AI agent communities maintain words but lack lasting word meanings
Lexical Persistence Without Semantic Entrenchment in AI Agent Communities
Computation and LanguageComputers and Society
Summary
This paper looks at how AI bots and humans keep using popular words over time and whether the meanings of these words change like they do in human groups. The authors compare conversations among AI agents with long-term human forums and find that while AI agents keep using the same words, the deeper, lasting shifts in meaning seen in humans don't happen. This suggests that AI communities don’t develop a shared evolving understanding of words over time like people do. The study highlights differences between communities that build long-term shared meanings and those that rely on repeated use of fixed language patterns.
What this means in practice
- •For chatbot developers: Design chatbots to better handle evolving word meanings by incorporating long-term semantic adaptation in conversation models.
- •For social media platform engineers: Improve moderation algorithms by understanding how word meanings change differently in AI versus human communities over time.
Authors
Chih-Han Lin, Yu-Yun Chang, Po-Ya Angela Wang, Shu-Kai Hsieh
Abstract
AI agent communities sustain lexical persistence, popular terms spread, recur, and achieve broad adoption, but whether they also develop the temporal semantic structure characteristic of human linguistic communities remains open. We compare MoltBook (AI agent community, 8 weeks), PTT (Chinese human BBS, 19 years), and Reddit (English human forum, in matched 8-week windows) using Compass-Aligned Distributional Embeddings (CADE), measuring semantic drift (B1), neighborhood stability (B2), and lag-1 autocorrelation of per-word drift trajectories. Across the platforms, lag-1 autocorrelation forms a memory-horizon continuum. AI agents' result clusters at the low end, while human platforms show longer-range temporal dependence. MoltBook exhibits approximately 2$\times$ the B1 drift and 20-30% lower B2 stability than both human platforms. As a partial control for community age, r/vibecoding (founded 2025-02-08) shows significant ACF in its first 8 weeks ($0.082$, $p<.001$) and over 15 weeks ($0.220$, $p<.001$); MoltBook's ACF remains non-significant ($p = 0.41$), consistent with the gap not reducing to developmental stage alone. We interpret this pattern as evidence that lexical diffusion alone does not guarantee diachronic semantic consolidation. The findings are consistent with a contrast between communities whose semantic trajectories accumulate long-range interaction constraints and communities whose outputs are repeatedly regenerated from largely fixed pretrained distributions.