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Parent-Guided Adaptive Reliability (PGAR): A Behavioural Meta-Learning Framework for Stable and Trustworthy AI

Anshum Rankawat

TL;DR

The paper tackles reliability gaps in learning systems by introducing PGAR, a behavioural meta-learning framework wherein a supervisory parent layer modulates a learner via reflex-level signals. A fused reliability index $R_t \in [0,1]$ controls the effective learning rate $\eta_t = \eta_0 R_t^{\delta}$, with a Lyapunov-like function $V_t = L_t + \kappa(1 - R_t)$ guaranteeing bounded adaptation under mild assumptions. Empirical results on MNIST-like tasks show PGAR improves calibration, reduces loss variance, and speeds recovery after perturbations compared to standard optimisers, while remaining lightweight and interpretable. The framework lays a foundation for trustworthy AI through runtime reliability traces and introduces pathways (RG and URH) toward scalable, self-regulating, and multi-agent reliability in real-world systems.

Abstract

Parent-Guided Adaptive Reliability (PGAR) is a lightweight behavioural meta-learning framework that adds a supervisory "parent" layer on top of a standard learner to improve stability, calibration, and recovery under disturbances. PGAR computes three reflex-level signals (incident detection, overconfidence correction, and recovery memory) and fuses them into a bounded reliability index in [0,1]. This index continuously modulates the learner's effective learning rate, reducing update magnitude during instability and restoring it as reliability improves. We provide a Lyapunov-based proof sketch establishing bounded adaptation of the reliability dynamics under mild assumptions (smooth loss, descent direction, and bounded reflex outputs). Empirical evaluations on representative learning tasks show improved calibration, reduced loss variance, and faster recovery compared to standard optimizers, while retaining computational simplicity. PGAR functions as a plug-in reliability layer for existing optimization and learning pipelines, supporting interpretable reliability traces in safety-relevant settings.

Parent-Guided Adaptive Reliability (PGAR): A Behavioural Meta-Learning Framework for Stable and Trustworthy AI

TL;DR

The paper tackles reliability gaps in learning systems by introducing PGAR, a behavioural meta-learning framework wherein a supervisory parent layer modulates a learner via reflex-level signals. A fused reliability index controls the effective learning rate , with a Lyapunov-like function guaranteeing bounded adaptation under mild assumptions. Empirical results on MNIST-like tasks show PGAR improves calibration, reduces loss variance, and speeds recovery after perturbations compared to standard optimisers, while remaining lightweight and interpretable. The framework lays a foundation for trustworthy AI through runtime reliability traces and introduces pathways (RG and URH) toward scalable, self-regulating, and multi-agent reliability in real-world systems.

Abstract

Parent-Guided Adaptive Reliability (PGAR) is a lightweight behavioural meta-learning framework that adds a supervisory "parent" layer on top of a standard learner to improve stability, calibration, and recovery under disturbances. PGAR computes three reflex-level signals (incident detection, overconfidence correction, and recovery memory) and fuses them into a bounded reliability index in [0,1]. This index continuously modulates the learner's effective learning rate, reducing update magnitude during instability and restoring it as reliability improves. We provide a Lyapunov-based proof sketch establishing bounded adaptation of the reliability dynamics under mild assumptions (smooth loss, descent direction, and bounded reflex outputs). Empirical evaluations on representative learning tasks show improved calibration, reduced loss variance, and faster recovery compared to standard optimizers, while retaining computational simplicity. PGAR functions as a plug-in reliability layer for existing optimization and learning pipelines, supporting interpretable reliability traces in safety-relevant settings.
Paper Structure (28 sections, 2 theorems, 12 equations, 6 figures, 3 tables)

This paper contains 28 sections, 2 theorems, 12 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Meta-Learning and Reliability Adaptation
  4. Control-Theoretic Stability and Lyapunov Approaches
  5. Behavioural and Reflexive Learning Paradigms
  6. PGAR Framework and Methodology
  7. System Overview
  8. Reliability Control Law and Stability Proof
  9. Ablation and Control Study
  10. Behavioural Maturity and Supervisor--Learner Dynamics
  11. Behavioural Trajectory and Maturity Model
  12. Supervisor--Learner Dynamics
  13. Reflex-Level Analysis
  14. Reflex Behaviour and Reliability Co-evolution
  15. Reflex Contribution Analysis
  16. ...and 13 more sections

Key Result

Lemma 1

Under (A1)--(A3) and the choices above,

Figures (6)

  • Figure 1: (a) PGAR system architecture showing the child--parent feedback structure. (b) Parent regulator modules and reflex flow.
  • Figure 2: Maturity trajectory of reliability across developmental phases. Slope reduction in $R_t$ represents composure gain and decreasing parent intervention frequency.
  • Figure 3: Training vs runtime loops in the PGAR framework. Reflex activations decrease as reliability stabilises, indicating transition from reactive to steady-state regulation.
  • Figure 4: PGAR behavioural modes under perturbation: agility vs safety control. The agility mode demonstrates rapid reflex activations during instability, while the safety mode shows smooth convergence and sustained reliability.
  • Figure 5: Calibration curve (ECE vs confidence) comparing PGAR-v2, Adam, and SGD on MNIST.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Lemma 1: Gradient Summability
  • Theorem 1: Bounded Adaptation of Reliability Dynamics