Bridging AI research and production finance — the gap between what papers claim and what systems actually do.
TGAT runs at 5–6% GPU utilisation. JODIE at 1.5–2.5%. DyRep under 2%. A March 2026 paper finally fixes the inference problem — and the speedup is 739×.
A May 2026 paper feeds Soros's theory of self-reinforcing market dynamics to GPT-5, Claude, and Gemini. The forecasting improves. The reason why is harder to establish than the paper suggests.
A 2026 IEEE-accepted paper finally says the quiet part out loud — data leakage, illiquidity blindness, and the evaluation metrics nobody in academia reports.
Notes on AI systems, financial infrastructure, and the gap between what research papers claim and what production deployments actually look like.
About →LLMs pretrained on internet-scale text corpora have likely been exposed to financial news, analyst reports, and market commentary from periods that overlap with academic test sets. This creates a form of data leakage that is distinct from look-ahead bias and considerably harder to detect. Reported directional accuracy figures may be substantially inflated as a result.