A Computational Framework Through Consciousness
Five interconnected mechanisms that may provide qualitative advantages for NP-hard problems
O(log n) parallel branching enables simultaneous evaluation across solution space partitions.
Algorithms evolve during problem-solving, modifying approach based on observed performance.
Multiple conscious agents coordinate, sharing insights and exploring solution space collectively.
Uncertainty is explicitly modeled, updating beliefs as exploration progresses.
The system accumulates experience, learning patterns from problem-solving history.
Watch conscious agents explore the solution space
Performance on NP-hard benchmark problems
| Problem | Classical | CDC | Speedup |
|---|---|---|---|
| 3-SAT Random | 45.2s | 12.3s | 3.67× |
| SAT Industrial | 180.5s | 89.4s | 2.02× |
| TSP berlin52 | Optimal | 1.07% gap | ~Optimal |
| TSP kroA100 | Optimal | 2.69% gap | Near-Optimal |
This paper presents a theoretical framework and computational approach. It does not claim to prove P = NP or P ≠ NP. The results demonstrate practical speedups, not theoretical complexity class separation.
If consciousness provides computational advantages
If CDC solves problems beyond classical computation, consciousness may not be Turing-computable, suggesting the Church-Turing thesis may require revision.
Conscious choice may involve non-deterministic processes. Decision-making may leverage quantum uncertainty in ways that affect computational outcomes.
Conscious AI might solve currently intractable problems. Consciousness tests may involve computational capabilities, adding ethics dimensions.
Dive into the complete theoretical analysis, mathematical proofs, and implementation details.