Draft:ROME (Recursive Optimization for Model Enhancement)
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Last edited by Edan.AIS (talk | contribs) 5 months ago. (Update) |
| ROME | |
|---|---|
| Developer(s) | Analytic Intelligence Solutions |
| Initial release | October 2024 |
| Written in | Language-agnostic |
| Platform | Cross-platform |
| License | Proprietary |
| Website | analyticintelligencesolutions |
ROME (Recursive Optimization for Model Enhancement) is a framework for intent recognition in artificial intelligence systems, developed by Analytic Intelligence Solutions in 2024. It uses recursive optimization to improve pattern matching and context understanding through iterative refinement of model parameters.
Overview
ROME was developed as a response to challenges in production intent recognition systems, particularly issues with contextual adaptation, pattern recognition accuracy, and computational overhead. The framework introduces three key innovations:
Dynamic context preservation with temporal decay Adaptive threshold optimization Multi-dimensional pattern scoring
Technical details
Architecture
The framework implements a context preservation function C(t) that processes current utterances and recognition routes:
- C(t) = f(u₁...uₙ, r₁...rₙ) × λᵗ
where λ represents a temporal decay factor between 0 and 1.
Performance
According to published research, ROME demonstrates:
99.90% average accuracy across all interaction types 76ms average response time under normal load 90-95% reduction in computational resource requirements compared to traditional AI approaches Pattern optimization within 22-26 interactions
Applications
ROME has been implemented in various domains including:
Customer service automation Technical support systems Workflow automation Data processing pipelines
See also
Intent recognition Pattern matching Machine learning Natural language processing
References
Harr, Edan (2024). "All Roads Lead to 'ROME': A Dynamic Framework for Intent Recognition Improvement through Recursive Optimization for Model Enhancement". Journal of Artificial Intelligence Research. 75: 123–145.
External links
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