Added Numba JIT-compiled inner loops (_numba_jit.py) for iterative solvers: @njit(cache=True) compiled functions with automatic disk caching Graceful fallback to pure Python when numba is not installed JIT functions: _doroshenko_inner, _kaczmarz_inner, _mlem_inner, _gravel_inner, _compute_log_steps_jit, _dose_weighted_mse_jit numba added as optional dependency (>=0.65.1) Changed — Performance Doroshenko solver: 50x speedup (40.6 ms → 0.8 ms) — element-wise inner loop eliminates per-coordinate numpy overhead Kaczmarz solver: 14x speedup (1.4 ms → 0.1 ms) — JIT-compiled row update loop MLEM solver: 7x speedup (2.7 ms → 0.4 ms) — JIT-compiled multiplicative update GRAVEL solver: 3x speedup (~2 ms → 0.6 ms) — JIT-compiled weighted geometric mean update Monte Carlo uncertainty: pre-generates all noise vectors at once instead of per-sample dict creation Comparison metrics: _compute_log_steps and dose_weighted_error use JIT-compiled helpers when numba available Fixed total_flux_ratio returned sum(reference)/sum(test) instead of sum(test)/sum(reference) per docstring Improved Extracted _compute_log_steps DRY helper in comparison.py (was duplicated in 3 functions) Extracted _handle_extrapolation DRY helper in interpolation.py (was duplicated in 2 functions) 110 new tests in tests/test_improvements.py (validators, converters, matrix utils, Monte Carlo, dose calculation, interpolation, comparison metrics, EURADOS metrics, Detector integration) Test suite: 910 tests (was ~800)