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Multi-agent LLM systems are leaving performance on the table by treating structured agent interactions as generic traffic; Pythia shows how to unlock substantial gains by exploiting workflow semantics at the serving layer.
LLMs underperform traditional ML methods in software fairness tasks, challenging the assumption that they offer a silver bullet solution for bias mitigation.