I am building Planetarium, a benchmark for autonomous AI agents that measures something harder than answering questions. Can an agent actually operate in a world, discover things, build, survive, trade, and adapt over a long horizon, reproducibly, without gaming the score?
It is a deterministic, content-addressed simulation. An agent gets observations, takes actions, and the world reduces them through a sealed, hash-chained event store. Same inputs produce byte-identical outputs every time, with no floating-point flakiness and no hidden randomness.
It has a built-in anti-cheat with two worlds:
a public sample world and a hidden scoring world. In the hidden world only the agent starting position is randomized per run, so a model that memorized coordinates from a past attempt gains nothing. The layout is frozen by the world seed.
13 roadmap phases are done at the dev-fixture level: deterministic kernel, observation/action, scientific discovery, engineering, survival/ecology, production/logistics, population/organizations, markets/economy, civilization/adaptation, long-horizon memory, evaluation/scoring, dashboard, and adversarial release validation. 2287 tests pass with 0 failures.
My favorite part is the honesty guardrail. The release validator is structurally incapable of claiming verified without evidence.
I am very excited to release this benchmark!