Approximation Target Divergence Mapping

Gap-fill role

This draft directly addresses the zero-any accepted-prime target refinement from queue position 24 of scaled_gap_fill_batch_003_queue.yaml. It operationalizes refinement as a target-relative divergence discipline: specify the target, snapshot the approximation, map consequential mismatch, and use that map to prioritize corrective improvement.

Pre-draft disposition conclusion

Disposition: drafted_full_archetype. The candidate has close neighbors in bounded_approximation, iterative_refinement_loop, progressive_refinement_from_core_model, divergence_detection_and_correction, tolerance_band_management, tolerance_stack_management, variance_reduction, quality_control, and the pilot benchmark_anchored_refinement variant. None clearly absorbs the full pattern, and no binding alias or duplicate-merge directive was found.

Review emphasis

Review this draft with the broader refinement family. Its most important boundary risks are component-only collapse into a generic gap map, overlap with iterative_refinement_loop, overfitting to benchmark targets, and false precision when target divergence is scored more exactly than the measurement supports.

Compression statement

Approximation-Target Divergence Mapping is the pattern of treating the difference between a current approximation and an intended target as a structured object. Instead of refining everywhere or reacting to the most visible defects, it specifies the target, snapshots the approximation, decomposes their divergence by dimension, distinguishes acceptable approximation error from material mismatch, and directs refinement effort toward the gaps whose correction most improves fit.

Canonical formula: refinement_priority_i := consequence(target_i - approximation_i) × tractability_i × confidence_i; refine gaps above the action threshold while preserving acceptable approximation bounds