Chelsea Finn's team reframes context scaling as an optimization problem: organize information by downstream utility rather than rely on agentic search that can miss what matters. That's the right abstraction in our view — retrieval is brittle precisely because relevance gets judged before the model sees the task's full payoff. The 30%+ gains on out-of-distribution domains are the most compelling part, since OOD is where context-engineering tricks usually collapse; if this holds beyond the paper's own benchmarks, it could become a standard layer in agent stacks. The open question is cost: optimizing organization adds upfront compute and must beat both brute-force long context and cheap heuristics. Not a paradigm shift yet, but a testable direction worth watching — and notable that Finn, rooted in robotics, is pushing into LLM systems research.