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Paper Citation Record · LEDGER

Large Language Models for Spatial Trajectory Patterns Mining

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.04942.

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

pith.paper-citation-record.v1
2310.04942 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:48:13.168574Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T05:09:31.871047Z

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 d12777c1-6101-4471-bfef-bae6d52af6fc · inbound

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction cites this paper.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Large Language Models for Spatial Trajectory Patterns Mining

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T00:48:13.168574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:48:13.168574Z digest=sha256:180838a87c2de4f581d20992e67fa1abd3e5e528ddb04851c58fb89c06027314

Observation e2d99eb2-798a-4c67-b971-3a478e1b5748 · inbound

Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation cites this paper.

Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation Large Language Models for Spatial Trajectory Patterns Mining

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-05T05:09:31.879494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-05T05:09:31.775673Z digest=sha256:4c9ab190aeb4498a727688ac4912eab700b1c4248be2e210855c338d62d94d9e