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Step 8: tool results
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{
"url": "https://arxiv.org/html/2607.27191v2",
"offset": 78000,
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}{"ok":true,"url":"https://arxiv.org/html/2607.27191v2","text":"isted log analysis of the agent logs, and editing the tables and figures. We verified AI outputs at each phase of the project. The core team takes full responsibility for the paper’s contents and all artifacts included with the paper.\nFunding\nWe are grateful to Coefficient Giving and Schmidt Sciences for funding to\nsupport this project, and to OpenAI for providing API credits to evaluate GPT-5.6 Sol.\nReferences\nAhmed et al. (2026)\nM. O. Ahmed, S. A. Amale, R. D. Bhavsar, P. Chopra, A. Jaimes, A. Kachhwah, C. D. Kalotra, P. Li, X. Li, Y. Liao, R. Roy, N. Senthilselvan, Y. Shao, A. D. Sharma, A. Shrivatsan, R. Xue, Y. You, A. Badkul, L. Xie, M. Oet, K. Lee, and A. V. 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Clark\nWhen AI Builds Itself: Our Progress Toward Recursive Self-Improvement, and Its Implications .\nNote: The Anthropic Institute https://www.anthropic.com/institute/recursive-self-improvement ,\naccessed 2026-07-28\nCited by: §1 ,\n§7.2 .\nGarikaparthi et al. (2026)\nA. Garikaparthi, M. Patwardhan, and A. Cohan\nResearchGym: evaluating language model agents on real-world AI research .\narXiv preprint arXiv:2602.15112 .\nExternal Links: Document ,\nLink\nCited by: Table 6 .\nHu et al. (2025)\nS. Hu, C. Lu, and J. Clune\nAutomated design of agentic systems .\nIn The Thirteenth International Conference on Learning\nRepresentations ,\nExternal Links: Link\nCited by: Table 6 .\nHuang et al. (2024)\nQ. Huang, J. Vora, P. Liang, and J. 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Dean\nAI 2027 .\nNote: AI Futures Project https://ai-2027.com/ , accessed 2026-07-28\nCited by: §1 .\nKon et al. (2025)\nP. T. J. Kon, J. Liu, X. Zhu, Q. Ding, J. Peng, J. Xing, Y. Huang, Y. Qiu, J. Srinivasa, M. Lee, M. Chowdhury, M. Zaharia, and A. Chen\nEXP-Bench: can AI conduct AI research experiments? .\narXiv preprint arXiv:2505.24785 .\nExternal Links: Document ,\nLink\nCited by: Table 6 .\nLiu et al. (2025)\nY. Liu, Y. Nan, W. Xu, X. Hu, L. Ye, Z. Qin, and P. Liu\nAlphaGo moment for model architecture discovery .\narXiv preprint arXiv:2507.18074 .\nExternal Links: Document ,\nLink\nCited by: Table 6 .\nLu et al. (2024)\nC. Lu, C. Lu, R. T. Lange, J. Foerster, J. Clune, and D. Ha\nThe AI scientist: towards fully automated open-ended scientific discovery .\narXiv preprint arXiv:2408.06292 .\nExternal Links: Document ,\nLink\nCited by: Table 6 .\nLupidi et al. (2026)\nA. Lupidi, B. Gauri, T. S. Foster, B. A. Omari, D. Magka, A. Pepe, A. Audran-Reiss, M. Aghamelu, N. Baldwin, L. 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Orona, S. Rabanser, T. Bayer, A. Shetty, Y. Ling, D. Chan-Sew, R. Nakagawa, S. Utpala, Z. S. Siegel, and A. Narayanan\nLife after benchmark saturation: a case study of CORE-Bench .\narXiv preprint arXiv:2606.26158 .\nExternal Links: Document ,\nLink\nCited by: §7.1 .\nNathani et al. (2025)\nD. Nathani, L. Madaan, N. Roberts, N. Bashlykov, A. Menon, V. Moens, A. Budhiraja, D. Magka, V. Vorotilov, G. Chaurasia, D. Hupkes, R. S. Cabral, T. Shavrina, J. Foerster, Y. Bachrach, W. Y. Wang, and R. Raileanu\nMLGym: a new framework and benchmark for advancing AI research agents .\narXiv preprint arXiv:2502.14499 .\nExternal Links: Document ,\nLink\nCited by: Table 6 .\nNeurIPS (2026)\nNeurIPS\nNeurIPS 2026 reviewing guidelines .\nNote: https://neurips.cc/Conferences/2026/ReviewerGuidelines Accessed: 2026-07-29\nCited by: §2 .\nNovikov et al. (2025)\nA. Novikov, N. Vũ, M. Eisenberger, E. Dupont, P. Huang, A. Z. Wagner, S. Shirobokov, B. Kozlovskii, F. J. R. Ruiz, A. Mehrabian, M. P. Kumar, A. See, S. Chaudhuri, G. Holland, A. Davies, S. Nowozin, P. Kohli, and M. Balog\nAlphaEvolve: a coding agent for scientific and algorithmic discovery .\narXiv preprint arXiv:2506.13131 .\nExternal Links: Document ,\nLink\nCited by: Table 2 ,\nTable 6 .\nOpenAI (2026a)\nOpenAI\nGPT-5.6 Launch Livestream .\nNote: YouTube https://www.youtube.com/watch?v=Wq45rvPGNHs , at 20:45;\naccessed 2026-07-28\nCited by: §1 .\nOpenAI (2026b)\nOpenAI\nGPT-5.6 System Card .\nNote: https://deploymentsafety.openai.com/gpt-5-6 , accessed 2026-07-28\nCited by: footnote 1 .\nOpenAI (2026c)\nOpenAI\nIntroducing LifeSciBench .\nNote: https://openai.com/index/introducing-life-sci-bench/\nCited by: Table 6 .\nOu and Burnham (2026)\nB. Ou and G. Burnham\nAI doesn’t get better at this board game with practice .\nNote: Accessed: 2026-08-05\nExternal Links: Link\nCited by: §5.2 .\nPostTrainBench Team (2026)\nPostTrainBench Team\nPostTrainBench Leaderboard .\nNote: https://posttrainbench.com/ , accessed 2026-07-28\nCited by: §5.4 .\nPrime Intellect Team (2026)\nPrime Intellect Team\nAutonomous AI research for nanogpt speedrun .\nPrime Intellect Blog .\nNote: Published May 14, 2026\nExternal Links: Link\nCited by: Table 2 ,\n§1 ,\nTable 6 .\nRank ","chars":10000,"total_chars":124208,"offset":78000,"truncated":true,"next_offset":88000}