Scalable, Symbiotic, AI and Non-AI Agent Based Parallel Discrete Event Simulations
Not on the map. Why?
Abstract (arXiv)
To fully leverage the potential of artificial intelligence (AI) systems in a trustworthy manner, it is desirable to couple multiple AI and non-AI systems together seamlessly for constraining and ensuring correctness of the output. This paper introduces a novel parallel discrete event simulation (PDES) based methodology to combine multiple AI and non-AI agents in a causal, rule-based way. Our approach tightly integrates the concept of passage of time, with each agent considered as an entity in the PDES framework and responding to prior requests from other agents. Such coupling mechanism enables the agents to work in a co-operative environment towards a common goal while many tasks run in parallel throughout the simulation. It further enables setting up boundaries to the outputs of the AI agents by applying necessary dynamic constraints using non-AI agents while allowing for scalability through deployment of hundreds of such agents in a larger compute cluster. Distributing smaller AI agents can enable extremely scalable simulations in the future, addressing local memory bottlenecks for model parameter storage. Within a PDES involving both AI and non-AI agents, we break down the problem at hand into structured steps, when necessary, providing a set of multiple choices to the AI agents, and then progressively solve these steps towards a final goal. At each step, the non-AI agents act as unbiased auditors, verifying each action by the AI agents so that certain rules of engagement are followed. We evaluate our approach by solving four problems from four different domains and comparing the results with those from AI models alone. Our results show greater accuracy in solving problems from various domains where the AI models struggle to solve the problems solely by themselves. Results show that overall accuracy of our approach is 68% where as the accuracy of vanilla models is less than 23%.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 28 May 2025.
Builds on
24 of 72 references.
References · 24
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
- 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
- 2024Mixtral of Expertscited by 1,823
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
- 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
- 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
- 2023Reasoning with Language Model is Planning with World Modelcited by 861
- 2022Mixture-of-Experts with Expert Choice Routingcited by 623
12 more
- 2024OpenMoE: An Early Effort on Open Mixture-of-Experts Language Modelscited by 187
- 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 177
- 2023Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificationcited by 166
- 2024Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solverscited by 138
- 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generationcited by 74
- 2024Learning to Route Among Specialized Experts for Zero-Shot Generalizationcited by 65
- 2024Learning to Decode Collaboratively with Multiple Language Modelscited by 62
- 2023Self-Verification Improves Few-Shot Clinical Information Extractioncited by 52
- 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
- 2018End-to-end Speech Recognition with Word-based RNN Language Modelscited by 23
- 2023GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Taskscited by 14
- 2023Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulationscited by 11
Built on by
Citing papers per year: 2026: 1.
1 paper.