Minimax can support robust decision-making over retrieved evidence by treating uncertainty as an adversarial factor and choosing actions that remain acceptable under the worst plausible interpretation of the retrieved context. This is the same “optimal opponent” assumption used in games, but here the “opponent” is not a player—it’s the risk that a retrieved item is misleading, outdated, or incorrectly matched. If your system has to make a decision based on retrieved snippets, a Minimax-like approach asks: “What if the most harmful plausible error happens—does this decision still hold up?”
Implementation-wise, you need two things: a way to generate alternatives and a scoring function that reflects risk. Start by retrieving a candidate pool (topK) and building a small decision tree where each action selects some subset of evidence and leads to an outcome score. Then define adversarial scenarios, such as “the top similarity snippet is wrong,” “the highest-confidence source is missing,” or “the evidence conflicts.” A Minimax policy chooses the action whose minimum score across these scenarios is maximal. You can implement this without an enormous tree by keeping the adversarial scenarios small and well-defined (for example, drop one candidate, flip a truth label among ambiguous candidates, or apply a worst-case penalty to low-provenance sources).
A concrete pipeline: retrieve candidates from Milvus or Zilliz Cloud, then classify each candidate with metadata-based trust signals (source type, timestamp, doc policy). For each possible action (answer strategy, escalation, request more info), score the outcome under multiple “stress tests” (e.g., best candidate removed, conflicting candidate dominates). The Minimax action is the one that performs best under the worst stress test. This tends to produce safer behavior: it prefers corroborated evidence and conservative actions when evidence is fragile. The important constraint is to keep the model honest: Minimax won’t magically fix retrieval quality, and if your scoring function doesn’t reflect real risk, you’ll optimize the wrong thing. But as an engineering pattern—evaluate under worst-case plausible uncertainty and choose the robust option—it can be a practical way to reduce catastrophic failures when you must act under imperfect retrieval.