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Research#llm📝 BlogAnalyzed: Dec 27, 2025 22:31

[D] NOMA update: reproducible self-growing XOR benchmark (shared init, N=10) + optimizer-state “preserve vs reset” ablation

Published:Dec 27, 2025 22:14
1 min read
r/MachineLearning

Analysis

This post details an update on NOMA, a system language and compiler focused on implementing reverse-mode autodiff as a compiler pass. The key addition is a reproducible benchmark for a "self-growing XOR" problem. This benchmark allows for controlled comparisons between different implementations, focusing on the impact of preserving or resetting optimizer state during parameter growth. The use of shared initial weights and a fixed growth trigger enhances reproducibility. While XOR is a simple problem, the focus is on validating the methodology for growth events and assessing the effect of optimizer state preservation, rather than achieving real-world speed.
Reference

“The goal here is methodology validation: making the growth event comparable, checking correctness parity, and measuring whether preserving optimizer state across resizing has a visible effect.”

Permalinkr/MachineLearning
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:22

SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis

Published:Dec 8, 2025 00:43
1 min read
ArXiv

Analysis

The article introduces SGEMAS, a novel approach for unsupervised online anomaly detection. The core concept revolves around a self-growing, ephemeral multi-agent system that leverages entropic homeostasis. This suggests a focus on adaptability and resilience in identifying unusual patterns within data streams. The use of 'ephemeral' agents implies a dynamic and potentially resource-efficient system. The 'entropic homeostasis' aspect hints at a mechanism for maintaining stability and detecting deviations from the norm. Further analysis would require examining the specific algorithms and experimental results presented in the ArXiv paper.
Reference

“Further analysis would require examining the specific algorithms and experimental results presented in the ArXiv paper.”

PermalinkArXiv