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Paper Citation Record · LEDGER

A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.15223.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.15223 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:07:33.432493Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T18:32:36.839975Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 38c6ce1d-6273-4331-886b-26fe0705dded · inbound

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems cites this paper.

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T15:07:33.432493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:07:33.432493Z digest=sha256:10a9dfb4a11e00943ebdad850802a083046fc0b5b4de69a08bc8758d087db00f

Observation fffca1ff-b4a7-453b-95be-27526d740ec1 · inbound

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services cites this paper.

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:32:36.863798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:32:33.458632Z digest=sha256:3e1d326101ee9953e041cafd4b93f820a796d8c920b130a46b13658b76877bb2