demonstrate training […]

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Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents […]

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FastAPI From First Principles — Free Course From Hello World to Kubernetes. Here’s everything I wish someone had told me. I’ve been building APIs with FastAPI for over four years now — production systems handling millions of requests, authentication flows that actually work, database patterns that don’t fall apart at scale. And I’ve made every mistake possible along the way. And don’t need to repeat the same. So I wrote the guide I wish I had when I started: FastAPI from first principles to production deployment. […]

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Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from diverse node features and structural topologies across multiple clients. To address both types of heterogeneity, we propose a novel graph Federated learning method via Semantic and Structural Alignment (FedSSA), which shares the knowledge of both node features and structural topologies. For node feature heterogeneity, we propose a novel variational model to infer class-wise node distributions, […]

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Catastrophic forgetting during knowledge injection severely undermines the continual learning capability of large language models (LLMs). Although existing methods attempt to mitigate this issue, they often lack a foundational theoretical explanation. We establish a gradient-based theoretical framework to explain catastrophic forgetting. We first prove that strongly negative gradient similarity is a fundamental cause of forgetting. We then use gradient similarity to identify two types of neurons: conflicting neurons that induce forgetting and account for 50%-75% of neurons, and […]

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Standard deep learning relies on Backpropagation (BP), which is constrained by biologically implausible weight symmetry and suffers from significant gradient interference within dense representations. To mitigate these bottlenecks, we propose Selective Adaptive Learning (SAL), a training method that combines selective parameter activation with adaptive area partitioning. Specifically, SAL decomposes the parameter space into mutually exclusive, sample-dependent regions. This decoupling mitigates gradient interference across divergent semantic patterns and addresses explicit weight symmetry requirements through our refined feedback alignment. Empirically, […]

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We’ve been experimenting with federated RL for heterogeneous robotic manipulation and ended up building a framework that separates representation federation from policy learning. Preprint is here. https://www.preprints.org/manuscript/202601.2257 I’d genuinely appreciate feedback on the design choices, especially around aggregation and stability. submitted by /u/EitherFox1242 [link] [comments]

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