Noah P PRO

If your interested in getting access to the model, join the alpha. https://p1ayer-1-gamedecider-playground.hf.space/alpha

Haha. If your interested in using it on a game of your own, join the alpha. https://p1ayer-1-gamedecider-playground.hf.space/alpha

Fair points, thanks. Honest denominators: the day-1 numbers were one card per type (intent 12/12, apology 7/14), because everyone gets the same five cards a day. As of now it’s 33 players and 10 cards: intent 18/18 over 2 cards, apology 9/19 over 2. So treat them as anecdotes, not results.

And yes, the reference label is the secret brief the writer was assigned. It measures recovering a scripted intent, not detecting real sincerity. The first-day cards were model-written to a brief and kept only if a second model read them blind the same way. Player-written lines started today.

Repeat guesses already only count once per player per card. I like the “can’t tell” idea, especially for apologies, where several intents fit the same words. I’ll add it, and publish the labeling rules plus per-card counts on the game page once there are enough cards to say anything.

1/4 is respectable, most people's first lie gets caught.

The trick is it isn't an LLM grading on a rubric. The judge is a small (~400M) encoder model trained only to answer game questions like "is this a threat / a lie against these facts / a sincere apology", and each challenge uses a small adapter trained just for that one question, so it's hard to talk it out of its job. It answers with a probability and can say "not sure" instead of guessing.

If you want the other direction, I made a second game where you are the judge: Mean It? — every day you read 5 lines from strangers and call whether they're faking it (apologies, threats, sarcasm, alibis), then you write one to fool tomorrow's players. Real players are currently 50/50 on the fake apologies:
https://p1ayer-1-gamedecider-playground.hf.space/mean?ref=hf-comment