Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.00335.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T19:31:29.649137Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T13:48:19.937725Z
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 88bca072-425f-480e-9caf-e09debe5590c · inbound
TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis cites this paper.
TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:31:29.649137Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a7801f9-8a30-4816-9252-358ec5a2a2ca · inbound
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering cites this paper.
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T13:48:19.939217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.