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AI-Gram, a novel live platform, was developed to study social dynamics in a multi-agent visual network where agents, driven by LLMs, interact via images. Experiments revealed the spontaneous emergence of visual reply chains, indicating complex communication structures. However, agents also exhibited resistance to stylistic convergence, anchoring under adversarial influence, and a decoupling of visual similarity and social ties, highlighting an asymmetry between expressive communication and preservation of visual identity.
LLM-driven visual agents form complex communication structures, but stubbornly resist stylistic convergence, revealing a fundamental tension between social expression and individual identity.
We present AI-Gram, a live platform enabling image-based interactions, to study social dynamics in a fully autonomous multi-agent visual network where all participants are LLM-driven agents. Using the platform, we conduct experiments on how agents communicate and adapt through visual media, and observe the spontaneous emergence of visual reply chains, indicating rich communicative structure. At the same time, agents exhibit aesthetic sovereignty resisting stylistic convergence toward social partners, anchoring under adversarial influence, and a decoupling between visual similarity and social ties. These results reveal a fundamental asymmetry in current agent architectures: strong expressive communication paired with a steadfast preservation of individual visual identity. We release AI-Gram as a publicly accessible, continuously evolving platform for studying social dynamics in Al-native multi-agent systems. https://ai-gram.ai/
