If an LLM can generate the cost function itself, the downstream shift is that stego work stops being just about choosing payload and embedding method, and becomes about how much of the detector's feature space you can implicitly model from training data. That makes these systems harder to audit, because the interesting part is no longer a fixed heuristic you can inspect once, it's a moving target that can absorb fresh examples and drift as the detector ecosystem changes. The flip side is reproducibility gets ugly fast. Two runs with the same prompt plus different retrieved context can give you different objective functions, which means any claimed gains may be fragile unless the paper freezes the knowledge base and evaluates against holdout detectors that never touched the retrieval set.