#statistical-rigor News & Analysis

2 articles tagged with #statistical-rigor. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBearisharXiv – CS AI · Jun 237/10

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How Much Coordination Gain Is Real? A Paired Noise-Floor Protocol for Multi-Agent LLM Benchmarks

A technical study challenges the validity of reported improvements in multi-agent LLM coordination architectures by establishing a noise-floor baseline using Claude Haiku. The research reveals that paired configuration-equivalent trials produce statistical gaps of ±5pp at best, suggesting that seven of ten recent coordination papers report headline effects within or below this noise floor, raising questions about reproducibility and the actual gains from proposed architectures.

🧠 Claude🧠 Haiku

AINeutralarXiv – CS AI · May 96/10

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CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors

Researchers introduce CatNet, an algorithm that controls False Discovery Rate (FDR) in LSTM neural networks by combining SHAP feature importance derivatives with a Gaussian Mirror statistical approach. The method addresses overfitting and model interpretability challenges in time-series deep learning through improved feature selection and a novel kernel-based independence measure.