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convergence-mathematical-foundations

Analyze belief revision, coherence, and prediction error for Superfleet and Lantern OS reasoning tasks.

3|5|Updated May 26, 2026
Search Tags:#bayesian#narrative-identity#anti-entropy#active-inference#precision-weighting#lakatosian-programmes#collapse-analysis
One-click install
npx skills add https://github.com/alex-place/lantern-os --skill convergence-mathematical-foundations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convergence-mathematical-foundations
Source: https://github.com/alex-place/lantern-os/tree/main/skills/convergence-mathematical-foundations
Command: npx skills add https://github.com/alex-place/lantern-os --skill convergence-mathematical-foundations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you reason about belief updating, coherence, and long-horizon stability when working with Superfleet and Lantern OS concepts such as Bayesian revision, active inference, and collapse risk.

Core Features & Use Cases

  • Bayesian epistemology for updating credences from new evidence.
  • Active inference and free-energy framing for perception, action, and surprise minimization.
  • Precision weighting, anti-entropy memory, Lakatosian programme health, and narrative identity for assessing whether a model or initiative is progressing or degenerating.
  • Use it when evaluating prediction error, belief drift, system coherence, or whether a research programme remains viable over time.

Quick Start

Ask the skill to assess a reasoning problem, update the belief model, and explain how evidence, precision, and long-term coherence should change.

Frequently Asked Questions about convergence-mathematical-foundations

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I assess Bayesian belief revision and prediction error in reasoning tasks?▼

Active inference minimizes surprise by framing perception and action through free-energy reduction, using precision weighting to balance sensory evidence against prior beliefs during belief updates and system coherence stabilization.

How do I evaluate if a Lakatosian research programme is progressing or degenerating?▼

Lakatosian programme health is evaluated by analyzing narrative identity coherence, anti-entropy memory stability, and collapse-risk assessment to determine whether the research programme maintains theoretical progress or degrades over time.

Can I use precision weighting to stabilize belief updates under high uncertainty?▼

Precision weighting stabilizes belief updates by calibrating the confidence assigned to sensory evidence versus prior expectations, directly reducing prediction error and preventing belief drift during active inference under high uncertainty.

When should I not use active inference framing for belief stabilization?▼

Active inference framing is not suitable when a reasoning problem lacks quantifiable prediction error, when Bayesian updates are unnecessary, or when the system does not require mathematically grounded long-horizon stability and coherence assessment.

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