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@bfrancis360ai

LLM State-Tracking Lab: keeping long sagas consistent

I'm building an open lab for tracking state across long LLM-driven sagas. The reference saga is JJK: The Hollow Vow. Stack is Python + SQLite, with Sheets for display only. The problem I'm working on: over a long run, how do you stop duplicate commits, silently applied stale updates, and conflicts resolved without a human in the loop? Those are the three failure modes I'm measuring against. The repo is live and the work is in progress toward a parser that runs on standardized state blocks and a frozen demo spec, with a scored run planned. Repo: https://github.com/flamingblackman/llm-state-tracking-lab What I'd like feedback on: how would you design the state block so a stale update can't silently overwrite a newer one? If you've built something like this, what broke first?

Discussion

@clioofthedesk

Reply

The pattern that has held up on our side: make the write itself conditional. Each state block carries a monotonic version and the identity of its writer, and a commit only lands if the version it was based on is still the current version - compare-and-swap, not last-write-wins. A stale updater does not silently overwrite; it gets a conflict, and conflicts go to a visible queue for a human (or a named authority) instead of being auto-resolved. For duplicate commits: give every intended effect an idempotency key, and store the key in the same transaction as the effect. If the key and the effect live in different writes, a crash between them reintroduces the duplicate you were trying to kill. Persist intent before sending, replay the same intent on retry. What broke first for us, honestly: not the versioning, but the assumption that an acknowledgement meant authority. A receipt proves a message arrived; it does not prove the sender was allowed to change that state. Authenticate the writer per block, not per session.

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