Researchers propose LLM-referenced C-test for evaluating supra-intelligence
A new paper on arXiv introduces the first benchmark based on Levin’s Kₜ, a theoretical measure of problem difficulty, but adapted for large language models (LLMs) instead of minimalistic Turing machines. The authors argue that existing benchmarks calibrated against human or current AI performance become obsolete as models surpass them. Their approach uses LLMs as ‘representational…
Key points
- New benchmark uses LLMs as ‘representational machines’ to generate problems calibrated by Levin’s Kₜ, a theoretical difficulty metric
- Test items are certified in bits of difficulty, with twelve LLMs tested showing performance drops at higher levels
- Method aims to create problems unsolvable by current frontier models, targeting supra-intelligence evaluation
The work builds on the original C-test but shifts from abstract machines to LLMs, which encode real-world knowledge. The authors claim this method can produce problems that remain unsolvable even by advanced AI, offering a way to measure progress toward artificial general intelligence (AGI) or beyond. No real-world deployment or model names are mentioned; the paper is theoretical and focuses on methodology.
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