Ken Thampiratwong | Searcharxiv

arXiv2026

DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning

Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems has thus far been limited by existing task suites. They suffer from many issues, such as: poor evaluation methodologies; data contamination; and containing saturated or very similar problems. Here, we introduce DiscoGen, a procedural generator of algorithm discovery tasks for machine learning, such as developing optimisers for reinforcement learning or loss functions for image classification. Motivated by the success of procedural generation in reinforcement learning, DiscoGen spans billions of tasks of varying difficulty and complexity from a range of machine learning fields. These tasks are specified by a small number of configuration parameters and can be used to optimise algorithm discovery agents (ADAs). We present DiscoBench, a fixed, small subset of DiscoGen tasks for principled evaluation of ADAs. Finally, we propose a number of ambitious, impactful research directions enabled by DiscoGen, and demonstrate its use for ADA optimisation through scaling experiments for automated prompt tuning. DiscoGen is released open-source at https://github.com/AlexGoldie/discogen.

cs.LG↗

arXiv2025

Leveraging Large Language Models to Contextualize Network Measurements

With the worldwide growth of remote communication and telepresence, network measurements form a cornerstone of effective performance assessment and diagnostics for Internet users. Most often, users seek for overall connection performance measurement using publicly available tools (also known as `speed tests') that provide an overview of their connection's throughput and latency. However, extracting meaningful insights from these measurements remains a challenging task for a non-technical audience. Interpreting network measurement data often requires considerable domain expertise to account not only for subtle variations of the connection stability and metrics, but even for simpler concepts such as latency under load or packet loss influence towards connection performance. In the absence of proper expertise, common misconceptions can easily arise. To address these issues, researchers should recognize the importance of making network measurements not only more comprehensive but also more accessible for wider audience without deep technical knowledge. A promising direction to achieve this goal involves leveraging recent advancements in large language models (LLMs), which have demonstrated capabilities in conducting an analysis of complex data in other fields, such as laboratory test results interpretation, news summarization, and personal assistance. In this paper, we describe an ongoing effort to apply large language models and historical data to enhance the interpretation of network measurements in real-world environments. We aim to automate the translation of low-level metric data into accessible explanations, allowing non-experts to make more informed decisions regarding network performance and reliability.

cs.NI↗