AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Together, these results show how readout scaling, the data distribution, and training shape sparse learning, and RC-DS provides an information-preserving method for studying and controlling the readout's contribution.
Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data. We use readout-canonicalized decision-space (RC-DS) coordinates to remove singular-value gains while preserving retained decision information. At a fixed readout condition number, changing the generating...
The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Hallucination: an LLM's tendency to issue non-functional mutations with unsubstantiated performance claims on already-optimized code. This is driven by the Evaluation Trap, wher...
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