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This paper proposes 'Trivium,' a framework that introduces long-horizon temporal regret and epistemic regret as first-class objectives alongside outcome regret for causal-memory controllers in agentic LLM systems. The authors prove that outcome-only learning cannot distinguish causal from spurious structure without an intervention channel, while their approach achieves O(log E) temporal regret on CausalBench-Seq experiments versus linear growth for baselines.