IdeaLens targets AI provenance at the idea level

AI-writing detectors usually estimate whether a model produced a document’s wording. Schools, journals, and newsrooms often need to assess the source of its thesis, examples, and reasoning. The IdeaLens paper, from researchers at UMass Amherst and collaborating institutions, introduces a detector designed to classify that intellectual provenance.

Policies care about provenance

Tools such as Pangram and GPTZero return document-level estimates based largely on patterns in the prose, including word choice, syntax, and rhythm. Those signals can identify text drafted directly by a model, but they reveal less about who developed the underlying argument.

Mixed-authorship workflows expose that limitation. A person can brainstorm with ChatGPT and write the result independently, producing human prose from AI-derived ideas. Someone else can create a detailed plan and ask Claude to turn it into polished paragraphs, producing model-written prose from human ideas. Academic and editorial policies may treat those cases differently even when a conventional detector gives the opposite verdict.

How IdeaLens hides the prose

IdeaLens changes the classifier’s input by converting each document into a paraphrased outline. The pipeline works as follows:

1. The document is divided into meaningful sections or claims.
2. Each item receives a discourse role, such as thesis, counterexample, or anecdote.
3. The item’s contribution is summarized in language that minimizes overlap with the source.
4. The classifier receives the resulting outline without access to the original document.