arXiv:2601.12283v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the quadratic cost of global attention. In this work, we observe that denoising dynamics are spatially non-uniform-background regions converge rapidly while edges and textured areas evolve much more actively. Building on this insight, we propose SDiT, a Semantic Region-Adaptive Diffusion Transformer that allocates computation according to regional complexity. SDiT introduces a training-free […]

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You’ve mastered streaming text. The AI types, the user reads, and the magic of perceived performance is achieved. But what if the AI could do more than just tell? What if it could show and let you interact? Welcome to the paradigm shift. We are moving Beyond Text. In this guide, we’ll explore how to stream actual React components directly from the server to the client. Using the Vercel AI SDK and React Server Components (RSCs), we’ll transform a static chat interface into […]

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TIL: Downloading archived Git repositories from archive.softwareheritage.org Back in February I blogged about a neat Python library called sqlite-s3vfs for accessing SQLite databases hosted in an S3 bucket, released as MIT licensed open source by the UK government’s Department for Business and Trade. I went looking for it today and found that the github.com/uktrade/sqlite-s3vfs repository is now a 404. Since this is taxpayer-funded open source software I saw it as my moral duty to try and restore access! […]

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In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs) for subgoal discovery and trajectory guidance. While LLMs can support exploration, frequent reliance on LLM calls raises concerns about scalability and reliability. We address these challenges by constructing a memory graph that encodes subgoals and trajectories from both LLM guidance and the agent’s […]

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arXiv:2604.15416v1 Announce Type: new Abstract: Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and training large foundation models. Despite their empirical superiority, SignSGD is known to diverge on non-smooth objectives, which are ubiquitous in modern machine learning due to ReLUs, max-pools, and mixture-of-experts. To overcome this fundamental limitation, we propose textbf{StoSignSGD}, an algorithm that injects structural stochasticity into the sign operator while maintaining an unbiased update step. In the […]

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arXiv:2603.10001v2 Announce Type: new Abstract: Large Language Models (LLMs) exhibit inequalities with respect to various cultural contexts. Most prominent open-weights models are trained on Global North data and show prejudicial behavior towards other cultures. Moreover, there is a notable lack of resources to detect biases in non-English languages, especially from Latin America (Latam), a continent containing various cultures, even though they share a common cultural ground. We propose to leverage the content of Wikipedia, the structure of the […]

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arXiv:2603.05577v1 Announce Type: new Abstract: Human speech contains both linguistic content and speaker dependent characteristics making speaker verification a key technology in identity critical applications. Modern deep learning speaker verification systems aim to learn speaker representations that are invariant to semantic content and nuisance factors such as ambient noise. However, many existing approaches depend on labelled data, textual supervision or large pretrained models as feature extractors, limiting scalability and practical deployment, raising sustainability concerns. We propose Deep Koopman […]

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arXiv:2602.05786v1 Announce Type: cross Abstract: Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular methods for hyperparameter optimization for tree-boosting including random grid search, the tree-structured Parzen estimator (TPE), Gaussian-process-based Bayesian optimization (GP-BO), Hyperband, the sequential model-based algorithm configuration (SMAC) method, and deterministic full grid search using $59$ regression and classification data sets. We find that the SMAC […]

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