Post
106
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.
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.
- 3 replies
