Kavli Affiliate: Ke Wang
| First 5 Authors: Xiaocai Zhang, Xiaocai Zhang, , ,
| Summary:
In this study, a modular, data-free pipeline for multi-label intention
recognition is proposed for agentic AI applications in transportation. Unlike
traditional intent recognition systems that depend on large, annotated corpora
and often struggle with fine-grained, multi-label discrimination, our approach
eliminates the need for costly data collection while enhancing the accuracy of
multi-label intention understanding. Specifically, the overall pipeline, named
DMTC, consists of three steps: 1) using prompt engineering to guide large
language models (LLMs) to generate diverse synthetic queries in different
transport scenarios; 2) encoding each textual query with a Sentence-T5 model to
obtain compact semantic embeddings; 3) training a lightweight classifier using
a novel online focal-contrastive (OFC) loss that emphasizes hard samples and
maximizes inter-class separability. The applicability of the proposed pipeline
is demonstrated in an agentic AI application in the maritime transportation
context. Extensive experiments show that DMTC achieves a Hamming loss of 5.35%
and an AUC of 95.92%, outperforming state-of-the-art multi-label classifiers
and recent end-to-end SOTA LLM-based baselines. Further analysis reveals that
Sentence-T5 embeddings improve subset accuracy by at least 3.29% over
alternative encoders, and integrating the OFC loss yields an additional 0.98%
gain compared to standard contrastive objectives. In conclusion, our system
seamlessly routes user queries to task-specific modules (e.g., ETA information,
traffic risk evaluation, and other typical scenarios in the transportation
domain), laying the groundwork for fully autonomous, intention-aware agents
without costly manual labelling.
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