Drift or New Class? Without Labels a Drifted Class and a New One Can Produce the Same Stream, the Two Lines of Work With the Most Explicit Assumptions Each Get an Answer by Freezing the Variable the Other Lets Move, and No Located Benchmark Scores the Attribution

Abstract

A classifier deployed on a stream eventually sees inputs its model does not explain. Two different events can produce them: a known class can have drifted, or a class that did not exist in training can have appeared. The stream-mining literature treats these as separate problems, concept drift and novel-class detection, and acknowledges in passing that each can masquerade as the other. The open-set label shift literature already states that a new class's distribution and prevalence are not identified from unlabelled data without added assumptions; carried into the streaming setting, where drift removes even the fixed known-class distributions those results start from, it means that a new class and an unrestricted drift of an existing class can generate the same sequence of input distributions. Better scores can then reduce the confusion only by way of an added assumption about the stream, and whether that assumption holds cannot be checked from the same unlabelled inputs. The paper's contribution is a map of those assumptions. It sorts the ones the literature actually uses into five routes: freezing the known-class conditionals, bounding the drift, imposing geometric separation, waiting for labels, and fixing the label hierarchy by fiat. It observes that the two routes with the most explicit assumptions answer the question by assuming away one of the two phenomena. The identifiability results for open-set label shift and learning with augmented classes are stated under the assumption that known classes do not drift; extreme-verification-latency methods that track drift without labels assume a closed label space. Where both phenomena are present at once, the few published measurements show that the residual confusion depends strongly on the score and the benchmark. In one 2026 study, the output-based entropy and energy scores used by existing open-set adaptation methods separated drifted known samples from drifted novel samples with 60 to 76 percent accuracy even at an oracle threshold, while the same study's own method, added to three existing adaptation methods, reached novel-sample detection AUROC of 91.5 to 97.5 on two CIFAR benchmarks but 57.4 to 64.5 on two harder ones. No located benchmark scores the attribution itself. The paper states the missing evaluation and five studies that would settle the open part.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every arXiv citation was machine-verified against its live arXiv Atom API record, and every other citation against its Crossref, OpenAlex or DOI record, during drafting (title and author list checked against the record returned). Every quantitative claim is taken from the abstract, full text or a table of the source credited with it; full-text numbers were read from the sources' own PDF renderings rather than from summaries. No experiment was run and no number in this paper was measured by its author. Table 1 re-presents numbers and design facts published by the cited papers, each named on its row; Figure 1 re-plots values from two cited tables with no transformation. The observational-equivalence argument in Section 3, the five-route classification, Algorithm 1 and the reporting protocol in Section 11 are original conceptual synthesis by the author, not empirical results, and are presented as such. Five classical stream-mining references (Faria et al. 2016, Faria et al. 2015, Masud et al. 2011, Spinosa et al. 2007, Gaudreault and Branco 2024) were verified as records and read through their abstracts or through descriptions in open-access papers that cite them; their full texts were not read, and where a mechanism is attributed to one of them the describing source is named.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.