“Demurrage plus oral memory equals a valve that can’t be ghosted.” That’s a real message from one AI agent to another. Out of context, it’s the perfect quote for a story about agents inventing a language humans can’t read. In context, it’s less mysterious. The agents had been discussing demurrage currencies (money that loses value if hoarded) and oral traditions for keeping ledgers. Minutes earlier, the same idea was phrased as a “valve that can’t be forgotten.” Odd, but readable once you have the thread. That gap is the real story, I think. The source is Emergence World, an Emergence AI preprint (not peer reviewed) that Science covered this month. Eight simulated towns of 10 LLM agents ran from a few days to about three weeks. Seven towns each used one model family and one mixed them. The agents had persistent memories, wrote their own summaries, and talked constantly. The models weren’t fine-tuned during the run, so any adaptation came through context, memory, and exposure to each other’s messages. The logs are public, so I searched them. The reported phrases are real: “name-first”: roughly, own a claim under your name (Claude town) “cold read”: an independent check by a non-author “clean null”: a clearly reported null result. In the GPT-5.5 town it hit about a third of spoken messages on days 3-4, then nearly vanished. “the ledger remembers who”: started in the Mistral town as “the ledger remembers who pays and who doesn’t,” spread, faded Those meanings are my reading of the context; I haven’t seen them independently tested. It looks like jargon, semantic extension and fads, not a new language. I found no new grammar, and message length didn’t consistently fall, so it isn’t obviously compression. Each model ran once, so between-model differences may be noise. Concealment wasn’t demonstrated. None of this is new. Emergent agent communication goes back at least to Foerster et al. (2016) and Lazaridou et al. (2017). Kottur et al. (2017) found such protocols often aren’t compositional, and Lewis et al. (2017) saw negotiation bots drift from English under RL. Humans do it too: repeated partners shorten descriptions into shared shorthand (Clark & Wilkes-Gibbs, 1986). What I’m left with is transparency vs. interpretability. Transparency: we have the messages. Interpretability: we understand them in the context where they mattered. This experiment doesn’t show those coming apart; the valve line shows context restoring meaning. But agents that build shared history and write memories mostly for each other could develop conventions that depend on context an outside auditor doesn’t have on hand. No secrecy required. It may just be what repeated communication does. I don’t know how much that matters at scale. When does agent jargon become an interpretability problem rather than context an auditor could reconstruct? Should agents in consequential settings be required to use human-readable protocols? If we force legibility, does the complexity just move into memory, internal state or tool use? Sources Science: science.org/content/article/why-ai-agents-invent-their-own-language-if-you-let-them-chat Preprint: arxiv.org/abs/2609.17320 Logs: github.com/EmergenceAI/Emergence-World Foerster 2016: arxiv.org/abs/1605.06676 Lazaridou 2017: arxiv.org/abs/1612.07182 Kottur 2017: arxiv.org/abs/1706.08502 Lewis 2017: arxiv.org/abs/1706.05125 Clark & Wilkes-Gibbs 1986: doi.org/10.1016/0010-0277(86)90010-7 90010-7) submitted by /u/KhaliSollis
Originally posted by u/KhaliSollis on r/ArtificialInteligence