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Recent research builds various patching agents that combine large language models (LLMs) with non-ML tools and achieve promising results on the state-of-the-art (SOTA) software patching benchmark, SWE-bench.
Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al · 2011
Earlier work this paper cites.
Program synthesis using conflict-driven learning
Feng, Y., Martins, R., Bastani, O., and Dillig, I · 2018
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Automatic software repair: A survey
Gazzola, L., Micucci, D., and Mariani, L · 2018
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Automatic software repair: A bibliography
Monperrus, M · 2018
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Using safety properties to generate vulnerability patches
Huang, Z., Lie, D., Tan, G., and Jaeger, T · 2019
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Cure: Code-aware neural machine translation for automatic program repair
Jiang, N., Lutellier, T., and Tan, L · 2021
Earlier work this paper cites.
Automatic program repair
Le Goues, C., Pradel, M., Roychoudhury, A., and Chandra, S · 2021
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CrossHair: An analysis tool for python that blurs the line between testing and type systems, 2022
Schanely, P · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Earlier work this paper cites.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Earlier work this paper cites.
Better patching using llm prompting, via self-consistency
Ahmed, T. and Devanbu, P · 2023
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Claude family, 2023
Anthropic · 2023
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Text-to-audio generation using instruction-tuned llm and latent diffusion model
Ghosal, D., Majumder, N., Mehrish, A., and Poria, S · 2023
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Swe-bench: Can language models resolve real-world github issues?
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K · 2023
Earlier work this paper cites.
Llm-grounded video diffusion models
Lian, L., Shi, B., Yala, A., Darrell, T., and Li, B · 2023
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A study of generative large language model for medical research and healthcare
Peng, C., Yang, X., Chen, A., Smith, K. E., PourNejatian, N., Costa, A. B., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., et al · 2023
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Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
Cited alongside, same era.
Aider, 2024
Aider · 2024
Cited alongside, same era.
Amazon q, 2024
Amazon · 2024
Cited alongside, same era.
Raising the bar on swe-bench verified with claude 3.5 sonnet, 2024
anthropic · 2024
Cited alongside, same era.
Introducing the next generation of Claude
Anthropic · 2024
Audiogpt: Understanding and generating speech, music, sound, and talking head
Huang, R., Li, M., Yang, D., Shi, J., Chang, X., Ye, Z., Wu, Y., Hong, Z., Huang, J., Liu, J., et al · 2024
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Ibm research swe-1.0, 2024
IBM · 2024
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Marscode agent: Ai-native automated bug fixing
Liu, Y., Gao, P., Wang, X., Liu, J., Shi, Y., Zhang, Z., and Peng, C · 2024
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Ma, Y., Cao, R., Cao, Y., Zhang, Y., Chen, J., Liu, Y., Liu, Y., Li, B., Huang, F., and Li, Y · 2024
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moatless, 2024
moatless · 2024
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Cited alongside, same era.
Swe-search: Enhancing software agents with monte carlo tree search and iterative refinement
Antoniades, A., Örwall, A., Zhang, K., Xie, Y., Goyal, A., and Wang, W · 2024
Cited alongside, same era.
Masai: Modular architecture for software-engineering ai agents
Arora, D., Sonwane, A., Wadhwa, N., Mehrotra, A., Utpala, S., Bairi, R., Kanade, A., and Natarajan, N · 2024
Cited alongside, same era.
Coder, 2024
CodeR · 2024
Cited alongside, same era.
Codestory midwit agent, 2024
CodeStory · 2024
Cited alongside, same era.
Composio swe-kit, 2024
Composio · 2024
Cited alongside, same era.
Fast ML Inference, Simple API
deepinfra · 2024
Cited alongside, same era.
Ouyang, S., Yu, W., Ma, K., Xiao, Z., Zhang, Z., Jia, M., Han, J., Zhang, H., and Yu, D · 2024
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Specrover: Code intent extraction via llms
Ruan, H., Zhang, Y., and Roychoudhury, A · 2024
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., and Yao, S · 2024
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Magis: Llm-based multi-agent framework for github issue resolution
Tao, W., Zhou, Y., Wang, Y., Zhang, W., Zhang, H., and Cheng, Y · 2024
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The AI Acceleration AccelerationCloud
together.ai · 2024
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OpenAI’s o3 vs o1: The Dawn of Hyper-Intelligent AI
Under, C. D · 2024
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Agentless: Demystifying llm-based software engineering agents
Xia, C. S., Deng, Y., Dunn, S., and Zhang, L · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence
Zhu, Q., Guo, D., Shao, Z., Yang, D., Wang, P., Xu, R., Wu, Y., Li, Y., Gao, H., Ma, S., et al · 2024
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Deepseek-r1 release, 2025
DeepSeek · 2025
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