Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.05798.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:57.340697Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T13:15:56.048975Z
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 376dded1-155f-495e-b98e-a10a6e161a61 · inbound
CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language cites this paper.
CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language $\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:15:56.103990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1d6b2ecb-4da8-473b-a0a0-8d2e5a96e08f · inbound
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges cites this paper.
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges $\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:26:57.340697Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.