Conflict-Core Extraction for Explainable Package Version Planning with Language Models
DOI:
https://doi.org/10.66238/fsrma122Keywords:
Package Management, Conflict Resolution, Large Language Models, Explainable Artificial IntelligenceAbstract
Modern software development relies heavily on open-source ecosystems, necessitating robust package management systems to handle complex dependency graphs. As applications scale, dependency networks often become deeply nested, leading to version conflicts that are computationally difficult to resolve and conceptually challenging for human developers to understand. Traditional package managers utilize Boolean satisfiability solvers to guarantee correct version planning; however, when constraints are unsatisfiable, these solvers typically output cryptic error messages. This lack of explainability creates a significant bottleneck in software engineering workflows. This paper proposes a novel framework for explainable package version planning by combining Conflict-Core Extraction with Large Language Models. By isolating the Minimal Unsatisfiable Subset of dependency constraints, the proposed system distills massive dependency graphs into a precise conflict core. This core is then translated into natural language prompts and processed by language models to generate highly contextual, human-readable explanations and actionable resolution strategies. Through extensive empirical analysis across multiple open-source ecosystems, we demonstrate that this methodology significantly reduces the cognitive load required to debug dependency conflicts. The results indicate a substantial improvement in diagnostic accuracy and a marked decrease in resolution time compared to traditional package management diagnostic outputs
References
1. Zhao, R., Tang, J., Zeng, W., Chen, Z., & Zhao, X. (2024, October). Zero-shot knowledge graph question generation via multi-agent llms and small models synthesis. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 3341-3351).
2. Huang, Y., Thede, L., Mancini, M., Xu, W., & Akata, Z. (2025, September). Investigating structural pruning and recovery techniques for compressing multimodal large language models: An empirical study. In DAGM german conference on pattern recognition (pp. 320-336). Cham: Springer Nature Switzerland.
3. Zhang, W., & Pei, S. (2026). Predictive prefetching for retrieval-augmented generation. Proceedings of Machine Learning Research. The Forty-third International Conference on Machine Learning, ICML 2026. https://par.nsf.gov/biblio/10686293
4. Wang, L., Shi, C., Du, S., Tao, Y., Shen, Y., Zheng, H., & Qiu, X. (2024, December). Performance Review on LLM for solving leetcode problems. In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM) (pp. 1050-1054). IEEE.
5. Sang, Y. (2025, July). Towards explainable rag: Interpreting the influence of retrieved passages on generation. In 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) (pp. 397-400). IEEE.
6. Sang, Y. (2025, October). AutoCrit: A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought. In 2025 6th International Conference on Machine Learning and Computer Application (ICMLCA) (pp. 1177-1180). IEEE.
7. Tang, J., Wang, Z., Gong, Z., Yu, J., Zhu, X., & Yin, J. (2025, April). Multi-grained query-guided set prediction network for grounded multimodal named entity recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 25246-25254).
8. Tang, J., Yang, Y., Yu, J., Wang, Z. X., Liang, H., Yao, L., & Yin, J. (2025, November). Unco: Uncertainty-driven collaborative framework of large and small models for grounded multimodal ner. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 7644-7662).
9. Zhang, Y., Zhao, M., Zhang, Y., & Cheung, Y. M. (2025). Trending applications of large language models: A user perspective survey. IEEE Transactions on Artificial Intelligence.
10. Su, Yiyun, et al. "Agentic-SQL Taxonomy: A Survey of Autonomous and Interactive Text-to-SQL with LLMs." (2026).
11. Cao, Y., Jing, L., Wang, Y., Shi, D., Yu, C., & Xing, J. (2026, June). Dual-Pathway Diffusion for Hand Correction in Synthetic Portraits: Global Context Aware and Local Structure Refinement. In Proceedings of the 2026 International Conference on Multimedia Retrieval (pp. 1899-1907).
