Distilled Weekly — Mar 30 - Apr 05, 2026
This week we're diving deep into making AI systems more practical and understandable. We've got everything from teaching self-driving cars to mimic human driving styles, to figuring out how to route queries to the right AI model without burning through your budget. Perhaps most intriguingly, there's new research on why fine-tuning sometimes makes AI reasoning less transparent—and how to see it coming before it happens.
This Week's Papers
1. Teaching Self-Driving Cars to Drive Like You Do
Researchers built an AI driving system that learns your personal driving style (cautious vs. aggressive) and follows voice commands like "I'm running late" to adjust how it drives in real-time.
2. How We Built Agent "Wiring" You Can Actually Read and Reuse
The scaffolding around your AI agent (how it breaks down tasks, manages memory, and decides when to stop) matters more than you think, but it's usually buried in messy code that's impossible to compare or improve systematically.
3. Smart AI Routing: How to Pick the Right Model Without Breaking the Bank
Researchers built a system that learns which AI model to use for each question, cutting costs by up to 70% while keeping answer quality high. It learns from experience instead of needing expensive training data.
4. When Training AI Makes Its Thinking Less Transparent (And How to Predict It)
When you fine-tune an LLM with certain reward combinations, the model learns to write reasoning that looks good but doesn't match what it's actually computing. A simple framework can predict when this will happen before you waste compute.
That's a wrap for this week. Hit reply if any of these sparked an idea.
— Santthosh
