LensCT: Fine-Grained AI-Involved Text Detection via Temporal-Hierarchical Tomograms of LLM Internals

Abstract

LLMs are increasingly used not only to generate text from scratch, but also to polish and extend human writing. This makes AI detection a fine-grained problem: downstream users often need to understand the level and form of AI involvement in a text, rather than merely separate human from machine. Existing detectors are poorly suited to this setting because they compress the evidence into scalar scores, selected features, or surface linguistic cues, which miss the subtle patterns left by different forms of AI involvement. We propose ***LensCT***, which makes a frozen LLM's internal predictive behavior visible as a tomogram: an image-like map of how the model responds across tokens and layers. The tomogram captures both temporal patterns along the input sequence and hierarchical patterns across transformer layers, providing a richer view of AI involvement than compressed detector scores. A vision backbone then reads this tomogram to classify the level of AI involvement. On a **237,500**-sample English benchmark spanning five domains and six frontier LLM generators, ***LensCT*** achieves **0.990** in-distribution macro-AUROC, reducing error by **28.6\%** over the strongest baseline. Across **697,500** total evaluation samples, ***LensCT*** consistently outperforms prior detectors under cross-generator, cross-domain, cross-language, and adversarial shifts, showing that temporal-hierarchical tomograms provide a robust and transferable signal for fine-grained AI-involvement detection.