[Paper] Emergent Language as an Approach to Conscious AI
Source: arXiv - 2606.06380v1
Overview
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.
Key Contributions
This paper presents research in the following areas:
- cs.CL
- cs.AI
- cs.MA
- cs.NE
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.CL.
Authors
- Zengqing Wu
- Chuan Xiao
Paper Information
- arXiv ID: 2606.06380v1
- Categories: cs.CL, cs.AI, cs.MA, cs.NE
- Published: June 4, 2026
- PDF: Download PDF