Tag: Frontier Models

In the early architecture of autonomous agent systems, the bridge connecting probabilistic neural reasoning to deterministic software execution was notoriously fragile. Developers spent thousands of engineering hours crafting elaborate system prompts that implored foundation models to “always return valid JSON,” wrapping outputs in markdown code fences, and writing complex regular expression parsers to scrub away […]

For the first decade of the modern deep learning expansion, progress followed a single primary vector: pre-training compute scaling laws. Empirical research from Kaplan and Chinchilla demonstrated that model capabilities scaled predictably as a power-law function of parameter counts, dataset volume, and training FLOPs. However, by late 2024, pre-training reached physical and economic friction points: […]