World models vs RAG: why agents need state tracking, not just retrieval
techNmak · x · 2026-09-19
- The author disentangles "world model" from context, memory, and RAG: agents on long tasks must track entities, relations, current facts, history, constraints, and action effects—an evolving state, not just stored text.
- Active context = what's fed to the model now; knowledge bases store; RAG retrieves; world models represent state and its dynamics over time.
- Example: knowing Alice worked at Acme, then joined Nova—retrieval is easy, maintaining the evolving state (what's true now, when transitions happened, what depends on them) is the hard part.
- Case study: Pocket FM's Sherpa builds a Narrative World Model for long-form fiction—a narratology-grounded typed temporal-state graph with query-conditioned hybrid retrieval—answering questions flat retrieval handles poorly (when a character learned something, event ordering, setup resolution).
- Core point: a world model tracks what is true now, not just what happened.
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