Yes, Minimax ideas can help with worst-case ranking in vector retrieval if you explicitly want robustness against the most harmful plausible errors. Vector retrieval usually ranks by similarity (plus filters and re-ranking), which optimizes for average relevance under your embedding model. A Minimax-inspired approach becomes useful when you suspect the top-ranked item might be misleading, low-quality, or risky, and you want a selection that remains acceptable even in the worst case. In that framing, the “opponent” isn’t a person; it’s uncertainty: ambiguous matches, noisy embeddings, adversarial content, or incomplete metadata.
A practical way to apply Minimax here is to define a utility function for using a retrieved item (or a set of items) and define what “worst case” means. For example, suppose you retrieve top 50 candidates, then choose 5 passages to show or to feed into downstream logic. You can define utility as “usefulness minus risk,” where risk includes low provenance, outdated content, or mismatched constraints. Then define the adversary as the worst plausible assignment of which items are truly correct among the ambiguous ones. A Minimax policy would choose the subset that maximizes the minimum utility under that uncertainty set. This often produces safer outputs: it prefers candidates with strong metadata, consistent corroboration, and fewer single points of failure, even if they are slightly lower similarity.
In concrete engineering terms, a robust pipeline could look like this: (1) use a vector database such as Milvus or Zilliz Cloud to retrieve an initial candidate pool; (2) enrich each candidate with metadata features (source class, timestamp, permissions, domain tags); (3) score candidates with a combined function that separates “relevance signal” from “risk signal”; and (4) select final items using a worst-case objective, such as maximizing the minimum “credibility-adjusted relevance” across the chosen set. You can implement that selection with simple greedy heuristics (choose items that improve the current worst-case score) rather than a full combinatorial search if latency matters. The key is to stay honest about tradeoffs: Minimax-style robustness will usually lower average relevance slightly but reduce catastrophic failures. That tradeoff is often worth it in high-stakes retrieval settings, as long as you define your risk model and constraints clearly and validate them with real queries rather than relying on intuition alone.