Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks
Quick summary
arXiv:2610.11794v1 Announce Type: new Abstract: Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives. Yet limited observations can support multiple world models that explain past interactions but predict different outcomes in unseen states. We introduce Memento 3, building on the Memento series to enable frozen LLM agents to continually learn explicit world models through external memory. The agent maintains a natural-language rulebook as persistent semantic memory, recording revisable hypotheses about env
Key takeaways
- arXiv:2610.11794v1 Announce Type: new Abstract: Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives.
- Yet limited observations can support multiple world models that explain past interactions but predict different outcomes in unseen states.
- We introduce Memento 3, building on the Memento series to enable frozen LLM agents to continually learn explicit world models through external memory.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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