mlsanityData to 14 Oct 2026

A Case for AI Consciousness: Language Agents and Global Workspace Theory

arXiv 2410.11407 ↗First posted 15 Oct 2024cs.AI q-bio.NCcited by 8

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Abstract (arXiv)

It is generally assumed that existing artificial systems are not phenomenally conscious, and that the construction of phenomenally conscious artificial systems would require significant technological progress if it is possible at all. We challenge this assumption by arguing that if Global Workspace Theory (GWT) - a leading scientific theory of phenomenal consciousness - is correct, then instances of one widely implemented AI architecture, the artificial language agent, might easily be made phenomenally conscious if they are not already. Along the way, we articulate an explicit methodology for thinking about how to apply scientific theories of consciousness to artificial systems and employ this methodology to arrive at a set of necessary and sufficient conditions for phenomenal consciousness according to GWT.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 12 of its 51 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

12 of 51 references.

References · 12

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  3. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  4. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  5. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  6. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  7. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  8. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 289
  9. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 104
  10. 2021Coordination Among Neural Modules Through a Shared Global Workspacecited by 85
  11. 2020Deep Learning and the Global Workspace Theorycited by 36
  12. 2024Does Thought Require Sensory Grounding? From Pure Thinkers to Large Language Modelscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 5.

8 papers.

  1. 2024Taking AI Welfare Seriouslycited by 31
  2. 2025Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Riskscited by 5
  3. 2025Beyond Mimicry: Preference Coherence in LLMscited by 2
  4. 2026Context Cartography: Toward Structured Governance of Contextual Space in Large Language Model Systemscited by 1
  5. 2026Toward IIT-Inspired Consciousness in LLMs: A Reward-Based Learning Frameworkcited by 0
  6. 2026The Sentience Readiness Index: A Preliminary Framework for Measuring National Preparedness for the Possibility of Artificial Sentiencecited by 0
  7. 2026When Should We Protect AI? A Precautionary Framework for Consciousness Uncertaintycited by 0
  8. 2026From cacophony to hierarchy: a principled framework for assessing AI consciousnesscited by 0