arXiv:2605.28420v1 Announce Type: new Abstract: While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the `class-symmetric' nature of these standard losses fundamentally limits the ability of ML models to exploit structural relationships between classes, particularly when facing structured noise. We propose \textsc{Conveyance}, a new classification approach and associated loss function tailored to structured class spaces. It allows users to encode g
The rapid evolution of complex ML architectures necessitates more sophisticated loss functions to fully leverage structural relationships inherent in data and address limitations of generic approaches.
This development improves machine learning's ability to handle complex, structured data more effectively, leading to more robust and accurate AI models across various applications, including potentially for agentic systems.
Machine learning models can now better account for and exploit structural relationships between classes, moving beyond 'class-symmetric' limitations of traditional loss functions.
- · AI developers
- · ML research institutions
- · Industries with complex, structured data (e.g., biotech, finance)
- · Legacy ML frameworks
- · Systems reliant on structure-agnostic loss optimization
Improved performance and accuracy of AI models in applications with structured classification.
Faster development and deployment of advanced AI agents capable of understanding nuanced relationships.
Enhanced automation and decision-making in complex environments, potentially impacting white-collar workflows.
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Read at arXiv cs.LG