Competitive Programming Solutions
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CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
arXiv:2602.20213v2 Announce Type: replace Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an automated agent framework dedicated to generating targeted adversarial test cases that expose latent vulnerabilities in program submissions.
Where Do Large Language Models Fail on Competitive Programming? A Taxonomy of Failures by Algorithm Type and Difficulty Rating
arXiv:2606.05228v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate increasing proficiency on competitive programming benchmarks, yet technical reports predominantly publish aggregate pass rates, obscuring domain-specific vulnerabilities. We present a systematic empirical study of LLM failure patterns using a balanced taxonomy of 315 Codeforces problems across seven algorithm categories and three difficulty tiers. We evaluate GPT-4o and Claude Sonnet 4.6 under strict...
CodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models
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Cast a Wider Net: Coordinated Pass@K Policy Optimization for Code Reasoning
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Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compression
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The need for a socialist planned economy (2021)
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Jan. 6 rally organizer getting another $1.2 million from taxpayers to promote ‘Trump Accounts’
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