LLM Agents Interview Questions, issue 16, Mar 10, 2026
The Vision Encoder Scaling Trap
Senior AI Engineer interview at Google DeepMind, and the interviewer asks:
“You upgraded your geometry autoformalization pipeline from a 70B text-only LLM to a state-of-the-art VLM. You feed it textbook diagrams alongside the text. Success rates barely nudge past 20%. Why?”
Don’t say: “The vision encoder is losing spatial granularity. We need to unfreeze the ViT and fine-tune on higher-resolution diagram crops to capture exact intersection points.”
Fine-tuning the ViT won’t break the 20% ceiling because the bottleneck isn’t perception, it’s the missing symbolic bridge between visual claims and provable geometry.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.