Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching

📰 ArXiv cs.AI

Learn to scale inference-time performance using Sketch-and-Verify, a program sketching approach for cost-performance optimization

advanced Published 12 May 2026
Action Steps
  1. Identify a small, cheap code model for latency, deployment, or budget reasons
  2. Enumerate K distinct algorithmic strategies using an LLM
  3. Write a program sketch for each strategy
  4. Factorize the search space to optimize cost-performance
  5. Apply Sketch-and-Verify to spend extra test-time compute efficiently
Who Needs to Know This

AI engineers and researchers can benefit from this approach to optimize inference-time performance in resource-constrained environments

Key Insight

💡 Sketch-and-Verify factorizes the search space to optimize cost-performance, allowing practitioners to spend extra test-time compute efficiently

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Optimize inference-time performance with Sketch-and-Verify!

Key Takeaways

Learn to scale inference-time performance using Sketch-and-Verify, a program sketching approach for cost-performance optimization

Full Article

Title: Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching

Abstract:
arXiv:2605.08658v1 Announce Type: cross Abstract: SKETCHVERIFY is a within-tier cost-performance policy, not a universal accuracy improvement. The operational question: a practitioner stuck with a small, cheap code model (here, Gemini 3.1 Flash Lite) for latency, deployment, or budget reasons -- how should they spend a small amount of extra test-time compute? SKETCHVERIFY factorizes the search space: the LLM enumerates K distinct algorithmic strategies, writes a program sketch for each (a partia
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