arXiv:2606.04421v1 Announce Type: cross Abstract: Many current agentic systems and LLM 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. Temporal regr
The proliferation of complex agentic systems and LLM pipelines highlights the current limitations of outcome-based optimization, driving the need for more sophisticated error correction mechanisms.
This research introduces a fundamental shift in how AI systems learn and adapt, moving beyond simple outcome rewards to address the deeper causes of failure, which is critical for robust and autonomous AI development.
The focus of AI optimization shifts from merely correcting 'what' went wrong to understanding 'why' and 'when' it went wrong, incorporating temporal and epistemic regret into the learning objective.
- · AI researchers
- · Agentic system developers
- · AI-driven automation platforms
- · Basic outcome-reward AI models
- · Systems with high error recurrence
More intelligent and self-correcting AI agents will emerge, reducing the need for constant human oversight.
This could accelerate the deployment of autonomous systems in complex, high-stakes environments where reliability is paramount.
The enhanced learning capabilities might lead to faster AI development cycles and new paradigms in AI safety and interpretability.
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Read at arXiv cs.LG