12. Zhu, R., Ma, Z., Wu, J., Gao, J., Wang, J., Lin, D., & He, C. (2025, April). Utilize the flow before stepping into the same river twice: Certainty represented knowledge flow for refusal-aware instruction tuning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 26157-26165).
13. Qiu, Z., Qiu, K., Lyu, H., Xiong, W., & Luo, J. (2024, December). Semantics preserving emoji recommendation with large language models. In 2024 IEEE International Conference on Big Data (BigData) (pp. 7131-7140). IEEE.
14. Wang, H., Xu, Q., Liu, C., Wu, J., Lin, F., & Chen, W. (2026, April). Emergent hierarchical reasoning in llms through reinforcement learning. In International Conference on Learning Representations (Vol. 2026, pp. 74519-74543).
15. Wang, Z. (2026). StreamReason-Bench: Can Large Language Models Reason about Event-Time Stream-Processing Semantics?. arXiv preprint arXiv:2608.12348.
16. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
17. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of ICLR.
18. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
19. Zhao, R., Xu, D., Jian, S., Tan, T., Sun, X., & Zhang, W. (2023, July). Quadratic Exponential Decrease Roll-Back: An Efficient Gradient Update Mechanism in Proximal Policy Optimization. In 2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) (pp. 65-70). IEEE.
20. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.
21. Li, S. (2025). Momentum, volume and investor sentiment study for us technology sector stocks—A hidden markov model based principal component analysis. PloS one, 20(9), e0331658.
22. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
23. Liang, R., Xu, S., Xie, C., Chen, J., Ren, F., Yang, S., & Yabe, T. (2026). Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency. Advances in Neural Information Processing Systems, 38, 99445-99471.
24. Liu, X., Yu, Z., Yao, Y., Yang, K., Zeng, H., & Chen, C. P. (2026). A Comprehensive Survey of Cross-Modal Retrieval: Breaking Through Modal Barriers. ACM Computing Surveys.
25. Zhang, Q., Yang, M., Sun, G., Xiang, Y., Wang, S., Zhang, J., & Li, S. (2026). Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics. Energy Storage Materials, 104897.
26. Wang, Y., Wang, Z., Kim, E. H., & Fu, Z. FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting. In Forty-third International Conference on Machine Learning.
27. Ghosh, R., Jia, X., Yin, L., Lin, C., Jin, Z., & Kumar, V. (2022, November). Clustering augmented self-supervised learning: an application to land cover mapping. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems (pp. 1-10).
28. Wang, Jiacheng, et al. "A Dynamic Factor Gating Architecture with Market Regime Awareness for Stock Return Forecasting." (2026).
29. Lyu, M., Torii, R., Liang, C., Li, Q., Wang, X., Ventikos, Y., & Chen, D. (2025). In silico comparison of two non-invasive pre-procedural virtual coronary revascularisation techniques for personalised cardiovascular medicine. Computer Methods and Programs in Biomedicine, 109046.
30. Liu, Z., Ye, Z., & Zhang, K. (2025). Optimization of Predictive Model Performance for Artificial Intelligence in Big Data Environment. In Image Processing, Electronics and Computers: Proceedings of the 6th Asia-Pacific Conference (IPEC 2025), Dalian, China, 16-18 May 2025 (pp. 1870-1878). 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP: SAGE Publications.
31. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Proceedings of ICLR.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gabriela Costa Soares, Larissa Monteiro Silva
This work is licensed under a Creative Commons Attribution 4.0 International License.
License Terms (Recommended – CC BY 4.0)
All articles published in Fundamental Scientific Reports in Multidisciplinary Areas are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly cited. Users are allowed to copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial use, as long as appropriate credit is given.
Authors retain copyright of their work while granting the journal the right of first publication. The journal provides immediate open access to its content on the principle that making research freely available supports a greater global exchange of knowledge.
For more information about this license, visit:
https://creativecommons.org/licenses/by/4.0/
