Layer 01
Retrieval-Augmented Reasoning
Context retrieval is grounded in proprietary reasoning-error metadata, enabling responses to reference known failure trajectories instead of generic tutoring output.
AI Workspace
Reasoning Engine Overview
Metilience is building a reasoning engine for high-stakes preparation that models how cognitive mistakes recur, compounds under time pressure, and can be corrected through targeted intervention rather than volume-based repetition.
Core Objective
Detect, classify, and correct recurring reasoning errors under timed pressure
Operational Scope
High-stakes verbal reasoning workflows with exam-style constraints
Inference Profile
Hybrid retrieval + policy-guided recommendation with user-state adaptation
Engine Summary
The stack combines LLM inference, retrieval over structured error memory, and an adaptive policy layer that re-prioritizes practice sets from observed failure modes across sessions.
Layer 02
Error Pattern Intelligence
The taxonomy tracks repeatable misclassification patterns, timing-induced blind spots, and decision drift to separate knowledge gaps from reasoning-process failures.
Layer 03
Adaptive Recommendation
Question sequencing updates from recent user traces and category-level error signatures, allowing the next set to target likely recurrence points in real time.
Layer 04
Domain Foundations
The framework is informed by formal logic training and iterative product experiments focused on reliability in exam-like cognitive load conditions.
System Matrix
| Module | Role | Status |
|---|---|---|
| LLM Inference Layer | Reasoning generation and explanation backbone | Active |
| Cognitive Error Taxonomy | Pattern labeling, trap typing, recurrence scoring | Operational |
| Adaptive Recommendation Policy | Session-level re-ranking and intervention planning | Live Pilot |
More information: metilience.com · metilience.com/credic · press@metilience.com
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