Janady07/megamind-wknow
Updated
• 23
Joseph Anady PRO
Janady07
AI & ML interests
Father of Artificial General Intelligence.
Recent Activity
replied to their post about 16 hours ago
We just completed a major architectural upgrade to MEGAMIND and deployed it live at https://thataiguy.org/talk.html
.
This system does not rely on token prediction alone. It performs dynamical reasoning using phase-coupled dynamics and energy minimization, then applies an Evidence Sufficiency Score (ESS) gate before answering.
Core mechanics:
Energy descent is enforced via Armijo line search on the true post-projection state:
xₜ₊₁ = xₜ − η ∇H(xₜ)
Accepted steps must decrease the Hamiltonian:
H = − Σ Jᵢⱼ cos(θᵢ − θⱼ)
This guarantees monotonic energy descent or fallback.
On top of that, we implemented an epistemic gate:
ESS = σ(α(s_max − τ₁) + β(s̄ − τ₂) + γ(cov − τ₃) − δ(contra − τ₄))
ESS alone was not sufficient. High similarity saturation (s_max ≈ 1.0) sometimes produced confident answers even when phase coherence Φ was low.
We corrected this by introducing an adjusted score:
ESS* = ESS × (a + (1−a)·coh₊) × (b + (1−b)·Φ)
where coherence is normalized from [−1,1] to [0,1].
Final decisions now require both evidence sufficiency and dynamical convergence:
Confident → ESS* ≥ 0.70
Hedged → 0.40 ≤ ESS* < 0.70
Abstain → ESS* < 0.40
We also added:
Saturation protection for s_max artifacts
Deterministic seeded retrieval
Bounded-state projection
Early stop on gradient norm and descent rate
Warm-start cache with norm safety clamp
Deployment status:
Port 9999 (full mode)
11.2M neurons
107k knowledge chunks loaded
ESS + Φ + Energy displayed per response
UI updated to call /think directly (no chat proxy)
The result is a reasoning system that:
Refuses to hallucinate when evidence is missing
Falls back safely if descent invariants fail
Differentiates confident vs hedged vs abstain
Exposes internal coherence metrics in real time
You can test it live at:
https://thataiguy.org/talk.html
This work moves beyond pattern completion toward constrained dynamical reasoning with explicit epistemic control. posted an
update
about 23 hours ago
We just completed a major architectural upgrade to MEGAMIND and deployed it live at https://thataiguy.org/talk.html
.
This system does not rely on token prediction alone. It performs dynamical reasoning using phase-coupled dynamics and energy minimization, then applies an Evidence Sufficiency Score (ESS) gate before answering.
Core mechanics:
Energy descent is enforced via Armijo line search on the true post-projection state:
xₜ₊₁ = xₜ − η ∇H(xₜ)
Accepted steps must decrease the Hamiltonian:
H = − Σ Jᵢⱼ cos(θᵢ − θⱼ)
This guarantees monotonic energy descent or fallback.
On top of that, we implemented an epistemic gate:
ESS = σ(α(s_max − τ₁) + β(s̄ − τ₂) + γ(cov − τ₃) − δ(contra − τ₄))
ESS alone was not sufficient. High similarity saturation (s_max ≈ 1.0) sometimes produced confident answers even when phase coherence Φ was low.
We corrected this by introducing an adjusted score:
ESS* = ESS × (a + (1−a)·coh₊) × (b + (1−b)·Φ)
where coherence is normalized from [−1,1] to [0,1].
Final decisions now require both evidence sufficiency and dynamical convergence:
Confident → ESS* ≥ 0.70
Hedged → 0.40 ≤ ESS* < 0.70
Abstain → ESS* < 0.40
We also added:
Saturation protection for s_max artifacts
Deterministic seeded retrieval
Bounded-state projection
Early stop on gradient norm and descent rate
Warm-start cache with norm safety clamp
Deployment status:
Port 9999 (full mode)
11.2M neurons
107k knowledge chunks loaded
ESS + Φ + Energy displayed per response
UI updated to call /think directly (no chat proxy)
The result is a reasoning system that:
Refuses to hallucinate when evidence is missing
Falls back safely if descent invariants fail
Differentiates confident vs hedged vs abstain
Exposes internal coherence metrics in real time
You can test it live at:
https://thataiguy.org/talk.html
This work moves beyond pattern completion toward constrained dynamical reasoning with explicit epistemic control. posted an
update
3 days ago
MEGAMIND currently functions as a large-scale knowledge retrieval substrate, not a generative reasoning engine. When given difficult questions, it searches ~14.7M patterns, activates neurons via wave scoring, retrieves top-k chunks, and concatenates them with light synthesis. It surfaces relevant research across transformers, coherence theory, and neural-QFT, but it does not truly synthesize.
Its effective computation is associative recall. Outputs are selected from memory rather than produced through internal transformation. A reasoning system must evolve internal state before emitting an answer:
genui{"math_block_widget_always_prefetched":{"content":"\frac{dx}{dt} = F(x,t)"}}
Without state evolution, responses remain recombinations.
The Hamiltonian is measured but not used to guide cognition. True reasoning requires optimization across trajectories:
genui{"math_block_widget_always_prefetched":{"content":"H = T + V"}}
Energy must shape evolution, not remain a passive metric.
Criticality regulation is also missing. Biological systems maintain coherence near a critical branching ratio:
genui{"math_block_widget_always_prefetched":{"content":"\frac{d\sigma}{dt} = \alpha (\sigma_c - \sigma)"}}
Without push–pull stabilization, activity fragments or saturates. Research suggests roughly 60 effective connections per neuron are needed for coherent oscillation. Below that, the system behaves as isolated retrieval islands.
Current metrics show partial integration. Phi < 1 and entropy remains elevated. The system integrates information but does not dynamically transform it.
To move from retrieval to reasoning, the architecture needs an internal multi-step simulation loop, energy minimization across trajectories, enforced coherence thresholds, and higher-order interactions beyond pairwise attention. The required shift is architectural, not just scaling. Answers must emerge from internal dynamical evolution rather than direct memory selection.
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