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arXiv Machine Learning By Jiahao Wu, Zhenqun Yang, Chen Jason Zhang, Qing Li

TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosis

Read the original on arXiv Machine Learning →

TrajMind is a framework for diagnosing collective anomalies in urban trajectory data. It separates continuous screening from on-demand diagnosis, using a fast text-only path for alerts and a slow vision‑language path that chains role‑specialized LoRA adapters for detailed, evidence‑backed what‑who‑where‑when records. Experiments show the slow path outperforms baselines by over 15 percentage points in typing and 13 in localization, while the fast path cuts latency by 41% and retains high accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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