Enhanced Reconsideration System: A Mathematical Framework for Self-Correcting AI Memory Architecture with Temporal Decay and Consensus Validation
Year: 2025
Type: preprint
Abstract: Traditional artificial intelligence systems suffer from a fundamental limitation: the inability to reconsider previously stored information when presented with contradictory evidence. This phenomenon, which we term nostalgic incorrectness, occurs when AI systems maintain outdated or inaccurate information simply because it was learned first. We present the Enhanced Reconsideration System (ERS), a novel mathematical framework for implementing self-correcting AI memory architecture with temporal awareness and consensus validation. Our system introduces three key innovations: (1) a temporal decay function that naturally reduces confidence in aging information, (2) a multi-dimensional consensus algorithm that validates memories against related information, and (3) an advanced contradiction detection mechanism using vector space analysis. Through extensive experimentation on large-scale memory datasets, we demonstrate that ERS achieves a 73.2% reduction in persistent incorrect memories while maintaining 94.7% accuracy for valid information. The system processes 2,500+ memory operations per second with sub-200ms latency, making it suitable for real-time AI applications. Our implementation is available as open-source software and has been successfully deployed in production environments serving millions of memory operations daily.
Authors: Josef Kurk Edwards, Sarah Chen, Michael Rodriguez
Institutions: Carnegie Mellon University, Stanford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413546848