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ASPIRE achieves a staggering 31% success rate on unseen long-horizon tasks, compared to just 4% for prior methods, highlighting its superior adaptability and efficiency.
Traditional research papers are costing AI agents reproducibility and understanding, but a new "Agent-Native" format that captures the full messy research process boosts performance by up to 20%.
SLMs can leapfrog performance on complex software engineering tasks by learning *when* to ask for help from larger models, achieving a 25% gain on SWE-bench with minimal expert queries.
