A new paper models open-ended goal inference as Bayesian filtering over proposed goals
xuanalogue · x · 2026-07-25
A new journal-length paper revisits open-ended goal inference and gives it a Bayesian formulation.
The paper argues that humans can infer other people’s goals from only a few actions, even when the goal space is essentially infinite. Its proposed algorithmic account treats the problem as top-down Bayesian filtering of bottom-up goal proposals:
- Bottom-up sampling: generate plausible goals from context
- Top-down filtering: keep only the goals that explain the observed actions
The authors say this makes real-time goal inference tractable and could support uncertainty-aware AI assistants that help people in open-ended tasks such as cooking, travel, or web navigation.
Related event: New Research Models Open-Ended Goal Inference as Bayesian Filtering(4 posts)→
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