Trivium: Temporal Regret as a First-Class Objective for Causal-Memory Controllers
Many agentic systems and LLM pipelines correct mistakes by optimizing outcome reward. This addresses only the what of failure; the why and when may go unlogged, allowing the same error to recur across episodes. We propose long-horizon temporal regret alongside outcome regret and epistemic regret. These are diagnostic quantities, not standard comparator-based online-learning regrets. Temporal regret captures how long an unresolved or incorrect causal model is tolerated; epistemic regret captures posterior error over that model. Over a stream of E episodes, we prove three conditional results under explicit probing, persistence, and detectability assumptions. First, under observationally equ...
| Authors | Edward Y. Chang |
|---|---|
| Primary category | cs.AI |
| Published | 3 June 2026 |
| Last updated at source | 4 August 2026 |
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