OptiArena: Can LLMs Improve Executable Algorithms under Fixed Resource Budgets?

arXiv:2609.32227 · cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.CL

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/WJ-Peng/OptiArena

Project page: https://trinkle23897.github.io

Terminology

• Benchmark — 35× in this paper · explained in 2 episodes
• Calibration — 16× in this paper · explained in 7 episodes
• Trace — 12× in this paper · explained in 2 episodes
• alpha — 8× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Agentic AI — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• In Weak-Start Optimization — 1× in this paper
• Resource Constraints — 1× in this paper · explained in 3 episodes

Sources

• Program Synthesis with Large Language Models
• Evaluating Large Language Models Trained on Code
• OpenSpiel: A Framework for Reinforcement Learning in Games
• Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• AlphaEvolve: A coding agent for scientific and algorithmic discovery
• ProgramBench: Can Language Models Rebuild Programs From Scratch?
• AI co-mathematician: Accelerating mathematicians with agentic AI

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