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ProfiLLM enhances ride-hailing dispatch with agentic LLM profiling

Researchers have developed ProfiLLM, a novel agentic LLM data pipeline designed to enhance industrial ride-hailing dispatch systems. This system addresses challenges in processing massive datasets by using tool-augmented knowledge mining and utility-aligned profile exploration. When deployed on DiDi's platform, ProfiLLM demonstrated significant improvements, including a +6.14% relative AUC increase in outcome prediction and a +4.35% GMV gain in simulations. AI

IMPACT Proposes a novel method for applying LLMs to large-scale industrial data, potentially improving efficiency in logistics and dispatch systems.

RANK_REASON This is a research paper detailing a new method for applying LLMs to a specific industrial problem.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Tengfei Lyu, Zirui Yuan, Xu Liu, Kai Wan, Zihao Lu, Li Ma, Hao Liu ·

    ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

    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 re…

  2. arXiv cs.AI TIER_1 English(EN) · Hao Liu ·

    ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

    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,…