ATM: Why Latent World Models Can Fail to Plan
Quick summary
arXiv:2606.09028v2 Announce Type: replace-cross Abstract: Latent world models can achieve accurate latent prediction yet still differ substantially in downstream planning performance. We argue that a key source of this discrepancy lies in the structure of action-induced latent transitions. We formalize action-identifiability through Bayes inverse risk, characterizing how much uncertainty about an action remains after observing the transition it induces. Model-predicted transitions can become highly self-decodable while encoding a domain-specific action relationship that fails to transfer to re
Key takeaways
- arXiv:2606.09028v2 Announce Type: replace-cross Abstract: Latent world models can achieve accurate latent prediction yet still differ substantially in downstream planning performance.
- We argue that a key source of this discrepancy lies in the structure of action-induced latent transitions.
- We formalize action-identifiability through Bayes inverse risk, characterizing how much uncertainty about an action remains after observing the transition it induces.
Why it matters
“ATM: Why Latent World Models Can Fail to Plan” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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