Drift Inspector: Exploring and Measuring Scientific Drift with Atomic Contribution Claims

Abstract:Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produces from what it discusses. We present Drift Inspector, an open-source system for measuring and exploring how a research field changes over time at the level of Atomic Contribution Claims (ACCs): decontextualized, contribution-bearing propositions an LLM extracts from each abstract before analysis. The system clusters these claims across years into an interactive map where every trend traces back to the claims and papers behind it. Applied to six years of EMNLP, it shows the field shifting away from classic NLP tasks toward LLM-era capabilities such as reasoning and multimodality -- a movement that keyword or whole-abstract counts blur. The released data extend beyond EMNLP: the same pipeline has processed the full ACL Anthology (346k claims, 80k abstracts, 423 venues). Extraction is human-validated and clustering checked against an external manually constructed taxonomy.

Accepted to EMNLP 2026 System Demonstrations. 11 pages. Live demo, code and data: this https URL

Computation and Language (cs.CL); Digital Libraries (cs.DL)

arXiv:2609.39710 [cs.CL]

(or arXiv:2609.39710v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.39710 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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