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  "text": "Outstanding paper on long-horizon agents.\n\n(bookmark it)\n\nSimilar to humans, how do you make agents persist on a difficult task, and how is that useful?\n\nAnd which models today work well on this?\n\nThis new work, AutoLab, explores this question and how encoding persistence in agents is beneficial for tasks such as auto research and engineering tasks.\n\nCan a model keep improving an artifact for hours, under a strict wall-clock budget, the way real research and engineering actually work?\n\nResults:\n\nAutoLab hands agents 36 expert-curated tasks across system optimization, model development, CUDA kernels, and puzzles, each starting from a correct but deliberately suboptimal baseline.\n\nAcross 17 frontier models, the dominant predictor of success was not the quality of the first attempt. It was persistence, repeatedly benchmarking, editing, and folding in empirical feedback.\n\nIt appears that Claude-opus-4.6 sustained that loop well. Most of the other models quit early or burned the budget, making almost no progress.\n\nPaper: https://t.co/jb8uYR0fpE\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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