MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation

Researchers introduce MobEvolve, an AI framework that generates realistic human mobility patterns by combining interpretable heuristics with LLM agents that self-evolve through iterative learning. The system outperforms existing deep learning and LLM approaches while maintaining computational efficiency and behavioral plausibility across Singapore and Montreal datasets.

MobEvolve represents a meaningful shift in how AI systems tackle complex real-world modeling tasks that demand both accuracy and explainability. Rather than relying solely on black-box deep neural networks or pure language model approaches, the framework hybridizes behavior-inspired heuristics with agentic LLM refinement, creating a system where each component serves a specific purpose. This design choice addresses a persistent tension in AI: maximizing performance often requires sacrificing interpretability, yet regulatory and practical requirements increasingly demand transparent decision-making.

The problem context is significant. Urban planners, transportation analysts, and epidemiologists depend on accurate human mobility models for infrastructure design, disease spread prediction, and resource allocation. Existing methods fail on at least one critical dimension—deep generative models lack interpretability, pure LLM approaches struggle with scalability and plausibility, and traditional heuristics cannot capture behavioral complexity. MobEvolve's iterative self-evolution mechanism, where the agent diagnoses validation set failures and proposes targeted logic updates, resembles human expert improvement cycles, making the system both theoretically grounded and practically effective.

For AI practitioners and organizations building mobility-dependent systems, this framework offers a template for balancing performance with transparency. The demonstrated superiority across multiple benchmarks and metrics suggests that hybrid approaches combining symbolic reasoning with modern language models may outperform specialized deep learning methods in constrained domains. The emphasis on interpretability and inference efficiency also makes the approach production-ready rather than purely academic, relevant to transportation platforms, smart city initiatives, and logistics companies seeking trustworthy predictions.

• →MobEvolve combines behavior-inspired heuristics with LLM agent self-evolution to generate interpretable human mobility patterns more effectively than existing methods.
• →The system achieves superior performance on individual trajectory fidelity, population-level distribution alignment, and behavioral plausibility while maintaining computational efficiency.
• →Iterative validation-driven evolution allows the framework to diagnose failures and propose targeted improvements, creating a cumulative learning process without manual retraining.
• →The hybrid symbolic-and-neural approach demonstrates that interpretability and state-of-the-art performance are not mutually exclusive in specialized AI applications.
• →Results across Singapore and Montreal benchmarks suggest broad applicability to urban mobility modeling for transportation, infrastructure, and epidemiological planning.