Recursive Adversarial Engine: A Stability-Oriented Architecture for Reliable Large Language Model Multi-Agent Systems
Keywords:
Convergence, Hallucination, Autonomy, Checks and balances, Robustness, InterpretabilityAbstract
Large language model (LLM) based multi-agent systems are rapidly migrating from laboratory prototypes to production deployments in finance, scientific research, industrial digital twins, and urban scheduling. As deployment scales, however, a family of systemic pathologies persists: decision oscillation driven by zero-sum agent interactions, cascade propagation of hallucinated content, long-horizon behavioral drift, value misalignment, and unresolved inter-agent conflict. Existing orchestration paradigms, which rely on instruction dispatch, greedy optimization, or static rules, address only shallow coordination and provide no principled mechanism for convergence. This paper proposes the Recursive Adversarial Engine (RAE), a runtime architecture for multi-agent systems organized around three propositions: bounded adversarial interaction supplies the error-detection signal, recursive correction supplies the error-elimination mechanism, and dynamic checks and balances supply the stability guarantee. We specify a four-layer reference architecture comprising adversarial verification, recursive correction, checks-and-balances scheduling, and steady-state output, and we characterize its convergence behavior through a Lyapunov-style stability argument. A qualitative comparison with self-play, multi-agent debate, alignment, and consensus frameworks delineates the paradigm difference. Engineering implications for high-stakes domains, long-horizon robustness, auditability, and middleware integration are analyzed, and open challenges are identified.
References
[1]Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680.
[2]Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2024). Improving factuality and reasoning in language models through multiagent debate. In Proceedings of the 41st International Conference on Machine Learning (ICML).
[3]Cemri, M., Pan, M. Z., Yang, S., Agrawal, L. A., Chopra, B., Tiwari, R., Keutzer, K., Parameswaran, A., & Song, D. (2025). Why do multi-agent LLM systems fail? arXiv preprint arXiv:2503.13657.
[4]Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-35.
[5]Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., & Liu, T. (2023). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232.
[6]Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems (Vol. 30).
[7]Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., et al. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073.
[8]Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (Vol. 27).
[9]Mescheder, L., Geiger, A., & Nowozin, S. (2018). Which training methods for GANs do actually converge? In Proceedings of the 35th International Conference on Machine Learning (ICML).
[10]Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning (ICML).
[11]Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved training of Wasserstein GANs. In Advances in Neural Information Processing Systems (Vol. 30).
[12]Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Advances in Neural Information Processing Systems (Vol. 30).
[13]Irving, G., Christiano, P., & Amodei, D. (2018). AI safety via debate. arXiv preprint arXiv:1805.00899.
[14]Ganguli, D., Hernandez, D., Lovitt, L., Askell, A., Bai, Y., Chen, A., et al. (2022). Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858.
[15]Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Gladstone, A., Irving, G., & Christiano, P. (2022). Red teaming language models with language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP).
[16]Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
[17]Hendrycks, D., Carlini, N., Schulman, J., & Steinhardt, J. (2021). Unsolved problems in machine learning safety. arXiv preprint arXiv:2109.13916.
[18]Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.
[19]Lyapunov, A. M. (1992). The general problem of the stability of motion. International Journal of Control, 55(3), 531-534.
[20]Slotine, J. J. E., & Li, W. (1991). Applied nonlinear control. Prentice Hall.
[21]Khalil, H. K. (2002). Nonlinear systems (3rd ed.). Prentice Hall.
[22]Varela, F. J. (1979). Principles of biological autonomy. North-Holland.
[23]Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review A, 38(1), 364-374.
[24]Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230-265.
[25]Banach, S. (1922). Sur les opérations dans les ensembles abstraits et leur application aux équations intégrales. Fundamenta Mathematicae, 3, 133-181.
[26]Hofstadter, D. R. (1979). Gödel, Escher, Bach: An eternal golden braid. Basic Books.
