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CP-Agent boosts LLM competitive programming success with feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CP-Agent, a new system designed to improve the performance of large language models in competitive programming tasks. The agent utilizes a calibrated stopped process model to effectively incorporate execution feedback, focusing on reducing false admissions and increasing evidence against incorrect programs. By implementing mechanisms like Dual-Granularity Verification and Test Augmentation, CP-Agent significantly boosts success rates on benchmarks like LiveCodeBench Pro and ICPC-Eval without requiring model parameter updates. AI

IMPACT Enhances LLM capabilities in complex problem-solving, potentially improving agent performance in specialized domains.

RANK_REASON This is a research paper detailing a new method for improving LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

CP-Agent boosts LLM competitive programming success with feedback

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This is a research paper detailing a new method for improving LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Peisong Wang, Bowen Liu, Zehua Li, Yuyao Wang, Zhiwei Ma, Yuhan Li, Jia Li ·

    CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming

    arXiv:2605.24693v1 Announce Type: new Abstract: Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive multi-stage post-training. We study when execution feedback reliably helps an LLM …

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