BU88 Living Network Intelligence under B_U Slime Mold, Mycelial Networks, Biofilms, and Co-Admissive Repository Formation

BU88 develops the concept of living network intelligence under the B_U framework through the structural examples of slime mold, mycelial networks, and bacterial biofilms. Its central claim is that these systems do not become intelligent through central command, symbolic representation, or explicit global planning. Their intelligence arises through local exploration, shared medium, feedback reinforcement, automatic pruning, residual traces, and reactivation on the same settlement surface. The paper therefore treats living network intelligence as a concrete biological analogy for the co-admissive repository and weak persistent morphism developed in BU80, BU81, BU82, BU86, and BU87. The paper begins by rejecting a common surface reading: decentralized intelligence is not explained merely by the absence of a central brain. The decisive structure is the presence of a shared medium in which local actions can leave traces, receive feedback, alter future pathways, and become re-admitted under changing conditions. Slime mold tubes, fungal hyphal networks, and biofilm signal environments all function as living settlement surfaces. They allow local trials to accumulate into network-level standing without requiring global representation at the level of any single unit. BU88 defines the core chain of living network intelligence as: local exploration, shared medium, feedback trace, reinforcement, pruning, residual repository, reactivation, and network standing. Slime mold demonstrates this chain through exploratory spreading, flow-based reinforcement, tube thickening, inefficient-path contraction, and path reactivation. Mycelial networks demonstrate it through underground distributed carriers that connect roots, nutrients, warning signals, and environmental gradients. Biofilms demonstrate it through quorum sensing, signal accumulation, threshold activation, protective structure formation, and collective behavioral switching. In all three cases, intelligence appears as a network-level readout produced by condition-responsive connection, not by isolated decision-making. The paper links these biological structures to the B_U chain M_U \rightarrow p^w \rightarrow L_s \rightarrow G_{tl} \rightarrow C_{fric} \rightarrow Standing_{new}. The shared biological medium corresponds to M_U; persistent traceable pathways correspond to p^w; adaptive low-impedance connections correspond to L_s; dynamic activation of usable paths corresponds to G_{tl}; energetic, spatial, chemical, and temporal costs correspond to C_{fric}; retained adaptive network patterns correspond to Standing_{new}. BU88 also clarifies the role of residual traces. A contracted tube, weak chemical mark, abandoned hyphal branch, or low-intensity signal is not automatically useless. It may serve as a residual repository that preserves future reactivation potential. This directly parallels BU87’s residual repository: retainable residuals must be classified, isolated, observed, recombined, and re-admitted rather than blindly eliminated. The final judgment is that living network intelligence demonstrates a general rule: intelligence can arise when fragments share the same settlement surface, weak signals can persist, successful paths are reinforced, inefficient paths are pruned, and residual structures retain future re-entry potential. For AI closure ecology, the lesson is direct: stronger central models are not enough. A robust AI ecosystem requires shared grammar, shared settlement surface, agent closures, feedback reinforcement, residual repositories, and reality-coupled re-admission.