arXiv:2512.22227v3 Announce Type: replace Abstract: We investigate whether graded states of mind form spectrum-like structure in transformer representation spaces. To do so, we construct a dataset of 636 short natural-language sentences annotated with both a continuous score from $-5$ to $5$ and one of seven ordered tiers, ranging from collapsed or scarcity-driven expressions to more coherent, reflective, and integrative ones. We evaluate five frozen transformer representations: four sentence-embedding models and one decoder-only residual-stream representation. Across all representations, simp
The proliferation of advanced transformer models has created an urgent need to understand their internal representations and cognitive parallels, driving new research into their 'states of mind' or internal processing structures.
Understanding how AI models process and organize complex human concepts like 'states of mind' is crucial for developing more robust, interpretable, and ethically aligned artificial general intelligence.
This research provides a methodology for inspecting and potentially influencing the 'cognitive' structures within large language models, moving beyond purely behavioral evaluations.
- · AI researchers
- · NLP developers
- · Developers of interpretable AI systems
- · Black-box AI development approaches
Improved understanding of transformer model internal mechanics and representation spaces.
Development of new techniques to align AI models' internal states with human cognitive frameworks, leading to more reliable AI.
Potential for AI systems to not just mimic but truly grasp and categorize nuanced human mental states, enabling advanced human-AI interaction applications.
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Read at arXiv cs.CL