Scaling Laws Hit Hard Ceiling: Data‑Driven ML Can't Master Symbolic Logic
The paper proves that supervised deep learning cannot achieve full syllogistic reasoning, no matter how much data or compute you throw at it. It shows training data can’t represent all 24 valid syllogism types and end‑to‑end mapping creates contradictory objectives, while sphere neural networks solve the task without any data.