AI imagination is fundamentally rigged toward its own comfort zone, causing a state of delusional optimism.
April 29, 2026
Original Paper
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
arXiv · 2604.25416
The Takeaway
Latent dynamics models like Dreamer learn by simulating potential future scenarios in their own heads. This research shows that these models exhibit attractor behavior, pulling their simulations toward well-represented regions of their internal memory. This bias causes the model to systematically overestimate the rewards it will receive in unfamiliar situations. The resulting delusional optimism can lead to dangerous failures when the model encounters the real world. Fixing this requires a fundamental change in how we measure uncertainty in the imaginary spaces of AI.
From the abstract
Model-Based Reinforcement Learning distinguishes between physical dynamics models operating on proprioceptive inputs and latent dynamics models operating on high-dimensional image observations. A prominent latent approach is the Recurrent State Space Model used in the Dreamer family. While epistemic uncertainty quantification to inform exploration and mitigate model exploitation is well established for physical dynamics models, its transfer to latent dynamics models has received limited scrutiny