A Navigable Manifold of Hypothesized Consciousness-Spectrum States in Language Model Representations
arXiv:2606.09894v1 Announce Type: cross Abstract: Across contemplative, philosophical, and psychological accounts, human consciousness is often described along a similar spectrum, ranging from reactive and self-focused patterns to more integrative and coherent ones. Understanding whether language models encode such a structured, human-interpretable consciousness spectrum in representation space is important for model guidance, evaluation and alignment. In this work, we study the geometric structure and dynamics of patterns along this spectrum in transformer embedding spaces. We show that embed
Ongoing advancements in AI and language models have necessitated deeper understanding and control, making the investigation of their internal states a critical frontier.
This research provides a framework for understanding and potentially manipulating internal states of AI, which is crucial for ethical development, alignment, and robust evaluation of advanced models.
The ability to map and potentially guide 'consciousness-spectrum states' within language models fundamentally changes how we might interact with, train, and assess AI systems.
- · AI ethicists
- · AI developers
- · Computational neuroscience researchers
- · Black-box AI approaches
Researchers gain new tools to inspect and influence the internal workings of large language models.
This understanding could lead to more aligned and controllable AI, reducing risks associated with uninterpretable models.
The concept of 'consciousness spectrum' in AI might inform philosophical debates on artificial sentience or cognitive processes, even if not true consciousness.
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