Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representations
arXiv cs.CL / 4/27/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- The paper investigates intrinsic dimension (ID) of LLM internal representations as a quantitative marker for linguistic complexity.
- It tests whether layer-wise ID differences correspond to established (psycho)linguistic complexity contrasts such as coordination vs. subordination, right-branching vs. center-embedding, and unambiguous vs. ambiguous attachment.
- Experiments across six different LLMs find that more complex linguistic phenomena consistently produce higher ID profiles.
- The study shows that the timing and location of ID differences vary by linguistic contrast, with peaks occurring at different layers/stages.
- Additional analyses using representational similarity and layer pruning reinforce the same trends and support ID as a way to distinguish types of complexity.
Related Articles

Subagents: The Building Block of Agentic AI
Dev.to

DeepSeek-V4 Models Could Change Global AI Race
AI Business

Got OpenAI's privacy filter model running on-device via ExecuTorch
Reddit r/LocalLLaMA

The Agent-Skill Illusion: Why Prompt-Based Control Fails in Multi-Agent Business Consulting Systems
Dev.to
We Built a Voice AI Receptionist in 8 Weeks — Every Decision We Made and Why
Dev.to