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This paper introduces Thomson, a frontier model developed through Continual Learning on open-weight models, enabling a broader range of institutions to achieve competitive performance in high-stakes professional tasks. By employing a modern mid- and post-training stack, the approach ensures both plasticity and stability, allowing for significant performance gains with minimal intervention compared to traditional fine-tuning methods. The results demonstrate that Thomson not only competes effectively with leading models across various domains but also addresses the common issue of catastrophic forgetting, thereby enhancing its utility for diverse applications.
Frontier model performance is now within reach for a wider array of institutions, thanks to a novel Continual Learning approach that minimizes forgetting while maximizing capability.
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse user base of modern AI. Recent public discourse acknowledges this concern, calling for SovereignAI (an organisation's capability to independently build, deploy and govern AI use), but offers little concrete advice on how this can be achieved in the short term under a diversity of funding settings. We argue that frontier performance is achievable by a wide range of institutions through Continual Learning on readily available open-weight models. Unlike limited approaches such as small-scale fine-tuning, prompt engineering, or tool-augmentation of a frozen model, our approach exploits a modern mid-&post-training stack while introducing safeguards that preserve both plasticity and stability at each stage, making the minimal number of high-impact interventions on the parameters. This yields gains comparable to those typically seen across multiple successive model generations, at compute and personnel budgets substantially lower than commonly thought, making ownership of large parts of the SovereignAI stack (model, tool infrastructure, values&data privacy) viable for far more actors. We demonstrate this with Thomson, a general-purpose frontier model trained with an enhanced focus on high-stakes professional work. Thomson performs competitively with recent frontier models across agentic tasks, safety, legal, tax&multilingualism, and large-scale Deep Research. Evaluations show a distinctive $\pi$-shaped pattern: distinct improvements across a wide range of capabilities, including those not explicitly targeted, while almost completely eliminating the forgetting problem common to narrow domain adaptation.
