ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
Predictive maintenance with large language models and transformer-based survival analysis
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LLMSurvival enables LLM-based survival analysis on tabular data by converting censored time-to-event tasks into pairwise comparisons, yielding small concordance gains over Cox and deep learning baselines on ICU mortality and fracture prediction.
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ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
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Towards end-to-end LLM-based censoring-aware survival analysis
LLMSurvival enables LLM-based survival analysis on tabular data by converting censored time-to-event tasks into pairwise comparisons, yielding small concordance gains over Cox and deep learning baselines on ICU mortality and fracture prediction.