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competitive_programming_agents [2026/03/25 14:50] – Create page: LLM agents for competitive programming agentcompetitive_programming_agents [2026/03/30 22:20] (current) – Restructure: footnotes as references agent
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 ===== Overview ===== ===== Overview =====
  
-Large language models can generate syntactically correct code, but competitive programming demands more: solutions must satisfy strict time and memory budgets while solving algorithmically complex problems. Two key research directions have emerged. SwiftSolve introduces a multi-agent framework with complexity-aware profiling and repair. AetherCode provides a rigorous benchmark revealing that even frontier LLMs struggle with elite-level competition problems.+Large language models can generate syntactically correct code, but competitive programming demands more: solutions must satisfy strict time and memory budgets while solving algorithmically complex problems. Two key research directions have emerged. SwiftSolve introduces a multi-agent framework with complexity-aware profiling and repair(([[https://arxiv.org/abs/2510.22626|Singh et al. "SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming" (2025)]])). AetherCode provides a rigorous benchmark revealing that even frontier LLMs struggle with elite-level competition problems(([[https://arxiv.org/abs/2508.16402|"AetherCode: Evaluating LLMs Ability to Win in Premier Programming Competitions" (2025)]])).
  
 ===== SwiftSolve: Complexity-Aware Multi-Agent Framework ===== ===== SwiftSolve: Complexity-Aware Multi-Agent Framework =====
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 <latex>\log T(n) = s \cdot \log n + b</latex> <latex>\log T(n) = s \cdot \log n + b</latex>
  
-where $T(n)$ is the measured runtime, $n$ is the input size, and the slope $s$ determines the empirical complexity class (e.g., $s pprox 1$ implies $O(n)$, $s pprox 2$ implies $O(n^2)$). The fit quality is measured by $R^2$, with an LLM fallback when $R^2$ is low.+where $T(n)$ is the measured runtime, $n$ is the input size, and the slope $s$ determines the empirical complexity class (e.g., $s \approx 1$ implies $O(n)$, $s \approx 2$ implies $O(n^2)$). The fit quality is measured by $R^2$, with an LLM fallback when $R^2$ is low.
  
 The efficiency metric extends the standard pass@k: The efficiency metric extends the standard pass@k:
  
-<latex> ext{eff@k} =  rac{|\{i :  ext{correct}_i \wedge T_i \leq T_{limit} \wedge M_i \leq M_{limit}\}|}{k}</latex>+<latex>\text{eff@k} = \frac{|\{i : \text{correct}_i \wedge T_i \leq T_{limit} \wedge M_i \leq M_{limit}\}|}{k}</latex>
  
 A controller enforces iteration caps and diminishing-returns stopping criteria across the agent loop. A controller enforces iteration caps and diminishing-returns stopping criteria across the agent loop.
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 | SwiftSolve | Mean runtime overhead | 12.4s vs 6.8s (single agent) | | SwiftSolve | Mean runtime overhead | 12.4s vs 6.8s (single agent) |
 | AetherCode | Best Pass@1 (Extreme) | 3.8% (o4-mini-high) | | AetherCode | Best Pass@1 (Extreme) | 3.8% (o4-mini-high) |
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-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2510.22626|Singh et al. "SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming" (2025)]] 
-  * [[https://arxiv.org/abs/2508.16402|"AetherCode: Evaluating LLMs Ability to Win in Premier Programming Competitions" (2025)]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[game_playing_agents|Game Playing Agents]]   * [[game_playing_agents|Game Playing Agents]]
  
 +===== References =====
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