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This paper proposes a statistical model to efficiently estimate uncertainty dynamics in text generation, smoothing noisy resampling data to significantly reduce computational costs while maintaining accuracy in analyzing LLM reasoning chains.
This article recommends the top 10 skills and tools for social science research, including Auto-Empirical-Research-Skills developed by the Stanford team, for using AI agents to conduct empirical research and write papers.
This paper introduces VESTA, a framework that equips vision-language models with dynamically growing toolkits for data exploration and statistical model refinement, outperforming prior agent-based methods on complex scientific modeling tasks. The authors also present Dawn, a benchmark for distribution fitting and time series modeling, including real-world astronomy challenges.
The paper introduces RGxEStat, a lightweight interactive tool that applies mixed-effect models to analyze gene-environment interactions, offering breeders a user-friendly alternative to complex SAS/R programming.