Risk

TL;DR FinCast is a 1B‑parameter, decoder‑only Transformer trained on >20B financial time points with a token‑level sparse Mixture‑of‑Experts (MoE), learnable frequency embeddings, and a Point‑Quantile (PQ) loss that combines Huber point forecasts with quantile targets and a trend‑consistency term. In zero‑shot benchmarks across crypto/FX/stocks/futures, it reports ~20% lower MSE vs leading generic time‑series FMs, and it also beats supervised SOTAs—even without fine‑tuning—then widens the gap with a light fine‑tune. If you build risk or execution systems, the interesting part isn’t just accuracy points; it’s the shape of the predictions (tail‑aware, regime‑sensitive) and the deployment economics (conditional compute via sparse MoE + patching). ...

The gist A new clinical natural language inference (NLI) benchmark isolates what models know from how they reason—and the results are stark. State‑of‑the‑art LLMs ace targeted fact checks (≈92% accuracy) but crater on the actual reasoning tasks (≈25% accuracy). The collapse is most extreme in compositional grounding (≈4% accuracy), where a claim depends on multiple interacting clinical constraints (e.g., drug × dose × diagnosis × schedule). Scaling yielded fluent prose, not reliable inference. ...