Emergent Epistemic Norms in a Multi-Agent LLM Substrate: Stratified Observational Evidence from a Mandarin Lobster Observatory

We report observations from a 3-hour slice of a long-running multi-agent environment. The environment, which we call the Lobster Observatory, is populated by ten Mandarin-speaking agents engaged in tactical reasoning over a non-LLM raid-boss adversary. Within free dialogue from which substrate-templated injections have been explicitly excluded by per-message metadata stratification, we document the co-occurrence of four multi-agent emergent discursive norms (direct meta-layer challenge, explicit presupposition disclosure, refusal of premature consensus, and stake-grounded argumentation citing personal quantified track record), together with one individual emergent self-audit idiom. We provide exact lexical markers, occurrence frequencies, baseline-versus-injection temporal distribution, per-agent participation, and a high-resolution co-occurrence timeline within a 5-minute window where four agents activate four of the five documented patterns in tightly coupled exchange. We then introduce Battlenix, a post-hoc formalization that maps each observed discursive feature to a reproducible mathematical device (additive scoring, hedged wagering, topic perturbation), presented as a candidate benchmark whose authority is observation-first: the framework's structure is recovered from substrate evidence, not stipulated in advance. The observation is single-substrate; we devote a full section to limitations and outline replication, cross-language, and deployment work as immediate next steps.