During March 2026, Loste developed the AIME 2026 Evaluation Benchmark Framework for the UKGovernmentBEIS/inspect_evals repository. Leveraging Python, data analysis, and machine learning, Loste integrated new datasets, implemented scoring logic, and established robust testing scaffolds to validate model performance. The work included introducing trajectory analysis artefacts for multiple GPT-nano variants, updating evaluation artefacts, and aligning tooling with previous benchmark structures. Loste also enhanced documentation, improved contributor attribution, and reorganized common utilities for maintainability. Through careful attention to code quality, linting, and CI hygiene, the framework now supports reproducible evaluation and accelerates iteration for AIME 2026 benchmarking initiatives.