Civilization Architecture: Structured Memory, State, and Rule Pathways for Auditable Hidden-State Computation

Анотація

Large language model systems are commonly extended with retrieval, tool use,
prompt templates, and parameter-efficient adapters, yet these mechanisms often
leave memory, state control, and rule constraints outside an explicitly
auditable latent computation path. This paper presents Civilization
Architecture, a model-system architecture in which a Transformer-like neural
core is coupled with structured Memory, State, and Rule pathways that enter
hidden-state computation and expose path-specific traces for ablation and
audit. The model is defined as the integrated computation graph, not as a
standalone language model plus external orchestration. We evaluate the
architecture through a staged validation protocol spanning a controlled
PyTorch CivilizationTransformer and a frozen Qwen3-0.6B instantiation with
trainable path-specific latent adapters. In the controlled backend, native
Memory/State/Rule fusion supports hidden-state injection, chain-state
alignment, and selective path-dependency stress tests. In the frozen-Qwen3
backend, the final Stage 42 multi-seed benchmark achieves raw full-hidden
fixed-centroid recovery of 1.0000 accuracy over three seeds, with
wrong-context drop 0.8000 and mean hidden-norm ratio 1.1270, while all Qwen3
base parameters remain frozen. Matched comparisons show that MLP adapters,
LoRA, and prefix tuning reach the same accuracy ceiling, and context-token-only
input reaches 0.9611 accuracy. Thus, the supported contribution is not
predictive-performance superiority, but a native latent-path intervention and
audit interface for structured memory and rule signals. Public benchmark
transfer, longer-context robustness, lifelong graph-memory maintenance, and
native full-architecture pretraining remain future work.