---
language: en
license: mit
tags:
  - metacognition
  - interpretability
  - control-theory
  - explainability
  - research
  - pytorch
  - dynamic-inference
  - safety
  - signal-modeling
model_name: "SCI: Surgical Cognitive Interpreter"
library_name: pytorch
papers:
  - https://arxiv.org/abs/2511.12240
---

# SCI: Surgical Cognitive Interpreter  
A Metacognitive Control Layer for Signal Dynamics

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  
\[
\Delta SP(t) = SP^\*(t) - SP(t),
\]  
under Lyapunov-style stability constraints.

---

## 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 computation, explanation quality, or clarification depth.  
In safety–critical domains (medicine, industrial monitoring, environmental sensing), this is brittle:

- Easy and ambiguous inputs receive the same computational budget.  
- Explanations are static and post hoc, with no adaptation under drift.  
- There is no explicit notion of “interpretive error” that can be monitored or controlled.

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

- Monitors a scalar interpretive state \( SP(t) \in [0,1] \).  
- Computes interpretive error \( \Delta SP = SP^\* - SP \) relative to a target clarity level.  
- Updates interpreter parameters \( \Theta \) according to a Lyapunov-inspired rule with safeguards.  
- Allocates more inference steps and adaptation to ambiguous or unstable inputs.  
- Exposes \( \Delta SP \) as a **safety signal** for abstention, escalation, or human-in-the-loop review.

Empirically, SCI:

- Allocates **3.6–3.8× more computation** to misclassified inputs than to correct ones.  
- Produces an effective scalar safety signal \( \Delta SP \) with **AUROC ≈ 0.70–0.86** for error detection across vision, medical, and industrial benchmarks.

---

## 2. Conceptual Overview

SCI is a modular architecture with four main components.

### 2.1 Decomposition \( \Pi \)

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

- Rhythmic components (frequency bands, oscillations)  
- Trend components (baselines, drifts)  
- Spatial / structural components (sensor topology, modes)  
- Cross-modal interactions (coherence, correlation, causal couplings)  
- Latent composites \( \Pi^\* \)  

Each feature is weighted by a reliability score \( w_f(t) \) derived from:

- Signal-to-noise ratio (SNR)  
- Temporal persistence  
- Cross-sensor coherence  

These weights ensure degraded or untrustworthy features are down-weighted.

---

### 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  

This component can be instantiated as a linear or shallow neural head on top of \( P(t, s) \), optionally constrained by domain rules or ontologies.

---

### 2.3 Surgical Precision (SP)

\( SP(t) \in [0,1] \) aggregates calibrated components such as:

- Clarity / selectivity  
- Pattern strength  
- Domain consistency  
- Predictive alignment  

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

---

### 2.4 Closed-Loop Controller

The controller monitors \( \Delta SP(t) \) and updates \( \Theta \) when interpretive clarity is insufficient.

\[
\Theta_{t+1} = \text{Proj}_{\mathcal{C}}\left[\Theta_t + \eta_t\left(\Delta SP(t)\nabla_\Theta SP(t) + \lambda_h u_h(t)\right)\right],
\]

where:

- \( \eta_t \): step-size schedule  
- \( \lambda_h \): human-gain budget  
- \( u_h(t) \): bounded human feedback signal (optional)  
- \( \text{Proj}_{\mathcal{C}} \): projection enforcing constraints (trust region, sparsity, parameter bounds)

Lyapunov-style analysis shows that, under suitable conditions on \( \eta_t \) and \( \lambda_h \), the “interpretive energy”

\[
V(t) = \tfrac{1}{2}(\Delta SP(t))^2
\]

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.

---

## 3. Repository Structure

The repository is organized as follows (file names may evolve slightly as the framework matures):

```text
sci/                  # Core SCI library
  __init__.py
  config.py
  controller.py       # SCIController: closed-loop update over Θ using ΔSP
  decomposition.py    # Decomposition Π and reliability-weighted feature bank
  interpreter.py      # Interpreter / marker head and SP computation
  reliability.py      # Reliability scores (SNR, persistence, coherence)
  sp.py               # SP scalar and related metrics
  utils.py            # Shared utilities and helper functions

configs/              # Example configuration files
  mnist.yaml
  mitbih.yaml
  bearings.yaml

examples/             # Jupyter notebooks (to be populated)
  mnist_sci_demo.ipynb
  ecg_sci_demo.ipynb
  bearings_sci_demo.ipynb

experiments/          # Experiment scripts, logs, and analysis

scripts/              # Training utilities, Hub utilities, etc.
  push_to_hub.py

run_sci_mitbih_fixed_k.py
run_sci_bearings.py
run_sci_signal_v2.py  # Signal-domain SCI experiments

plot_metacognition_hero.py  # Plotting script for metacognitive behavior
sc_arxiv.pdf                # Paper PDF (for convenience)
sci_latex.tex               # LaTeX source of the paper

pyproject.toml
setup.cfg
LICENSE
README.md