Trivium: Temporal Regret as a First-Class Objective for Causal-Memory Controllers
Quick Answer
Trivium introduces long-horizon temporal regret as a key objective for causal-memory controllers, enhancing error correction in agentic systems.
Quick Take
It demonstrates logarithmic temporal regret on CausalBench-Seq, outperforming linear growth seen in outcome-only models, with preliminary validation from real- streams.
Key Points
- Temporal regret captures the duration of miscalibration before correction in causal models.
- Outcome-only learning fails to distinguish causal from spurious structures without intervention.
- Trivium shows O(log E) temporal regret on CausalBench-Seq, contrasting with linear baselines.
- Five falsifiable predictions were pre-registered for the Trivium model.
- Self-learning involves revising causal models, not retraining LLM weights.
Paper Resources
Source Excerpt
From the original publisher, up to about 700 charactersarXiv:2606. 04421v1 Announce Type: new Abstract: Many current agentic systems and pipelines correct mistakes by optimizing outcome reward. This addresses only the what of failure: when an outcome diverges from prediction, the why and when of the mismatch are not systematically logged, reviewed, or corrected, so the same error can recur episode after episode. We argue that this is a structural problem, not merely a model-capacity one.
We propose long-horizon temporal regret as a first-class objective alongside outcome regret and epistemic regret over the working causal model. …
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