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This work proposes FlexST, a novel pre-training framework that introduces modularity and adaptivity for traffic modeling, and devise a unified periodic encoding strategy that injects resolution- and domain-aware inductive biases to harmonize periodic inconsistencies across datasets.
This workshop seeks to consolidate efforts by providing an interdisciplinary forum for presenting cutting-edge research, sharing deployment experiences, and showcasing real-world systems in this rapidly evolving field of AI Data Scientist.
ClinPRISM, a cost-effective multimodal LLM reasoning framework for question answering over ICTS data, is proposed and achieves state-of-the-art performance on the held-out evaluation benchmark while using only 16 time-series tokens and achieving an average inference latency of 0.15 seconds per question.
As data volumes and analytical demands grow, traditional data science workflows struggle to meet the need for efficiency, scalability, and reliability. The rapid advancement of large language models (LLMs) has opened new possibilities for AI-powered agents to augment or automate end-to-end data science pipelines—from d...
CLINLENS is introduced, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms, which exposes a substantial gap between runnable submissions and correct clinical analyses.
CLIR-Bench is introduced, a benchmark for irregular clinical time series QA constructed from de-identified ICU records through a principled four-stage pipeline, enabling evaluation of both answer accuracy and evidence use.
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