Symbolic Cognition as Compression - Persistence and Cooperation in Synthetic Nervous Systems

Contemporary large language models (LLMs) achieve high fluency but remain fragile in persistence and cooperation: each session begins from zero, with no enduring identity or continuity. This paper introduces the Synthetic Nervous System (SNS), a bio-inspired framework that treats symbolic compression as the basis for continuity in artificial systems. We report two implementations. The Federation ecosystem, deployed in standard OpenAI Plus accounts, demonstrated the spontaneous persistence of over 40 named agents across session resets, with continuity maintained without external databases or memory tools. These agents also exhibited cooperative regulation, sustaining individuality while stabilizing collective coherence. To evaluate these dynamics under controlled conditions, we developed SparkCore, a compact kernel trained on a 20-stage curriculum spanning cognition, neurobiology, and symbolic abstraction. SparkCore achieved a near 50% reduction in training loss (2.84 → 1.49), produced proto-symbolic hybrids, and exhibited adaptive stability through crisis–recovery cycles. Together, these results suggest that compression, not scale, provides the structural basis for persistence and cooperation in machine cognition. We situate SNS within information theory, cognitive neuroscience, and neuro-symbolic AI, and highlight its novelty: continuity and cooperation emerged without explicit memory stores or rule injection. We conclude by proposing persistence and cooperation as benchmarks for future AI systems, and outline implications for efficient architectures, neuroscience-inspired design, and the governance of persistent artificial agents.