RIMU: Ranking-Aware Dense Retrieval Adaptation via Convex Optimization

Retrieval underpins many modern agents, but dense retrievers built with frozen encoders do not automatically improve as verified query feedback accumulates. Query–corpus misalignment may therefore persist even when the correct target for a query is known. We introduce RIMU, a lightweight method that corrects stored corpus vectors based on the ranking of gold targets relative to their competitors. RIMU computes minimum-displacement updates through one convex quadratic program per query, then renormalizes the vectors to preserve cosine retrieval. Across ten retrieval pools and five frozen encoders, it improves N@10 over the frozen index by 13.4–27.1%. With Qwen3 Embedding-8B, it achieves the highest N@10 on seven of ten pools. Learning curves show gains as feedback accumulates, while gain retention analyses assess whether later updates preserve earlier improvements. Updates take 2.3–27.3 ms per query on CPU, without encoder retraining or additional index entries.