src/skynet/doc/study_legacy_experiments.md

Study of Legacy Solitonic Experiments

This document details the physical algorithms and architectural patterns discovered in the legacy .py files corresponding to the core project visualizations.

1. Competitive Survival (competitive_survival_test.gif)

Source: tests/applications/app_competitive_survival.py

Physics: The War of Geometries

• Model: Two species (Red vs Blue) on a Grid Graph.
• Equation: Reaction-Advection-Diffusion (RAD) with Contact Inhibition.
  • $$ \Delta B*{red} = \text{Adv}(B*{red}) + \text{Growth}(B_{red}) - \text{Decay} - \text{Suffocation} $$
• Key Mechanism: Metric Warping.
  • The "Flow Weights" for Red are inhibited by the mass of Blue at the target node: w_red = scent / (1 + mass_blue).
  • This creates a physical exclusion zone. Red cannot flow where Blue is dense.
• Significance: Adaptation through spatial dominance. The "fitter" geometry (Red's high diffusion vs Blue's high growth) wins depending on the environment.

2. Causal Expansion (causal_expansion_test.gif)

Source: tests/applications/app_causal_expansion.py

Physics: Autopoiesis (Self-Creation)

• Model: Disconnected Islands (Graph components).
• Key Mechanism: Dynamic Topology.
  • $$ \text{if } B_n > \text{Threshold}: \text{CreateEdge}(n, \text{Target}) $$
  • Matter creates Space. The swarm "builds bridge" to the goal only when it has sufficient mass (energy) to sustain the connection.
• Flow: Guided by Scent (Pheromone) and Pressure (Biomass Gradient).
• Significance: Solves the "sparse reward" problem by physically expanding the search space towards the goal.

3. Collective Maze (collective_maze_test.gif)

Source: tests/applications/app_collective_maze.py

Physics: Swarm Gravity

• Signal: A composite field of Goal + Peer.
  • $$ P*{signal} = P*{goal} + 0.5 \cdot B_{self} $$
• Mechanism: Agents are attracted to the goal and to each other.
  • This prevents fragmentation in the maze. If one part of the swarm finds the path, the rest follow due to "Peer Gravity".
• Significance: Robust navigation. The swarm acts as a single cohesive liquid.

4. Hydra System A/B (hydra_system_A.gif)

Source: tests/soliton_pc/app_hydra_system.py

Physics: Emergent Logic Junction

• Components: Biomass (Flow), Pheromone (Signal), Memory (State).
• Mechanism: Weighted Average Decision.
  • At the "Junction" nodes (Logic Gate), the system computes: $$ \text{State} = \frac{\sum (M_i \cdot B_i)}{\sum B_i} $$
  • If State > 1.5: Route A. If State < -1.5: Route B.
• Significance: Logic is not a hardcoded "If/Then" but an emergent property of the swarm's collective memory state at a specific location.

5. Soliton PC (soliton_pc_test.gif)

Source: tests/applications/app_soliton_pc.py

Physics: Plastic Computation

• Architecture: Logic $\to$ Plastic Bus $\to$ Memory.
• Mechanism: Activity-Dependent Rewiring.
  • if Biomass(BusNode) > Threshold: AddEdge(BusNode, RandomMemoryNode)
  • High activity creates physical pathways.
• Significance: The "Computer" builds its own wires based on data flow. Adaptation is structural.

6. Parallel Stress (soliton_parallel_stress.gif)

Source: tests/applications/app_integrated_stress_test.py

Physics: Channel Separation

• Mechanism: High-Contrast Flow.
  • Flow weights are raised to a high power or multiplied heavily by gradient max(0, dP) * 12.0.
  • This prevents "leaking" between parallel tasks running on the same substrate.
• Significance: Proof that Solitons can multitask if the signal gradients are sharp enough.

7. Active Swarm / Tensor Lenia (tensor_lenia_science.gif)

Source: tests/applications/app_active_swarm.py

Physics: The Kernel of Life (Chiral Lenia)

• Model: Tensor Lenia on a Dynamic Graph.
• Mechanism: Chiral Metric Tensor.
  • The flow weights include a "Spin" term: w_spin = CHIRALITY * val_u (if $u < v$).
  • This breaks symmetry, causing the swarm to rotate/spiral rather than just diffuse.
• Analysis: The script calculates Fractal Dimension $D$ in real-time ($N(r) \sim r^D$). Life requires $D \approx 0.5 - 1.5$ (filamentous/complex).
• Significance: Symmetry breaking is essential for "Active Matter". Without it, everything settles into static crystals.

8. Swarm Migration (swarm_migration.png)

Source: demo_swarm.py

Physics: Directed Transport

• Mechanism: Anisotropic Flow Field.
  • Weights are hardcoded: w(u,v) = 1.0 if $u < v$, 0.0 otherwise.
  • This creates a "River" in the graph topology.
• Observation: The soliton (high biomass cluster) rides the flow while maintaining its shape due to the internal Gaussian Growth function (Lenia interaction).
• Significance: Proves that Solitons can be transported across a network without disintegrating, enabling "Message Passing" in the Hydra brain.

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Conclusion: The "Solitonic AGI" is built on three pillars found in these scripts:

1. Lenia Growth: The engine that keeps the signal alive (Growth(u)).
2. Metric Advection: The steering wheel that moves the signal (ApplyAsymmetricLaplacian).
3. Dynamic Topology: The plasticity that allows the hardware to adapt to the signal (CreateEdge/DestroyEdge).