arXiv:2606.18803v1 Announce Type: new Abstract: Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e.g., a driver's habitual aversion to certain regions) are inherently contextual and naturally expressible as LLM-generated user profiles. However, scaling such profiling to a live, millisecond-latency dispatcher faces three intertwined c
The increasing maturity of Large Language Models (LLMs) and the growing need for more nuanced, real-time decision-making in complex logistical systems like ride-hailing make this development timely.
This item highlights the practical application of LLMs beyond mere text generation, demonstrating their utility for complex feature extraction and personalized real-time operations, which can significantly enhance efficiency and user experience.
Production matching pipelines in industrial logistics can now move beyond structured numerical features to incorporate rich, contextual behavioral signals derived from LLM-generated profiles, enabling more adaptive and personalized dispatch decisions.
- · Ride-hailing platforms
- · LLM developers
- · Data scientists in logistics
- · End consumers (better service)
- · Traditional feature engineering approaches
- · Companies relying solely on static user profiles
LLMs are integrated into real-time operational systems for dynamic user profiling and decision support.
Ride-hailing services become significantly more efficient and personalized, leading to increased customer satisfaction and market share for early adopters.
The methodology is widely adopted across other complex logistical and service industries, accelerating the deployment of agentic AI systems for user understanding and personalized service delivery.
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Read at arXiv cs.AI