competitive programming platforms

Abstract: The role of coding agents is expanding beyond writing new code toward maintaining existing systems as AI teammates. Yet recent studies show that agent refactorings favor superficial, consistency-oriented edits over structural transformations needed to resolve Technical Debt (TD). We investigate if explicit static-analysis feedback closes this gap. Through the CodeScene MCP server, we give an open-source agent (gptme) access to code smell reports and CodeHealth (CH) scores (1--10, higher is better), and compare guided against unguided refactorings in a paired experiment covering $24{,}620$ single-file competitive programming solutions in C++, Java, and Python. We find that guidance shifts the agent from performing cosmetic edits toward structural operations, raising the rate of effective refactorings by $21$--$26$ percentage points. The benefit depends on initial code quality, with language-dependent break-even points at CH $4.9$--$7.1$, below where the structural transformations pushed by guidance increasingly risk regression, indicating where the agent needs more support to refactor. Guided runs also shift the failure profile toward logical test failures, introduce new coupling smells, and consume $\sim$$3$--$5\times$ more tokens over $\sim$$2\times$ as many interaction turns. Our results indicate that AI teammates can benefit from similar static-analysis feedback that human developers already use, enabling structural transformations toward more effective TD remediation, at a higher token cost and more interaction turns.

Abstract: Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and revision, we need a verifier that can estimate candidate correctness and provide evidence of failure. In competitive programming, however, official judging systems provide authoritative verdicts under finite submission budgets, without revealing failure explanations or failing inputs. To provide informative feedback without consuming official submissions, we formulate a task in which a model judges and diagnoses a candidate program using only the problem statement and source code, with generated counterexamples serving as checkable evidence. To evaluate this task, we build USACO-JUDGE from the USACO 2026 contests, using accepted and unaccepted programs written by human experts and multiple LLMs. We then train ARBITER, a 35B mixture-of-experts (MoE) agentic verifier, with reinforcement learning in a generic execution sandbox. Training raises counterexample success on incorrect programs from 40.61% to 62.67% and mean binary verdict accuracy on USACO-JUDGE from 71.41% to 87.41%, approaching frontier verification performance. Beyond verification, ARBITER outperforms baselines in both code selection and refinement. In an IOI 2026 case study, ARBITER-guided refinement raises GPT-OSS-120B's expected score above the gold-medal threshold under the official submission limit.