Counterfactual Debugging: causal attribution over 1M steps to localize sim2real gaps in world-model agents
MichaelD1729 · x · 2026-09-03
Agents trained with world models often fail at deployment, and identifying the real sim2real gap is hard. The authors propose Counterfactual Debugging, which uses causal attribution at the 1M-step scale to pinpoint root causes of failure, helping distinguish model defects from environment mismatch.
Related event: Counterfactual Debugging Pins Down Sim2Real Failures at Million-Step Scale(4 posts)→
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