New preprint from @lightningrodai, this time with Philip Tetlock and Ville Satopää! We post-train 5 versions of the same LLM, changing only the scoring rule used as the RL reward. Similar aggregate scores, but very different BIN profiles. A good Brier score alone doesn't tell you if a forecaster can distinguish likely from unlikely events. One might discern well but lose if its probabilities run systematically too high. A less discerning one might score better by hugging the base rate. BIN splits forecast performance into bias, information, and noise. Bias is a systematic shift in the probabilities. Noise is random scatter. Information is real signal about which outcomes are more likely — the part you need a powerful LLM for. Different uses call for different profiles. Reward choice is one lever shaping which forecaster you get. Congrats to co-authors @indiequant @KSkotheim64001 @VSatopaa @PTetlock 🙌 Full paper:

New preprint from @lightningrodai! We trained AI to predict clinical events — ICU transfers, new diagnoses, complications, procedures, ventilation, mortality — directly from raw clinical notes. No labeled data required – Foresight Learning infers outcomes from what happens later in patient records. Using Tinker from @thinkymachines , we trained a lightweight adapter on GPT-OSS-120B, resulting in a specialized predictor that runs on a single GPU. Results: 🎯 ~70% lower calibration error 📈 Brier skill score: ~0% → 27% 🧠 84% win-rate vs the base model in blind reasoning review 🥇 Slightly better Brier than GPT-5, despite being a fraction of the size Hospitals and specialty clinics often treat unique patient populations that out-of-the-box models don't have training data for. This makes it possible to build frontier-quality predictors for highly specific patient groups, with nothing but raw clinical records. Congrats to the team — @indiequant @KSkotheim64001 🙌 Full paper 👇