Metacognitive Control Layer for Signal Dynamics

SCI

2025

This repository contains the reference implementation of SCI, a closed-loop metacognitive controller that wraps existing models and turns prediction into a regulated process rather than a one-shot function evaluation.

SCI is introduced in:

Vishal Joshua Meesala
SCI: A Metacognitive Control for Signal Dynamics.
arXiv:2511.12240, 2025
https://arxiv.org/abs/2511.12240

The paper formalizes interpretability as a feedback-regulated state: SCI monitors a scalar interpretive signal SP(t), defined over reliability-weighted, multi-scale features, and adaptively adjusts an interpreter’s parameters to reduce interpretive error

DeltaSP(t) = SP_target(t) - SP(t)

under Lyapunov-style stability constraints.

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Overview

This codebase provides:

• A modular implementation of SCI’s metacognitive control loop
• Tools to construct reliability-weighted feature decompositions for time-series and other signals
• A controller that monitors interpretive stability and adapts parameters over time
• Experiment scripts and utilities to reproduce key empirical findings from the paper

SCI sits on top of existing models and signal pipelines; it does not replace them.
It adds a control-theoretic layer for inference-time regulation, safety, and interpretive stability.

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1. Motivation

Most neural networks are deployed as open-loop function approximators: they map inputs to outputs in a single forward pass, with no explicit mechanism to regulate how much computation, explanation quality, or clarification is applied to a given case. In safety–critical domains (medicine, industrial monitoring, environmental sensing), this is brittle:

• Easy and ambiguous inputs receive the same computational budget
• Explanations are static, post hoc, and do not adapt under drift
• There is no explicit notion of “interpretive error” that can be monitored and controlled

SCI addresses this by introducing a closed-loop metacognitive layer that:

• Monitors a scalar interpretive state SP(t) in [0, 1] over time
• Computes interpretive error DeltaSP = SP_target - SP relative to a target clarity level SP_target
• Updates interpreter parameters Theta according to a stability-aware rule with safeguards
• Allocates more inference steps and adaptation to ambiguous or unstable inputs
• Exposes DeltaSP as a safety signal for abstention, escalation, or human-in-the-loop review

Empirically, SCI:

• Allocates roughly 3.6–3.8x more computation to misclassified inputs than to correct ones under matched budgets
• Produces a scalar safety signal DeltaSP with AUROC around 0.70–0.86 for detecting errors across vision, medical, and industrial benchmarks

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2. Conceptual Overview

SCI is a modular architecture with four core components.

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2.1 Decomposition Pi

A multi-scale, multimodal feature bank P(t, s) that organizes raw signals X(t) into interpretable blocks:

• Rhythmic components (frequency bands, oscillatory structure)
• Trend components (low-frequency baselines, drifts)
• Spatial / structural components (sensor topology, modes)
• Cross-modal interactions (coherence, cross-correlation, causal couplings)
• Compact but auditable latent composites Pi_star

Each feature is associated with a reliability weight w_f(t), derived from quantities such as:

• Signal-to-noise ratio (SNR)
• Temporal persistence
• Multi-sensor or cross-modal coherence

These weights allow SCI to emphasize trustworthy features and down-weight degraded sensors or spurious patterns.

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2.2 Interpreter psi_Theta

A knowledge-guided interpreter that maps the reliability-weighted feature bank into:

• Markers m_k: human-meaningful states or concepts
• Confidences p_k(t): calibrated probabilities
• Rationales r_k(t): sparse feature-level attributions and/or templated text

The interpreter can be instantiated as a modest neural head (e.g., linear layer or shallow MLP) on top of P(t, s), optionally constrained by ontologies or domain rules.

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2.3 Surgical Precision (SP)

A scalar interpretive signal SP(t) in [0, 1] that aggregates calibrated components such as:

• Clarity or selectivity
• Pattern strength
• Domain consistency
• Predictive alignment

In the minimal implementation, SP is instantiated as normalized entropy of a marker or predictive distribution: high SP corresponds to focused, confident internal usage of markers; low SP indicates a diffuse or ambiguous internal state.

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2.4 Closed-Loop Controller

The controller monitors DeltaSP(t) and updates the interpreter parameters over time.

1. Compute the interpretive error:

   DeltaSP(t) = SP_target(t) - SP(t)

2. If |DeltaSP(t)| is large, update parameters:

   Theta_{t+1} = Proj_C( Theta_t + eta_t * ( DeltaSP(t) * grad_Theta_SP(t) + lambda_h * u_h(t) ) )

   where:

   • eta_t is a step-size schedule
   • lambda_h is a human-gain budget
   • u_h(t) is an optional, bounded human feedback signal
   • Proj_C enforces constraints (e.g., trust region, sparsity, parameter bounds)

3. Define the interpretive energy:

   V(t) = 0.5 * ( DeltaSP(t) )^2

Under suitable conditions on eta_t and lambda_h, the energy V(t) decreases monotonically up to bounded noise, so explanations become more stable and consistent over time.

This yields a reactive interpretability layer that not only explains but also stabilizes explanations under drift, feedback, and evolving conditions.

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3. Installation

Clone the repository and install in editable mode:

git clone https://github.com/vishal-1344/sci.git
cd sci
pip install -e .

Or install from requirements:
pip install -r requirements.txt