Building Resilient AI: A New Loss Function for Error-Tolerant Neural Networks

A novel training approach enhances the robustness of quantized neural networks against bit errors, paving the way for more reliable deployment in resource-constrained environments.

/|\text{Baseline}-\text{Human}|[/latex]- achieved by large language models on the ASAP-SAS dataset; a transformer ensemble (point A, Ormerod, 2022) and a fine-tuned GPT ensemble (point B, Ormerod & Kwako, 2024) progressively surpass earlier baselines-with a subsequent GPT-4 implementation (point C, Jiang & Bosch, 2024) projected to reach a normalized score of -1.52-indicating a substantial, ongoing refinement in automated assessment capabilities.](https://arxiv.org/html/2603.04820v1/2603.04820v1/x1.png)





