First scale, then enrich: How the right execution strategy helped us reach state-of-the-art on SWE-rebench

A recent study has set a new benchmark with a 60.9% issue resolve rate on the SWE-rebench by revising the conventional approach to context extraction and solution generation in coding agents. Traditionally, the process involves enriching context first and then generating solutions, but the researchers reversed this order, starting with solution generation to better inform context extraction. This new approach, combined with horizontal scaling and focused context enrichment, allows for more precise codebase exploration, significantly enhancing the agent's accuracy without increasing costs. The study highlights how leveraging initial solution rollouts to guide context enrichment reduces blind spots and optimizes the agent's performance beyond the baseline ReAct loop. By maintaining a cost-effective strategy that uses existing computational resources wisely, the researchers demonstrated an improved agent architecture that could serve as a model for developing more accurate and efficient AI software engineers.