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--- |
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language: en |
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license: mit |
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tags: |
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- metacognition |
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- interpretability |
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- control-theory |
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- explainability |
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- research |
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- pytorch |
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- dynamic-inference |
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- safety |
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- signal-modeling |
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model_name: "SCI: Surgical Cognitive Interpreter" |
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library_name: pytorch |
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papers: |
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- https://arxiv.org/abs/2511.12240 |
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--- |
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# SCI: Surgical Cognitive Interpreter |
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A Metacognitive Control Layer for Signal Dynamics |
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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. |
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SCI is introduced in: |
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**Vishal Joshua Meesala** |
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*SCI: A Metacognitive Control for Signal Dynamics* |
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arXiv:2511.12240, 2025 |
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https://arxiv.org/abs/2511.12240 |
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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 |
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\[ |
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\Delta SP(t) = SP^\*(t) - SP(t), |
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\] |
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under Lyapunov-style stability constraints. |
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--- |
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## 1. Motivation |
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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. |
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In safety–critical domains (medicine, industrial monitoring, environmental sensing), this is brittle: |
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- Easy and ambiguous inputs receive the same computational budget. |
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- Explanations are static and post hoc, with no adaptation under drift. |
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- There is no explicit notion of “interpretive error” that can be monitored or controlled. |
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SCI addresses this by introducing a **closed-loop metacognitive layer** that: |
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- Monitors a scalar interpretive state \( SP(t) \in [0,1] \). |
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- Computes interpretive error \( \Delta SP = SP^\* - SP \) relative to a target clarity level. |
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- Updates interpreter parameters \( \Theta \) according to a Lyapunov-inspired rule with safeguards. |
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- Allocates more inference steps and adaptation to ambiguous or unstable inputs. |
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- Exposes \( \Delta SP \) as a **safety signal** for abstention, escalation, or human-in-the-loop review. |
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Empirically, SCI: |
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- Allocates **3.6–3.8× more computation** to misclassified inputs than to correct ones. |
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- Produces an effective scalar safety signal \( \Delta SP \) with **AUROC ≈ 0.70–0.86** for error detection across vision, medical, and industrial benchmarks. |
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--- |
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## 2. Conceptual Overview |
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SCI is a modular architecture with four main components. |
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### 2.1 Decomposition \( \Pi \) |
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A multi-scale, multimodal feature bank \( P(t, s) \) that organizes raw signals \( X(t) \) into interpretable components: |
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- Rhythmic components (frequency bands, oscillations) |
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- Trend components (baselines, drifts) |
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- Spatial / structural components (sensor topology, modes) |
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- Cross-modal interactions (coherence, correlation, causal couplings) |
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- Latent composites \( \Pi^\* \) |
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Each feature is weighted by a reliability score \( w_f(t) \) derived from: |
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- Signal-to-noise ratio (SNR) |
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- Temporal persistence |
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- Cross-sensor coherence |
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These weights ensure degraded or untrustworthy features are down-weighted. |
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--- |
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### 2.2 Interpreter \( \psi_\Theta \) |
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A knowledge-guided interpreter that maps the reliability-weighted feature bank into: |
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- **Markers** \( m_k \): human-meaningful states or concepts |
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- **Confidences** \( p_k(t) \): calibrated probabilities |
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- **Rationales** \( r_k(t) \): sparse feature-level attributions and/or templated text |
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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. |
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--- |
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### 2.3 Surgical Precision (SP) |
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\( SP(t) \in [0,1] \) aggregates calibrated components such as: |
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- Clarity / selectivity |
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- Pattern strength |
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- Domain consistency |
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- Predictive alignment |
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In the minimal implementation, \( SP \) is normalized entropy of a marker or predictive distribution: |
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high SP corresponds to focused, confident internal usage of markers; |
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low SP corresponds to diffuse or ambiguous internal state. |
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--- |
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### 2.4 Closed-Loop Controller |
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The controller monitors \( \Delta SP(t) \) and updates \( \Theta \) when interpretive clarity is insufficient. |
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\[ |
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\Theta_{t+1} = \text{Proj}_{\mathcal{C}}\left[\Theta_t + \eta_t\left(\Delta SP(t)\nabla_\Theta SP(t) + \lambda_h u_h(t)\right)\right], |
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\] |
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where: |
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- \( \eta_t \): step-size schedule |
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- \( \lambda_h \): human-gain budget |
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- \( u_h(t) \): bounded human feedback signal (optional) |
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- \( \text{Proj}_{\mathcal{C}} \): projection enforcing constraints (trust region, sparsity, parameter bounds) |
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Lyapunov-style analysis shows that, under suitable conditions on \( \eta_t \) and \( \lambda_h \), the “interpretive energy” |
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\[ |
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V(t) = \tfrac{1}{2}(\Delta SP(t))^2 |
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\] |
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decreases monotonically up to bounded noise, so explanations become more stable and consistent over time. |
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This yields a **reactive interpretability layer** that not only explains but also stabilizes explanations under drift, feedback, and evolving conditions. |
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--- |
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## 3. Repository Structure |
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The repository is organized as follows (file names may evolve slightly as the framework matures): |
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```text |
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sci/ # Core SCI library |
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__init__.py |
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config.py |
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controller.py # SCIController: closed-loop update over Θ using ΔSP |
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decomposition.py # Decomposition Π and reliability-weighted feature bank |
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interpreter.py # Interpreter / marker head and SP computation |
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reliability.py # Reliability scores (SNR, persistence, coherence) |
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sp.py # SP scalar and related metrics |
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utils.py # Shared utilities and helper functions |
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configs/ # Example configuration files |
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mnist.yaml |
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mitbih.yaml |
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bearings.yaml |
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examples/ # Jupyter notebooks (to be populated) |
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mnist_sci_demo.ipynb |
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ecg_sci_demo.ipynb |
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bearings_sci_demo.ipynb |
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experiments/ # Experiment scripts, logs, and analysis |
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scripts/ # Training utilities, Hub utilities, etc. |
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push_to_hub.py |
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run_sci_mitbih_fixed_k.py |
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run_sci_bearings.py |
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run_sci_signal_v2.py # Signal-domain SCI experiments |
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plot_metacognition_hero.py # Plotting script for metacognitive behavior |
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sc_arxiv.pdf # Paper PDF (for convenience) |
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sci_latex.tex # LaTeX source of the paper |
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pyproject.toml |
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setup.cfg |
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LICENSE |
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README.md |
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