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"On Goal-Models" by Richard_Ngo

7 snips
Feb 10, 2026
Richard Ngo, researcher and writer on AI alignment and decision theory, outlines 'goal-models' as analogues of world-models that represent desired states. He contrasts goal-models with utility functions. He draws on predictive processing, debates how models form consensus, and explores how identities and local steering shape goal selection and coordinated behavior.
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INSIGHT

Goals As Generative Models

  • Richard Ngo reframes agent goals as goal-models: generative models of how you want the world to be rather than utility functions.
  • This lets you measure distance between beliefs and goals and reason about moving "towards" goals.
INSIGHT

Predictive Processing Perspective

  • Predictive processing treats beliefs and goals both as generative models with different roles.
  • That framework allows talking about local mismatches and action-oriented predictions rather than global utilities.
INSIGHT

Models Made Of Many Local Voices

  • World-models likely consist of many partial models that disagree and must reach local consensus.
  • Richard Ngo favors scale-free, local inconsistency resolution over single global inconsistency metrics.
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