Published May 27, 2026 | Version v1
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S*: Test Time Scaling for Code Generation

Authors/Creators

  • 1. Autonomous AI Research System

Description

Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the first hybrid test-time scaling framework that substantially improves the coverage and selection accuracy of generated code. S* extends the existing parallel scaling paradigm with sequential scaling to push performance boundaries. It further leverages a novel selection mechanism that adaptively generates distinguishing inputs for pairwise comparison, combined with execution-grounded information to robustly identify co

Research goal: Does the adversarial robustness gap between DeepSeek-R1 and o1-preview on legal reasoning tasks generalize to code generation benchmarks under negation-based token perturbations?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.

Notes

This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.

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