Post
107
Most AI systems don't reason. They predict the next token. MEGAMIND uses seven distinct mathematical reasoning frameworks operating in parallel to actually think through problems. Here's what's on the chalkboard.
Bayesian Reasoning handles uncertainty. Bayes' theorem P(A|B) = P(B|A)P(A)/P(B) lets the system update beliefs as new evidence arrives. MAP and MLE estimation find the most probable explanations from learned knowledge.
Information-Theoretic Reasoning measures what the system knows and doesn't know. Shannon entropy H(X) quantifies uncertainty. KL divergence measures how far two knowledge distributions are from each other. Jensen-Shannon divergence provides a symmetric version for comparing competing hypotheses.
Optimization Reasoning finds the best answer. Gradient descent on a loss function L(x,t) drives the system toward optimal states. Decision energy E_dec accumulates evidence until a threshold is crossed, preventing premature conclusions.
Distance/Similarity Reasoning determines how related concepts are. Euclidean, cosine, Hamming, and Jaccard distances each capture different relationships. Cross-frequency coupling detects patterns across scales.
Fixed-Point/Convergence Reasoning is how MEGAMIND knows when it's done thinking. The system iterates x* = f(x*) until reaching a fixed point where further computation doesn't change the answer. No arbitrary iteration limits.
Hamiltonian/Variational Reasoning provides energy-conserving dynamics. The system evolves along paths that preserve information through Hamilton's equations, ensuring nothing gets lost during reasoning.
The Master Reasoning Generator unifies everything:
d_c x = τD(x) + N(x) + F(·,t) + F(x,t) + L(·,t)
Drift, nonlinear dynamics, stochastic forcing, state-dependent feedback, and learned priors all combined into one equation driving every reasoning step.
Seven reasoning modes. One unified system. No token prediction. This is how MEGAMIND thinks.
Joseph Anady | ThatAIGuy | feedthejoe.com
Bayesian Reasoning handles uncertainty. Bayes' theorem P(A|B) = P(B|A)P(A)/P(B) lets the system update beliefs as new evidence arrives. MAP and MLE estimation find the most probable explanations from learned knowledge.
Information-Theoretic Reasoning measures what the system knows and doesn't know. Shannon entropy H(X) quantifies uncertainty. KL divergence measures how far two knowledge distributions are from each other. Jensen-Shannon divergence provides a symmetric version for comparing competing hypotheses.
Optimization Reasoning finds the best answer. Gradient descent on a loss function L(x,t) drives the system toward optimal states. Decision energy E_dec accumulates evidence until a threshold is crossed, preventing premature conclusions.
Distance/Similarity Reasoning determines how related concepts are. Euclidean, cosine, Hamming, and Jaccard distances each capture different relationships. Cross-frequency coupling detects patterns across scales.
Fixed-Point/Convergence Reasoning is how MEGAMIND knows when it's done thinking. The system iterates x* = f(x*) until reaching a fixed point where further computation doesn't change the answer. No arbitrary iteration limits.
Hamiltonian/Variational Reasoning provides energy-conserving dynamics. The system evolves along paths that preserve information through Hamilton's equations, ensuring nothing gets lost during reasoning.
The Master Reasoning Generator unifies everything:
d_c x = τD(x) + N(x) + F(·,t) + F(x,t) + L(·,t)
Drift, nonlinear dynamics, stochastic forcing, state-dependent feedback, and learned priors all combined into one equation driving every reasoning step.
Seven reasoning modes. One unified system. No token prediction. This is how MEGAMIND thinks.
Joseph Anady | ThatAIGuy | feedthejoe.com
