[2410.13874] Chain-Oriented Objective Logic with Neural Network Feedback Control and Cascade Filtering for Dynamic Multi-DSL Regulation

Computer Science > Software Engineering arXiv:2410.13874 (cs) [Submitted on 2 Oct 2024 (v1), last revised 25 Mar 2026 (this version, v5)] Title:Chain-Oriented Objective Logic with Neural Network Feedback Control and Cascade Filtering for Dynamic Multi-DSL Regulation Authors:Jipeng Han View a PDF of the paper titled Chain-Oriented Objective Logic with Neural Network Feedback Control and Cascade Filtering for Dynamic Multi-DSL Regulation, by Jipeng Han View PDF HTML (experimental) Abstract:Contributions to AI: This paper proposes a neuro-symbolic search architecture integrating discrete rule-based logic with lightweight Neural Network Feedback Control (NNFC). Utilizing cascade filtering to isolate neural mispredictions while dynamically compensating for static heuristic biases, the framework theoretically guarantees search stability and efficiency in massive discrete state spaces. Contributions to Engineering Applications: The framework provides a scalable, divide-and-conquer solution coordinating heterogeneous rule-sets in knowledge-intensive industrial systems (e.g., multi-domain relational inference and symbolic derivation), eliminating maintenance bottlenecks and state-space explosion of monolithic reasoning engines. Modern industrial AI requires dynamic orchestration of modular domain logic, yet reliable cross-domain rule management remains lacking. We address this with Chain-Oriented Objective Logic (COOL), a high-performance neuro-symbolic framework introducing: (1) C...