resED is a modular generative framework derived from the Representation-Level Control Surfaces (RLCS) paradigm. It treats reliability as a managed system property rather than a learned model attribute.
Status: IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Regular Paper submission ready.
The system consists of four governed layers:
- resENC (Encoder): Deterministic feature extraction with a statistical side-channel.
- RLCS (Governance): A statistical control surface that evaluates latent representations against a reference manifold.
- resTR (Transformer): A strictly residual refinement module gated by governance signals.
- resDEC (Decoder): A controlled decoder that implements a "fail-safe" mechanism (ABSTAIN/DEFER).
"Reliability is a system property, not a component property." Individual deep learning modules are opaque and volatile. resED provides observability and governance to ensure that generative models only execute within validated statistical bounds.
- Domain Agnostic: Validated across Vision (CIFAR-10) and Biology (Bioteque Gene Embeddings).
- Structural Calibration: Normalizes risk scores using Z-mapping to handle dimensionality scaling.
- Deterministic Control: Purely functional control logic without learned safety discriminators.
- Scientific Manuscript (IEEE TNNLS Format): Comprehensive report with formal methodology and empirical validation.
- Methodology: Mathematical definitions.
- System Architecture: Structural overview.
Please refer to CITATION.cff for authoritative metadata.
@article{arshad2026resed,
title={Reliability is a System Property: Formal Methodology and Empirical Validation of the resED Architecture},
author={Arshad, MD.},
year={2026},
journal={IEEE Transactions on Neural Networks and Learning Systems (Submitted)}
}- resED does not fix model errors; it suppresses them.
- The system is semantically blind; it monitors statistical typicality.
- Reliability depends on the representativeness of the reference population.