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READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis — arXiv:2609.32123

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32123
License (as archived): CC0-1.0
Archived: 2026-10-07T20:55:08.707Z
SHA-256 of the archived text: f79cd099bb7115758b3e27c6b5b63566d5a299105fc42a3d47e3df25c7654144
Stable URL of this page: https://metraipolis.com/documento/dc_4ecefa226037

How to cite

READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis — arXiv:2609.32123 — arXiv (en), https://arxiv.org/abs/2609.32123 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ecefa226037 (Metraipolis archive dc_4ecefa226037, sha256 f79cd099bb7115758b3e27c6b5b63566d5a299105fc42a3d47e3df25c7654144)

BibTeX

@misc{dc4ecefa226037,
  title = {READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis — arXiv:2609.32123},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32123},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ecefa226037; sha256 f79cd099bb7115758b3e27c6b5b63566d5a299105fc42a3d47e3df25c7654144},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32123. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Time-series diagnostic systems rarely rely on retrieving relevant historical cases, and when they do, retrieval is evaluated only indirectly through downstream prediction. We introduce READ-Bench, a benchmark for historical-case retrieval across 12 diagnostic datasets, centered on multivariate time series, that defines relevance by shared fault or event type rather than signal shape, so visually different traces of the same fault count as relevant while similar-looking traces of different faults do not. Treating retrieval as a base retriever followed by a reranker, we evaluate classical distances, symbolic retrievers, self-supervised and foundation-model embedders, and their fusion, plus label-aware and language-model rerankers, under one protocol that varies supervision, pollution, and corpus scale with significance testing. Under a common channel-independent interface, pretrained representations offer no statistically detectable advantage over strong classical and symbolic baselines for search alone. The decisive factor is a small amount of resolved-case supervision at reranking, namely a Gaussian-process reranker that propagates a few neighbor labels in embedding space, which helps far more than more sophisticated representations or language-model reasoning and holds under pollution and at full corpus scale. Guided by these findings, we fuse a normal-residual-scored embedder with a dynamic time warping leg via reciprocal-rank fusion, then rerank with the Gaussian-process reranker, improving NDCG@10 over its own search stage on all 12 datasets, by +0.11 from reranking and +0.16 over the strongest single base retriever.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.32123 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:00.713Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).