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COMPAS method optimizes code generation by jointly tuning models, prompts, and settings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed COMPAS, a novel method for optimizing code generation by jointly searching over models, prompts, and decoding settings. This difficulty-aware approach learns group-specific quality-cost fronts, allowing it to route test tasks to the most efficient configuration without additional search. COMPAS significantly improves performance on benchmarks like LiveCodeBench and SWE-bench, increasing pass@1 rates and task resolution while substantially reducing costs. AI

IMPACT This method could lead to more efficient and cost-effective AI-powered code generation tools.

RANK_REASON The cluster contains a research paper detailing a new method for code generation optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COMPAS method optimizes code generation by jointly tuning models, prompts, and settings

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The cluster contains a research paper detailing a new method for code generation optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova, Dong Huang, Mohammad Reza Mousavi, Mark Harman ·

    COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation

    arXiv:2608.04336v1 Announce Type: cross Abstract: Code generation systems make each LLM call with a model, a prompt, and decoding settings. However, existing optimization methods usually tune only part of these choices or use one fixed configuration for all tasks: global optimize…

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