Try to Break Our AI Memory Benchmark

Facts change. An earnings forecast is revised. A policy is amended. A medication dose is corrected. An entity record is updated.

Many AI memory systems retain both the old and new versions. When retrieval ranks both highly, stale information can silently enter the model context.

We built .

Please include the fact pair or dataset, your environment, the command or script, the expected result, and the actual result.

If a failure is reproducible, we will turn it into a regression test and credit the contributor. People who find meaningful edge cases will also be invited to a technical pairing session with the maintainers.

Why this matters

A current answer and a historical reconstruction are different products.

A system reviewing a past financial decision, clinical recommendation, legal analysis, or policy action must preserve what was knowable at the time. Later corrections should improve current answers without rewriting the original decision context.

That is the standard we want Lians to meet. The fastest way to improve the product is to expose the benchmark, make the claims falsifiable, and welcome critical results.

If your team is deploying an agent that depends on changing facts, you can also request a free temporal-memory audit at lians.ai. We will examine one sanitized workflow and identify where stale facts or missing evidence could affect reliability.