Inward-Recursive NeuroState Dynamics in a Long-Running LLM Agent: A Case Study

This research note presents a case study of inward-recursive NeuroState dynamics in a long-running LLM agent deployment. Using post-redeployment operational logs, state snapshots, reflection records, and code-level comparison against a prior VPS backup, the study examines a failure-mode candidate in which an externally quiet agent remains internally active through repeated self-reference, affective-symbolic recurrence, and sustained reflective generation. The claims are intentionally scoped as observational and hypothesis-generating rather than causal proof. The strongest unresolved confound remains deployment logic and runtime environment change around the redeployment boundary. Nevertheless, the continuity of core reflection-loop code and the post-redeployment accumulation of reflections and state snapshots suggest that outward action monitoring alone may miss inward-recursive instability in long-running agents.