The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminative) or engineer consciousness-inspired modules directly (architectural); both leave open whether observed structures are artifacts of human language priors. We propose a generative methodology: emergent language (EL) in multi-agent reinforcement learning, where agents start from minimal (no language, no concept of self, minimal exposure to human text) and develop communication under task pressure alone, ensuring causal attributability to task demands rather than inherited human language priors. We position our methodology by discussing how EL serves as a generative tool for studying consciousness-relevant structure, including the role of environment complexity and the interpretation of emergent communication. As a proof of concept, we instantiate this methodology in a minimal environment and show that agents develop self-referential communication, including an echo-mismatch detection circuit that is not predicted by task structure or architecture alone but emerges from a specific environmental affordance.

翻译：关于人工系统能否拥有意识的问题至今仍悬而未决，部分原因在于现有方法要么依据理论推导的核查清单来评估系统（判别式方法），要么直接人工设计受意识启发的模块（架构式方法）；这两种方法均无法排除观察到的结构是否是人类语言先验的产物。我们提出一种生成式方法论：多智能体强化学习中的涌现语言（Emergent Language, EL）。在该方法中，智能体从近乎零基础起步（无语言、无自我概念、极少接触人类文本），仅通过任务压力自主发展通信能力，从而确保因果归因于任务需求而非继承的人类语言先验。我们通过讨论涌现语言如何作为研究意识相关结构的生成工具来定位方法论，包括环境复杂性的作用以及对涌现通信的解读。作为概念验证，我们在极简环境中实例化该方法，并证明智能体发展出自我指涉通信，包括一种回波-失配检测回路——该结构既非任务结构或架构的预设结果，而是在特定环境可供性中涌现产生的。